Can machine learning improve discrete optimization algorithms without sacrificing theoretical guarantees? This was the central question of the talk I gave this spring at a few schools (UCSD, UIC, Yale, Penn): Machine Learning for Discrete Optimization: Theoretical Foundations. (🧵 1/7)

More time to submit to LAMP 2026! The deadline for spotlight talks, posters, and open problems has been extended to May 14. Learn more and submit: dravy.ttic.edu/lamp26.html

Nina Balcan, Avrim Blum, Piotr Indyk & Ali Vakilian are organizing a #STOC2026 Workshop on Machine Learning for Algorithms, featuring tutorials, talks & a poster session. Learn more and submit your poster by June 1: buff.ly/JdQci1c

We’re excited to announce the call for submissions for our workshop on Learning-driven Algorithms and Machine-aided Proofs (LAMP) at Toyota Technical Institute of Chicago (@tticconnect.bsky.social) on August 6–7. Huge thanks to my co-organizers Sandeep Silwal and Dravyansh Sharma.

This week at the Innovations in Theoretical Computer Science (ITCS) conference, Mingwei Yang is presenting our paper: 𝗦𝗺𝗼𝗼𝘁𝗵𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗲𝘁𝗿𝗶𝗰 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗮 𝗦𝗶𝗻𝗴𝗹𝗲 𝗦𝗮𝗺𝗽𝗹𝗲: 𝗕𝗲𝘆𝗼𝗻𝗱 𝗠𝗲𝘁𝗿𝗶𝗰 𝗗𝗶𝘀𝘁𝗼𝗿𝘁𝗶𝗼𝗻 by Yingxi Li, myself, and Mingwei Yang See Mingwei's talk here: youtu.be/yEBPI9c7OE8?...

@lawlessopt.bsky.social and I are excited to present our #AAAI2026 tutorial on “LLMs for Optimization: Modeling, Solving, and Validating with Generative AI.” When: Tuesday, Jan 20, 2026, 8:30am–12:30pm SGT Where: Garnet 216 (Singapore EXPO) (Connor’s intro slides are shown here.) CC @aaai.org

I’m excited to share the materials from my Stanford seminar course, “AI for Algorithmic Reasoning and Optimization”: vitercik.github.io/ai4algs_25/. It covered formal algorithmic frameworks for analyzing LLM reasoning, GNNs for combinatorial/mathematical optimization, and theoretical guarantees.

Please keep an eye out for Connor Lawless (@lawlessopt.bsky.social) on the faculty job market! Connor is a Stanford Human-Centered AI Postdoc, co-hosted by myself and Madeleine Udell. His research combines ML, computational optimization, and HCI, with the goal of building human-centered AI systems.

Excited to be chatting about our new paper "Understanding Fixed Predictions via Confined Regions" (joint work with @berkustun.bsky.social, Lily Weng, and Madeleine Udell) at #ICML2025! 🕐 Wed 16 Jul 4:30 p.m. PDT — 7 p.m. PDT 📍East Exhibition Hall A-B #E-1104 🔗 arxiv.org/abs/2502.16380

Our ✨spotlight paper✨ "Primal-Dual Neural Algorithmic Reasoning" is coming to #ICML2025! We bring Neural Algorithmic Reasoning (NAR) to the NP-hard frontier 💥 🗓 Poster session: Tuesday 11:00–13:30 📍 East Exhibition Hall A-B, # E-3003 🔗 openreview.net/pdf?id=iBpkz... 🧵

Join us for a Wikipedia edit-a-thon at #ACMEC25! When: July 8th, 8PM-10PM Where: Stanford Econ Landau 139 Website: sites.google.com/view/econcs-... Come hangout, grab snacks, and edit/create Wikipedia pages for EC topics. Suggest topics/articles that need attention: docs.google.com/spreadsheets...

Super excited about this new work with Yingxi Li, Anders Wikun, @ellen-v.bsky.social, and Madeleine Udell forthcoming at CPAIOR2025: LLMs for Cold-Start Cutting Plane Separator Configuration 🔗: arxiv.org/abs/2412.12038

Pulled a shoulder muscle trying to stay cool on the golf course in front of my PhD students and postdoc 😅 🏌‍♀️

📢 Join us at #NeurIPS2024 for an in-person Learning Theory Alliance mentorship event! 📅 When: Thurs, Dec 12 | 7:30-9:30 PM PST 🔥 What: Fireside chat w/ Misha Belkin (UCSD) on Learning Theory Research in the Era of LLMs, + mentoring tables w/ amazing mentors. Don’t miss it if you’re at NeurIPS!