Seminar archive

Foundations of Generative Discovery Beyond the Data with Flow and Diffusion Models

Riccardo De Santi · PhD Student, ETH AI Center

Making the most of your Healthcare Data for Reliable AI

Dr. Aahlad Puli · Faculty Fellow, Center for Data Science at NYU

A Gentle Introduction to Neural Network Verification

Daniel Neider · Professor of Verification and Formal Guarantees of Machine Learning, Department of Computer Science, TU Dortmund

From Models to Medicine: Theoretical Foundations and Practical Impact of Machine Learning in Healthcare

Julia Vogt · Assistant Professor, Department of Computer Science, ETH Zurich

Safe Reinforcement Learning: Building Agents You Can Trust

Davide Corsi · Postdoctoral Researcher, Department of Computer Science, University of California, Irvine

Radiance Fields Advancing 3D Scene Understanding for Robotics and Autonomous Driving

Matúš Dopiriak · PhD Student, Department of Computers and Informatics, Technical University in Košice

Fast and Flexible Generative Modeling with Free-Form Flows

Felix Draxler · Postdoctoral Researcher, Department of Computer Science, University of California, Irvine

Advancing Multimodal Models Beyond Human Supervision

XuDong Wang · PhD Student, Berkeley AI Research Lab, University of California, Berkeley

Demystifying the Inner Workings of Language Models

Sarah Wiegreffe · Postdoctoral Researcher, Allen Institute for AI and University of Washington

Neural Operator for Scientific Computing

Zongyi Li · PhD Student, Computing and Mathematical Sciences, Caltech

Reasoning in the Wild

Wenting Zhao · PhD Student, Department of Computer Science, Cornell University

What Generative Visual Models Understand (and Don't) About the Physical World

Anand Bhattad · Research Assistant Professor, Toyota Technological Institute at Chicago

Enabling Language Models to Process Information at Scale

Tianyu Gao · PhD Student, Department of Computer Science, Princeton University

Deep Learning Theory in the Age of Generative AI

Sadhika Malladi · PhD Student, Department of Computer Science, Princeton University

Toward Spatial Intelligence with Limited Data

Guandao Yang · Postdoctoral Scholar, Stanford University

Understanding Generative Models Inside Out: From Representation to Data

Yanai Elazar · Postdoctoral Researcher, Allen Institute for AI and University of Washington

Training Precise Language Models for Imprecise Humans

Valentina Pyatkin · Postdoctoral Researcher, Allen Institute for AI and University of Washington

Aligning Language Model Agents to Environment Dynamics

Kolby Nottingham · PhD Student, Department of Computer Science, University of California, Irvine

Transferring Climate Change Physical Knowledge

Francesco Immorlano · Postdoctoral Researcher, Department of Computer Science, University of California, Irvine

Revisiting FastMap: New Applications

Satish Kumar Thittamaranahalli · Research Associate Professor, Department of Computer Science, University of Southern California

Uncertainty Quantification for Scientific Machine Learning

Dongxia Wu · PhD Student, Dept. of Computer Science & Engineering, University of California, San Diego

End-to-end Learnable Particle Filters and Smoothers

Ali Younis · PhD Student, Department of Computer Science, University of California, Irvine

Evaluating Foundational Models Using Insights from Their Pretraining Data

Yasaman Razeghi · PhD Student, Department of Computer Science, University of California, Irvine

In Pursuit of Dexterous and Generalizable Robot Manipulation using Reinforcement Learning, Imitation Learning, and Foundation Models

Daniel Seita · Assistant Professor of Computer Science, University of Southern California

Future Health: Harnessing Multimodal Data and GenAI for Health Promotion

Amir Rahmani · Professor of Nursing and Computer Science, University of California, Irvine

AI-based multimodal data fusion for outcome prediction in oncology

Jana Lipkova · Assistant Professor, Department of Pathology, School of Medicine, University of California, Irvine

Causal inference and machine learning in mobile health: Modeling time-varying effects using longitudinal functional data

Tianchen Qian · Assistant Professor of Statistics, University of California, Irvine

Conjugate Integrators for Fast Sampling in Diffusion Models

Kushagra Pandey · PhD Student, Department of Computer Science, University of California, Irvine

AppWorld: Reliable Evaluation of Interactive Agents in a World of Apps and People

Harsh Trivedi · PhD Student, Department of Computer Science, Stony Brook University

Hidden Capabilities and Counterintuitive Limits in Large Language Models

Peter West · PhD Student, School of Computer Science & Engineering, University of Washington

Unlocking Language Models: Controlling LMs to Enable NLP for All

Hila Gonen · Postdoctoral Researcher, School of Computer Science & Engineering, University of Washington

Challenges in Improving and Applying Generative Models

Karen Ullrich · Research Scientist, Fundamental AI Research (FAIR) at Meta, New York

Jump-starting Embodied Intelligence

Unnat Jain · Postdoctoral Researcher, Carnegie Mellon University, Fundamental AI Research (FAIR) at Meta

Causal Representation Learning: Discovery of the Hidden World

Kun Zhang · Professor and Director, Center for Integrative AI, Mohamed bin Zayed University of Artificial Intelligence

Learning Generalized Knowledge for AI with Limited Supervision

Chen Wei · PhD Student, Department of Computer Science, Johns Hopkins University

Building Planetary-Scale Collaborative Intelligence

Sai Praneeth Karimireddy · Postdoctoral Researcher, University of California, Berkeley

Steering Textual Reasoning with Explanations

Xi Ye · PhD Student, Department of Computer Science, University of Texas at Austin

More from Less: Learning with Limited Annotated Data in Vision and Language

Paola Cascante-Bonilla · Postdoctoral Associate, University of Maryland Institute for Advanced Computer Studies (UMIACS)

Breaking the Curse of Multilinguality in Language Models

Terra Blevins · PhD Student, School of Computer Science and Engineering, University of Washington

Scaling Generative AI in the Enterprise

Bratin Saha · Vice President of Machine Learning and AI Services, Amazon Web Services

The Synergy between Machine Learning and the Natural Sciences

Max Welling · Professor and Research Chair in Machine Learning, University of Amsterdam

Informative Example Selection for In-Context Learning

Shivanshu Gupta · PhD Student, Department of Computer Science, UC Irvine

Deep Generative Models in Infinite-Dimensional Spaces

Gavin Kerrigan · PhD Student, Department of Computer Science, UC Irvine

From Heatmaps to Structural and Counterfactual Explanations

Fuxin Li · Associate Professor of Electrical Engineering and Computer Science, Oregon State University

Handformer2T: A Lightweight Regression-based model for Interacting Hands Pose Estimation from a single RGB Image

Deying Kong · Software Engineer, Google

Stochastic thermodynamics: Diffusion models for information and energy transfer

Tryphon Georgiou · Distinguished Professor of Mechanical and Aerospace Engineering, University of California, Irvine

Possible Impossibilities and Impossible Possibilities

Yejin Choi · Wissner-Slivka Professor of Computer Science and Engineering, University of Washington and Allen Institute for Artificial Intelligence

Continuous-in-time Limit for Multi-armed Bandit

Yuhua Zhu · Assistant Professor, Halicioglu Data Science Institute and Department of Mathematics, University of California, San Diego

MCMC, variational inference, and reverse diffusion Monte Carlo

Yian Ma · Assistant Professor, Halıcıoğlu Data Science Institute, University of California, San Diego

Provably Personalized and Robust Federated Learning

Mariel Werner · PhD Student, Department of Electrical Engineering and Computer Science, UC Berkeley

Losing bits and finding meaning: Efficient compression underlies meaning in language

Noga Zaslavsky · Assistant Professor of Language Science, University of California, Irvine

Towards Transparent Language Models

Sarah Wiegreffe · Postdoctoral Researcher, Allen Institute for AI and University of Washington

Deep Anomaly Detection

Marius Kloft · Professor of Computer Science, RPTU Kaiserslautern-Landau, Germany

Use Cases for Bayesian Deep Learning in the Age of ChatGPT

Vincent Fortuin · Research group leader in Machine Learning, Helmholtz AI

CrystalBox: Future-Based Explanations for Deep RL Network Controllers

Sangeetha Abdu Jyothi · Assistant Professor of Computer Science, University of California, Irvine

Semi-Supervised Learning with Prediction-Constrained Variational Autoencoders

Gabe Hope · PhD Student, Computer Science, University of California, Irvine

AI can Learn from Data. But can it Learn to Reason?

Guy Van den Broeck · Associate Professor of Computer Science, University of California, Los Angeles

Deep Learning in Science

Pierre Baldi and Alexander Shmakov · Department of Computer Science, UC Irvine

Towards Causal Revolution: On Learning Heterogeneity and Non-Spuriousness in Causal Graphs

Hengrui Cai · Assistant Professor of Statistics, University of California, Irvine

Researching and Revising What Language Models Say, Using Language Models

Anthony Chen · PhD Student, Department of Computer Science, UC Irvine

Evaluating Explanations

Danish Pruthi · Assistant Professor, Department of Computational and Data Sciences, Indian Institute of Science (IISc), Bangalore

Understanding the Diffusion Objective

Durk Kingma · Research Scientist, Google Research

Competence-Aware Systems

Shlomo Zilberstein · Professor of Computer Science, University of Massachusetts, Amherst

Variational Methods for Bayesian Optimal Experimental Design

Noble Kennamer · PhD Student, Department of Computer Science, University of California, Irvine

Large Language Models as External Knowledge Sources for Sequential Decision Making

Kolby Nottingham · PhD Student, Department of Computer Science, University of California, Irvine

Behavioral science research at a corporate research laboratory

Maarten Bos · Lead Research Scientist, Snap Research

Statistical implications of group invariance of distributions

Peter Orbanz · Professor of Machine Learning, Gatsby Computational Neuroscience Unit, University College London

Robust and Indirectly Supervised Information Extraction

Muhao Chen · Assistant Research Professor of Computer Science, University of Southern California

Adaptive Online Scalable Learning with Graph Feedback

Yanning Shen · Assistant Professor of Electrical Engineering and Computer Science, University of California, Irvine

Predictive Querying for Autoregressive Neural Sequence Models

Alex Boyd · PhD Student, Department of Statistics, University of California, Irvine

Human-AI collaboration

Mark Steyvers · Professor of Cognitive Sciences, University of California, Irvine

Effective, explainable, and equitable predictions in NLP models with world knowledge and conversations

Bodhisattwa Prasad Majumder · PhD Student, Department of Computer Science and Engineering, University of California, San Diego

Trustworthy Machine Learning in Complex Environments

Furong Huang · Assistant Professor of Computer Science, University of Maryland

AND/OR Branch-and-Bound for Computational Protein Design Optimizing K*

Bobak Pezeshki · PhD Student, Department of Computer Science, University of California, Irvine

Out-of-Distribution Evaluation: The How, the Which, and the What?!

Robin Jia · Assistant Professor of Computer Science, University of Southern California

Instance-adaptive data compression: Improving Neural Codecs by Training on the Test Set

Ties van Rozendaal · Senior Machine Learning Researcher, Qualcomm AI Research

Functional Priors for Bayesian Deep Learning

Maurizio Filippone and Ba-Hien Tran · Associate Professor and PhD Student, EURECOM

Analyzing deep neural networks using attribution

Mukund Sundararajan · Principal Research Scientist, Google

Towards Inclusive and Socially Aware Language Technologies

Sunipa Dev · Research Scientist, Ethical AI Team, Google AI

Advanced training of energy-based models

Ruiqi Gao · Research Scientist, Google Brain

Modeling Irregular Time Series with Continuous Recurrent Units

Maja Rudolph · Senior Research Scientist, Bosch Center for AI

Exposing Shortcomings and Improving the Reliability of Machine Learning Explanations

Dylan Slack · PhD Student, Department of Computer Science, University of California, Irvine

Propagating Uncertainty from Modeling into Decision-Making for Trustworthy Autonomy

Ransalu Senanayake · Postdoctoral Scholar, Department of Computer Science, Stanford University

Curiously effective ensemble and double-oracle reinforcement-learning methods

Roy Fox · Assistant Professor, Department of Computer Science, University of California, Irvine

Effective representation learning to dissect the gene regulatory grammar

Jing Zhang · Assistant Professor, Department of Computer Science, University of California, Irvine

How do we evaluate our own uncertainty? Uncovering our metacognitive computations

Megan Peters · Assistant Professor, Department of Cognitive Sciences, University of California, Irvine

Ideal made real: machine learning with limited data and interpretable outputs

David Alvarez-Melis · Postdoctoral Researcher, Microsoft Research New England

Policy and Heuristic-Guided Tree Search Algorithms

Levi Lelis · Assistant Professor, Department of Computer Science, University of Alberta

Exploring the limits of lossy data compression with deep learning

Yibo Yang · PhD Student, Department of Computer Science, University of California, Irvine

Towards an Atlas of AI: A Conversation with Kate Crawford

Kate Crawford · Senior Principal Researcher, Microsoft Research, New York

The Measurement and Mismeasurement of Trustworthy ML

Sanmi Koyejo · Assistant Professor, Department of Computer Science, University of Illinois at Urbana-Champaign

Fill in the ___: Prompt-Based Solutions for NLP

Robert Logan · PhD Student, Department of Computer Science, University of California, Irvine

Decomposition Bounds for Influence Diagrams

Junkyu Lee · AI Planning Group, IBM Research

Connecting Variational Autoencoders Back to the Brain

Joe Marino · PhD Student, Computation and Neural Systems, California Institute of Technology

Visual Recognition beyond Appearances, and its Robotic Applications

Yezhou Yang · Assistant Professor, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University

Towards Reliable Deep Learning

Florian Wenzel · Postdoctoral Researcher, Google Brain Berlin