Raymond Chua

After a fantastic week of discussions at COSYNE, I’m excited to now be in Amsterdam for the Winter School on Foundation Models. I am interesting in developing foundation models that can continuously learn, adapt, and reason. If you’re around, I would love to connect! 🇳🇱

📢AI for Science Conference 2026: one week to go! Last chance to register to hear from our esteemed speakers & explore how AI is transforming scientific discovery & innovation. 📅31 Mar 2026 📍@royalsociety, London (in-person only) 🎫Secure your place: bit.ly/4anI4ZA

I am experiencing the same. Most of the papers are not well written, statistical information of the empirical results were not reported, no baseline comparisons, only cite papers from the last 1-2 years despite the field existed for decades.

This is a joint work with my wonderful advisors Profs. Doina Precup and @tyrell_turing! It is also the last project of my PhD thesis and I’m grateful that all three contributions in my PhD thesis are and were presented at Cosyne during my PhD studies!

Excited to present my poster at COSYNE 2026! We explore whether Successor Features (SFs) in reinforcement learning exhibit properties similar to hippocampal place cells, using analytical tools from neuroscience. Come say hi if you’re around!

Two business school professors from the University of Technology in Sydney have sounded the alarm on the declining quality of academic literature in a new publication titled “The junkification of research”. Drawing parallels to the “enshittification” of online platforms, they argue that similar forces are now overwhelming scholarly publishing. The key drivers are threefold: 1) relentless “publish or perish” pressures in academia, 2) scientific publisher’s incentive to publish more to make more money, and 3) AI making paper production faster and easier. Taken together, they say, these drivers are a recipe for disaster. The authors call for a shift to not-for-profit models of scientific publishing and better evaluation systems. I strongly doubt either is going to happen. The problem is of course not new, and you all know that I have been drawing attention to this trend for more than a decade. It is interesting to see, however, that the awareness for the issue is increasing. Paper: Rhodes, C., & Linnenluecke, M. K., “The junkification of research” Organization (2025).

@sarahookr Totally agree! Went there twice during my recent trip to London :)

Hands down one of the best meals yet I have had in London.

@spacecadet_kels @UniofOxford Congrats!

Introducing Dr. Doerksen 😎 On Friday, I successfully defended and passed my @UniofOxford PhD viva with minor corrections - I’m going to be the first in my family to be a Dr!! In true Kelsey fashion, I defended at 6:30am at NeurIPS in San Diego - not a bad place to celebrate 🤠

@MavorParker @VmaxAI @SEAWorkshop 90% Dark?

At @VmaxAI, we take the bitter lesson seriously. Come get your Vmax chocolate at the scaling environments workshop @SEAWorkshop

@ReplyGrinder @arna_ghosh @dwarkesh_sp @tyrell_turing nature.com/articles/s4159…

@ReplyGrinder @arna_ghosh @dwarkesh_sp @tyrell_turing One example would be this article he and co-authors published in Nature Neuroscience in 2019 on deep learning architecture, loss functions, and learning rules, and how they can be used as theoretical models in neuroscience.

Looking for a neuroscientist to interview on my podcast. Keen for someone who can draw ML analogies for how the brain works (what's the architecture & loss/reward function of different parts, why can we generalize so well, how important is the particular hardware, etc).

@arna_ghosh @dwarkesh_sp @tyrell_turing I also second @tyrell_turing !

@dwarkesh_sp @tyrell_turing fits the bill perfectly! Neuroscientist ✅ Neuro-AI connections ✅ Amazing communicator ✅ 🙌

An excellent article on how models are treated differently in AI vs in Neuroscience. While the field of AI goes rampant on creating bigger, faster and more capable models, interpretability and verifiability struggles to keep up at the same pace. How should we address this issue?

@alec_helbling @karanjagtiani04 Would also like to use a version of tsne and Umap!

@karanjagtiani04 Thanks! Perhaps in the future

Data often lie on a low-dimensional manifold embedded in a high-dimensional space. But these manifolds are often highly non-linear, making linear dimensionality reduction methods like PCA insufficient. This has motivated the development of non-linear dimensionality reduction.

@arna_ghosh @nsaphra Today.

@nsaphra When is this due?

ICLR review deadline update: 9/13 papers in my AC batch are below the required review count. 5/13 don't even have 2 reviews. If you want a ticket to my 2026 emergency reviewer appreciation party, this is YOUR CHANCE to DM me for an LM interp/analysis review assignment.

@mariyaivasileva Sorry to hear that Mariya. Hope you find a new opportunity soon!

I was impacted by the Meta PAR/FAIR layoffs today. I’m a research scientist passionate about computer vision, generative models, multimodal understanding, and AI safety. Over the past decade, I’ve contributed to advancing a broad range of applied machine learning problems — from multimodal generative models and visual recommender systems, to 2D-to-3D human modeling, large-scale data generation for foundation model training, mechanistic interpretability, and rigorous evaluations for trust & alignment of large-scale AI systems. I’m actively seeking new opportunities — please reach out if you have any openings!

@mariyaivasileva @rohanbanerjeee @LynnCherif @MandanaSamiei Thanks for visiting us and looking forward to crossing paths again!

Lovely to meet you @rohanbanerjeee, @LynnCherif, @MandanaSamiei and @RaymondRChua! Thank you for the thoughtful discussions on anything and everything AI, the field at large, academia vs industry, work-life balance, and open research questions — looking forward to the next time we cross paths at conferences 😊

Hanging out at @Mila_Quebec for the next couple of hours — if you’re free for coffee, say hi and chat with us! 🤗

@rpatrik96 @klindt_david @Mila_Quebec Hey Patrik! Would love to chat. Drop me a DM!

I am heading to @Mila_Quebec for 2 months to work together with Dhanya Sridhar, Simon Lacoste-Julien and their groups. Reach out if you want to talk about (causal) representation learning, OOD generalization, self-supervised learning, and compositionality—in LLMs and beyond.

@mariyaivasileva @Mila_Quebec DM me!

Spontaneously in Montréal for three days! Any friends from @Mila_Quebec want to grab coffee, walk around and chat?