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Nicholas Tomlin Profile
Nicholas Tomlin

@NickATomlin

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Incoming assistant professor at TTIC, current faculty fellow at NYU CDS, and previous PhD student at Berkeley. Natural language processing. He/him.

New York, NY
Joined November 2013
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@NickATomlin
Nicholas Tomlin
7 months
I'm incredibly excited to share that I'll be joining @TTIC_Connect as an assistant professor in Fall 2026! Until then, I'm wrapping up my PhD at Berkeley, and after that I'll be a faculty fellow at @NYUDataScience
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@gabe_grand
Gabe Grand
6 days
Do AI agents ask good questions? We built “Collaborative Battleship” to find out—and discovered that weaker LMs + Bayesian inference can beat GPT-5 at 1% of the cost. Paper, code & demos: https://t.co/lV76HRKR3d Here's what we learned about building rational information-seeking
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@NickATomlin
Nicholas Tomlin
10 days
NYU is recruiting faculty fellows! Happy to chat with anyone who is considering this as an option:
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@NickATomlin
Nicholas Tomlin
16 days
We're also located in the wonderful city of Chicago, which has surprisingly low CoL given all it offers
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@NickATomlin
Nicholas Tomlin
16 days
In many ways, I think TTIC is the ideal form of an academic research group. A more manageable balance of teaching and research, a tight-knit group of faculty who are actively engaged in their work, and excellent PhD students
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@NickATomlin
Nicholas Tomlin
16 days
TTIC is hiring for both tenure-track and research assistant professor positions:
@TTIC_Connect
TTIC
16 days
TTIC is hiring Research Assistant Professors in #ML, #Robotics, #Algorithms & more! 3-year, fully funded role — no teaching required, full research freedom, strong mentorship & collaborations. Join a top #CS research community in Chicago. Apply by Dec 1: https://t.co/LIXnlkH597
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@ethayarajh
Kawin Ethayarajh
20 days
The Applied AI group at UChicago Booth is hiring this year! This was the only non-CS faculty position I applied to during my search, and it turned out to be an incredible fit: more compute than most CS depts I interviewed with, tons of research freedom, and (best of all!) no
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@tallinzen
Tal Linzen
20 days
Prediction is central in human language comprehension. LLMs are trained to predict the next word. Match made in heaven? Turns out the better mainstream LLMs become, the less useful they are as cognitive models. @byungdoh and I wrote a position paper on why that is, and how to
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@YGandelsman
Yossi Gandelsman
21 days
I’m hiring PhD students for 2026 @TTIC_Connect. More details here:
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@alexisjross
Alexis Ross
22 days
One of my takeaways from #COLM2025 was that people are thinking a lot about user simulation (have been thinking about this myself in the context of tutoring!) Really exciting to see this work on the topic 🤩
@tareknaous
Tarek Naous
23 days
Simulating user–AI conversations helps us understand how LMs work in multi-turn settings. Prompting LMs like GPT-4o to simulate users is common, but their assistant nature makes it hard to replicate user behavior. We introduce User LMs - trained to be users, not assistants.
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@NickATomlin
Nicholas Tomlin
1 year
COLM is the best conference I have attended throughout the entirety of my PhD, really looking forward to future iterations
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@NickATomlin
Nicholas Tomlin
1 month
I'll be at COLM in Montreal next week! 🇨🇦 Currently thinking about: scalable environments for RL, user sims, more data-efficient learning, and recruiting PhD students for my new group at TTIC
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@NickATomlin
Nicholas Tomlin
1 month
We wrote a blogpost outlining some open questions for building more useful + human-like user simulators Also: if you're applying to PhDs this cycle and interested in working on topics like these, or reasoning, interaction, and AI capabilities more broadly, please reach out!
@realJessyLin
Jessy Lin
1 month
What does it take to build a human-like user simulator? // To train collaborative agents, we need better user sims. In blog post pt 2, @NickATomlin and I sketch a framework for building user simulators + open questions for research: https://t.co/FD0dRt22lR
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@realJessyLin
Jessy Lin
1 month
What does it take to build a human-like user simulator? // To train collaborative agents, we need better user sims. In blog post pt 2, @NickATomlin and I sketch a framework for building user simulators + open questions for research: https://t.co/FD0dRt22lR
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jessylin.com
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@realJessyLin
Jessy Lin
2 months
🔍 How do we teach an LLM to 𝘮𝘢𝘴𝘵𝘦𝘳 a body of knowledge? In new work with @AIatMeta, we propose Active Reading 📙: a way for models to teach themselves new things by self-studying their training data. Results: * 𝟔𝟔% on SimpleQA w/ an 8B model by studying the wikipedia
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@gregd_nlp
Greg Durrett
3 months
📢I'm joining NYU (Courant CS + Center for Data Science) starting this fall! I’m excited to connect with new NYU colleagues and keep working on LLM reasoning, reliability, coding, creativity, and more! I’m also looking to build connections in the NYC area more broadly. Please
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@TTIC_Connect
TTIC
3 months
Happy #InternationalCatDay! 🐈
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@NickATomlin
Nicholas Tomlin
4 months
For many interactive tasks, we might want to train LLMs in conjunction with simulated users. In this blogpost, we discuss some of the challenges with this approach:
@realJessyLin
Jessy Lin
4 months
User simulators bridge RL with real-world interaction // https://t.co/bsrYxVHuVo How do we get the RL paradigm to work on tasks beyond math & code? Instead of designing datasets, RL requires designing environments. Given that most non-trivial real-world tasks involve
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@TTIC_Connect
TTIC
4 months
We’re proud to announce three new tenure-track assistant professors joining TTIC in Fall 2026: Yossi Gandelsman (@YGandelsman), Will Merrill (@lambdaviking), and Nick Tomlin (@NickATomlin). Meet them here: https://t.co/8UzLmuytNe
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@wellecks
Sean Welleck
5 months
New paper by Andre He: Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening https://t.co/R3oKGqWwOw Tired of sharpening the distribution? Try unlikeliness reward to learn new things from the roads less traveled
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@gregd_nlp
Greg Durrett
5 months
Revoking visas to Chinese PhD students is economically shortsighted and inhumane. Most Chinese PhD students stay in the U.S. after graduation (first image, stats from 2022). They're staying and building technology in the U.S., not taking it to China. Immigrant students create
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