🚨 Just 1 month away: Rice Paris Summit on Large-Scale Learning & Optimization! 🇫🇷 Join top minds in #AI & #Optimization: 💥 Chamon (X): ML under constraints 💥 Heckel (TUM): ML x Sci Discovery 💥 Taylor (INRIA): Systematic opt. via PEPit 💥 Zardini (MIT): Future Mobility 💥

The AI research paradox we're all living: 📚 Hundreds of papers hit ArXiv daily. Keeping up = impossible. Two paths: 🎯 Follow your genuine ideas (risk: get scooped) 🏃‍♂️ Chase what "big dogs" do (risk: lose your soul) I'm jealous of researchers from eras when ideas could breathe

🚀 AI will disrupt jobs in 5 years - Google DeepMind CEO Perfect timing: Rice University CS just launched our new AI major! For decades, CS = discrete algorithms (sorting, graphs, complexity). But AI revealed the other half: continuous algorithms like gradient descent -

🧠 The theory-to-practice gap is real, but we're closing it one rank at a time! Just published online: "Cascading Error Dynamics in Sequential Learning" - tackling how errors propagate when AI learns complex tasks step-by-step. 🔍 Key insights: • Sequential learning as a form

AI's evolution: We've moved from "building better algorithms" to "finding better problems" 🎯 (highly recommended read: ysymyth.github.io/The-Second-Hal…) AlphaEvolve (deepmind.google/discover/blog/…) just beat Strassen's 56-year-old matrix multiplication record & solved dozens of open math

🚀 Exciting insights from our latest research at Rice University, in collaboration with MSR! Our paper introduces Dynamically Decentralized Orchestration of Mixtures of Experts (DDOME), highlighting: - Joint Training is Key: Combining gating functions with experts leads to

🌟 Just wrapped up the RiP-SILO workshop! Huge thanks to all the speakers for their insightful talks in such an academically-friendly environment. The event was refreshing, with friendly vibes and ample time for dialogue. We embraced diverse perspectives—from graph theory to AI

🚀 Since the NeurIPS deadline, I’m feeling like a PhD student again! 🎓 I’ve been working on a polynomial, fully parallelizable algorithm for MaxCUT, a project that traces back to my Master’s thesis. Here’s why: 0. Refreshing Detachment: Stepping back from the LLM madness has

🚀 Dear PhD Students, (Boilerplate but good to remind) As you navigate the world of research, remember this: 1. Ideas are abundant. But execution? That's where the challenge lies. What looks great on paper may stumble in practice. 2. Small successes are just the beginning.

🚀 The Future of Computer Science: An Indispensable Partner in Innovation 🚀 In an era dominated by AI, it's easy to misunderstand the role of computer science (CS). Many well-meaning voices, including some high-profile figures, have propagated this myth, but let’s set the

Had a night chat with a friend back home that made me rethink AGI's "general" intelligence claim. We have linguistic, spatial, emotional, kinetic, interpersonal intelligence - yet we act like next-word prediction covers it all. Society shifted from "strongest wins" to "smartest

Recently, I delved into the role of AI in PK-8 schools, a topic that resonates with my commitment to blending technology and education, as part of the AI Major at Rice University. And I will focus on this age range as I feel it more gentle and "innocent": Kids should be kids, and

No hype, just breakthroughs: Rice AI summit quietly makes waves in Paris. 🔗 Learn more: news.rice.edu/news/2025/no-h… The summit was sponsored by Rice University, Rice Global, Rice Engineering and Computing and The Ken Kennedy Institute. Rice Electrical & Computer Engineering Rice Computer Science #SolvingforGreaterGood

🌟 Reflections on AI and Computer Science Research 🌟 Reading the recent paper "Hierarchical Reasoning Model" (lnkd.in/d5vzcEck) sparked a thought: What purpose does AI (and CS) research truly fulfill? Other disciplines claim grand challenges (with a huge grain of salt):

1/ 29k “ideas” ≠ 29k novelties. AAAI-26 saw ~29k submissions (~23k under review). We’re producing faster than we can digest; flooding the system and blurring true novelty. 2/ Minor tweaks, new data, recombinations aren’t bad. But at this volume, reviews drift from shared ground

AAAI-26 just added AI-generated reviews to every paper. If these work “better” than humans, it’s not that we’re worse;it’s that we’re busy. Open questions: - How do we track long-term bias drift (topic, citation gravity, method conservatism)? - Will authors “jailbreak” the

Hot takes about AI are fun. Designing great courses is underrated exciting. As we build “Math of AI” (COMP 282) for Rice CS’s new AI major, I’m reflecting on COMP 414 (Optimization). Highlights: 150+ in the project Slack, 100+ pages of double-column notes (w/ exercises), two

As a greek scientist, I'm obliged to read this

Hot take: “Superintelligence” is the wrong yardstick for lab automation. Intelligence is lumpy: internet-scale text → strong literacy; wet labs/physics/clinics ≠ day-one mastery. You can get real value without “super.” One new angle on domain data is a win. Labs need

Using LLMs ≠ building them ≠ studying smaller ones (that’s me). Question: Do LLMs “reread your entire chat” every turn? My mental model: - Finite context window = working memory. - New message + most relevant recent history get packed in. - If too long,