AI Seminars
Fall 2025 | Spring 2025 | Spring 2024 | Winter 2024 | Spring 2023 | Winter 2023
Upcoming
Biologically Plausible Computing: Navigating Energy Landscapes
Francesco Bullo, Department of Mechanical Engineering, UCSB
To be announced
Winter 2026
Parameter identifiability in RNN models of neural computations
Fatih Dinç, Kavli Institute for Theoretical Physics, UCSB
01/30/2026
Fall 2025
Vector Search: High-Throuhgput and Robust Query Processing and Modern
Benchmarks
Teemu Roos, Department of Computer Science, University of Helsinki
12/08/2025
Reliable and Efficient AI for Robotics
Christian Shewmake, New Theory
11/14/2025
Learning Cellular Dynamics through Neural and Geometric Models of Imaging and Genomics Data
Wenjun Zhao, Department of Mathematics, Wake Forest University
11/07/2025
Predictive navigation emerges from real and artificial neural networks
Andy Alexander, Department of Psychological and Brain Sciences, UCSB
10/17/2025
“Double Machine Learning” for Causal Inference
Alex Franks, Department of Statistics and Applied Probability, UCSB
10/03/2025
Spring 2025
When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Plugins
Yigitcan Kaya, Department of Computer Science, UCSB
05/30/2025
Learning Mechanics of Neural Networks: Conservation Laws, Implicit Bias, and Feature Learning
Daniel Kunin, Institute for Computational and Mathematical Engineering, Stanford
05/16/2025
Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images
Tian Qiu, Department of Electrical & Computer Engineering, UCSB
05/02/2025
Methods combining networks and machine learning for complex systems: from hate speech to personalized predictive medicine
Sanjukta Krishnagopal, Department of Computer Science, UCSB
04/08/2025
Spring 2024
Understanding and harnessing reinforcement learning for security purposes
Wenbo Guo, Department of Computer Science 05/31/2024
Initialization Matters for Adversarial Transfer Learning
Andong Hua, Department of Electrical & Computer Engineering 05/17/2024 Read More
Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
Antonis Antoniades, Department of Computer Science 05/03/2024 Read More
Organoids and Topology
Eve Bodnia, Department of Physics 04/19/2024
Winter 2024
Neural Emulators and Hamiltonian Monte Carlo for Parameter Inferences
Diego Gonzalez, Department of Electrical & Computer Engineering 03/07/2024
Geometry of Neural Manifolds
Francisco Acosta, Department of Physics, and Electrical & Computer Engineering 02/22/2024 Read More
No Dataset Left Behind: DIET for Stable and Data Efficient Self-Supervised Learning
David Klindt, Department of Electrical & Computer Engineering 01/25/2024
Spring 2023
Architectures of Topological Deep Learning
Mathilde Papillon, Department of Physics, and Electrical & Computer Engineering 05/19/2023 Read More
Probabilistic ML with p-bits
Kerem Camsari, Department of Electrical & Computer Engineering 05/05/2023 Read More
Symbolic Regression for Dynamical Systems with Neural ODEs
Colby Fronk, Department of Chemical Engineering 04/21/2023 Read More
Winter 2023
Trustworthy and Explainable ML for Networks
Arpit Gupta, Department of Computer Science 03/10/2023
ML Algorithms for Imaging and Graphics
Pradeep Sen, Department of Electrical & Computer Engineering 03/03/2023
Manifold Learning for Non-Linear Dynamics
Paul Atzberger, Department of Mathematics, and Mechanical Engineering 02/17/2023
ML for Neuroprostheses
Michael Beyeler, Department of Computer Science 02/10/2023
Federated Learning
Ramtin Pedarsani, Department of Electrical & Computer Engineering 02/03/2023
Tensor-Compressed Neural Networks for Edge AI
Zheng Zhang, Department of Electrical & Computer Engineering 01/27/2023
Fairness in Machine Learning
Haewon Jeong, Department of Electrical & Computer Engineering 01/20/2023
Geometric Machine Learning
Nina Miolane, Department of Electrical & Computer Engineering 01/13/2023
