I'm an AI researcher who graduated with CS honors from Carnegie Mellon last year. I've won 10+ hackathons and published several papers at ML conferences.
Currently, I'm researching compute-efficient model architectures on OpenAI's Training team. My undergrad thesis explored semantics in vision-language transformers.
Before the LLM era, I fine-tuned language models at Microsoft AI and advised startups deploying NLP for various B2B verticals. In my free time, I enjoy reading Sanskrit literature & learning weird math.
As a Research Resident on the Training team, I develop architectures that get more intelligent with compute.
Our deep learning research is the foundation for the intelligence of models like GPT-4o and o1.
I was Founding Research Engineer, and explored multimodal code-generation for extracting web data at scale.
We raised $4M from Paul Graham, General Catalyst, AI Grant (Nat Friedman & Daniel Gross), SV Angel, Y Combinator, and founders of Reddit, Instacart, & Cruise.
SEO content writers have to deeply research their topic to know what to write about. Ousia automates research.
As technical co-founder, I built NLP & LLM solutions to 10x our users' article writing ability. Exited via co-founder buyout.
Vision-Language Models drastically fail to represent & align compositional structure (e.g. "mug in grass" vs "grass in mug").
In my Honors Thesis, we explore various vectorial approaches inspired by linguistic theory to address this problem, with papers at NeurIPS, ACL, EACL, and ICCV.
The AI Platform group at Microsoft builds infrastructure for enterprise-scale machine learning lifecycles on Azure.
I fine-tuned distilled LLMs to aid annotators in natural language data labeling, saving compute & improving speed.
Are large language models just learning co-occurence statistics, or can they capture compositional relations as encoded by semantic formalisms?
We applied graph algorithms to Abstract Meaning Representation to create a task that probes compositional ability. I presented our work at the 2021 SCS Research Fair.
Vizerto is a digital sales assistant that makes domain-specific knowledge easily available to B2B sellers.
I advised their ML team on novel approaches to information retrieval, graphical knowledge representations, and more.
Our conversational socialbot interacted with thousands of Amazon Alexa users every day, maintaining the top average user rating for 2 months straight against teams from Stanford, USC, and more.
My work on user modeling and entity graphs was included in our paper at EMNLP 2021.
SapientX builds white label intelligent voice assistants for cars, phones, fridges, and stores.
I fine-tuned state-of-the-art models for extractive question answering to give Tele the ability to answer domain-specific user queries from large, unorganized document corpora.
Can deep reinforcement learning model how humans learn to parse syntax trees from experience?
We built a family of cognitively realistic parsing environments to explore how novel neural architectures & RL algorithms could inform psycholinguistic theory. Our work was accepted at NeurIPS 2021 Deep RL workshop.
Wordcab summarizes business meetings using the latest in abstractive neural summarization tech.
I worked with Aleks (CEO) to build topic-based summarization, a highly-demanded but technologically challenging feature.
Intheon builds neural data processing infrastructure used by labs across the world to simplify their brainwave analysis pipelines.
I undertook NSF-funded research to investigate how language models could aid brain-computer interfaces in assisting users.
#1 HN, #2 r/LocalLlama, Github Trending, 900+ Stars
Fine-tune LLM agents with online reinforcement learning
Won 2nd @ AGI House SF Launch an LLM Hackathon
2D Positional Embeddings for Web Structure Understanding
Helped out with my little sister's first LLM project!
LLMs as Collaboratively Edited Knowledge Bases
Deployed with active users
Morphology visualizer for Sanskrit literature research & education
It's 5:46am. Good morning 🫡
— Rohan Pandey (e/acc) (@khoomeik) March 6, 2024
I spent the last few hours writing a character-count constrained decoding algorithm for Llama2-13B to de-redact Elon's email to Ilya from 2018.
Here's one of the completions it proposed that perfectly matches the length constraints of the redaction. pic.twitter.com/TUWBXXVAln
Got $28M? You can soon permanently halt open-source AI progress in California by training a suboptimal model with 10^26 FLOPs.
— Rohan Pandey (e/acc) (@khoomeik) May 26, 2024
Then, all small compute-optimal models with similar performance to your large suboptimal model get covered by SB 1047.
Chinchilla scaling laws tell us: https://t.co/pX3AFfze25 pic.twitter.com/c2TxBOV3G9
anon, have you done your part to decelerate dealflow to decel VCs? https://t.co/7B6BDkpEOf pic.twitter.com/ANblQXzgsa
— Rohan Pandey (e/acc) (@khoomeik) November 15, 2023
quick schizo theory:
— Rohan Pandey (e/acc) (@khoomeik) December 6, 2023
top and bottom quarks were originally called "truth" and "beauty" quarks
"satya" in sanskrit = truth
"sundar" in sanskrit = beauty
the 2 mfs competing in the race to AGI are named after opposing fundamental particles
the simulation is fucking with us pic.twitter.com/5V9t3GSSNg
u ppl r not ready for whats coming
— Rohan Pandey (e/acc) (@khoomeik) October 5, 2023
(we're all in ML research) pic.twitter.com/8CRt3nmEhR
After organizing this hackathon @AGIHouseSF with @kylejohnmorris last weekend, we wrote an assembler that generates RDNA3 to target AMD GPUs for tinygrad (with @realGeorgeHotz's help).
— Rohan Pandey (e/acc) (@khoomeik) July 3, 2024
Here's what I learned going from AI Researcher to GPU Compiler Engineer in one day 🧵⬇️ https://t.co/2HnYApfq06
e/acc Meetup @AGIHouseSF last night was inspiring. Thank you to @BasedBeffJezos, @garrytan, @wagieeacc, @chrisprucha, @NexusVP & everyone else for the pro-homo-techno-capital energy.
— Rohan Pandey (e/acc) (@khoomeik) September 18, 2023
Feeling optimistic af. Details for October meetup & talk recordings out soon.
Back to build 🫡 pic.twitter.com/fMsf92c4vH
Interruptions make conversations feel natural.
— Rohan Pandey (e/acc) (@khoomeik) March 31, 2024
Much work has focused on AI voice assistants that can be interrupted by humans, but systems that know much more than us should be able to interrupt us too.
At @AGIHouseSF's Launchathon today, I'm launching Interrupting Cow 🐮📢 pic.twitter.com/8HgPSZ92Ie
wonder if we'll ever get an indian @natfriedman to sponsor an indus valley script decipherment prize
— Rohan Pandey (e/acc) (@khoomeik) May 18, 2024
even just putting together a dataset of the 6000 inscriptions discovered so far would be a huge win pic.twitter.com/rMMoCvUKhX
So it turns out you can replace backprop with fully parallelizable next token prediction losses at each layer and reach kinda comparable performance?
— Rohan Pandey (e/acc) (@khoomeik) June 11, 2024
Slightly less sample-efficient but 4x faster to train when parallelized. Not sure how well it scales yet tho. https://t.co/qJ3i2qPUz9 pic.twitter.com/xAtJhTwYku