Fall Seminar Series 2023 *Distinguished Speaker* Dr. Yiran Chen of Duke University

Unleashing the Potential of Edge Computing with AI Hardware and Software Innovations

Dr. Yiran Chen

Professor of Electrical and Computer Engineering

Director of the NSF AI Institute for Edge Computing Leveraging the Next-generation Networks (Athena),

Director of NSF Industry-University Cooperative Research Center (IUCRC) for Alternative Sustainable and Intelligent Computing (ASIC)

Co-director of Duke Center for Computational Evolutionary Intelligence (DCEI)

Duke University

10/13/2023

11AM – 12PM CST

Location: UTSA Main Campus, Student Union Denman Room 2.01.28 (Second Floor)

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

Abstract: Edge computing has emerged as a powerful paradigm, shifting data processing and analysis closer to its origin points, resulting in real-time insights, reduced latency, and improved privacy. AI hardware and software innovations further unleash the potential of edge computing by enhancing the intelligence, efficiency, and elasticity of the systems. In this talk, we will discuss the customization of AI hardware and the optimization of AI models, considering the computing resource constraints and communication bandwidth limitations of edge devices, the security and privacy requirements of the systems, and the heterogeneity of data and devices.

Fall Seminar Series 2023 – Dr. Julia Lindberg

Estimating Gaussian mixtures using sparse polynomial moment systems

Dr. Julia Lindberg

The University of Texas Austin

9/29/2023

11AM – 12PM CST

Location: SAN PEDRO 1 YOTTA ROOM 430

506 Dolorosa St, San Antonio, TX 78204

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

Abstract: The method of moments is a statistical technique for density estimation that solves a system of moment equations to estimate the parameters of an unknown distribution. A fundamental question critical to understanding identifiability asks how many moment equations are needed to get finitely many solutions and how many solutions there are. We answer this question for classes of Gaussian mixture models using the tools of polyhedral geometry. Using these results, we present a homotopy method to perform parameter recovery, and therefore density estimation, for high dimensional Gaussian mixture models. The number of paths tracked in our method scales linearly in the dimension.

Fall Seminar Series 2023 – Toufeeq Ahmed Syed

Workforce Diversity, Education Informatics, and AI: Challenges, Innovation, and Future Directions

Toufeeq Ahmed Syed, PhD, MS

Associate Professor

Assistant Dean of Education Informatics

McWilliams School of Biomedical Informatics

McGovern Medical School

The University of Texas Health Science Center at Houston

9/22/2023

11am – 12pm CST

Location: SP1 Room 530

506 Dolorosa St, San Antonio, TX 78204

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

In this talk, Dr. Toufeeq Syed will discuss the role AI and Informatics can play in supporting life-long self-directed learners, increasing researcher diversity, creating national mentoring networks, and reducing health disparities. Dr. Ahmed co-leads National Research Mentoring Network (NRMN). The goal of NRMN is to increase the diversity of the biomedical workforce necessary to meet the projected needs by providing mentorship and networking activities, as well as serve as a national resource for best practices in mentoring and networking. For NRMN, Dr. Ahmed designed and developed MyNRMN (https://my.nrmnet.net), a powerful mentoring and networking platform custom-built to support NRMN mentors, mentees, and the community. With over 7,600 mentors and 14,600 mentees participating on the platform, MyNRMN enables mentees and mentors to connect professionally (11,300 mentoring connections made), support mentoring relationships, and build their professional networks. Our members represent more than 3,700 institutions from all 50 states. Dr. Ahmed serves as (multiple) Principal Investigator of the NIH-funded AIM-AHEAD program (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity). With other national leaders in health equity and AI/ML, he is building a national consortium with goals to enhance the participation and representation of underrepresented minority students, faculty, and researchers in developing AI/ML models and reducing health disparities and health inequities. For this project, he has developed an online national platform, AIM-AHEAD Connect (https://connect.aim-ahead.net), to support participant registrations, the proposal application process, fellows/students, and AI experts to network/mentor, online collaborations, projects, helpdesk, and online courses. Previously, he served as Executive Director in Health IT at Vanderbilt University Medical Center and Assistant Dean of Education Informatics at Vanderbilt School of Medicine, and developed VSTAR (https://vstar.app.vanderbilt.edu), an integrated learning management platform to deliver competency-based education (Curriculum 2.0), and designed and developed innovative learning and assessment applications including QuizTime (https://quiztime.app.vumc.org), Compass, Portfolio, TurnOut (https://turnout.app.vumc.org), and Spark. He provides technical leadership in national and international medical education technology and standards committees.

Fall Seminar Series 2023 – Ke Yang

Mitigating Bias in Data-driven Tasks

Dr. Ke Yang

University of Texas San Antonio

9/15/2023

11am – 12pm CST

Location: SP1 Room 530

506 Dolorosa St, San Antonio, TX 78204

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

Data-driven tasks are used in assisting decisions in many domains that greatly affect people’s lives. Though these tasks are efficient, they inevitably inherit bias in the input data and show problematic behaviors. Deploying unfair algorithms in critical domains will reinforce and legitimize the inequitable treatment of groups who are already disadvantaged. Moreover, the deployment of these tasks lacks transparency and accountability.

In this talk, I will introduce my research on mitigating bias in two types of data-driven tasks: rankings and classifications, which are frequently set up to automate decision-making processes. I will discuss three projects that demonstrate how fairness and diversity are ensured in ranking tasks. Then, I will talk about how fair classifications can be achieved by modeling bias as a data drift problem, which is a new approach to mitigate bias in classification tasks. At last, I will also highlight future work of promoting responsible data science by building solutions with the ethical concerns in the picture.

Fall Seminar Series 2023 – Kuldeep Meel

Automated Synthesis: An Ideal Meeting Ground for Symbolic Reasoning and Machine Learning

Kuldeep Meel, PhD

Associate Professor of Computer Science

University of Toronto

9/8/2023

11AM – 12PM CST

Location: San Pedro 1, Room 530

506 Dolorosa St, San Antonio, TX 78204

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

In this talk, I will focus on one of the most fundamental problems in computer science: functional synthesis, which seeks to synthesize a system from a given relational specification. The synthesis problem is as old as propositional logic, tracing its origins to Boole’s seminal work in the 1850’s. Despite decades of work, scalability remains the fundamental challenge in functional synthesis. I will discuss a new data-driven approach, Manthan, that combines the power of machine learning with symbolic reasoning to achieve dramatic improvements in scalability.  In particular, Manthan views functional synthesis as a classification problem, relies on advances in constrained sampling for data generation, and advances in automated reasoning for a novel proof-guided refinement and provable verification. The significant performance improvements call for interesting future work at the intersection of machine learning and symbolic reasoning.

Fall Seminar Series – Dharanidhar Dang

Silicon Photonics Computing: Remedy for Energy-draining AI

Dharanidhar Dang, PhD

Assistant Professor in the Department of Electrical & Computer Engineering

University of Texas, San Antonio (UTSA)

9/1/2023

11AM- 12PM CST

Location: SAN PEDRO 1 YOTTA ROOM 430

506 Dolorosa St, San Antonio, TX 78204

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

Abstract: AI and in particular deep learning compute demand is growing exponentially. In fact, 10 times a year. What makes it worse is, the AI growth is 5 times more than that of Moore’s Law. To give a perspective, training the latest GPT model with the best of GPUs costs 4.6M $ of electricity and it is going to get worse further. Now, to think of fast-decision making with limited power budget, traditional computing systems cannot offer the required energy efficiency. Gladly, silicon photonics with its ultra-low power characteristics, high-speed nature, and large-scale parallelism gives us immense hope to address these challenges.

In this talk, I will demonstrate how I utilize a variety of silicon photonic components along with emerging memory such as resistive memory to design next-generation end-to-end deep learning accelerator which can demonstrate two order or more improvement in energy efficiency and speedup compared to the state-of-the-art. The talk also includes the showcase of a novel photonic backpropagation accelerator which is a first of its kind. Finally, I will end the talk with several future directions to tackle critical problems in the real world.