Lecture Series Archive

The Coming 6th Generation (6G) of Mobile Wireless

Roger Nichols

Mr. Roger Nichols
6G Program Manager
Keysight Technologies, Santa Rosa, CA

Thu, 02/01/2024

Abstract: The first commercial 5G deployments were in March of 2019 - now almost 5 years ago--and the path to 6G is well under way. It is without a doubt that 6G will be evolution and revolution beyond 5G, but its territory is still rife with speculation. However, some of the differences are already clear. Not only is the technology going to be different, the change in commercial and government approach to commercial wireless systems has changed in a dramatic fashion. This talk will cover a little of what remains to be realized from the original 5G vision and what to expect from the work on 6G in the coming decade.

Bio: Mr. Roger Nichols is an acknowledged subject matter expert in mobile wireless communications design and measurement technologies. He has 39 years of engineering and management experience at Hewlett-Packard, Agilent, and Keysight spanning roles in R&D, marketing, and manufacturing. With experience in mobile wireless technology on all five previous generations, he has been directing Keysight’s 6G program since its inception in 2019. He is a member of the FCC Technological Advisory Council and is also the strategic director of Keysight’s work in wireless standards. Roger holds a BSEE from the University of Colorado, Boulder.

A Highly Linear Distributed Amplifier Using Ultra-wideband Intermodulation Feedforward Linearization

Alex Stameroff

Dr. Alex Stameroff
Operating Manager
Signal Conditioning and Subsystems, Keysight Technologies, Santa Rosa, CA

Thu, 11/16/2023

Abstract: Distributed amplifiers (DA) are critical components in various applications such as electronic measurement instruments, high-speed wireless systems, RADAR, and optical networks. In this lecture an introduction to distributed amplifiers and their applications will be presented. After this a feedforward intermodulation design technique to improve linearization will be discussed. Finally, a prototype utilizing this new technique will be presented.

Bio: Dr. Alexander Stameroff received a BS, MS, and PhD in Electrical Engineering from the University of California, Davis in 2007, 2011, and 2013, respectively. His PhD was in the field of high efficiency power amplifiers in X-band for RADAR applications. He joined Keysight Technologies (then Agilent) in 2013 as an Integrated Circuit designer. In 2020 he became manager of Keysight’s RF/mmWave Subsystem Design group. He has also been an adjunct professor with Santa Rosa Junior College since 2015. He has authored and co-authored over 20 publications in his field.

AI Alignment and RLHF: What we've accomplished, what we've learned, and what's missing!

Anca Dragan

Dr. Anca Dragan
Associate Professor
EECS Department, UC Berkeley, Berkeley, CA

Thu, 11/02/2023

Abstract: I've been thinking about how robots and AI agents more broadly can optimize for what we actually want as end users for a while now. I'll take the opportunity to reflect on what we've been able to accomplish in this area, as well as what's missing.
(RLHF = Reinforcement Learning from Human Feedback)

Bio: Prof. Anca Dragan is an Associate Professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. She runs the InterACT Lab, where they focus on algorithms for human-robot interaction -- algorithms that move beyond the robot's function in isolation, and generate robot behavior that coordinates well with people, and is aligned with what they actually want the robot to do and work across different applications, from assistive arms, to quadrotors, to autonomous cars, and draw from optimal control, game theory, reinforcement learning, Bayesian inference, and cognitive science. She also helped found and serve on the steering committee for the Berkeley AI Research (BAIR) Lab, and is a co-PI of the Center for Human-Compatible AI. She has been honored by the Sloan Fellowship, MIT TR35, the Okawa award, an NSF CAREER award, and the PECASE award.

Warm-Start Reinforcement Learning: From Function Approximation Error to Sub-Optimality Gap

Junshan Zhang

Dr. Junshan Zhang
Department of Electrical and Computer Engineering, UC Davis, Davis

Thu, 10/19/2023

Abstract: Conventional reinforcement learning (RL) techniques face the formidable challenge of high sample complexity and intensive computation load, which hinders RL's applicability in real-world tasks.  To tackle this challenge, Warm-Start RL is emerging as a promising new paradigm, with the basic idea being to accelerate online learning  by starting with an initial policy trained offline. Indeed,  owing to the knowledge transfer from an initial policy,  Warm-Start RL  has been  successfully applied in AlphaZero and ChatGPT, demonstrating its great potential to speed up  online learning. Despite these remarkable successes, a fundamental understanding of  Warm-Start RL is lacking. The primary objective of this study is to quantify the impact of function approximation errors on the sub-optimality gap  for Warm-Start RL. We consider the widely used "Actor-Critic" method for RL. Our findings reveal that   a 'good' warm-start policy (obtained by offline training) may be insufficient, and bias reduction in online learning also plays an essential role to lower the suboptimality gap.

Bio: Junshan Zhang is a professor in the ECE Department at University of California Davis. He received his Ph.D. degree from the School of ECE at Purdue University in Aug. 2000, and was on the faculty of the School of ECEE at Arizona State University from 2000 to 2021. His research interests fall in the general field of information networks and data science, including edge AI, reinforcement learning, continual learning, network optimization and control, game theory. He is a Fellow of the IEEE, and a recipient of the ONR Young Investigator Award in 2005 and the NSF CAREER award in 2003. His papers have won a few awards, including the Best Student paper at WiOPT 2018, the Kenneth C. Sevcik Outstanding Student Paper Award of ACM SIGMETRICS/IFIP Performance 2016, the Best Paper Runner-up Award of IEEE INFOCOM 2009 and IEEE INFOCOM 2014, and the Best Paper Award at IEEE ICC 2008 and ICC 2017.

Fair Machine Learning for Education - An Information Theorist’s Perspective

Haewon Jeong

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

Thu, 10/05/2023

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.