Exactly the goal of our open ended projects! @BrownCSDept Seeing students enjoy it worths every effort you pour into course prep. Here is why I love teaching CS/Optimization:

Forge: Foundational Optimization Representations from Graph Embeddings. #Forge is a self-supervised method for embedding optimization problems, just like your favorite text embedding. A single pre-trained model works across tasks, problems, and sizes 🤯

Balans: Bandit-based Adaptive Large Neighborhood Search ⁦⁦@IJCAIconf⁩ Online, meta-solver for solving mixed-integer programming (MIP) problems. Install: pip install balans Library: https://t.co/Cq4zAbaYnN Paper: https://t.co/I8Va1tQAYi

In Montreal for #IJCAI2025? Stop @CSatUSC PhD student Junyang Cai's poster presentation Wed. 10 am. BALANS is an open-source, solver-agnostic meta-solver for #MIPs. Paper: https://t.co/1mq6bsRam1 @BDilkina @serdar_kadioglu @AI4OPT @USCViterbi @IJCAIconf #Optimization #OpenSource

📢 If you're solving real-world problems with AI, we want to hear from you! As Vice Chair of IAAI'26, I invite you to submit your work to Innovative Applications of AI 2026 🚀 🗓️ Deadline: Aug 18, 2025 📍 Conference: Jan 2026, Singapore as part of #AAAI

CORS 2025! June 9–11, 2025, Edmonton, AB

another excellent #NYTechWeek panel with @IBM re: Open Source AI 👏👏👏

Thank you for the kind invitation @KuisAICenter @ftm_guney —looking forward to it!

https://t.co/uEeMJAdiy7

Driven by the complexity and the sheer scale of real-world problems in industry while seeking their abstractions & generalizations. Check out our latest applied research at @Fidelity Ai Center #ai #ml #fidelityassociate https://t.co/uxU2d0LkEL

🤖AWS Quantum Solutions Lab and @FCATalyst implemented an interpretable ML classification model for #ExplainableAI (XAI) via expressive Boolean formulas, which could be sped up by quantum computers. @serdar_kadioglu 🔗 https://t.co/L1kgWD0C84

Earth modellers.. Clearly, they are doing some cool stuff 🔥🔥

You don’t always see a cool advertisement like this: “We’re Hiring — Constraint Programmers” #cpaior #orms #mobi #travel

Thanks to our friends at @huggingface making demos and sharing pre-trained models a possibility 🤗 If you want to extract optimization entities (objective, variables, limits, etc.) You can install our library via “pip install ner4opt” https://t.co/ujQwVGwFK7

Thank you for sharing @thserra —impressive how you can follow talks and tweet them 👏👏 We are seeing a paradigm shift with audio/text becoming the common medium for human-computer interaction #ner4opt is our attempt to combine #NLP and #Optimization https://t.co/Kys64xqPXJ

Bu aplikasyonun üstüne hala çalışıyoruz, google sheets'e AFAD'ın istediği halde post request atması kaldı. Pipeline easyocr ve @OpenAI davinci üstünden çalışıyor. @Gradio'nun API call özelliği sayesinde görüntülerle istek atabilirsiniz. @huggingface'e GPU için teşekkür ederiz.

Last year, we criticized LMs for performing “too well” with pathological prompts, and many papers have now shown similar results with corrupted ICL or CoT. In our new work, we find that *humans* also perform surprisingly well with irrelevant prompts! (But not misleading ones.) ⅕

New MOOC on Constraint Programming and #MiniCP @PVanHentenryck @AIA_UCLouvain @GeoffreyDeSmet @serdar_kadioglu @JFPuget @thserra @chtruchet @ozgurakgun @TiasGuns @JeffLinderoth @BDilkina @TobyWalsh @parubin @hakankj

There is the #iris dataset in #ml and then there is the #iris from Claude #Monet.. Dialogue Monet - Mitchell @FondationLV

I am looking forward to hosting @serdar_kadioglu at @BucknellU tomorrow for a talk on multi-objective optimization in recommender systems. #orms If you would like to attend virtually, DM for the Zoom link!

Join us Nov. 21st for a new webinar! In this roundtable with @ak_ronay from Nvidia, @serdar_kadioglu at Fidelity Investments, @jacopotagliabue, and moderated by Comet CEO Gideon Mendels, we will be discussing how to build better recommender systems. https://t.co/VlnwsjWzV0