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 Berkeley DeepDrive. The seminars are every Thursday afternoon, from 2:10P-3P, and are open to BAIR faculty, students, and sponsors. Talks will take place in person on the 8th floor of Berkeley Way West (pending any new guidelines from the CDC and the University). There will also be a link to join via Zoom in case you are unable to attend in person. For any questions, please email bair-admin@berkeley.edu.

Spring 2022 Schedule

Date	Speaker 1: 2:10 PM -3:00 PM or 2:10-2:30 PM	Speaker 2: N/A or 2:30-2:50 PM
Jan 20	Nick Rhinehart: Decision-Centric Predictive Understanding: Enabling Learning Systems to See and Affect the Future	N/A
Jan 27	Andrea Bajcsy: Bridging Safety and Learning in Human-Robot Interaction	N/A
Feb 3	TBD	TBD
Feb 10	Lydia Liu: Rethinking the Machine Learning Life Cycle for Social Good: Education as a Case Study	N/A
Feb 17	TBD	TBD
Feb 24	Ashvin Nair: Scalable Robot Learning	N/A
Mar 3	TBD	TBD
Mar 10	Niloufar Salehi: TBD	TBD
Mar 17	TBD	TBD
Mar 31	TBD	TBD
Apr 7	TBD	TBD
Apr 14	TBD	TBD
Apr 21	TBD	TBD
Apr 28	Frederik Ebert: Autonomous Real-World Robot Learning	N/A

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Fall 2021 Schedule

Date	Speaker 1: 11:10 AM -12:00 PM or 11:10-11:30 AM	Speaker 2: N/A or  11:30-11:50 AM
Aug 31	Priya Moorjani: Big Data and Genomics: Insights into Human Evolutionary History	N/A
Sep 7	Christian Borgs: Network Structure and Epidemics	N/A
Sep 14	Wenshuo Guo: Towards Adaptive and Robust Learning in Data-Driven Mechanism Design	N/A
Sep 21	Ashish Kumar: Rapid Motor Adaptation for Legged Robots	N/A
Sep 28	Tijana Zrnic: Who leads and who follows in strategic classification?	N/A
Oct 5	Assaf Shocher: Deep Internal Learning	N/A
Oct 12	Zhongqi Miao: Deep learning and real-world datasets	N/A
Oct 19	Kirthevasan Kandasamy: Mechanism Design under Bandit Feedback with Unknown Agent Preferences	N/A
Oct 26	Jacob Steinhardt: Science of Measurement in Machine Learning	N/A
Nov 2	Yixin Wang: Representation Learning: A Causal Perspective	N/A
Nov 9	Anil Aswani: Engineering Health Systems using Artificial Intelligence	N/A
Nov 16	Natasha Jaques: Social Reinforcement Learning	N/A

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Spring 2021 Schedule

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Fall 2020 Schedule

Date	Speaker 1: 11:10 AM -12:00 PM or 11:10-11:30 AM	Speaker 2: N/A or  11:30-11:50 AM
Sep 1	Alvin Wan: What Explainable AI Fails to Explain (and how we fix that)	Amir Gholami, Zhewei Yao: ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
Sep 8	Nikita Kitaev: Is Unstructured Computation All You Need for Natural Language Processing?	Lucy Li: Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks
Sep 15	Georgios Pavlakos: Learning to Reconstruct 3D Humans	N/A
Sep 22	Lerrel Pinto: Rich Robotic Supervision through Cheap Human Demonstrations	N/A
Sep 29	Philippe Laban: Text Summarization Without The Summaries	Katie Stasaski: More Diverse Dialogue Datasets via Diversity-Informed Data Collection
Oct 6	Aldo Pacchiano:  Learning to Score Behaviors for Guided Policy Optimization	N/A
Oct 13	Paria Rashidinejad: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory	Zhen Dong: Efficient Neural Networks Through Systematic Quantization
Oct 20	Eugene Vinitsky: Optimizing Energy Efficiency of Traffic at Scale via Multi-agent Deep RL	Anastasios Angelopoulos: Uncertainty for Black Box Models
Oct 27	Misha Laskin: Escaping the Simulator: Accelerating Real Robot Learning through Unsupervised Learning	N/A
Nov 3	Wei Zhan: Close-loop Evaluation of Prediction Algorithms with Complex Scenarios from Naturalistic Driving Data	Liting Sun: Modeling Human Behaviors in Human-robot Interactions
Nov 10	Daniel Brown: Safe and Efficient Imitation Learning	N/A
Nov 17	Edward Kim: Scenic: Dynamic Scenario Description Language for Autonomous Systems	Kimin Lee: Ensemble methods in reinforcement learning
Nov 24	Xin Wang: Last-Mile Delivery of Computer Vision with Test-Time Adaptation	N/A
Dec 1	Huijuan Xu: Temporal Action Detection in Videos with Multi-Level Supervision	N/A

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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
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

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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

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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

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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 Cheng: Langevin 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

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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

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Spring 2017 Schedule

Date	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 Li: Learning 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 Gupta: Cognitive 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

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Fall 2016 Schedule

Date	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