Join us at our next event.

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

Next in our fall seminar series, we welcome Dr. David Z. Pan, Department of Electrical and Computer Engineering at the The University of Texas at Austin.

Abstract:
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.

You can see the presentation live at the UTSA Main Campus, Student Union Hidalgo Room (2.214).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/91049981814.

Topic: Efficient Continual Deep Learning

Next in our fall seminar series, we welcome Dr. Christopher Kanan from the University of Rochester.

Abstract:
Continual learning methods enable progressive knowledge accumulation in deep neural networks, and it has the potential to make training much more efficient and has many exciting applications such as on-device learning. Whereas conventional deep learning can suffer from catastrophic forgetting of previously acquired abilities when updating a network, over the past five years, continual learning has largely overcome this limitation. Despite this achievement, I argue the vast majority of continual learning algorithms have little practical value. In this talk, I show that methods are highly inefficient in terms of compute, memory, and storage. Some methods even require more computation than re-training from scratch as a dataset grows! For continual learning to have real-world applicability, the research community cannot ignore the resources used by these algorithms. Subsequently, I demonstrate that three new neuroscience-inspired algorithms created by my lab can vastly improve computational efficiency on large-scale vision and text understanding datasets. Our work has the potential to make continual learning more practical and widely adopted as well as advancing the goals of GreenAI.

You can see the presentation live at UTSA San Pedro I Yotta room 430.

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/91049981814.

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

Next in our fall seminar series, we welcome 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), and Co-director of Duke Center for Computational Evolutionary Intelligence (DCEI) at Duke University.

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.

You can see the presentation live at UTSA Main Campus, Student Union Denman Room 2.01.28 (Second Floor).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/91049981814.

Topic: Estimating Gaussian mixtures using sparse polynomial moment systems

Next in our fall seminar series, we welcome Dr. Julia Lindberg from the University of Texas at Austin.

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.

You can see the presentation live at UTSA San Pedro I in the Yotta Room430.

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/91049981814.

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

Next in our fall seminar series, we welcome Dr. Toufeeq Ahmed Syed, Associate Professor and Assistant Dean of Education Informatics at McWilliams School of Biomedical Informatics and McGovern Medical School at the The University of Texas Health Science Center at Houston.

Abstract:
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.

You can see the presentation live at UTSA San Pedro I room 530.

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/91049981814.

Topic: Mitigating Bias in Data-driven Tasks

Next in our fall seminar series, we welcome Dr. Ke Yang from the University of Texas San Antonio.

Abstract:
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.

You can see the presentation live at UTSA San Pedro I room 530.

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/91049981814.