Pre-ICML event 2025 – Sample Space
Last week (on 3rd July), we co-organised the pre-ICML 2025 event at UCL, bringing together around 100 Machine Leaning researchers (students and professionals alike) from the greater London area to sha...
A bit belated, but I co-authored a 3-minute-read blog post about our experience in co-hosting the Pre ICML event at UCL; have a look here: shorturl.at/Cihwm
This was a great experience, of big credit of which goes to an amazing team of the co-organisers, and the event chairs.
14.07.2025 07:40 — 👍 0 🔁 0 💬 0 📌 0
Pre ICML @ London 2025
The goal of this meetup is to bring together students, researchers, and engineers from the greater London area to discuss machine learning research presented at International Conference on Machine Lea...
Our department’s CSML theme is co-organising a ‘pre-ICML’ event to showcase the exciting research in machine learning taking place at UCL and across London on the 3rd July. Details on registration and the call for talks/posters is available here: sites.google.com/view/pre-icm...
09.06.2025 09:30 — 👍 0 🔁 1 💬 0 📌 0
An ICML workshop on scaling up intervention models!
If you're working on predicting the effects of novel/unseen interventions by combining evidence from different intervention regimes, consider submitting to the workshop.
29.03.2025 09:09 — 👍 0 🔁 0 💬 0 📌 0
Many congratulations! Do you intend to publicise the thesis?
04.03.2025 09:25 — 👍 1 🔁 0 💬 1 📌 0
no pain au chocolat, no gain au chocolat
29.11.2024 12:58 — 👍 946 🔁 177 💬 29 📌 9
PhD researcher in Machine Learning at Imperial College. Visiting at University of Oxford.
Interested in all things involving causality and Bayesian machine learning. Recently I have also been interested in scaling theory.
https://anish144.github.io/
Researcher in machine learning
Research Director, Founding Faculty, Canada CIFAR AI Chair @VectorInst.
Full Prof @UofT - Statistics and Computer Sci. (x-appt) danroy.org
I study assumption-free prediction and decision making under uncertainty, with inference emerging from optimality.
Machine learning, environmental modeling, sustainability, robotics
Professor @UCL
He/him
🏳️🌈👨👨👧👦 interested in causal inference, experimentation, optimization, RL, statML, econML, fairness
Cornell & Netflix
www.nathankallus.com
Assistant prof in the Amsterdam Machine Learning Lab at the University of Amsterdam | ELLIS scholar | #causality #causalML anything #causal | 🇮🇹🇸🇮 in 🇳🇱 | #UAI2025 program chair and #UAI2026 general chair
https://saramagliacane.github.io/
Assistant Professor of Biostatistics at Columbia.
I study causal inference, graphical models, machine learning, algorithmic (un)fairness, social + environmental determinants of health, etc. Opinions my own.
http://www.dmalinsky.com
Machine Learner by day, 🦮 Statistician at ❤️
In search of statistical intuition for modern ML & simple explanations for complex things👀
Interested in the mysteries of modern ML, causality & all of stats. Opinions my own.
https://aliciacurth.github.io
Postgraduate researcher (PhD) at Imperial College London and visiting researcher at the University of Oxford. Working on probabilistic machine learning.
Research fellow @OxfordStats @OxCSML, spent time at FAIR and MSR
Former quant 📈 (@GoldmanSachs), former former gymnast 🤸♀️
My opinions are my own
🇧🇬-🇬🇧 sh/ssh
Associate Professor in Machine Learning at the University of Oxford.
Interested in automatic inductive bias selection using Bayesian tools.
Professor of Statistics and Machine Learning at UCL Statistical Science. Interested in computational statistics, machine learning and applications in the sciences & engineering.
Machine learning researcher, working on causal inference and healthcare applications
PhD student at UCL Statistical Science working at the intersection of Bayesian modelling and computation
PhD student in Computational Statistics and Machine Learning at STOR-i CDT, Lancaster University, UK.
Research Interests: Sampling Algorithms, Bayesian Experiment Designs, Neural Amortization.
https://shusheng3927.github.io/
PhD in Statistical ML at University of Oxford. Interested in the intersection of causal inference and ML.
