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Current Seminar Series

CSAIL Forum
Dertouzos Distinguished Lecture
Hot Topics in Computing
Algorithms and Complexity (A&C) 2024 - 2025
Algorithms and Complexity (A&C) 2025 - 2026
Bioinformatics Seminar Series 2025
Biomedical Imaging and Analysis 2024 - 2025
Boston IEEE/ACM 2024 -2025
Brains, Minds and Machines 2024 - 2025
CIS Seminar 2024 - 2025
Cryptography and Information Security (CIS) 2025 - 2026
CSAIL Security Seminar 2024 - 2025
EECS Special Seminar
Embodied Intelligence 2024-2025
ML Tea
Theory of Computation (ToC) 2025 - 2026
Thesis Defense
Previous Seminar Series

May 23, 2025

Collapse-Proof Non-Contrastive Self-Supervised Learning

Emanuele Sansone
MIT (CSAIL) and KU Leuven (ESAT)
4:00P
- 5:00P

Location

32-G449
Kiva
Add to Calendar 2025-05-23 16:00:00 2025-05-23 17:00:00 America/New_York Collapse-Proof Non-Contrastive Self-Supervised Learning Abstract: Self-supervised learning (SSL) has unlocked the potential of learning general-purpose representations from large amounts of unlabeled data. Despite its successes, important challenges remain, hindering the applicability and democratisation of SSL. One such challenge is the presence of failure modes occurring during the training of SSL models. In this talk, we aim to distill the essential principles to guarantee the avoidance of known collapses. We present a principled and simplified design of the projector and loss function for non-contrastive SSL based on hyperdimensional computing. We theoretically demonstrate that this design introduces an inductive bias that encourages representations to be simultaneously decorrelated and clustered, without explicitly enforcing these properties. This bias provably enhances generalization and suffices to avoid known training failure modes, such as representation, dimensional, cluster, and intracluster collapses. We validate our theoretical findings on image datasets, including SVHN, CIFAR-10, CIFAR-100, and ImageNet-100. Our approach effectively combines the strengths of feature decorrelation and cluster-based SSL methods, overcoming training failure modes while achieving strong generalization in clustering and linear classification tasks. Bio: Emanuele Sansone is a Postdoctoral Fellow jointly affiliated with MIT (CSAIL) and KU Leuven (ESAT). His research interests lie at the intersection between unsupervised learning and mathematical logic. His research ambition is to empower machines with the capability to acquire and discover knowledge from data in an autonomous manner. He was recently awarded the Marie Curie Global Fellowship for the program titled “Discovering the World through Unsupervised Statistical Relational Learning”.  TBD

May 27, 2025

Neural Robot Navigation with Foundational and Bio-inspired Models

Younès Raoui
Mohammed V University in Rabat, Morocco

Part Of

Embodied Intelligence 2024-2025
4:00P
- 5:00P

Location

32-G449
Stata Center (Building 32) (32 Vassar Street)
Add to Calendar 2025-05-27 16:00:00 2025-05-27 17:00:00 America/New_York Neural Robot Navigation with Foundational and Bio-inspired Models Abstract: Recent neural robot navigation methods use both end-to-end vector-based and bio-inspired approaches. Foundational models based on reinforcement learning, transformers, or liquid neural networks can be applied for planning, navigation, or visual place recognition. Bio-inspired methods model place cells, grid cells, and head direction cells using continuous attractor networks or spiking neural networks.My research focuses on both of these approaches. In this seminar, I will present a tutorial on neural robot navigation methods, followed by our method, called Neural Object SLAM, which creates experience maps by training a neural model of place cells, grid cells, and head direction cells using inputs from visual objects and internal sensory data.Bio: Dr. Younès Raoui is an Assistant Professor at the Faculty of Sciences (FSR) in the physics department at Mohammed V University in Rabat, Morocco. His current research focuses on neuro-robotics, navigation and mapping for autonomous mobile robots, and nonlinear and optimal control for mobile robots. He has published numerous journal and conference articles in these areas. He has also held visiting appointments with the Knowledge Technology Lab at the University of Hamburg and the French National Institute of Health and Medical Research (INSERM).Dr. Younès Raoui earned his PhD in a co-supervision program between Mohammed V University and the Institut National Polytechnique de Toulouse (INPT). His doctoral research was conducted at the Laboratoire d Analyse et d Architecture des Systèmes (LAAS-CNRS) and the Faculty of Sciences of Rabat (LIMIARF Lab) team. He obtained a Masters degree in Computer Science, Telecommunications, and Multimedia from the Faculty of Sciences and the National Institute of Posts and Telecommunications (INPT) in Rabat. He also holds a Maîtrise Sciences et Techniques degree in Informatique, Électronique, Électrotechnique et Automatique from the Faculty of Sciences and Techniques in Settat (Morocco).https://www.raouiyounes.com/ TBD

May 29, 2025

[Thesis Defense] Generalizable Robot Manipulation through Unified Perception, Policy Learning, and Planning

Xiaolin Fang
MIT CSAIL

Part Of

Thesis Defense
10:00A
- 12:00P

Location

45-792
Add to Calendar 2025-05-29 10:00:00 2025-05-29 12:00:00 America/New_York [Thesis Defense] Generalizable Robot Manipulation through Unified Perception, Policy Learning, and Planning Abstract:Advancing robotic manipulation to achieve generalization across diverse goals, environments, and embodiments is a critical challenge in robotics research. While the availability of data and large-scale training has brought exciting progress in robotics manipulation, current methods often struggle with generalizing to unseen, unstructured environments and solving long-horizon tasks. In this thesis, I will present my contributions that bridge structured decision-making frameworks with learned perceptual and policy components to enable multi-step manipulation in partially observable environments. Specifically, I will talk about my work in 1) constructing a modular framework that estimates affordances using learned perceptual models with task and motion planning (TAMP) for object rearrangement in unstructured scenes, 2) learning generative diffusion models of robot skills, which can be composed to solve unseen combination of environmental constraints through infeference-time optimization, 3) leveraging large vision-language models (VLMs) in building task-oriented visual abstractions, allowing skills to generalize across different environments with only 5 to 10 demonstrations. Together, these approaches contribute to the generality and scalability of embodied agents towards solving real-world manipulation in unstructured environments.Thesis Committee: Leslie Kaelbling, Tomás Lozano-Pérez, Russ Tedrake TBD

June 02, 2025

[Thesis Defense] Personalizing Robot Assistance under Uncertainty about the Human

Shen Li
MIT

Part Of

Thesis Defense
9:00A
- 11:00A

Location

TBD
32-155
Add to Calendar 2025-06-02 9:00:00 2025-06-02 11:00:00 America/New_York [Thesis Defense] Personalizing Robot Assistance under Uncertainty about the Human Date: June 2Time: 9:00-11:00 AM ETLocation: 32-155Zoom: https://mit.zoom.us/j/9731989629Title: Personalizing Robot Assistance under Uncertainty about the HumanAbstractRobots have the potential to improve the quality of life by assisting with daily tasks, such as helping older adults and people with disabilities get dressed. But meaningful assistance requires personalization: each person has unique preferences, behaviors, and needs.A central challenge is that robots often operate under uncertainty about the human they are helping. This uncertainty may involve the person's preferences, hidden physical states, or reactions to assistance. If not properly addressed, such uncertainty can lead to ineffective, undesired, or even unsafe outcomes.This thesis asks: How should a robot behave when it is uncertain about the human? I present a unified framework for uncertainty-aware personalization in human-robot interaction, spanning three core components of robot intelligence: preference learning, state estimation, and motion planning.1. Preference learning: I introduce the first method that uses response time, a subtle but informative cognitive signal, as implicit feedback. By combining human choices with response times, robots can infer not only what a person prefers but also how strongly they prefer it. This reduces uncertainty and accelerates preference learning.2. State estimation: To support safe physical assistance when parts of the human body (e.g., the elbow) are occluded, I introduce a state estimator that models uncertainty in learned human dynamics and robot sensing. It constructs a geometric set (e.g., a 3D box) that reliably contains the true hidden human state, enabling safer and more precise robot behavior.3. Motion planning: When a robot is uncertain about future human motion, it may behave overly conservatively to avoid causing harm, resulting in ineffective assistance. To address this, I propose a relaxed safety formulation that allows the robot to either avoid collisions or make low-impact contact. This approach enables the robot to act more effectively while still maintaining safety under uncertainty.Together, these contributions lay a foundation for assistive robots that personalize their behavior while adapting to the uncertain and dynamic nature of human needs.Thesis Supervisor: Julie A. ShahCommittee Members: Julie A. Shah, Dylan Hadfield-Menell, Na (Lina) Li, Aude BillardThesis Readers: Vaibhav Unhelkar, Tariq IqbalContact: shenli@mit.edu TBD

June 06, 2025

Bayesian Deep Learning: From Reliable Neural Networks to Interpretable Foundation Models

Hao Wang
Rutgers University

Part Of

Biomedical Imaging and Analysis 2024 - 2025
11:00A
- 12:00P

Location

32-D407
Add to Calendar 2025-06-06 11:00:00 2025-06-06 12:00:00 America/New_York Bayesian Deep Learning: From Reliable Neural Networks to Interpretable Foundation Models While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning, and planning require an even higher level of intelligence.  The past decade has seen major advances in many perception tasks using deep learning models. In terms of higher-level inference, however, probabilistic graphical models, with their ability to expressively describe properties of variables and various probabilistic relations among variables, are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, we have been exploring along a research direction, which we call Bayesian deep learning, to tightly integrate deep learning and Bayesian models within a principled probabilistic framework. In this talk, I will present the proposed unified framework and some of our recent work on Bayesian deep learning with various applications including interpretable large language models, network analysis, and healthcare. TBD

June 24, 2025

Boltz: Towards a Unified Approach for Biomolecular Interaction Modeling

Gabriele Corso and Jeremy Wohlwend

Part Of

Boston IEEE/ACM 2024 -2025
7:00P
- 9:00P

Location

32-G449
Add to Calendar 2025-06-24 19:00:00 2025-06-24 21:00:00 America/New_York Boltz: Towards a Unified Approach for Biomolecular Interaction Modeling 7:00 PM, Tuesday, 24 June 2025MIT Room 32-G449 (Kiva) and online via ZoomBoltz: Towards a Unified Approach for Biomolecular Interaction ModelingGabriele Corso and Jeremy WohlwendPlease register in advance for this seminar even if you plan to attend in person athttps://acm-org.zoom.us/webinar/register/9317463194385/WN_U1yhFblMQO-I4n1cBhkBFwAfter registering, you will receive a confirmation email containing information about joining the webinar.Indicate on the registration form if you plan to attend in person.  This will help us determine whether the room is close to reaching capacity. We plan to serve light refreshments (probably pizza) beforethe talk starting at around 6:30 pm. Letting us know you will come in person will help us determine how much pizza to order.                We may make some auxiliary material such as slides and access to the recording available after the seminar to people who have registered.Abstract:Understanding biomolecular interactions is fundamental to advancing fields like drug discovery and protein design. In this talk, we introduce Boltz-1, an open-source deep learning model incorporatinginnovations in model architecture, speed optimization, and data processing achieving AlphaFold3-level accuracy in predicting the 3D structures of biomolecular complexes. Boltz-1 demonstrates aperformance on-par with state-of-the-art commercial models on a range of diverse benchmarks, setting a new benchmark for commercially accessible tools in structural biology. By releasing the training andinference code, model weights, datasets, and benchmarks under the MIT open license, we aim to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecularmodeling.Bio: Jeremy Wohlwend and Gabriele Corso are PhD students at the MIT Computer Science and Artificial Intelligence Laboratory where their research focuses on developing novel ML frameworks to tacklechallenging problems in drug discovery and immunology.Directions to 32-G449 - MIT Stata Center, 32 Vassar Street, Cambridge, MA: Please use the main entrance to the Stata Center at 32 Vassar Street (the entrance closest to Main street) as those doors will beunlocked. Upon entering, proceed to the elevators which will be on the right after passing a large set of stairs and a MITAC kiosk. Take the elevator to the 4th floor and turn right, following the hall to anopen area; 32-G449 will be on the left. Location of Stata on campus map.This joint meeting of the Boston Chapter of the IEEE Computer and EMBS Societies and GBC/ACM will be hybrid (in person and online).Up-to-date information about this and other talks is available online at https://ewh.ieee.org/r1/boston/computer/. You can sign up to receive updated status information about this talk and informationalemails about future talks at https://mailman.mit.edu/mailman/listinfo/ieee-cs, our self-administered mailing list.  TBD

September 23, 2025

Explicit Lossless Vertex Expanders

Rachel Zhang
CSAIL, EECS

Part Of

Theory of Computation (ToC) 2025 - 2026
4:15P
- 5:15P

Location

32-G449
Refreshments at 4:00 PM
Add to Calendar 2025-09-23 16:15:00 2025-09-23 17:15:00 America/New_York Explicit Lossless Vertex Expanders We give the first explicit construction of lossless vertex expanders. These are d-regular graphs where every small set S of vertices has (1-eps)d|S| distinct neighbors. Previously, the strongest known explicit vertex expanders were those given by Ramanujan graphs, whose spectral properties imply that every small set S of vertices has 0.5d|S| distinct neighbors.Based on joint work with Jun-Ting Hsieh, Ting-Chun Lin, Alex Lubotzky, Sidhanth Mohanty, Ryan O'Donnell, and Assaf Reiner. TBD
  • CSAIL Forum
  • Dertouzos Distinguished Lecture
  • Hot Topics in Computing
  • Algorithms and Complexity (A&C) 2024 - 2025
  • Biomedical Imaging and Analysis 2024 - 2025
  • Boston IEEE/ACM 2024 -2025
  • Brains, Minds and Machines 2024 - 2025
  • CIS Seminar 2024 - 2025
  • CSAIL Security Seminar 2024 - 2025
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  • ML Tea
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  • Theory of Computation (ToC) 2025 - 2026
  • Thesis Defense
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