#solved paper MIT

[knsolutionedu]

[jellyfishsthings]

The Equation of Distraction

navigation , dc navigation

WARNINGS: none really

requests are open

dividers by @cafekitsune

Dick Grayson wasn’t used to competing for attention. Not in the way that actually mattered.

Sure, in the field, he competed with Bruce for control. With Jason, for who could kick in a door with more dramatic flair. With Damian, for sheer stubbornness. But when it came to relationships—real ones, ones with something soft and sacred curled at the center—he had always been attentive. Loving. Present.

So how the hell did he find himself third-wheeling to his own girlfriend, Tim, and a whiteboard full of integrals?

"Okay, stop. Stop right there," you said, stepping between Tim and the tangle of numbers he’d just scrawled. You were wearing one of Dick’s old hoodies, hair twisted into a bun, marker ink on your fingertips.

Tim leaned forward, eyebrows furrowed behind his glasses. "What? That’s the limit of the function as x approaches negative infinity."

"It should be," you said, tapping the board, "but this entire partial fraction decomposition is botched. You factored wrong."

Tim blinked. “I did?”

Dick, sprawled on the living room couch and pretending to read a book, smirked to himself. “Rookie mistake.”

You didn’t look away from the whiteboard. “Grayson, don’t snipe from the peanut gallery unless you want to solve this integral by hand.”

Dick shut his mouth.

Tim looked victorious. Dick glared.

The first time you met the family, you accidentally corrected Bruce on a quantum theory reference.

He had blinked at you.

You had flushed.

Alfred had smiled very faintly into his tea.

Dick, meanwhile, had fallen in love a little harder.

You were brilliant. Not just brilliant, but terrifyingly multidisciplinary brilliant. You knew literature and physics and evolutionary biology, and spoke with the unshakeable confidence of someone who had once gotten into an argument with a professor and emerged victorious.

You didn’t brag. You were just curious. A sponge for information. You asked questions and listened to the answers. And somehow, in a household full of detective minds and vigilante instincts, you were still the smartest person in the room.

So when Tim, swamped with his joint MIT-Gotham U coursework, mentioned offhandedly that he was struggling with differential equations, you offered to help.

Dick hadn’t realized what a tactical error that would be.

Then came Damian.

The kid walked in on one tutoring session, glanced at the diagrams you were sketching, and said, “That’s wrong.”

You turned, brow arched. “Excuse me?”

"The mitosis illustration. You’re using a generalized mammalian model. That isn’t accurate for marsupial chromosomes."

You blinked once. Slowly. “Are you studying marsupial mitosis in school right now?”

Damian scowled. "No. I already completed the human unit. I'm reading ahead."

Tim didn’t even look up. “He’s trying to skip grades again.”

You tapped your pen against the diagram, thinking. Then you shifted a few lines, adjusted a chromatid angle, and said, “There. Better?”

Damian squinted. “Acceptable.”

And that was that.

He joined the study sessions.

Suddenly, Dick’s evenings with you turned into academic triage.

Tim asked about imaginary numbers. Damian demanded enzyme pathways. You, looking entirely unbothered, juggled both while sipping lukewarm tea and wearing your glasses slightly crooked.

It was like watching a goddess of learning hold court.

And Dick? Dick got to sit there, watching you solve everyone else’s problems, while his half-written texts and longing stares went unanswered.

He tried not to pout.

It didn’t work.

The next Friday, Dick walked into the manor living room with takeout and three movies tucked under his arm. He had plans. Cozy night. Cuddles. Maybe make-out session #437.

Instead?

He found you, Tim, and Damian on the floor, surrounded by papers. You had a biology model of a nephron drawn across two pieces of poster board.

Dick stared.

You looked up. "Hey, love. You want to quiz Damian on the loop of Henle while I explain countercurrent multiplication?"

He dropped the takeout. "Absolutely not."

You blinked.

Tim smirked. Damian looked smug.

Dick folded his arms. “Babe, I love you. But I am not quizzing a fourteen-year-old on renal function on a Friday night.”

"Fifteen," Damian muttered.

You smiled sweetly. "We’ll be done soon. I promise."

Dick sulked off into the kitchen.

Alfred found him twenty minutes later, brooding into a cup of tea.

"Something the matter, Master Richard?"

Dick sighed. "She's supposed to be my girlfriend, not the tutor of every prodigy in this house."

Alfred didn’t flinch. "You are, perhaps, experiencing what Master Timothy and Master Damian have often felt about you."

Dick blinked. "What?"

"You have a history of... commanding attention."

Dick opened his mouth. Closed it. "Damn it."

Alfred handed him a second cup. "Jealousy, in moderation, is a sign of attachment. I suggest you redirect it.”

Dick took a breath. Sipped. Nodded.

Then promptly marched back into the living room.

"Alright, nerds. Move over."

You glanced up, amused. "Joining us after all?"

He plopped down beside you, tugging you into his lap. “No, I’m kidnapping my girlfriend."

Tim: “Rude.”

Damian: “Good riddance.”

Dick ignored them. Nuzzled into your neck. "Tell the mitochondria to wait."

You laughed. Warm and real. "That was biology. We're doing organ systems now."

"Whatever it is, it can survive without you for one hour."

You looked at him, eyes soft. "Are you jealous, Nightwing?"

"Me? Jealous? Never. Just asserting my dibs."

Tim made a gagging noise. Damian threw a pen.

You kissed him.

The study session ended shortly after.

And if Dick helped grade practice tests with glitter pens the next day just to feel useful? Well. No one had the heart to mention it.

Not even Tim.

(Okay, Tim did take a picture. But he sent it only to Kon, and Dick pretended not to notice.)

Eventually, things settled.

Tutoring became once a week. You started leaving time just for Dick. You told him how much you loved his patience, how good he was with his family, how your favorite part of the week was still movie night with him.

You even let him teach you something, once—acrobatics, on the mats in the cave. You fell on your ass laughing, legs tangled with his, and kissed him like you didn’t need textbooks to understand what you had.

And for once, Dick Grayson didn’t mind not being the smartest person in the room.

Not when he got to be yours.

[mellowmadds]

Casual Study Dates | Peter Parker

(MCU) Peter Parker/Fem Stark Reader

Warnings - slightly suggestive

Summary - Avenger’s compound a usually busy place hustling with activity seems unusually quiet for the day. leaving y/n and Peter in a sticky situation (pun intended)

Word Count: 1,237

°°••....••°°

Avenger’s Compound, a place that’s usually bustling with activities and combat training sometime’s has quiet days like this where super-powered humans who have insanely intense hearing can hear a pin drop from across campus. For you though being one of the youngest on the team you hated those days because it seemed as if everybody always wanted to see what you were up to. You weren’t necessarily an avenger but you were extremely smart and helped out around the lab and worked on some Stark Industries projects with your dad every once and a while. And that’s how you met Peter Parker and during those first two years of awkward conversations and study dates you two seemed to find some comfort in all that awkwardness.

“Are you nervous about MIT sending out their decisions soon?” Peter asked while getting comfortable on your bed while staring out at the beautiful city view.

“Why would I be nervous Peter? Most of my family are MIT Alumni.” You said a bit cocky if you really think about it.

“I- know it’s just I figured maybe you’d be experiencing the same nerves I was. It was a stupid question nevermind sorry” Peter stuttered out.

“You don’t have to be sorry Peter and you definitely don’t have to worry my dad put in a good word about you. You’re one hundred percent getting into MIT” You told him confidently.

You knew Peter was an anxious person and you’d do anything to take his nerves away.

“Now are we going to keep stressing about MIT or are we going to figure out these formulas that Bruce gave us to solve?” You asked while holding up the stack of papers labeled ‘Top Secret Formulas’.

Peter nodded his head yes while lifting his body off your bed to instead sit on the edge of the bed closer to your desk where all of your work was scattered across your laptop.

“But first I need to put some music on or else I won’t be able to focus” You said before sliding the miscellaneous papers off your laptop.

“That’s the Stark in you talking, how can you focus better with music blasting in your ears?” Peter asked while laughing.

“I guess you are right, that is a classic trait of my dads. But it just helps me focus better. I don't know, I can't explain it.” You turned on your playlist before flipping to the first page of the stack of formulas Bruce assigned you to solve.

Your speaker was loud but who cares it’s not like anyone cared or was listening everyone was off doing their own things. The first few songs were upbeat and fun but the farther you got into your playlist the more guilty pleasure songs started playing, but Peter didn’t mind he was blocking out the music anyways so he could focus better on the formulas in front of him. What you didn’t know was that Steve and Nat were standing outside your room listening.

“Knee deep where? doing what?” Steve said worriedly looking over at Nat.

“It’s just a song Steve stop being so old-school” Nat smirked back at him.

“But Peter’s in there with her, what if they aren’t actually studying?” Steve asked as any worried uncle would.

“The song is talking about having relations in the bathroom during dinner time, that’s not appropriate Nat” Steve said firmly not accepting any excuse now.

Nat wasn’t interested in continuing this conversation any further and started walking toward the living quarters where there sat Bucky, Clint, Bruce and of course Tony.

“What’s got you so tense Cap? Your boyfriends right here if you have to relieve some tension” Tony laughed making fun of Steve and Bucky’s unusual bromance.

“I think you should worry more about what your daughter and Peter are doing upstairs” Steve said, crossing his arms.

“What? What are you talking about Cap? His vigilant ass better not be corrupting my innocent perfect daughter” Tony angrily stated as his face turned a shade of red nobody expected.

“They are listening to a song about having relations in the car and bathroom” Steve said pointing upstairs to your room.

“And you didn’t shut it down the moment you heard that? What kind of uncle are you?” Tony asked running up the stairs to take a listen for himself.

“Oh my gosh the lyrics are filthy but it sounds so calming, how does an artist achieve that?” Tony muttered under his breath before harshly knocking on your bedroom door and bursting in unannounced.

“What’s going on here?” Tony yelled loudly only to be met with a view of you sitting at your desk and Peter sitting on your bed leaning against the headboard with a textbook and stack of papers sitting on his lap.

“What dad? We are busy figuring out the formulas Bruce gave us. Why the hell is everyone crowding outside my room?” You asked, pointing towards Steve, Bucky, Nat, Clint and Bruce all huddling in a circle outside your bedroom door.

“Well we heard the song you guys were listening to and were a bit concerned. You guys aren’t acting on those lyrics are you? You guys better not be under my roof” Tony questioned with a look of disgust on his face.

“What the hell are you going on about dad?” You asked looking over at Peter who looked like he'd seen a ghost.

“Are you guys having sexual relations?” Tony asked in disgust as your playlist suddenly skipped to the next song which would make your case even worse.

“Head so good, she's an honor roll she’ll ride your what like a carnival?” Tony repeated the lyrics.

“I am on the honor roll though, so it’s not entirely a lie” You replied back smirking like a smartass.

“This is not a laughing matter young lady, we are talking about something serious here, answer my question right now” Tony stated with a straight face not joking around anymore.

“Yeah we are and what about it?” You said, shrugging your shoulders.

“Y/n not in front of everybody” Peter said shyly.

“Who cares Peter they were going to find out sooner or later anyways, might as well just tell them now” You said looking back at everyone’s shocked faces. As you looked past your father behind him stood Bucky handing Clint a ten dollar bill.

“You guys had a bet going on about us?” Peter asked, looking back and forth between them but also keeping one eye on Tony just in case he might try to kill him.

“This conversation is not over and from now on this door stays open” Tony said sternly ignoring all the giggles and snarky remarks coming from his fellow avengers. Your playlist then starts playing a different song which lightens up the mood just a little.

“This one has a dance to go along with it, watch H-O-T-T-O-G-O it’s like the YMCA'' You said while doing the dance.

“I like doing the YMCA” Steve said, smiling now entering your room.

“Of course you do because you're ancient” Peter said jokingly.

As you can expect you didn’t think you’d be ending your day teaching Steve Rogers the Hot To Go dance however you wouldn’t trade the quiet days at the compound for anything because at the end of the day you’re just one big family and you wouldn’t trade them for the world.

[amathslutsguidetofandom]

Infinite Solutions - 1

PAIRINGS: Law!Professor!Andy Barber x Math!Professor!Reader

SUMMARY: MIT is famously known for its high level of education. What happens if it's not only filled with knowledge? What if it is also filled with confusion? Confusion of a new, hard-working Mathematics professor? A professor who might be falling in love with her fellow co-worker? What if that co-worker was in the Law faculty? What if that Law professor is Andrew "Andy" Barber?

WARNINGS: Swearing (if you squint).

WORD COUNT: 1,905

ENJOY!

"Shit!" You feel the puddle soak your new, expensive black slacks. Swearing at your recklessness, you ignore the mishap and continue to walk on the cobblestone. The Massachusetts chill is cooler than usual, and that’s why you have your coffee in hand. The sip you take instantly warms and floods your entire being with comfort.

You see students sitting on the lawn with textbooks and papers surrounding them like an iron fence; you lightly smile at the reminder of how you were in their exact position and place when you were in your undergrad.

MIT is filled to the brim with students as they walk to their respective classes. You see the building of your destination, and you trek towards it. The feeling of your wet pant leg sticking to your skin is something that you were not really into, but at this point, you really don’t care. You were going to be late if you pay any more attention to that mistake.

You push open the doors and walk in and make your way to an office you used to frequent back in your young adulthood.

-------

"I must say, that when I got your application, I was quite shocked," Schmidt says from his seat with a light smile on his face. You give him one of your downwards smiles, “I can assure you, Mr. Schmidt, sending my application in was something I thought I never had to do. I mean, it was an arbitrary decision; I wasn’t really thinking about it when I sent it in.”

He laughs at your response before taking a sip from his coffee. "And please, call me Tobias," the German mathematician replies kindly before gathering some papers on his desk and standing, and you follow suit. "Let me show you to your office; I heard it has one of the greatest views," he opens the door and lets you go ahead first.

The halls of the building were old and gave you some sort of idea of how much mathematical knowledge has soaked into its walls over the years. You used to walk these halls all the time, use some of their empty classrooms all alone, and solve the most complex problems on their blackboards.

Now, you’re here walking beside your boss, the Dean. But in a previous life, that was your bachelor's, he was your first-year Multivariable Calculus professor.

The little journey to your new workspace isn’t long, but it consisted of you and Tobias chatting in the first half. “If I may, may I ask what happened over there,” he points at the wet splotch on your pant leg. You shake your head and wave him off, “a long story you do not want to listen to, I assure you.” The rest of the walk is just the two of you recollecting about your time back when you were an undergrad.

“You used to send emails at 3 am,” he says with a throaty chuckle. You widen your eyes as you remember what type of student you were. “Oh dear, I did, didn’t I?” You both share a laugh until he stops in front of a dark oak door.

"Well, here we are," he smiles warmly and unlocks the door with a key before handing it to you. You nod your thanks and follow him inside when he opens the door.

When he mentioned that the view was going to be gorgeous, you thought he was overstating it. But now that you are here, and you are looking out of the window with your eyes. You are stunned.

“And I know how much you love the blackboards, so I recommended them to book this office, specifically, for you,” he states before setting the papers he’s carrying on the, your, desk.

You look at him confused, then see the blackboard attached to the wall opposite your window. God, you think you could die at ease now. Your desk sits in the space between the window and the blackboard.

There are metal drawers at two corners of the room, and lamps situated on top of them illuminating the room in a decent glow. “This—” you scoff shockingly, “this is amazing, it’s literally more than I could ask for.” The 50-something man chuckles and smiles at your reaction. “Welcome to the School of Mathematics, Professor.”

-------

You type furiously at your keyboard, the monitor taking in your input as you type the last of the lecture notes for week 5. Finally, you click on the period.

Sighing, you lean back and stretch your arms. Your back tenses as you finally straighten your posture from the hunched position you were in.

The clock above your door shows that it's half to midnight. You do a few finishing touches to your notes before posting it on the website so the keener few of the students can get their studying done.

At the end of it all, you shut down your desktop and get your stuff packed. Your phone pings as you receive messages from friends and family congratulating you and liking your post on Instagram.

The picture you posted was of the view you had from your desk, and it really was Pinterest-worthy, so you decided why not and post it on social media.

You leave your office and lock it before exiting the building and returning to the Cambridge chilling weather.

-------

You're nervous.

Really, really nervous.

It’s the first day of classes, and students are already starting to file in. You thought there would only be a handful at your 8 AM class, but here you are, seeing that the whole class is full.

The hand on your watch strikes 8 AM, and you look up and see all the different types of students waiting for you to start the class.

Taking in a deep breath, you adjust the microphone that’s clipped on your navy silk blouse and switch it on. Then you rub your hands together before walking to stand in front of the blackboard.

“Morning everyone!” You start with a bright smile on your face. “I’ll be your professor for this unit, for this semester.” You tell the class your name and what title you prefer to be called. “You really don’t have to call me Professor; you can call me by my first name. I’m not that much of a pain in the ass,” the majority of the class chuckles at your swearing.

“Welcome to Multivariable Calculus (ADVANCED),” your grin widens, and you rub your hands down your thighs. “It really isn’t for the faint-hearted,” you state with a slightly serious expression.

“But you can push through if you put in the hard work. Mathematics is a beautiful subject; it’s one of the few technical subjects where you can actually express your creativity and think in so many different ways to come to one answer,” you talk with your hands as you talk about the subject you're most passionate about.

“So, really do not be scared. Just put in the hard work, and if you do feel like you're falling behind, please, please, please contact me or the TAs that are assigned to this unit. We are here to help you with any mathematical problems you have,” you smile reassuringly, and you smile even wider when you see some of the students nod at your words.

“Alright, before we get started, do any of you have any questions about the unit or in general?” you ask before crossing your arms and adjusting the microphone a bit.

A lanky, you assume, first-year student raises his arm immediately after you asked that question. You look at him with a smile. “Yes?”

“Um, you are—” he says your full name in a questioning tone, as though waiting for you to correct him. You furrow your brows a bit but maintain a small smile. “Uh, yeah, that would be me.”

His eyes brighten a bit, and he asks a follow-up question. “You worked for NASA for three years, right? You were the main mathematician that calculated the landing trajectory and coordinates of the latest Mars rover.”

You are speechless; you thought that you’d be able to leave that life of yours behind you. “Uh, yeah, th-that’s true,” you answer with a pursed smile.

“Alright, any other questions?” you pointedly try not to look at the same student, and no one raises their hand. You clap your hands. “Alright, let's get started.”

-------

You look at your watch and see that two hours have gone by quickly. “Alright, I think I must wrap up in a minute. So, just a few late things,” you pause and look back at everyone and face your back to the used blackboard.

“Please do the practice questions; they really are helpful. And if you do have any questions, please either email me or any of the TAs, and we will reply. Just give us at least twenty-four hours to reply,” you smile and cross your arms.

“Ok, I think that’ll be it for today. Have a good one, y’all,” you nod and smile as you see your students start to flood out of the lecture theatre.

A few students line up to ask you a few questions about today's content, and you happily answer them and make sure that your explanations are detailed and clear for them to understand.

Soon, you are packing your stuff and wiping your writings off the blackboard. You switch off all the electronics, then finally the lights, before walking out of the classroom.

-------

It’s the end of the workday; you taught a total of three classes. And for each of them, you were equally enthusiastic about enlightening the minds of everyone present in the room with you.

You do your final routine of closing your office. You are quite happy with how today turned out; a few students took advantage of your office hours and asked you a lot of advanced questions, which tickled your brain in a really fun way.

You finally lock the door of your office and adjust the strap of your messenger bag over your shoulder.

You exit the building and head down the stairs. As you do, you hear someone call out your name.

You stop in your tracks as you sort of recognize the voice. You turn and see a 6’3 man jogging to catch up to you. His hair is the same since you first met him, his beard is much fuller, and his eyes. His eyes have always been the bluest you’ve ever seen.

But he's broader, stronger, and much taller.

“Oh gosh, it really is you, Hey! I thought my mind was playing tricks on me, you look different, but the same,” he chuckles as he pulls you into a hug. You hug him back, but you’re still in shock.

It’s been years since you both have seen the other.

“God, the last time I saw you, you were on TV,” he scoffs and laughs at the same time. “You were wearing the NASA uniform and giving that speech about your work,” he smiles at you so brightly; you think it’s the most gorgeous thing you’ve ever seen.

When did he get so handsome?

You just nod to whatever he’s saying; you seriously don’t think you can speak right now.

“How are you?” he smiles, gripping harder onto his briefcase as he waits for you to answer. And you smile.

“I’m well, Andy.”

🎀🎀🎀

TAGLIST <3: @sarahdonald87 , @yiiiikesmish , @jamneuromain

Here we are babes, with the first chapter of Infinite Solutions.

Took a while, but we made it. 😌😌😌

Again, if you want to be tagged, please comment so I can keep a list my loves.🤗🤗🤗

Till' then

Stay Coquette-y,

Anya 🫶🏽🕊️🎀

[betterbooktitles]

My great grandfather “Bud” Wilbur gave his son Jack an Erector Set one Christmas then took it back the same day. The Erector Set was a children’s toy made of metal pieces that allowed kids to build various model structures like bridges and poorly made bridges. Before video games, children had very few choices for entertainment: marbles, Erector Sets, or becoming a Peeping Tom. Those were the choices. My grandpa Jack was going to be an engineer like his father, and to seal his fate, great grandpa Bud bought him the tools to try his hand at building. Bud, seeing the pieces scattered on the floor must have thought “pearls before swine” while having his eureka moment. Using the toy he had bought his son, he built a model of what he called The Simultaneous Calculator, what the American papers in 1937 called “Robot-Einstein,” and what the Japanese dubbed “The Wilbur Machine.”

He didn’t build the first calculator. I believe that honor technically belongs to the Mesopotamians who made the first abacus. Nor did the calculator conceptually resemble the digital computing systems we have now that employ ones and zeros and a lot of electricity. The Wilbur Machine was an analog computing system with pulleys and brass bars that solved 9 equations simultaneously (or 9x9 systems according to an MIT grad’s thesis that I can only comprehend up to page 4). Math equations that once took a full day to solve now took roughly 1-3 hours. It sped up the production of large structures, power grids, and for one country it seems, planes. It was a big advancement in 1936-37, an advancement that was eclipsed by better smaller machines soon after. In the United States, that is. In Japan, a 3x3 system Wilbur Machine was replicated in the late 30s and a fully functioning 9x9 calculator was completed in 1944 at the Tokyo Imperial University’s Aviation Laboratory.

You read that correctly. My great-grandfather Bud Wilbur built a machine that was stolen by an Axis power right before World War II. Japan continued to use the machine until the war’s end. So, uh…sorry about that? It wasn’t Bud’s intention.

Read the rest of the essay here.

[oneefin]

because my living space is kinda small + not incredibly permanent, i usually like not having a lot of stuff and only holding onto the actual essential items i need to exist and work online. but that means i've sort of heavily biased myself into doing digital puzzles only, and although that's not a bad thing i do sometimes wonder if i had a bigger space (and more propensity to spend money) would i like having and solving more physical puzzles, like wooden packing puzzles or jigsaws or rubik's cubes or etc. or maybe even just crosswords + logic puzzles but on paper. could be a thing for future me to think about

when i went to mit mystery hunt 2025 in person for the first time i did get to look at a bunch of physical puzzles, but mit mystery hunt isn't really the space to dive deep on any of them lol. but it does seem like trying to own stuff like that is an expensive hobby, more so than the ones i have today

[pariskylar]

The Interview

Monday: August 2, 2021

in 2, 3 …hold… out 2,3. in 2, 3 …hold… out 2,3.

she was practicing her breathing techniques in the car. today was one of the most exciting and nerve wrecking days of her life. she was sat outside of thee Harlan Thombey's mansion, waiting for an interview.

she was interviewing for an assistant editor position. a job she dedicated four-and-a-half years of her life studying for. a literal dream opportunity, to be able to work under her idol.

as exciting as that thought was the possibility of rejection triggered her anxiety. which sent her on a spiral, in turn triggering her imposter syndrome. usually those thoughts would consume her, but last night she had an emergency session with her therapist.

just take a few deep breaths. in 2, 3 …hold… out 2,3.

the instruction rang in her head.

and remember, you're Jazlyn, fucking, Reed. you worked your tail off for this. you got this.

she continued her breathing techniques until being scared half to death by her 8:15 alarm. that was the sign for her to actually go into the home.

grabbing the phone she silenced it tossing it into the cup holder. pulling down her visor she checked her hair and makeup. rolling her eyes at the flyaway she reached into the glove box pulling out the spare brush, laying them down.

semi-satisfied at her appearance she grabbed her old messenger bag, leaving the car.

hi, nice to meet you. i'm Jazlyn Reed. i graduated summa cum laude from MIT-no.

hello, my name is Jazlyn Reed, nice to meet you. i graduated from MIT, summa cum laude.

she practiced her introduction making her way up the step.

remember, you're Jazlyn, fucking, Reed. you worked your tail off for this. you got this. she repeated the advice before knocking on the door.

after a full minute the door opened revealing a middle aged white woman with brown hair and brown eyes.

“yes?” she asked.

“um, hi, i'm Jazlyn Reed. i’m here for the 8:30 interview with Mr. Thrombey” her palms began to sweat.

“oh, yes. come in” she stepped aside letting her in “you can sit in the foyer and i’ll tell Harlan you’re here”

she nodded, taking a seat in one of the chairs, crossing one leg over the other.

nice to meet you my name is Jazlyn Reed. i graduated summa cum laude from MIT where i worked as writer and editor on The Tech. as she began to practice her introduction again her leg began to shake.

ok ok ok, calm down she closed her eyes working on her breathing techniques, again.

remember, you're Jazlyn, fucking, Reed. you worked your tail off for this. you got this. she chanted in her head beginning to calm down.

“Ms.Reed” the voice pulled her out the oasis she was creating in her head “Mrs.Drysdale will see you now.”

she nodded standing with her bag following the lady to the in home office. she bid adieu before pushing the already cracked door further open.

in the office sat a white older woman with white hair and blue eyes behind the desk reading over papers. the same face you'd see on any for sale sign in any major city of Massachusetts, Linda Drysdale.

in the corner of the room sat a cute brown skinned woman next to the iconic Harlan Thrombey. she could credit this man for a lot of things in her life.

her love for reading, her love for writing and the reason she double majored in Creative Writing and Publishing. she stayed up countless nights trying to finish his books and hopefully solve the mystery of Whodunit. if there was a book released by this man she would be sure to get it one way or another.

she stood there in awe, staring at him.

“Jasmine Reed?” Linda asked, pulling her from her trance like state.

“um, yes. hi, i’m Jazlyn Reed,” she walked further into the room “nice to meet you.” she stuck her hand out.

“i’m Linda Drysdale.” she accepted. “and this is my dad Harlan Thrombey-"

no shit!

"and his nurse Marta.”

Jazlyn smiled over at them.

“please have a seat.” Linda waved to the seat in front of her “so, Jasmine, tell us about yourself.”

“i graduated summa cum laude from MIT last spring with a 4.8 GPA. my junior and senior year i was head writer and editor for The Tech. i interned with Candlewick Press and externed with the Beacon Press." reaching in her bag she pulled out a folder, containing her resume and excerpts, handing it over to Linda. "during both i assisted multiple editors and written and edited pieces of my own."

while Linda looked over her resume, she mentally praised herself for that introduction. it was smooth and well articulated.

"impressive" she nodded "first question,"

the interview went on as usual—strengths, weaknesses, the greatest accomplishments. she was knocking it out the park when Linda tossed a curve ball passing the questions over to Harlan.

“dad, any questions?” all the attention turned to Mr. Thrombey

he sat quietly for a few breaths before asking the most odd question.

“why are you shaking?"

"excuse me?" she asked, genuinely confused.

"you haven’t stopped bouncing your leg since you’ve sat down.”

she looked down and realized she was, indeed, bouncing her leg. she hadn't noticed that at all.

“oh, i’m sorry.” she immediately stopped, crossing her legs.

“why?” he asked pointed.

she went to answer, but he was sat there staring. it felt like being scowled down on by a hawk, it was a bit unnerving.

“why am i sorry?” she felt her leg beginning to shake again, as she grew more nervous than before.

he continued to sit and stare. he stared at her for an unsettling amount of time. it began to psych her out, her mind began to race with everything she’s said during the interview. what she was once praising herself for she began to doubt.

“you’re quite dull, aren’t you?” he stated, earnestly.

that hit her like a ton of bricks. the person she aspire to be thinks she's dull.

“i-i” she turned to Linda not sure what to say.

“please ignore that.” she waved him off "it was nice talking to you. we will call you." she stood up extending her hand.

that’s it? she blinked twice before standing up, accepting her hand and making a quick exit out the house. she went out to her car, deflated and hurt.

the man she grew up idolizing called her dull. dull?

he didn't read her resume yet alone any of her excerpts but him calling her dull hurt like crazy. she dedicated her entire life to literature, wanting to make people feel the way he did with his writing. with a single word she began reconsidering everything, and the old feelings of imposter syndrome washed over her.

she had worked so long and so hard, with years of therapy, to get over those feelings. then with one word a man she just met sent her spiraling.

she hopped in the car and slammed the door behind her. with jittery hands, she set her phone in the phone holder and connected it to the charger. opening the FaceTime app she started her group call.

“hey, love.” Tia was the first to answer, the camera was facing the ceiling and she heard the clinking of dishes.

“hi.” she replied putting on her seatbelt and started the car.

“how was the interview?”

“oh my gosh!” she let out a shaky breath before shifting her gears.

“what happened?” Tia asked setting up the phone.

“hihi” Ash answered the call from Kiara's closet.

they returned the greetings.

“how was the interview?”

“she was finna tell me about it.” Tia cracked an egg and began whisking.

she pulled off and began explaining the entire interview to them, even the off the wall shit Harlan said. for some reason they found it entertaining.

Tia tried stifling a laugh while Ashley full on cackled.

"what?" she asked pulling up to a stop light.

“that’s such a weird thing to say to somebody you just met.” Ash laughed more, taking a hanger from the closet rod.

"yeah." she sighed beginning to think how she could start again.

should i look for another job in the same field? should i go back to school and study something different? am i going to be stuck at CVS my whole life?

T looked at her disconsolate friend and could see her having an internal crisis.

"hey, don't let some old white man cause you to spiral." she spoke first.

"yeah." Ash joined in "he called you boring? so what? is it kinda funny? yes. but he doesn't know you. you've worked too hard to regress."

"thanks." she gave them a small smile pulling up to work.

“hey, y’all. my bad i was rehearsing.” Zariana finally answered with sweat dripping down her face.

she earned a chorus of replies

“how was the interview?” she asked taking a swig from the water bottle.

“i have to tell you later, i just pulled up to work.” Jazlyn cut off the car.

“yeah and i gotta serve breakfast.” Tiana slid three plates on a serving platter.

“i have to take Kiara her fits for the day.” Ashley held up the outfits.

“dang! love y'all. see y'all later, i guess.” she blew them all a kiss

when the call ended she reached into the back grabbing her red collard shirt. leaving the car she walked into CVS ready for a long and draining shift.

"hey, Jaz, i need you to show this customer where the eye drops are." Drew, her supervisor, ordered as soon as she stepped inside.

i just got here

"it's on aisle 5." she said trying to walk past them.

"she can't find them" he put his arm out in front of her.

she rolled her eyes, vexed already, "ok, i need to clock in then i'll help her."

"just show her on your way."

today is gonna be a long ass day.

and it was. after 12 hours of getting yelled at by customers, berated by her supervisor, and a serious back ache she was relieved to finally go home.

back home she flopped on the couch next to Tia, tired.

“long day?” Tia asked pouring her a glass of wine.

“too long.” Jaz handed her the pint of Ben and Jerry’s

‘what’s the sitch, Wade?’ Kim Possible played on the TV.

scooted closer to Tia resting her head on her shoulder.

“fall asleep watching K.P with me?” Tia asked.

“yeah, but i need to take my makeup off first.” she said eyes still glued to the t.v.

thirty minutes and two glasses of wine later they were both knocked out on the couch.

- - - -

"KILL YOURSELF!"

"FUCK YOU!"

she was scared awake by arguing. she rolled out of bed—grabbing her phone—and rushed down to the living room. in the center of the room stood Tia and Shawn yelling at each other at full volume.

she sighed to herself and walked towards the kitchen.

"how bout you stop bein a bum and get a job!" Tia yelled

"how bout you get a life and stop worrying bout what me and Jaz got going on?" Shawn shouted back.

Jazlyn grabbed a naked juice from the fridge and sat at the table waiting for the arguing to stop.

"i have a life. i also have a career, a car and a place of my own. what do you have? no money, a dorm on campus and a school shuttle bus."

"i'm a med school student."

"on your eighth year."

"seventh." he corrected

"ouuu, seventh." she mocked him.

they began yelling at and over each other, it was barely audible. Tia and Shawn hated each other's guts. they couldn't be in the same room without arguing. she used to try and play peacemaker between the two but it was useless. she knew eventually one of them would piss the other off enough that they'd leave.

she pulled out her phone checking the notifications she got while asleep

core four 👯‍♀️👯‍♀️:

Ash 👒: is it weird to share panties with ur gf

core four 👯‍♀️👯‍♀️:

Zari 🪐: bro wtf

core four 👯‍♀️👯‍♀️:

Tia 🫧: i'm blockin u

Shawn: otw, in the Uber

before she could get further down Shawn screamed in frustration, stomping up stairs

"y'all done?" she locked the phone

"you need to get your friend." he slammed a door.

"no, she need to dump your sorry ass!" Tia yelled up at him. "i have to go, love you" she placed a kiss on her forehead.

she went over and flopped down on the couch, finally getting a moment of silence with herself. unlocking the phone she checked her email, hoping for some type of update on her interview. she had been waiting for two weeks.

she couldn't take another shift at CVS. her body hurts from working 12 hour shift and the feeling of another psychotic break was brewing. she had been averaging four hours of sleep a night and it began to affect her.

the mailbox was full of spam, promotional emails and her bank telling her she had payed her bills. tossing the phone to the side she laid back trying to take a nap, but couldn't. this was her first day off in months, and her mind wouldn't let her relax—she had to stay busy.

standing up she paced the townhouse trying to find something to busy herself. she landed in front of the refrigerator. five minutes later the fridge was empty and she had on rubber gloves scrubbing it until it looked brand new.

finishing the fridge she realized how nasty the floors were. filling a bucket with cleaning solution and water, she grabbed her scrub brush getting to work. while on her hands and knees her phone rang.

"hello?" she answered, wiping the sweat from her forehead.

“Jasmine Reed?” the familiar voice asked

finally!

“yes, this is she.” she tried keeping her cool

“this is Linda Drysdale, i interviewed you two weeks ago”

"Linda? hi."

"hi, i was calling because you stood out to me when we talked. and if you are still interested, we'd love for you to work for my dad."

are you insane

"yes, i'm still interested."

"are you able to start Monday?"

"yes."

"ok. well i'll send over the contract. see you Monday"

"thank you." she hung up

"YES! YES! YES!" she hopped up, jumping in a circle.

she opened the group chat and shot the message

BITCH I CAN FINALLY QUIT

[roseamongroses]

bare with me

tags: canon-typical violence, surviors guilt, near death experience, POV riri williams, some canon dialogue included, oneshot a/n: delayed cause i had to make it gayer. ao3 here.

OXYGEN LEVELS AT 55%

It was cold.

A numbness had long since spread throughout Riri’s body—pricking her finger tips and toes. The wind crushed her paper thin until all she could do was think and feel and feel.

But she had to keep going—if she lived through this—if she outlived another person, she wouldn’t be able to stand it.

She’d rather the atmosphere take her--suck the breath from her and all the worries that follow.

The sky and its endless color scared her, but she didn’t dare stop her ascent. She heard the system warnings, the shouts through the comms—but then the sky folded around her, her vision blurred—the stars became one unending white.

Riri couldn’t think, she couldn’t feel, not until it was over.

She couldn’t think—she couldn’t feel—She couldn’t.

OXYGEN LEVELS AT 0%

She had imagined her death before. Many times.

Death had taken her loved ones bloody and quick—so it was inevitable that she’d leave the same. Death terrified her, she didn’t crave it, still…

Each time Riri ascends, she fully expects to never return.

This time is no different, but Riri isn’t entirely fucking stupid. She has some failsafes--otherwise her Mom would’ve snitched on her whole operation.

After those seconds of nothing—life flooded her once again. It was solid beneath her, the smell of asphalt and sea-salt. The thick, humid air burrowed into her lungs causing her stomach to lurch and eyes fly open.

Life was loud, a clash of colors and people and—fish people, what the fuck I’m really fighting fish people— disoriented her causing her to stumble back into the pile of burnt machinery.

Life was the cool hands that followed her down.

Riri doesn’t know when her face plate retracted—but her skin felt like it was burning against the air, her back throbbing.

Shuri’s touch was constant, her eyes guarded as she inspected the other girl. Hands cool against Riri’s forehead, breath close as she checked each eye.

Riri searched their face, the slope of their eyes, the shallow curve of their lips--unsure of the attention.

Riri had expected a lecture--everyone had something to say about how she does shit--respecting her own life, her body, whatever-- but she got none. Shuri didn’t say a word, a fact that only worried Riri further.

Dumb considering just minutes ago Riri was pissed off at well--everything regarding this situation. Wakanda for showing up at her door, FBI in tow. At herself because she’d been so caught up in trying to prove herself she forgot to protect her work.

When she first got to MIT she thought she had to prove that she was smart enough. Now she knows that it doesn’t matter if she’s smart, not to the rich and fluorescent.

It only mattered if she was useful. If she was usable.

Deep down, Riri Williams was just a little girl kicking and screaming to be told she did good. And now it was no different. In fact it was worst. Ever since Wakanda went public, Riri had been obsessed. Every interview, every report--public facing or not--she had seen it. She was never sure if she wanted to be Shuri or be w--Now wasn't the time for that.

She needed Shuri to say something. Anything--

Shuri had stopped her inspection. Still close, she leaned down to Riri's ear, murmuring, "Stay still." Before Riri could protest, Shuri’s head whipped around as she abruptly stood, “Wait--” her voice cracked.

ACTIVATING INTERPRETATION…

Riri couldn’t turn her head, black dots swimming in her vision as she strained to listen. What was Shuri d--

“I am Shuri….”

Riri’s attention crumbled, her body succumbing to the pain.

“Princess of Wakanda….”

The conversation seemed to tunnel in her head, only Shuri’s voice rang clear. She supposes it would be easier this way--negotiation. If her only crime was knowing too much, the easiest way to solve it would be her death

“I demand you take me to Namor.”

Riri’s sky blotted out, black all over.

“Do not bring harm to this girl.”

[spacetimewithstuartgary]

Expert explains evidence for planetary formation through gravitational instability

Exoplanets form in protoplanetary disks, a collection of space dust and gas orbiting a star. The leading theory of planetary formation, called core accretion, occurs when grains of dust in the disk collect and grow to form a planetary core, like a snowball rolling downhill. Once it has a strong enough gravitational pull, other material collapses around it to form the atmosphere.

A secondary theory of planetary formation is gravitational collapse. In this scenario, the disk itself becomes gravitationally unstable and collapses to form the planet, like snow being plowed into a pile. This process requires the disk to be massive, and until recently there were no known viable candidates to observe; previous research had detected the snow pile, but not what made it.

But in a new paper published today in Nature, MIT Kerr-McGee Career Development Professor Richard Teague and his colleagues report evidence that the movement of the gas surrounding the star AB Aurigae behaves as one would expect in a gravitationally unstable disk, matching numerical predictions.

Their finding is akin to detecting the snowplow that made the pile. This indicates that gravitational collapse is a viable method of planetary formation. Here, Teague, who studies the formation of planetary systems in MIT's Department of Earth, Atmospheric and Planetary Sciences (EAPS), answers a few questions about the new work.

What made the AB Aurigae system a good candidate for observation?

There have been plenty of observations that have suggested some interesting dynamics going on in the system. Groups have seen spiral arms within the disk; people have found hot spots, which some groups have interpreted as a planet; others have explained it as some other instability. But it was really a disk and we knew there were lots of interesting motions going on. The data that we had previously was enough to see that it was interesting, but not really good enough to detail what was going on.

What is gravitational instability when it comes to protoplanetary disks?

Gravitational instabilities are where the gravity from the disk itself is strong enough to perturb motions within the disk. Usually, we assume that the gravitational potential is dominated by the central star, which is the case when the mass of the disk is less than 10% of the stellar mass (which is most of the time).

When the disk mass gets too large, gravitational potential will affect it in different ways and drive these very large spiral arms in the disk. These can have lots of different effects: They can trap the gas, they can heat it up, they can allow for angular momentum to be transported very rapidly within the disk.

If it's unstable, the disk can fragment and collapse directly to form a planet in an incredibly short period of time. Rather than the tens of thousands of years that it would take for a core accretion to happen, this would happen at a fraction of that time.

How does this discovery challenge conventional wisdom around planetary formation?

It shows that this alternative path of forming planets via direct collapse is a way that we can form planets. This is particularly important because we're finding more and more evidence of very large planets—say, Jupiter mass or larger—that are sitting very far away from their star.

Those sorts of planets are incredibly hard to form with core accretion, because you typically need them close to the star where things happen quickly. So to form something so massive, so far away from the star is a real challenge.

If we're able to show that there are sources that are massive enough that they're gravitationally unstable, this solves that problem. It's a way that perhaps newer systems can be formed, because they've always been a bit of a challenge to understand how they came about with core accretion.

IMAGE: Global spirals in the AB Aur disk. Credit: Nature (2024). DOI: 10.1038/s41586-024-07877-0

[mit]

How are cities managing record-setting temperatures?

Professor of urban and environmental planning David Hsu explains what municipal governments are doing as climate change accelerates.

Peter Dizikes | MIT News

July 2023 was the hottest month globally since humans began keeping records. People all over the U.S. experienced punishingly high temperatures this summer. In Phoenix, there were a record-setting 31 consecutive days with a high temperature of 110 degrees Fahrenheit or more. July was the hottest month on record in Miami. A scan of high temperatures around the country often yielded some startlingly high numbers: Dallas, 110 F; Reno, 108 F; Salt Lake City, 106 F; Portland, 105 F.

Climate change is a global and national crisis that cannot be solved by city governments alone, but cities suffering from it can try to enact new policies reducing emissions and adapting its effects. MIT’s David Hsu, an associate professor of urban and environmental planning, is an expert on metropolitan and regional climate policy. In one 2017 paper, Hsu and some colleagues estimated how 11 major U.S. cities could best reduce their carbon dioxide emissions, through energy-efficient home construction and retrofitting, improvements in vehicle gas mileage, more housing density, robust transit systems, and more. As we near the end of this historically hot summer, MIT News talked to Hsu about what cities are now doing in response to record heat, and the possibilities for new policy measures.

Q: We’ve had record-setting temperatures in many cities across the U.S. this summer. Dealing with climate change certainly isn’t just the responsibility of those cities, but what have they been doing to make a difference, to the extent they can?

A: I think this is a very top-of-mind question because even 10 or 15 years ago, we talked about adapting to a changed climate future, which seemed further off. But literally every week this summer we can refer to [dramatic] things that are already happening, clearly linked to climate change, and are going to get worse. We had wildfire smoke in the Northeast and throughout the Eastern Seaboard in June, this tragic wildfire in Hawaii that led to more deaths than any other wildfire in the U.S., [plus record high temperatures]. A lot of city leaders face climate challenges they thought were maybe 20 or 30 years in the future, and didn’t expect to see happen with this severity and intensity.

One thing you’re seeing is changes in governance. A lot of cities have recently appointed a chief heat officer. Miami and Phoenix have them now, and this is someone responsible for coordinating response to heat waves, which turn out to be one of the biggest killers among climatological effects. There is an increasing realization not only among local governments, but insurance companies and the building industry, that flooding is going to affect many places. We have already seen flooding in the seaport area in Boston, the most recently built part of our city. In some sense just the realization among local governments, insurers, building owners, and residents, that some risks are here and now, already is changing how people think about those risks.

Q: To what extent does a city being active about climate change at least signal to everyone, at the state or national level, that we have to do more? At the same time, some states are reacting against cities that are trying to institute climate initiatives and trying to prevent clean energy advances. What is possible at this point?

A: We have this very large, heterogeneous and polarized country, and we have differences between states and within states in how they’re approaching climate change. You’ve got some cities trying to enact things like natural gas bans, or trying to limit greenhouse gas emissions, with some state governments trying to preempt them entirely. I think cities have a role in showing leadership. But one thing I harp on, having worked in city government myself, is that sometimes in cities we can be complacent. While we pride ourselves on being centers of innovation and less per-capita emissions — we’re using less than rural areas, and you’ll see people celebrating New York City as the greenest in the world — cities are responsible for consumption that produces a majority of emissions in most countries. If we’re going to decarbonize society, we have to get to zero altogether, and that requires cities to act much more aggressively.

There is not only a pessimistic narrative. With the Inflation Reduction Act, which is rapidly accelerating the production of renewable energy, you see many of those subsidies going to build new manufacturing in red states. There’s a possibility people will see there are plenty of better paying, less dangerous jobs in [clean energy]. People don’t like monopolies wherever they live, so even places people consider fairly conservative would like local control [of energy], and that might mean greener jobs and lower prices. Yes, there is a doomscrolling loop of thinking polarization is insurmountable, but I feel surprisingly optimistic sometimes.

Large parts of the Midwest, even in places people think of as being more conservative, have chosen to build a lot of wind energy, partly because it’s profitable. Historically, some farmers were self-reliant and had wind power before the electrical grid came. Even now in some places where people don’t want to address climate change, they’re more than happy to have wind power.

Q: You’ve published work on which cities can pursue which policies to reduce emissions the most: better housing construction, more transit, more fuel-efficient vehicles, possibly higher housing density, and more. The exact recipe varies from place to place. But what are the common threads people can think about?

A: It’s important to think about what the status quo is, and what we should be preparing for. The status quo simply doesn’t serve large parts of the population right now. Heat risk, flooding, and wildfires all disproportionately affect populations that are already vulnerable. If you’re elderly, or lack access to mobility, information, or warnings, you probably have a lower risk of surviving a wildfire. Many people do not have high-quality housing, and may be more exposed to heat or smoke. We know the climate has already changed, and is going to change more, but we have failed to prepare for foreseeable changes that already here. Lots of things that are climate-related but not only about climate change, like affordable housing, transportation, energy access for everyone so they can have services like cooking and the internet — those are things that we can change going forward. The hopeful message is: Cities are always changing and being built, so we should make them better. The urgent message is: We shouldn’t accept the status quo.

Make sure to follow us on Tumblr!

[sunaleisocial]

How the brain solves complicated problems

New Post has been published on https://sunalei.org/news/how-the-brain-solves-complicated-problems/

How the brain solves complicated problems

The human brain is very good at solving complicated problems. One reason for that is that humans can��break problems apart into manageable subtasks that are easy to solve one at a time.

This allows us to complete a daily task like going out for coffee by breaking it into steps: getting out of our office building, navigating to the coffee shop, and once there, obtaining the coffee. This strategy helps us to handle obstacles easily. For example, if the elevator is broken, we can revise how we get out of the building without changing the other steps.

While there is a great deal of behavioral evidence demonstrating humans’ skill at these complicated tasks, it has been difficult to devise experimental scenarios that allow precise characterization of the computational strategies we use to solve problems.

In a new study, MIT researchers have successfully modeled how people deploy different decision-making strategies to solve a complicated task — in this case, predicting how a ball will travel through a maze when the ball is hidden from view. The human brain cannot perform this task perfectly because it is impossible to track all of the possible trajectories in parallel, but the researchers found that people can perform reasonably well by flexibly adopting two strategies known as hierarchical reasoning and counterfactual reasoning.

The researchers were also able to determine the circumstances under which people choose each of those strategies.

“What humans are capable of doing is to break down the maze into subsections, and then solve each step using relatively simple algorithms. Effectively, when we don’t have the means to solve a complex problem, we manage by using simpler heuristics that get the job done,” says Mehrdad Jazayeri, a professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, an investigator at the Howard Hughes Medical Institute, and the senior author of the study.

Mahdi Ramadan PhD ’24 and graduate student Cheng Tang are the lead authors of the paper, which appears today in Nature Human Behavior. Nicholas Watters PhD ’25 is also a co-author.

Rational strategies

When humans perform simple tasks that have a clear correct answer, such as categorizing objects, they perform extremely well. When tasks become more complex, such as planning a trip to your favorite cafe, there may no longer be one clearly superior answer. And, at each step, there are many things that could go wrong. In these cases, humans are very good at working out a solution that will get the task done, even though it may not be the optimal solution.

Those solutions often involve problem-solving shortcuts, or heuristics. Two prominent heuristics humans commonly rely on are hierarchical and counterfactual reasoning. Hierarchical reasoning is the process of breaking down a problem into layers, starting from the general and proceeding toward specifics. Counterfactual reasoning involves imagining what would have happened if you had made a different choice. While these strategies are well-known, scientists don’t know much about how the brain decides which one to use in a given situation.

“This is really a big question in cognitive science: How do we problem-solve in a suboptimal way, by coming up with clever heuristics that we chain together in a way that ends up getting us closer and closer until we solve the problem?” Jazayeri says.

To overcome this, Jazayeri and his colleagues devised a task that is just complex enough to require these strategies, yet simple enough that the outcomes and the calculations that go into them can be measured.

The task requires participants to predict the path of a ball as it moves through four possible trajectories in a maze. Once the ball enters the maze, people cannot see which path it travels. At two junctions in the maze, they hear an auditory cue when the ball reaches that point. Predicting the ball’s path is a task that is impossible for humans to solve with perfect accuracy.

“It requires four parallel simulations in your mind, and no human can do that. It’s analogous to having four conversations at a time,” Jazayeri says. “The task allows us to tap into this set of algorithms that the humans use, because you just can’t solve it optimally.”

The researchers recruited about 150 human volunteers to participate in the study. Before each subject began the ball-tracking task, the researchers evaluated how accurately they could estimate timespans of several hundred milliseconds, about the length of time it takes the ball to travel along one arm of the maze.

For each participant, the researchers created computational models that could predict the patterns of errors that would be seen for that participant (based on their timing skill) if they were running parallel simulations, using hierarchical reasoning alone, counterfactual reasoning alone, or combinations of the two reasoning strategies.

The researchers compared the subjects’ performance with the models’ predictions and found that for every subject, their performance was most closely associated with a model that used hierarchical reasoning but sometimes switched to counterfactual reasoning.

That suggests that instead of tracking all the possible paths that the ball could take, people broke up the task. First, they picked the direction (left or right), in which they thought the ball turned at the first junction, and continued to track the ball as it headed for the next turn. If the timing of the next sound they heard wasn’t compatible with the path they had chosen, they would go back and revise their first prediction — but only some of the time.

Switching back to the other side, which represents a shift to counterfactual reasoning, requires people to review their memory of the tones that they heard. However, it turns out that these memories are not always reliable, and the researchers found that people decided whether to go back or not based on how good they believed their memory to be.

“People rely on counterfactuals to the degree that it’s helpful,” Jazayeri says. “People who take a big performance loss when they do counterfactuals avoid doing them. But if you are someone who’s really good at retrieving information from the recent past, you may go back to the other side.”

Human limitations

To further validate their results, the researchers created a machine-learning neural network and trained it to complete the task. A machine-learning model trained on this task will track the ball’s path accurately and make the correct prediction every time, unless the researchers impose limitations on its performance.

When the researchers added cognitive limitations similar to those faced by humans, they found that the model altered its strategies. When they eliminated the model’s ability to follow all possible trajectories, it began to employ hierarchical and counterfactual strategies like humans do. If the researchers reduced the model’s memory recall ability, it began to switch to hierarchical only if it thought its recall would be good enough to get the right answer — just as humans do.

“What we found is that networks mimic human behavior when we impose on them those computational constraints that we found in human behavior,” Jazayeri says. “This is really saying that humans are acting rationally under the constraints that they have to function under.”

By slightly varying the amount of memory impairment programmed into the models, the researchers also saw hints that the switching of strategies appears to happen gradually, rather than at a distinct cut-off point. They are now performing further studies to try to determine what is happening in the brain as these shifts in strategy occur.

The research was funded by a Lisa K. Yang ICoN Fellowship, a Friends of the McGovern Institute Student Fellowship, a National Science Foundation Graduate Research Fellowship, the Simons Foundation, the Howard Hughes Medical Institute, and the McGovern Institute.

[eliast-94]

Coding with Copilot: Innovation, Ethics, and the Future of Junior Developers

Not long ago, writing code meant facing down a blank screen, building every function from scratch a debugging line by line. It was a craft that had a mix of logic, persistence, and creative problem solving. Now? You type a few words, and entire blocks of code appear, written by a machine that’s read more GitHub repos than any human ever will. Tools like GitHub Copilot, trained on billions of lines of public code, can autocomplete functions, suggest algorithms, and even write full files in seconds. For many developers, it feels like magic. Taks that once took hours can now be done over lunch. But that speed comes with risks. As AI-assisted, coding becomes more common in the tech industry, important questions are starting to appear. Who really owns the code that Copilot suggests? Can companies trust the legal safety of using AI-generated code in commercial products? And what happens to the career path of a junior developer when much of their entry-level work can be done by a machine in seconds? In this blog, I’ll explore the ethical and legal challenges of using AI-generated code in the software industry. These tools may boost productivity and change how we work, but they also raise serious challenges that need to be faced head-on.

(https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.amitmerchant.com%2Fcode-explanation-using-github-copilot%2F&psig=AOvVaw1F-03FUgh-p5yKc-qw4b7f&ust=1746673769915000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCNjPs_6wkI0DFQAAAAAdAAAAABAW)

The Rise of Copilot: Fast Code at What Cost?

GitHub Copilot, launched in 2021 by GitHub and Open AI, was advertised as a “pair programmer” that could autocomplete entire blocks of code based on just a comment or a function name. It uses the Codex model, trained in public code from GitHub repositories. According to GitHub’s official documentation, Copilot “suggests whole lines or blocks of code as you type,” and its training data includes “publicly available source code and natural language from public repositories on GitHub.” On paper, this is a game-changer. According to GitHubs’s 2023 productivity report, developers using Copilot complete tasks up to 55% faster. Some claim it reduces mental fatigue and gives more time for “higher-level thinking.” But while GitHub emphasizes productivity gains, others have been raising a red flag about what’s happening behind the scenes. While AI tools like Copilot bring undeniable benefits in speed and convenience, I believe their adoption must be balanced with strong legal safeguards and thoughtful workforce planning. The enthusiasm around automation shouldn’t blind us to its legal and ethical blind spots or the impact it may have on the careers of tomorrow's engineers like me.

Legal & Copyright issues: Who Owns This Code? One of the biggest legal questions around Copilot is whether its code suggestions inherit the licenses of the open-source code it was trained on. Many public repositories use licenses like MIT or GPL, which require attribution. If Copilot suggests code without including that, is it violating copyright? That’s the basis of an ongoing lawsuit filed by the Joseph Saveri Law Firm, which argues that GitHub, Microsoft, and OpenAI are failing to respect open-source license terms by removing credit or legal context in Copilot’s output. This raises serious concerns about how AI tools interact with the rules and values of open-source software. In response, Microsoft introduced its Copilot Copyright Commitment, promising to defend commercial users from legal claims if they follow proper use guidelines. It’s a bold move, but one that doesn’t resolve the deeper uncertainty developers face when using AI-generated code in real-world projects.

Ethical Concerns: Transparency and Trust While Copilot boosts productivity, it also raises real ethical concerns, especially around bias, transparency, and accountability. Because it was trained on billions of lines of public code, it can unintentionally reproduce outdated practices or even embed discriminatory logic. A 2024 study in ScienceDirect highlights how AI models often “inherit and reinforce societal biases” when trained on massive, unfiltered datasets. Transparency is another issue. Copilot doesn't explain why it suggests a certain solution, making it hard to verify the quality or origin of its code. As noted by the team at Human Made, this “black box” behavior limits the developer’s ability to fully trust or audit what the tool produces. Then there's the matter of responsibility. If AI-generated code introduces a flaw or causes harm, who is accountable, the developer who used it, or the creators of the tool? USC Annenberg’s research points out that as AI becomes more involved in creative and technical work, the boundaries of liability are becoming harder to define. These tools are undeniably powerful, but we shouldn’t treat them as neutral. Ethical risks need to be part of the conversation every time we choose to rely on AI for building the systems people depend on.

What happens to Junior Developers? As AI tools like GitHub Copilot become more sophisticated, many developers are left questioning what the future holds for entry-level roles. Microsoft CEO Satya Nadella recently revealed that AI now writes between 20% and 30% of the code in the company's repositories, depending on the programming language, with better results in Python than in C++. While this certainly boosts efficiency, it also means that tasks once reserved for junior engineers are being automated shifting the landscape of what it means to start a career in tech. This change, as highlighted in a Business Insider report, could disrupt traditional career paths, making it harder for newcomers to gain the hands-on experience that’s crucial for growth. Instead of writing boilerplate code, entry-level developers might find themselves grappling with debugging AI-generated output, code they barely understand. However, many experts argue that the human touch is irreplaceable. AI researcher Anima Anandkumar offers a hopeful reminder: “Curiosity is irreplaceable”. While AI may transform the nature of work, it’s unlikely to eliminate the need for human insight, creativity, and problem-solving skills that are still crucial to innovation in tech. Conclusion AI tools like Copilot are changing how we write code, but faster isn’t always better. As we embrace these technologies, we need to stay curious, question the impact, and ensure that progress doesn’t come at the cost of ethics, opportunity, or human creativity.

Coding with Copilot: Innovation, Ethics, and the Future of Junior Developers by Elias Berhe is marked with CC0 1.0 Universal

[desklibai]

AI Researcher - Desklib: Pioneering the Future of Artificial Intelligence

In the rapidly evolving world of artificial intelligence, the role of an AI Researcher has never been more crucial. AI researchers are the driving force behind groundbreaking innovations, ensuring that AI technology enhances industries such as healthcare, finance, education, and automation. If you aspire to become an AI researcher or are looking for the best opportunities, Desklib is here to guide you.

Why Become an AI Researcher?

Artificial Intelligence is reshaping the way businesses and societies operate. As an AI researcher, you will:

Solve complex problems using machine learning and deep learning.

Develop innovative AI models that enhance decision-making.

Contribute to the ethical use of AI in different industries.

Work with cutting-edge technology in a fast-growing field.

AI Researcher in the United States

The United States is a global leader in AI research, with top institutions like MIT, Stanford, and Google AI leading advancements. AI researchers in the U.S. enjoy access to vast funding opportunities, collaborations with tech giants, and a dynamic research environment. If you're looking for AI research opportunities in the U.S., check out Desklib for resources and career insights.

Job Prospects and Opportunities in the U.S.

High demand in Silicon Valley, New York, and Boston.

Average salary: $110,000 - $150,000 per year.

AI research funding from institutions like NSF and DARPA.

AI Researcher in Canada

Canada is an emerging AI hub, with cities like Toronto and Montreal housing renowned AI research labs. With government-backed AI initiatives and organizations such as the Vector Institute and MILA, AI researchers in Canada are at the forefront of global AI developments.

Why Choose Canada for AI Research?

Strong AI research community and academic support.

Competitive salaries ranging from $90,000 - $130,000.

Immigration-friendly policies for skilled AI professionals.

Explore more opportunities in Canada at Desklib.

AI Researcher in the United Kingdom

The United Kingdom is home to top AI research institutions like Oxford, Cambridge, and DeepMind. The UK's AI sector is booming, with extensive government and private sector investments.

Career Benefits for AI Researchers in the UK

Access to top universities and research labs.

Growing AI startup ecosystem in London, Manchester, and Edinburgh.

AI researcher salaries ranging from ��70,000 - £100,000.

For more insights into AI research in the UK, visit Desklib.

AI Researcher in Australia

Australia is a rising player in the AI research field, with institutions like the University of Sydney and CSIRO leading AI advancements. AI researchers in Australia contribute significantly to fields such as healthcare, robotics, and environmental sustainability.

Why Australia for AI Research?

Government-backed AI funding initiatives.

Growing AI job market in Sydney, Melbourne, and Brisbane.

Competitive salaries between AUD 95,000 - AUD 130,000.

Find out more about AI research in Australia on Desklib.

How Desklib Supports AI Researchers

Desklib is your go-to platform for AI research insights, career guidance, and academic resources. Whether you're a student, a professional, or an AI enthusiast, Desklib provides:

Research papers and AI study materials.

AI project ideas and guides.

Career insights and job opportunities in AI research.

Take the Next Step in Your AI Research Career!

Artificial Intelligence is the future, and AI researchers are leading the way. Whether you're in the United States, Canada, the United Kingdom, or Australia, Desklib is here to support your journey.

Explore AI research opportunities now at Desklib and kickstart your AI career today!

[rixdigital]

How to get admission in MIT WPU Pune for Btech ?

Discover proven strategies for Direct Admission in MIT WPU Pune BTech programs. Learn about entrance exams, management quota, cutoffs, fees & expert admission tips.

Introduction: Your Blueprint for MIT WPU BTech Admission

Every year, over 50,000 students compete for 1,200 BTech seats at MIT World Peace University, Pune. As an NAAC A++ accredited institution with 92% placement records, MIT WPU stands among Maharashtra's top engineering colleges. But with increasing competition, many aspirants wonder - what's the most effective way to secure admission?

Having guided 300+ students to successful MIT WPU admissions, we reveal insider strategies covering:

Entrance exam cutoffs and preparation

Direct Admission in MIT WPU Pune through management quota

Branch-wise seat availability

Financial planning and scholarships

Career outcomes and ROI analysis

Understanding MIT WPU's BTech Programs

1.1 Program Specializations

MIT WPU offers cutting-edge BTech programs in:

Computer Science & Engineering (Most competitive)

Artificial Intelligence & Data Science (Fastest growing)

Electronics & Telecommunications

Mechanical Engineering

Civil Engineering

1.2 Academic Excellence Factors

Faculty: 85% hold PhDs with industry experience

Infrastructure: ₹250 crore campus with 18 specialized labs

Industry Connect: 350+ MoUs with tech giants

Research: 200+ patents filed by faculty and students

Section 2: Comprehensive Admission Pathways

2.1 Entrance Exam Based Admission (75% Seats)

Accepted Exams:

MIT-WPU CET (Computer Science cutoff: 90+ percentile)

JEE Main (70+ percentile for CSE)

MHT-CET (75+ percentile for E&TC)

Selection Criteria:

60% weightage to entrance score

25% to 12th PCM marks

15% to personal interview

2.2 Direct Admission Through Management Quota (25% Seats)

For students with strong academics but average entrance scores:

Key Advantages:

No minimum entrance cutoff requirement

Faster admission process (7-10 days)

Equal degree value and placement opportunities

Eligibility Parameters:

60% in 12th PCM (55% for reserved categories)

Strong extracurricular profile

Early application recommended

Section 3: Step-by-Step Admission Process

3.1 For Entrance-Based Applicants

Exam Preparation (6-12 months prior)

Focus on PCM concepts

Solve previous years' papers

Take mock tests regularly

Application Timeline

MIT-WPU CET registration: Nov-Feb

Form submission deadline: April 30

Counselling: May-June

Document Checklist

10th/12th mark sheets

Entrance exam scorecard

Category certificate (if applicable)

Passport size photographs

3.2 For Direct Admission Applicants

Initial Assessment

Academic record evaluation

Branch preference analysis

Document Preparation

Attested academic certificates

Transfer/migration certificates

Gap year justification (if any)

Admission Timeline

Applications open: October

Early bird advantage: Apply by January

Seat confirmation: Within 2 weeks

Section 4: Financial Planning & ROI

4.1 Detailed Fee Structure (2024-25)

Tuition Fees (Annual):

Regular admission: ₹3.75-4.85 lakhs

Direct admission: ₹4.85-6.35 lakhs

Additional Costs:

Hostel (AC): ₹1.85 lakhs

Examination fees: ₹12,000/year

Industry certification programs: ₹8,000-15,000

4.2 Scholarship Opportunities

Merit Scholarships: Up to 50% fee waiver

Special Categories:

Defense wards (25% waiver)

Sportspersons (30% waiver)

Economically disadvantaged (20-40% waiver)

4.3 Education Loan Options

SBI Scholar Loan: 100% coverage at 8.5% interest

HDFC Credila: Flexible repayment options

Axis Bank: Moratorium period until placement

Section 5: Career Outcomes & Placements

5.1 Placement Statistics (2023)

Placement Rate: 94% for CSE, 89% overall

Average Package: ₹7.2 LPA

Highest Package: ₹42 LPA (Microsoft)

Top Recruiters: Amazon, TCS, L&T, Accenture, Infosys

5.2 Alumni Network Benefits

28,000+ engineering alumni worldwide

Strong Silicon Valley presence

Exclusive job referrals

Startup mentorship programs

Why Choose Our Admission Guidance?

6.1 Our Exclusive Services

Admission Strategy Session: Personalized roadmap

Document Verification: Error-free processing

Scholarship Optimization: Maximum fee savings

Post-Admission Support: Orientation to placements

6.2 Office Location & Contact

Visit Us At: SAUDAMINI COMMERCIAL COMPLEX, C1 - 203, Paud Road, Kothrud, Pune - 411038 (2 minute walk from Kothrud metro station)

Get in Touch: 📞 +91 81496 89468 (24/7 WhatsApp support) 📍 Google Maps Location

We also assist with admissions to COEP, PICT, VIT and other top engineering colleges.

Brows more collages

Direct Admission in MMCOE Pune

Direct Admission in D. Y. Patil Pune

Direct Admission in Bharati Vidyapeeth

Frequently Asked Questions

1. Can I get CSE with 75 percentile in JEE Main? Yes, through direct admission in MIT Pune if seats are available, though competition is intense.

2. What's the best time to apply for management quota? October-January for maximum branch options and potential early-bird discounts.

3. How does MIT WPU compare to NITs for placements? While NITs have brand value, MIT's industry-aligned curriculum often results in better corporate readiness.

4. Are international exchange programs available? Yes, partnerships with 40+ universities including Purdue, NTU Singapore and University of California.

5. What's the class size for Computer Science? Limited to 180 students (120 regular + 60 management quota) ensuring quality attention.

Conclusion: Your Engineering Future Starts Here

Securing BTech admission at MIT WPU Pune requires strategic planning whether through competitive exams or Direct Admission in MIT WPU Pune. With its world-class infrastructure, industry-connected curriculum and exceptional placement records, MIT WPU offers transformational engineering education.

Take the First Step Today: Book a personalized counseling session with our admission experts to create your customized admission roadmap.

[jcmarchi]

Scientists use generative AI to answer complex questions in physics

New Post has been published on https://thedigitalinsider.com/scientists-use-generative-ai-to-answer-complex-questions-in-physics/

Scientists use generative AI to answer complex questions in physics

When water freezes, it transitions from a liquid phase to a solid phase, resulting in a drastic change in properties like density and volume. Phase transitions in water are so common most of us probably don’t even think about them, but phase transitions in novel materials or complex physical systems are an important area of study.

To fully understand these systems, scientists must be able to recognize phases and detect the transitions between. But how to quantify phase changes in an unknown system is often unclear, especially when data are scarce.

Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.

Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.

Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.

“If you have a new system with fully unknown properties, how would you choose which observable quantity to study? The hope, at least with data-driven tools, is that you could scan large new systems in an automated way, and it will point you to important changes in the system. This might be a tool in the pipeline of automated scientific discovery of new, exotic properties of phases,” says Frank Schäfer, a postdoc in the Julia Lab in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author of a paper on this approach.

Joining Schäfer on the paper are first author Julian Arnold, a graduate student at the University of Basel; Alan Edelman, applied mathematics professor in the Department of Mathematics and leader of the Julia Lab; and senior author Christoph Bruder, professor in the Department of Physics at the University of Basel. The research is published today in Physical Review Letters.

Detecting phase transitions using AI

While water transitioning to ice might be among the most obvious examples of a phase change, more exotic phase changes, like when a material transitions from being a normal conductor to a superconductor, are of keen interest to scientists.

These transitions can be detected by identifying an “order parameter,” a quantity that is important and expected to change. For instance, water freezes and transitions to a solid phase (ice) when its temperature drops below 0 degrees Celsius. In this case, an appropriate order parameter could be defined in terms of the proportion of water molecules that are part of the crystalline lattice versus those that remain in a disordered state.

In the past, researchers have relied on physics expertise to build phase diagrams manually, drawing on theoretical understanding to know which order parameters are important. Not only is this tedious for complex systems, and perhaps impossible for unknown systems with new behaviors, but it also introduces human bias into the solution.

More recently, researchers have begun using machine learning to build discriminative classifiers that can solve this task by learning to classify a measurement statistic as coming from a particular phase of the physical system, the same way such models classify an image as a cat or dog.

The MIT researchers demonstrated how generative models can be used to solve this classification task much more efficiently, and in a physics-informed manner.

The Julia Programming Language, a popular language for scientific computing that is also used in MIT’s introductory linear algebra classes, offers many tools that make it invaluable for constructing such generative models, Schäfer adds.

Generative models, like those that underlie ChatGPT and Dall-E, typically work by estimating the probability distribution of some data, which they use to generate new data points that fit the distribution (such as new cat images that are similar to existing cat images).

However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free. This distribution describes the measurement statistics of the physical system.

A more knowledgeable model

The MIT team’s insight is that this probability distribution also defines a generative model upon which a classifier can be constructed. They plug the generative model into standard statistical formulas to directly construct a classifier instead of learning it from samples, as was done with discriminative approaches.

“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.

This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.

This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.

At the end of the day, similar to how one might ask ChatGPT to solve a math problem, the researchers can ask the generative classifier questions like “does this sample belong to phase I or phase II?” or “was this sample generated at high temperature or low temperature?”

Scientists could also use this approach to solve different binary classification tasks in physical systems, possibly to detect entanglement in quantum systems (Is the state entangled or not?) or determine whether theory A or B is best suited to solve a particular problem. They could also use this approach to better understand and improve large language models like ChatGPT by identifying how certain parameters should be tuned so the chatbot gives the best outputs.

In the future, the researchers also want to study theoretical guarantees regarding how many measurements they would need to effectively detect phase transitions and estimate the amount of computation that would require.

This work was funded, in part, by the Swiss National Science Foundation, the MIT-Switzerland Lockheed Martin Seed Fund, and MIT International Science and Technology Initiatives.

[adhyansh]

Pioneering Institutes in Pune Offering Premier Engineering Education

Pune has long been a hub for technical education, drawing students from across India and beyond. With its rich academic culture, advanced infrastructure, and industry connections, the city offers some of the finest institutions for engineering aspirants. Top Engineering Colleges in Pune provide students with the perfect blend of theoretical knowledge and practical exposure, ensuring they excel in their careers.

Entrance Examinations for Engineering Admission

Gaining admission to reputed institutions in Pune requires candidates to clear competitive examinations. National and state-level entrance exams serve as the primary criteria for selection. The most significant among these include:

JEE Main & JEE Advanced – Conducted by the National Testing Agency (NTA), these exams are essential for admission to premier institutions, including IITs, NITs, and select private colleges.

MHT CET – A state-level exam specifically for Maharashtra, determining eligibility for government and private engineering institutions.

BITSAT – Organized by BITS Pilani, this test is mandatory for securing admission to BITS campuses, including Pune.

VITEEE & SRMJEEE – These exams are for securing seats in private universities with a strong engineering curriculum.

Aspiring engineers should start their preparation well in advance, focusing on subjects like Mathematics, Physics, and Chemistry. Solving past question papers and taking mock tests significantly improve performance.

Scholarships and Financial Aid for Engineering Students

Education costs can be a concern for many students. Fortunately, various scholarships help alleviate financial burdens. Some of the key scholarship programs available for engineering aspirants in Pune include:

State Government Scholarships – Maharashtra Government offers scholarships like Rajarshi Chhatrapati Shahu Maharaj Scholarship for meritorious students.

AICTE and Central Government Scholarships – These are provided for students from economically weaker backgrounds and those belonging to reserved categories.

Institution-Specific Scholarships – Many colleges offer merit-based and need-based financial assistance, including tuition fee waivers and fellowships.

Corporate and Private Trust Scholarships – Companies like Tata, L&T, and Infosys fund scholarships for deserving candidates.

Students should regularly check the official websites of colleges and government portals to stay updated about eligibility and application deadlines.

Best Engineering Colleges Offering Quality Education

Pune is home to renowned institutions that provide world-class education in various engineering streams. Here are some of the most sought-after ones:

College of Engineering Pune (COEP) – One of the oldest engineering colleges in India, offering exceptional faculty and industry exposure.

Vishwakarma Institute of Technology (VIT) – Known for its academic excellence and collaborations with global industries.

MIT World Peace University (MIT-WPU) – Provides a holistic educational environment with a focus on innovation and research.

Bharati Vidyapeeth College of Engineering – Offers state-of-the-art infrastructure and strong placement support.

DY Patil College of Engineering – Well-regarded for its cutting-edge curriculum and research opportunities.

Pimpri Chinchwad College of Engineering (PCCOE) – Recognized for its focus on practical learning and strong corporate affiliations.

Symbiosis Institute of Technology (SIT) – Part of the prestigious Symbiosis International University, excelling in technology-driven education.

Each of these institutions offers diverse engineering disciplines, including Computer Science, Mechanical, Civil, Electrical, and more.

Life and Career Prospects After Engineering in Pune

Apart from academics, Pune provides an excellent environment for students, with its cosmopolitan culture, affordable living, and thriving job market. Many reputed IT firms, automobile companies, and core engineering industries have their bases in Pune, offering ample job opportunities.

Engineering graduates from Pune have strong placement prospects, with companies like TCS, Infosys, Tata Motors, and Mercedes-Benz actively recruiting. Additionally, students inclined towards research and entrepreneurship can leverage Pune’s startup ecosystem and innovation centers.

Final Thoughts

For those aspiring to build a successful career in engineering, Pune presents unmatched opportunities. With top-notch institutions, competitive exams, and numerous scholarships, the city remains a preferred destination for technical education. Choosing the right college and preparing strategically for admissions can pave the way for a bright future in the engineering field.