Events

Campus Shutdown Notice

In light of the ongoing coronavirus (COVID-19) situation, we have decided to close our administrative offices starting Monday, March 16, 2020 until further notice.  Cory and Soda Hall are closed.  Classes are being held remotely.  All events in Cory and Soda Halls will either be cancelled or held remotely, and staff will be working remotely during this time.

Apr20

Dissertation Talk: Towards Content-Creative AI

https://berkeley.zoom.us/meeting/register/tJIqcuigqDkqGtyKBMnInwg8vmRnGEsVOHkJ
  • Samaneh Azadi, PhD Candidate, Berkeley AI Research (BAIR)
In this talk, I will present our efforts towards creating new content in structural image domains from hand-designed fonts to natural complex scenes.
Apr21

EECS Colloquium: End-to-end learning for computational microscopy

Zoom: https://berkeley.zoom.us/s/99825370819
  • Laura Waller, Associate Professor of Electrical Engineering, UC Berkeley
Computational imaging involves the joint design of imaging system hardware and software, optimizing across the entire pipeline from acquisition to reconstruction. Computers can replace bulky and expensive optics by solving computational inverse problems. This talk will describe end-to-end learning for development of new microscopes that use computational imaging to enable 3D fluorescence and...
Apr22

Dissertation Talk: Statistical Complexity and Regret in Linear Control

Zoom
  • Max Simchowitz
The field of linear control has seen broad application in fields as diverse as robotics, aviation, and power-grid maintenance. Accordingly, decades of research have been dedicated to learning to control systems when the dynamics are not known a priori, but must be inferred from data. However, much of the classical work considered asymptotic regimes, which shed little light on precisely how much...
Apr22

Dissertation Talk: Interaction History for Building Human Data Interfaces

Zoom
  • Yifan Wu, UC Berkeley
History provides context for the present. Applied to data analysis, past user interactions provide context for present explorations. This thesis investigates different ways to reify user interaction history to help address emerging challenges in the design and programming of data analysis tools. We present three research artifacts that leverage interaction history in different but connected ways.
Apr23

Large-Scale Photonics for Quantum Information and Machine Learning: Nano Webinar Series

Zoom Session
  • Prof. Dirk Englund, MIT, EECS
The zoom link for this event is https://berkeley.zoom.us/j/98904362605?pwd=cXpkQ3p0K3dyMTlLUG9BWHoyN1c5dz09 After decades of intensive theoretical and experimental efforts, the field of quantum information processing is at a critical moment: special-purpose quantum information processors are cutting into a regime of quantum complexity where classical computers can no longer predict their...
Apr23

Applications of Data Science and AI to Equity, Race, and Inclusion in Mobility and Transportation: The Perils of Learning from Biased Data

Online
  • Judy Hoffman, Assistant Professor, Georgia Tech University
This spring, Berkeley's Institute of Transportation Studies is hosting a Zoom seminar series on "Applications of Data Science and AI to Equity, Race, and Inclusion in Mobility and Transportation" with the College of Engineering and The Center for Information Technology Research in the Interest of Society and the Banatao Institute. This topic brings a unique and innovative perspective to existing...
Apr26

Dissertation Talk: One-Shot Interactions with Intelligent Assistants in Unfamiliar Smart Spaces

Zoom
  • Meghan Clark
Smart space technologies have entered the mainstream home market. Most current smart space users interact with smart homes that they (or an acquaintance) have set up and know well. However, as these technologies spread to commercial or public environments, users will need to frequently interact with unfamiliar smart spaces. In such spaces, users will be unaware of the available capabilities and...
Apr26

Dissertation Talk: Offline Learning for Scalable Decision Making

Zoom
  • Justin Fu, UC Berkeley
The remarkable success of modern machine learning has arguably been due to the ability of algorithms to combine powerful models, such as neural networks, with large-scale datasets. However, the majority of these successes have been in prediction problems, such as supervised learning, whereas many real-world applications of machine learning involve complex decision making problems. In this talk, I...