Multi-disciplinary Faculty Research Talks
Jennifer Jacobs, Assistant Professor, Media Arts & Technology
Title: Expressive computation: integrating programming and physical making
Abstract: Creators in many different fields use their hands. Computers are increasingly displacing many manual practices in favor of procedural description and automated production. Despite this trend, computational and manual forms of creation are not mutually exclusive. By developing methods to integrate computational and physical making, we can dramatically expand the expressive potential of computers and broaden participation in computational production. I will present research across three categories: 1) Integrating physical and manual creation with computer programming through domain-specific programming environments. 2) Broadening professional computational making through computational fabrication technologies. 3) Broadening entry points into computer science learning by blending programming with art, craft, and design. Collectively, this work demonstrates how developing computational workflows, representations, and interfaces for manual and physical making can enable manual creators to leverage existing knowledge and skills. Furthermore, I’ll discuss how collaborating with practitioners from art, craft, and manufacturing science can diversify approaches to knowledge production in systems engineering and open new research opportunities in computer science.
Bio: Jennifer Jacobs is Assistant Professor at the University of California Santa Barbara in Media Arts and Technology and Computer Science by courtesy. At UCSB, she directs the Expressive Computation Lab, which investigates ways to support expressive computer-aided design, art, craft, and manufacturing by developing new computational tools, abstractions, and systems that integrate emerging forms of computational creation and digital fabrication with traditional materials, manual control, and non-linear design practices. Prior to joining UCSB, Jennifer received her Ph.D. from the Massachusetts Institute of Technology and was a Postdoctoral Fellow at the Brown Institute of Media Innovation within the Department of Computer Science at Stanford University. She also received an M.F.A. and B.F.A from Hunter College and the University of Oregon respectively. Her research has been presented at leading human-computer interaction research venues and journals including UIST, DIS, SIGGRAPH, and, most prominently, at the flagship ACM Conference on Human Factors in Computing Systems (CHI), where she received two best paper awards and one best paper honorable mention award in the past four years. As primary investigator, she has received two research grants in 2020 and 2021 from the National Science Foundation Division of Information and Intelligent Systems in computational fabrication for manufacturing and commercial craft.
Katie Byl, Associate Professor, Electrical & Computer Engineering
Title: Connections Between Reinforcement Learning Algorithms and Classical Dynamics Theory in Robot Control
Abstract: Developing control policies for robots and other autonomous systems is a challenging task. There is currently a lot of interest in using a variety of reinforcement learning (RL) algorithms, including deep learning, to do so efficiently, fueled by a range of anecdotally exciting demonstrations in simulations and for a few real robot systems, too. However, from a control theory perspective, we typically lack even approximate and/or probabilistic guarantees of stability or reliability for dynamic systems controlled by RL policies. Compared with model-based control strategies, it’s arguable that we also lack fundamental intuition for the closed-loop dynamics of such systems. This talk focuses at an intuitive level on a few potential connections between classical dynamics and control theory and various computer science algorithms for learning control policies. Specifically, encouraging low-dimensional structure within a much higher-dimensional state space has the complementary advantages of speeding up search time and of potentially enabling one to map out a low-dimensional subset of “reachable” states, toward analyzing closed-loop behavior of an autonomous system.
Bio: Katie Byl received her B.S., M.S., and Ph.D. degrees in mechanical engineering from MIT. Her research is in dynamic systems and control, with particular interest in modeling and control techniques to deal with the inherent challenges of underactuation and stochasticity that characterize bio-inspired robot locomotion and manipulation in real-world environments. Past research funding includes DARPA's M3 program, the DARPA Robotics Challenge (with JPL), the Army's Institute for Collaborative Biotechnologies (ICB) and Robotics CTA programs, an NSF CAREER award (2013), the Hellman Foundation (2012), and an Alfred P. Sloan Research Fellowship in Neuroscience (2011). Katie has worked on a wide range of research topics in the control of dynamic systems, including magnetic bearing control, flapping-wing microrobotics, piezoelectic noise cancellation for aircraft, and vibration isolation for gravity wave detection, and she was once a professional gambler on the now-infamous MIT Blackjack Team.
Nina Miolane, Assistant Professor, Electrical & Computer Engineering
Title: Geometric Learning for Biomedical Shape Analysis
Abstract: The advances in bioimaging techniques have enabled us to access the 3D shapes of a variety of structures: organs, cells, proteins. Since biological shapes are related to physiological functions, statistical analyses in biomedical research are poised to incorporate more shape data. This leads to the question: how do we define quantitative and reliable descriptions of shape variability from images? Mathematically, landmarks’ shapes, curve shapes, surface shapes, or shapes of objects in images are data that belong to non-Euclidean spaces, for example to Lie groups or quotient spaces. In this context, we introduce “Geometric Learning”, a framework for machine learning on non-Euclidean spaces, together with its open-source implementation Geomstats, and explore its impact on biomedical applications
Bio: Nina Miolane received her M.S. in Mathematics from Ecole Polytechnique (France) & Imperial College (UK), and her Ph.D. in Computer Science from INRIA (France) in collaboration with Stanford University. After her studies, Nina spent two years at Stanford University in Statistics as a postdoctoral fellow, and then worked as a deep learning software engineer in the Silicon Valley. At UCSB, Nina directs the BioShape Lab, whose goal is to explore the "geometries of life". Her research investigates how the shapes of proteins, cells, and organs relate to their biological functions, how abnormal shape changes correlate with pathologies, and how these findings can help design new automatic diagnosis tools. Her team also co-develops the open-source Geomstats library, a software that provides methods at the intersection of geometry and machine learning, to compute with geometric data such as biological shape data.