Alex Tong
@AlexanderTong7
Postdoc at Mila studying cell dynamics with Yoshua Bengio. I work on generative modeling and apply this to cells and proteins.
Wrapping up #ICML2025 on a high note — thrilled (and pleasantly surprised!) to win the Best Paper Award at @genbio_workshop 🎉 Big shoutout to the team that made this happen! Paper: Forward-Only Regression Training of Normalizing Flows (arxiv.org/abs/2506.01158) @Mila_Quebec
🚨 Our workshop on Frontiers of Probabilistic Inference: Learning meets Sampling got accepted to #NeurIPS2025!! After the incredible success of the first edition. The second edition is aimed to be bolder, bigger, and more ambitious in outlining key challenges in the natural…
1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #NeurIPS2025! 📢 FPI@NeurIPS 2025: Frontiers in Probabilistic Inference – Learning meets Sampling Learn more and submit → fpiworkshop.org…
Thrilled to be co-organizing FPI at #NeurIPS2025! I'm particularly excited about our new 'Call for Open Problems'track. If you have a tough, cross-disciplinary challenge, we want you to share it and inspire new collaborations. A unique opportunity! Learn more below.
1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #NeurIPS2025! 📢 FPI@NeurIPS 2025: Frontiers in Probabilistic Inference – Learning meets Sampling Learn more and submit → fpiworkshop.org…
we’re not kfc but come watch us cook with our feynman-kac correctors, 4:30 pm today (july 16) at @icmlconf poster session — east exhibition hall #3109 @k_neklyudov @AlexanderTong7 @tara_aksa @OhanesianViktor
Come check out SBG happening now! W-115 11-1:30 with @charliebtan @bose_joey Chen Lin @leonklein26 @mmbronstein
🚨 ICML 2025 Paper 🚨 "On Measuring Long-Range Interactions in Graph Neural Networks" We formalize the long-range problem in GNNs: 💡Derive a principled range measure 🔧 Tools to assess models & benchmarks 🔬Critically assess LRGB 🧵 Thread below 👇 #ICML2025
🎉Personal update: I'm thrilled to announce that I'm joining Imperial College London @imperialcollege as an Assistant Professor of Computing @ICComputing starting January 2026. My future lab and I will continue to work on building better Generative Models 🤖, the hardest…
New OpenProblems paper out! 📝 Led by Malte Lücken with Smita Krishnaswamy, we present openproblems.bio – a community-driven platform benchmarking single-cell analysis methods. Excited about transparent, evolving best practices for the field! 🔗 nature.com/articles/s4158…
Yes! We were heavily influenced by your work @JamesTThorn although I think still quite challenging to train energy based models
Nice to see energy based diffusion models + SMC/ Feynman Kac for temp annealing is useful for non-toy examples!
🚨 I heard people saying that Diffusion Samplers are actually not more efficient than MD? Well, if that's you, check out our new paper PITA done in collab with a dream team in the 🧵 below👇. Finally, a diffusion sampler that is more efficient in # energy evals compared to…
(1/n) Sampling from the Boltzmann density better than Molecular Dynamics (MD)? It is possible with PITA 🫓 Progressive Inference Time Annealing! A spotlight @genbio_workshop of @icmlconf 2025! PITA learns from "hot," easy-to-explore molecular states 🔥 and then cleverly "cools"…
Given q_t, r_t as diffusion model(s), an SDE w/drift β ∇ log q_t + α ∇ log r_t doesn’t sample the sequence of geometric avg/product/tempered marginals! To correct this, we derive an SMC scheme via PDE perspective Resampling weights are ‘free’, depend only on (exact) scores!
🧵(1/6) Delighted to share our @icmlconf 2025 spotlight paper: the Feynman-Kac Correctors (FKCs) in Diffusion Picture this: it’s inference time and we want to generate new samples from our diffusion model. But we don’t want to just copy the training data – we may want to sample…
Why do we keep sampling from the same distribution the model was trained on? We rethink this old paradigm by introducing Feynman-Kac Correctors (FKCs) – a flexible framework for controlling the distribution of samples at inference time in diffusion models! Without re-training…
🧵(1/6) Delighted to share our @icmlconf 2025 spotlight paper: the Feynman-Kac Correctors (FKCs) in Diffusion Picture this: it’s inference time and we want to generate new samples from our diffusion model. But we don’t want to just copy the training data – we may want to sample…
Check out FKCs! A principled flexible approach for diffusion sampling. I was surprised how well it scaled to high dimensions given its reliance on importance reweighting. Thanks to great collaborators @Mila_Quebec @VectorInst @imperialcollege and @GoogleDeepMind. Thread👇🧵
🧵(1/6) Delighted to share our @icmlconf 2025 spotlight paper: the Feynman-Kac Correctors (FKCs) in Diffusion Picture this: it’s inference time and we want to generate new samples from our diffusion model. But we don’t want to just copy the training data – we may want to sample…
Using ESM2 through hugging face? Might as well speed it up with flash attention!
FlashAttention-accelerated Protein Language Models ESM2 now supports Huggingface. One line change, up to 70% faster and 60% less memory! 🧬⚡ Huggingface: huggingface.co/fredzzp/esm2_t… Github: github.com/pengzhangzhi/f…
The supervision signal in AI4Science is so crisp that we can solve very complicated problems almost without any data or RL! In this project, we train a model to solve the Schrödinger equation for different molecular conformations using Density Functional Theory (DFT) In the…
(1/n)🚨You can train a model solving DFT for any geometry almost without training data!🚨 Introducing Self-Refining Training for Amortized Density Functional Theory — a variational framework for learning a DFT solver that predicts the ground-state solutions for different…
Really excited about this new paper. As someone who spent a ton of time training regular flows with MLE and got burned FORT training actually works making flows cool again 🌊
Excited to release FORT, a new regression-based approach for training normalizing flows 🔥! 🔗 Paper available here: arxiv.org/abs/2506.01158 New paper w/ @osclsd @jiarlu @tangjianpku @mmbronstein @Yoshua_Bengio @AlexanderTong7 @bose_joey 🧵1/6