BAIR/CPAR/BDD Internal Weekly Seminar

The Berkeley Artificial Intelligence Research Lab co-hosts a weekly internal seminar series with the CITRIS People and Robots Initiative and the Berkeley DeepDrive. The seminars are every Friday afternoon from 3:10-4:10 PM, and are open to BAIR faculty, students, and sponsors. Talk locations will be announced as they are available. For any questions, please email

Spring 2020 Schedule
Date Speaker 1: 3:10-4:00 or 3:10-3:30 Speaker 2: N/A or 3:30-3:50
Jan. 24 Dylan Hadfield-Menell: The Principal-Agent Value Alignment Problem N/A
Jan. 31 Somil Bansal: Safe and Data-efficient Learning for Physical Systems N/A
Feb. 7 Nick Antipa: Lensless Computational Imaging: Seeing More with Less N/A
Feb. 14 Ruoxi Jia: Towards a Responsible Data Economy: Fairness, Privacy, and Security N/A
Feb. 21 Chandan Singh: Interpreting and Improving Neural Networks via Disentangled Attributions N/A
Feb. 28 Alexei (Alyosha) Efros: Image Manipulation… And Ways to Detect It N/A
Mar. 6 Brijen Thananjeyan and Ashwin Balakrishna:  Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks Jeff Ichnowski: Fog-Robotics Serverless and Deep Grasp-Optimized Robot Motion Planning
Mar. 13 Aravind Srinivas: Self-Supervised Visual Representation Learning N/A
Mar. 20 Ronghang Hu: Structured Models for Vision-and-Language Reasoning N/A
Mar. 27 No Seminar N/A
Apr. 3 Misha Laskin: Improving Reinforcement Learning with Unsupervised and Self-Supervised Learning N/A
Apr. 10 Dequan Wang: Object-Centric Representation for Perception, Prediction, and Planning N/A
Apr. 17 Coline Devin: Learning with Modularity and Compositionality for Robotics N/A
Apr. 24 Cecilia Zhang: Bringing Cinema Quality Rendering into Casual Photos and Videos. N/A
May 1 Sylvia Herbert: Safe Real-World Autonomy in Uncertain and Unstructured Environments N/A
May 8 Sasha Sax: Robust Learning Through Cross-Task Consistency N/A
May 15 Carlos Florensa: What Supervision Scales? Practical Learning Through Interaction N/A
Spring 2019 Schedule
Date Speaker 1: 3:10-4:00 or 3:10-3:30 Speaker 2: N/A or 3:30-3:50
Jan. 18 Eric Jonas: Structured Prediction via Machine Learning for Inverse Problems N/A
Jan. 25 Dinesh Jayaraman: Towards Embodied Visual Intelligence N/A
Feb. 1 Ke Li: Advances in Machine Learning: Learning to Optimize, Generative Modelling and Nearest Neighbour Search N/A
Feb. 8 Deepak Pathak: Self-Directed Learning N/A
Feb. 15 Samaneh Azadi: Rectify the GAN Generator Distribution by Rejecting Bad Samples Sasha Sax: On Perception for Robotics: Mid-level Visual Representations Improve Generalization and Sample Complexity for Learning Active Tasks
Feb. 22 Jaime Fisac: Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are Wrong N/A
Mar. 1 Fisher Yu: Towards Human-Level Recognition via Contextual, Dynamic, and Predictive Representations N/A
Mar. 8 Andrew Owens: Sight and Sound N/A
Mar. 15 Daniel Fried: Pragmatic Models for Generating and Following Grounded Instructions Nikita Kitaev: Syntactic Parsing with Self-Attention
Mar. 22 **Oriol Vinyals: AlphaStar: Mastering the Real-Time Strategy Game StarCraft II (**This will be held 11am-12pm in 250 SDH**) N/A
Mar. 22 Jacob Steinhardt: What Makes Neural Networks (Non-)Robust? N/A
Mar. 29 Holiday Holiday
Apr. 5 Alex Lee: Visual Dynamics Models for Robotic Planning and Control N/A
Apr. 12 Lisa Anne Hendricks: Visual Understanding through Natural Language N/A
Apr. 19 Evan Shelhamer: Blurring the Line between Structure and Learning for Adaptive Local Recognition N/A
Apr. 26 Yian Ma: Bridging MCMC and Optimization N/A
May 3 Sandy Huang: Optimizing for Robot Transparency N/A
May 10 Jeffrey Regier: Statistical Inference for Cataloging the Visible Universe N/A
Fall 2018 Schedule
Date Speaker 1: 3:10-4:00 or 3:10-3:30 Speaker 2: N/A or 3:30-3:50
Aug. 24 Jaime Fisac, Andrea Bajcsy, Sylvia Herbert: Probabilistically Safe Robot Planning with Confidence-Based Human Predictions N/A
Aug. 31 Aviv Tamar: Learning Representations for Planning N/A
Sept. 7 Chi Jin: Is Q-learning Provably Efficient? N/A
Sept. 14 Wojciech Zaremba: Learning dexterity N/A
Sept. 21 Tuomas Haarnoja: Acquiring Diverse Robot Skills via Maximum Entropy Reinforcement Learning N/A
Sept. 28 Professor Ruzena Bajcsy: Data Driven vs. Model Driven Analysis of Human Ability for Human Robot Interaction N/A
Oct. 5 Ke Li: Implicit Maximum Likelihood Estimation N/A
Oct. 12 Roy Fox: Multi-Task Hierarchical Imitation Learning of Robot Skills N/A
Oct. 19 Ankush Desai: DRONA: A Framework for Programming Safe Robotics Systems N/A
Oct. 26 Ajay Tanwani: A Fog Robotics Approach to Large Scale Robot Learning Laura Hallock: Human Muscle Force Modeling for Enhanced Assistive Device Control
Nov. 2 Abhishek Gupta: Unsupervised (Meta) RL Anja Rohrbach: Diagnosing and correcting bias in captioning models
Nov. 9 Holiday Holiday
Nov. 16 Daniel Fried: Pragmatic Models for Generating and Following Grounded Instructions Nikita Kitaev: Syntactic Parsing with Self-Attention
Nov. 23 Holiday Holiday
Dec. 7 Jiantao Jiao: Deconstructing Generative Adversarial Networks N/A
Spring 2018 Schedule
Date Speaker 1: 3:10-4:00 or 3:10-3:30 Speaker 2: N/A or 3:30-3:50
Jan. 19 Pulkit Agrawal: Continually Evolving Machines: Learning by Experimenting N/A
Jan. 26 Sanjay Krishnan: Dirty Data, Robotics, and Artificial Intelligence N/A
Feb. 2 David Fouhey: Towards a 3D World of Interaction N/A
Feb. 9 Saurabh Gupta: Visual Perception and Navigation in 3D Scenes N/A
Feb. 16 Jacob Andreas: Learning from Language N/A
Feb. 23 Chelsea Finn: Generalization and Self-Supervision in Deep Robotic Learning N/A
Mar. 2 Jennifer Listgarten: Where genetics and biology meet machine learning N/A
Mar. 9 Yi Ma: Low-dimensional Structures and Deep Models for High-dimensional Data TBD
Mar. 16 Cathy Wu: Mixed-autonomy mobility: scalable learning and optimization N/A
Mar. 23 Xiang ChengLangevin MCMC as gradient flow over the probability space Eric Tzeng and Andreea Bobu: Domain Adaptation for Fixed and Continuously Varying Domains
Apr. 6 Shubham Tulsiani: Learning Single-view 3D Reconstruction of Objects and Scenes TBD
Apr. 13 Tinghui Zhou: Beyond Direct Supervision: Visual Learning via Data-driven Consistency Consistency (Location: SWARM Lab 490 Cory) N/A
Apr. 20 Richard Zhang: Image Synthesis for Self-Supervised Representation Learning N/A
Apr. 27 Reza Abbasi-Asl: Structural compression of Convolutional Neural Networks Fereshteh Sadeghi: Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control
Fall 2017 Schedule
Date Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10
Aug. 25 Jitendra Malik: What have we learned from datasets in computer vision? Angjoo Kanazawa: Single-View 3D Reconstruction of Deformable Objects like Animals and People
Sept. 1 Chang Liu: Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection Claire Tomlin: Safe Learning
Sept. 8 Andrew Critch: Open-source game theory is weird Anna Rohrbach: Generation and grounding of natural language descriptions for visual data
Sept. 15 Bo Li: Secure learning in adversarial environments Ching-Yao Chan: Safety of Automated Driving Systems (ADS) and AI/ML – A Dialogue
Sept. 22 Alex Anderson: The High-Dimensional Geometry of Binary Neural Networks Jeff Mahler: Learning Deep Policies for Robot Bin Picking using Discrete-Event Simulation of Robust Grasping Sequences
Sept. 29 Fisher Yu: Towards Universal Representation for Image Recognition Max Rabinovich: Abstract Syntax Networks for Code Generation and Semantic Parsing
Oct. 6 Michael Laskey: Learning Home Robotics Manipulation Tasks from Remote Supervision Pulkit Agrawal: Continually Evolving Agents: Curiosity & Experimentation
Oct. 13 Ron Fearing: Dextrous Locomotion Sylvia Herbert and David Fridovich: Planning, Fast and Slow with FaSTrack: A Framework for Adaptive Real-Time Safe Trajectory Planning
Oct. 20 Emrah Bostan: Learning Convex Regularizers for Optimal Bayesian Denoising Somil Bansal: Overcoming Model Bias in Model-based Learning
Oct. 27 Roberto Calandra Aaditya Ramdas: Sequential testing and online false discovery rate control
Nov. 3 Roy Fox: Discovery of Hierarchical Structures for Robot Learning and Neural Programming Leila Wehbe: Modeling brain responses to natural language stimuli
Nov. 10 Holiday Holiday
Nov. 17 Jacob Andreas: Learning with Latent Language Niladri Chatterji: Alternating minimization for dictionary learning with random initialization
Nov. 24 Holiday Holiday
Dec. 1 Eric Jonas: Could a neuroscientist understand a microprocessor Tuomas Haarnoja: Soft Q-Learning
Spring 2017 Schedule


Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10

Jan. 20

Jack Gallant: Melding neuroscience and computer science Javad Lavaei: On optimization theory, numerical algorithms and machine learning for nationwide energy systems

Jan. 27

Trevor Darrell: Adaptive Learning of Driving Models from Large-scale Video Datasets Coline Devin: Transfer learning for robotics

Feb. 3

Phillip Isola: Image-to-Image Translation with Conditional Adversarial Networks Anil Aswani: Human Modeling Using Inverse Optimization

Feb. 10

Zeynep Akata: Generating Fine-Grained Visual Explanations and Realistic Images Deirdre Mulligan: Hand-offs in AI: Functional Fidelity, Values, and Governance

Feb. 17

(3:10- 3:25) Jaime Fernandez Fisac: Generating plans that predict themselves/ (3:25-3:40) Aaron Bestick: Implicitly Assisting Humans to Choose Good Grasps in Robot to Human Handovers William Guss: WaveLayers: Neural Topology Inspired by Topology

Feb. 24

Laurent El Ghaoui: Safe feature elimination in sparse learning Laura Waller: Computational Microscopy with sparsity

Mar. 3

Chi Jin: How to Escape Saddle Points Efficiently Horia Mania: Universality of Mallows’ and degeneracy of Kendall’s kernels for rankings

Mar. 10

Matthew Matl: Automated Grasp Transfer with Mesh Segmentation and Point Cloud Registration Paul Grigas: An Extended Frank-Wolfe Method with “In-Face” Directions, and its Application to Low-Rank Matrix Completion

Mar. 17

Roy Fox: Multi-Level Discovery of Deep Options Parvez Ahammad: Beyond navigation metrics: Teaching computers how users perceive web application performance
Mar. 24 Ke LiLearning to Optimize and Fast k-Nearest Neighbour Search Jessica Hamrick: Metacontrol for Imagination-Based Optimization
Apr. 7 Deepak Pathak: Exploring Four Axes of Self-Supervision Saurabh GuptaCognitive Mapping and Planning for Visual Navigation
Apr. 14 Jeff Mahler: Dex-Net: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics Judy Hoffman: A General Framework for Domain Adversarial Learning
Apr. 21 Fereshteh Sadeghi: Sim2Real Collision Avoidance for Indoor Navigation of Mobile Robots via Deep Reinforcement Learning Evan Shelhamer: Loss is its own Reward: Self-Supervision for Reinforcement Learning
Apr. 28 Roberto Calandra: Robustness in Multi-objective Bayesian optimization Richard Zhang: Cross-Channel Visual Prediction
May 5 Abhishek Gupta: Imitation Learning for Dexterous Manipulation and Transfer in Reinforcement Learning Tinghui Zhou: 3D Visual Synthesis and Understanding from 2D Views
May 12 Karl Zipser: New Trajectories in the Autonomous Model Car Project Daniel Drew: Silent Swarms: Flying Microrobots Using Atmospheric Ion Thrusters
Fall 2016 Schedule


Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10

Aug. 26

Ken Goldberg: Deep Dexterity Sanjay Krishnan: Inverse Reinforcement Learning For Sequential Robotic Tasks

Sep 2

Anca Dragan: How Robots Influence Our Actions Jeff Donahue: Adversarial Feature Learning

Sep 9

Robert Nishihara & Philipp Moritz: Distributed Machine Learning with Ray John DeNero: Interactive machine translation

Sep 16

Joseph Gonzalez: Prediction Serving and RISE Kevin Jamieson: Bayesian Optimization and other bad ideas for hyperparameter tuning

Sep 23

Stuart Russell: Human-Compatible AI Marcus Rohrbach: Explain and Answer: Intelligent systems which can communicate about what they see.

Sep 30

Richard Zhang: Colorful Image Colorization Virginia Smith: A General Framework for Communication-Efficient Distributed Optimization

Oct 7

Gregory Kahn: Learning Control Policies for Partially Observable Safety-Critical Systems John Canny: Accountable Deep Networks

Oct 14

Ruzena Bajcsy: Individualized Human Models for Cyberphysical Interaction Jacob Andreas: Neural module networks

Oct 21

Dave Moore: Bayesian seismic monitoring from raw waveforms Greg Durrett: Data-Driven Text Analysis with Joint Models

Oct 28

Jun-Yan Zhu: Visual Manipulation and Synthesis on the Natural Image Manifold Dylan Hadfield-Menell: The Off-Switch

Nov 4

Bin Yu: Artificial neurons meet real neurons: building stable interpretations of V4 neurons from CNN+regression models Pulkit Agrawal: Forecasting from Pixels: Intuitive Physics and Intuitive Behavior

Dec 02

Chelsea Finn: Adversarial Inverse Reinforcement Learning Aviv Tamar: Deep Policy Representations based on a Planning Computation

Dec 09

Michael Laskey: A Human-Centric Approach to Deep Robotic Learning from Demonstrations. Michael Oliver: Using artificial neural networks to model visual neurons

Dec 16

Lisa Anne Hendricks: Localizing Moments in Video with Natural Language Andrew Owens: Learning visual models from paired audio-visual examples