Fall Seminar Series 2023 *Distinguished Speaker* Dr. David Pan of UT Austin

Closing the Virtuous Cycle of Photonics for AI and AI for Photonics

Dr. David Z. Pan

Department of Electrical and Computer Engineering

The University of Texas at Austin

11/3/2023

11AM – 12PM CST

Location: UTSA Main Campus, Student Union Hidalgo Room (2.214)

Zoom: https://utsa.zoom.us/j/91049981814

Photonics is a promising emerging technology that offers huge performance and efficiency benefits for AI/ML computing. There has been increasing interest in developing photonic AI accelerators for the next generation datacenters, smart sensing, intelligent edge, and automotive computing. However, realizing its full potential requires overcoming significant design challenges through a cross-layer co-design approach, where devices, circuits, architectures, and algorithms are optimized together. In this talk, I will show how photonics and AI interplay with each other. On the front of Photonics for AI, I will show some case studies of customized photonic device, circuit, architecture, and software co-design for efficient machine learning. On the front of AI for Photonics, I will show a novel machine learning framework for real-time photonic device simulations, which has the potential to speed up the customized photonic structure design through AI-enhanced optimizations. Closing the virtuous cycle of photonics for AI and AI for photonics shall hold great potential to significantly advance the state-of-the-art of each other.

Fall Seminar Series 2023 – Dr. Srikanth Saripalli

Autonomy in the Wild: Good, Bad and the Ugly

Dr. Srikanth Saripalli

 Professor in Department of Mechanical Engineering, Director for Center for Autonomous Vehicles and Sensor Systems (CANVASS)

Texas A&M University

11/10/2023

11AM – 12PM CST

Location: UTSA San Pedro 1, Yotta Room 430

Zoom: https://utsa.zoom.us/j/91049981814

UxVs (Unmanned Aerial/Ground/Surface/Underwater Vehicles) are expected to expand their applications to several civilian domains such as Autonomous Driving, precision agriculture, infrastructure monitoring & disaster response. However for these UxVs to perform these tasks with full autonomy they must satisfy multiple requirements: 1) Navigate in GPS-denied Environments 2) Sense and Avoid Obstacles 3) Able to determine interesting phenomenon. In this talk, I will give an overview of our algorithms on combining vision with Inertial Measurement Unit and GPS for accurate state estimation of the vehicle. I will describe our algorithms for mapping using thermal, visual and LIDAR sensors that enable autonomous navigation in various challenging conditions.  A major portion of the talk will be on applications of the above algorithms to real vehicles (aerial, ground) and the lessons that we have learned i.e. what worked and what didn’t and how we should go about building such systems.

Fall Seminar Series 2023 – Dr. Debashis Sahoo

Data Science for Translational Research

Dr. Debashis Sahoo, PhD

Associate Professor

Pediatrics and Computer Science and Engineering

University of California San Diego

11/17/2023

11AM – 12PM CST

Location: UTSA Main Campus, Student Union Hidalgo Room (2.214)

Zoom: https://utsa.zoom.us/j/91049981814

Translational research aims to swiftly apply basic science discoveries in practical healthcare. Productivity hurdles can impede the quick translation of research into healthcare breakthroughs, with issues like reproducibility and generalizability of biological results in clinical settings posing significant challenges. Concerns about irreproducible results are on the rise, with over 70% of researchers reportedly facing challenges reproducing others’ experiments, and more than half struggling with their own. Drug development, a crucial facet of translational research, maintains a low success rate despite past efforts.

Part of the problem lies in the tools of data science and statistics used. I’ll introduce you to a new approach—Boolean analysis of large and diverse biological datasets. In my research group, we’ve pioneered this method and developed computational tools to uncover invariant logical relationships between genes. Join me as we explore how leveraging these invariant relationships can enhance the reproducibility of current approaches. We will discuss how Boolean analysis can transform the power of AI/ML approaches.