Machine Learning Seminar Series

Hosted by Ruben Coen-Cagli

Machine learning (ML) is a rapidly developing branch of artificial intelligence (AI), with a long history rooted in statistics, computer science, and physics. Its recent successes in AI applications—from self-driving cars to content generation—have featured prominently in the scientific and popular news. The excitement for ML is also growing in the biomedical community as it becomes clear that ML could assist and improve practical applications ranging from medical image analysis to discovering patterns in patient databases, as well as to address basic science questions such as protein structure prediction, interpreting genetic networks and explaining the function of brain circuits.

In August 2016, faculty of systems and computational biology started the Reading Group on Recent Advances in Machine Learning, an informal, monthly meeting in which we discuss the newest publications and techniques in ML. The meeting offers the opportunity to discover new applications of ML, learn the techniques that make such advances possible, and discuss higher-level conceptual issues.

The meeting typically lasts 1 to 1.5 hours, with slide presentation, questions and discussions. Everyone is welcome to attend and join the interactive discussion. Please see below to view the full calendar and meeting locations (which can vary month to month), and contact Dr. Ruben Coen-Cagli at ruben.coen-cagli@einsteinmed.edu for information. Also, be sure to bookmark this page for easy reference and updates.

Calendar for 2026-27

Past Meetings

  • 05/04/2026 – Lyle Muller (Western University)
  • 03/02/2026 – Tai-Sing Lee (Carnegie Mellon University)
  • 11/24/2025 – Sam Nastase (University of Southern California)
  • 11/03/2025 – Erica L. Busch (Yale University)
  • 05/20/2025 – SueYeon Chun (New York University)
  • 04/07/2025 – Hossein Adeli (Columbia University) (Price Center Room 451)
  • 02/10/2025 – Steven Zucker (Yale University)
  • 01/20/2025 – Zhongming Liu (University of Michigan, Ann Arbor)
  • 11/25/2024 – Shuyang Sun (Oxford University)
  • 10/23/2023 – Aviv Bergman (Albert Einstein College of Medicine)
  • 11/20/2023 – Vittorio Caggiano (FAIR, Meta AI)
  • 12/4/2023 – Paolo Napoletano (University of Milano Bicocca)
  • 5/15/2023 – Zachary Flamholz (Einstein, Kelly lab) - Large Language Models for Biologic Discovery
  • 4/24/2023 – Maryam Shanechi (University of Southern California)
  • 3/27/2023 – Bo Wang (NIH/NCI-CCR) – “Machine Learning and the Mutational Effects Problem”
  • 01/23/2023 – Kohitij Kar (MIT and York University) – “Probing the neural mechanisms of primate visual cognition”
  • 12/27/2022 – Ulisse Ferrari (Sorbonne University, Paris) – “How do natural neuronal networks deal with noise?”
  • 05/2022 – Ilker Yildirim (Yale) - “Reverse-engineering the neural code in the language of objects and generative models”
  • 04/2022 – Olivier Henaff (Google Deep Mind) - “Towards general self-supervised learning”
  • 03/2022  – Carsen Stringer (HHMI Janelia) - "Making sense of large-scale neural and behavioral data"
  • 02/2022  – Yinghao Wu’s lab (Einstein) - "A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes"
  • 11/2021 – Thomas Serre (Brown) - “Feedforward and feedback processes in visual recognition”
  • 11/2021 – Ben Cowley (Princeton) - "Finding compact models of visual cortical neurons in macaque V4"
  • 05/2021 – Stephane Deni (Facebook AI) – Self-Supervised Learning Inspired by the Visual System.
  • 04/2021 – Luigi Acerbi (Helsinki University) – Practical sample-efficient Bayesian inference for models with and without likelihoods.
  • 03/2021 – Saad Kahn (Kelly lab, Einstein) – Reinforcement Learning.
  • 02/2021 – Odelia Schwartz (University of Miami) – Normalization in neuroscience and deep neural networks.
  • 01/2021 – Judy Wawira Gichoya (Emory) – Machine Learning for Health in Real Life.
  • 12/2020 – Ingmar Kanitscheider (Open AI) – Emergent tool use from multi-agent autocurricula.
  • 11/2020 – Theofanis Karaletsos (Uber AI Labs) – Structured priors for neural networks.
  • 05/2020 – Nikolaus Kriegeskorte (Columbia University, Director of Cognitive Imaging). Testing deep neural network models of human vision with brain and behavioral data.
  • 04/2020 – Special session on AI/ML initiatives for COVID-19.
  • 02/2020 – Aude Genevay (MIT, Geometric Data Processing Group). Optimal transport and applications.
  • 01/2020 – Youtube videolecture by Surya Ganguli (Stanford). Deep Learning Theory: From Generalization to the Brain.
  • 12/2019 – Sacha Sokoloski (Einstein, Coen-Cagli lab). State-of-the-Art of Artificial General Intelligence.
  • 11/2019 – Saad Kahn (Einstein, Kelly lab). Interpretation methods for deep learning models: saliency mappings.
  • 10/2019 – Ruben Coen-Cagli (Einstein). Neural population control using ‘mind-blowing’ synthetic images.
  • 05/2019 – Rajesh Ranganath (NYU Courant Institute)
  • 03/2019 – Multiscale interpretable models of neural dynamics, presented by Memming Park (Stony Brook)
  • 02/2019 – Probabilistic segmentation with U-NET, presented by Ruben Coen-Cagli (Einstein)
  • 01/2019 – Enhancing fluorescence microscopy with deep learning, presented by Adrian Jacobo (Rockefeller)
  • 11/2018 – Geometric deep learning, presented by Saad Kahn (Einstein)
  • 10/2018 – Adversarial networks, presented by Ruben Coen-Cagli (Einstein)
  • 05/2018 – Multiscale Methods for Networks, presented by Bo Wang
  • 04/2018 – The scattering transform, presented by Jonathan Vacher
  • 03/2018 – Topological data analysis, presented by Michoel Snow
  • 02/2018 – Recurrent Neural Networks for Sequence Learning, presented by Sacha Sokoloski
  • 01/2018 – Opening the black box of Deep Neural Networks via Information, presented by Saad Khan
  • 12/2017 – Probabilistic programming with STAN, presented by Dylan Festa
  • 11/2017 – Visualizing Data using t-SNE, presented by Daniel Pique
  • 10/2017 – Deep Convolutional Neural Networks, presented by Sacha Sokoloski
  • 09/2017 – Bayesian sparse priors and shrinkage, presented by Shuonan Chen
  • 08/2017 – The Variational Autoencoder, presented by Ruben Coen-Cagli