Ellen Vitercik

Topic 4: Theoretical Guarantees

- Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods (Caramanis et al., NeurIPS’23)
- Approximation Algorithms for Combinatorial Optimization with Predictions (Antoniadis et al., ICLR’25)

Topic 3: Math Optimization

- OptiMUS-0.3: Using LLMs to Model and Solve Optimization Problems at Scale (AhmadiTeshnizi et al., arXiv’25)
- Contrastive Predict-and-Search for Mixed Integer Linear Programs (Huang et al., ICML’24)
- Differentiable Integer Linear Programming (Geng et al., ICLR’25)

Topic 2: Graph Neural Networks

- One Model, Any CSP: GNNs as Fast Global Search Heuristics for Constraint Satisfaction (Tönshoff et al., IJCAI’23)
- Dual Algorithmic Reasoning (Numeroso et al., ICLR’23)
- DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization (Sun & Yang, NeurIPS’23)

Topic 1: Transformers & LLMs

- What Learning Algorithm is In-Context Learning? (Akyürek et al., ICLR’23)
- Transformers as Statisticians (Bai et al., NeurIPS’23)
- We Need An Algorithmic Understanding of Generative AI (Eberle et al., ICML’25)
- Evolution of Heuristics (Liu et al., ICML’24)

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.

On top of his research, my PhD students and I can attest that he’s a thoughtful, generous collaborator and mentor.

Please don’t hesitate to reach out if you’d like me to share my very strong recommendation letter.

(Photo credit: @cpaior.bsky.social.)

OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-t...

Connor has done exciting work on leveraging LLMs to model and solve large-scale optimization problems (arxiv.org/abs/2407.19633, arxiv.org/abs/2412.12038), developing mathematical optimization tools to make ML models more interpretable (arxiv.org/abs/2502.16380), among many other contributions.

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.

Understanding Fixed Predictions via Confined Regions Machine learning models can assign fixed predictions that preclude individuals from changing their outcome. Existing approaches to audit fixed predictions do so on a pointwise basis, which requires ac...

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...

Congrats Kira!!

LLMs for Cold-Start Cutting Plane Separator Configuration Mixed integer linear programming (MILP) solvers ship with a staggering number of parameters that are challenging to select a priori for all but expert optimization users, but can have an outsized impa...

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!

Hi Emily, could you please add me? Thanks for making it!

Can you add me? 😀