Lecture Series - View - Full Content - Upcoming

Dr. Haewon Jeong
Assistant Professor ECE Department, UC Santa Barbara, Santa Barbara CA

Salazar Hall 2009A
Wednesday, November 19, 2025

Abstract: Is it a good idea to use machine learning (ML) predictions in education? Would machine learning models treat all students fairly? I will start this talk with our recent analysis on middle school and high school datasets that reveal potential fairness risks of applying vanilla ML on students. To improve fairness in ML for education, there are several practical challenges. First, there are missing values in the datasets that are not evenly distributed across groups (e.g., female and male) which could aggravate the ML model's bias. I will show a fundamental limit of learning with missing values and propose a decision-tree-based algorithm that outperforms state-of-the-art fair ML methods that do not consider missing values. In the second part, we address how to correct bias in a classifier with low-cost post-processing when we have multi-class labels and sensitive attributes. I will introduce the Fair Projection algorithm which utilizes the idea of “information projection” and how it can be applied to a wide range of classifiers while maintaining a competitive fairness-accuracy trade-off.

Bio: Haewon Jeong is an assistant professor of Electrical and Computer Engineering at the University of California Santa Barbara. She received the B.S. degree ('14) in Electrical Engineering from KAIST and the M.Sc. ('16) and Ph.D. ('20) degrees in Electrical and Computer Engineering from Carnegie Mellon University. From 2020 to 2022, she was a postdoctoral scholar at Harvard University. Her research interests include information theory, distributed computing, machine learning, and ethics of AI systems.

Dr. William Putnam
Assistant Professor, ECE Department, UC Davis, Davis, CA

Salazar Hall 2009A
Monday, December 1, 2025

Abstract: In electronic devices like field-effect transistors, applied electric fields, up to hundreds of gigahertz in frequency, control electron motion. Recently, it has been demonstrated that the electric fields of ultra-intense laser pulses, i.e. electric fields in the tera- to petahertz (THz to PHz) regime, can similarly control electric currents around gas-phase atoms. In this talk, I will describe recent efforts to extend these early demonstrations of THz- and PHz-speed electronics from gaseous media to solid-state, microelectronic devices. Specifically, I will describe nanoantenna-based devices in which electric currents can be switched on and off by individual oscillations of the electric field of an infrared laser pulse. I will show that these devices can be used to sample >30-THz electric field waveforms in the time domain, and I will overview one of the exciting potential applications of these devices: field-resolved infrared spectroscopy.

Bio: William Putnam received his Ph.D. in EECS from MIT as well as undergraduate degrees in EECS and physics. Following his Ph.D. he held postdoctoral positions at MIT and the Center for Free-Electron Laser Science (CFEL) at the University of Hamburg, Germany. After his postdoctoral work and prior to joining the faculty at UC Davis, William spent several years as a staff scientist at Northrop Grumman’s basic research laboratory, NG Next where he worked on ultrafast electronics and frequency comb technology. From his undergraduate years to his postdoctoral work to his time in industry, William’s research has centered around both fundamental and applied studies of optics and quantum electronics.