Join us at our next event.

Topic: Advancing Intermittent IoT Systems with Resiliency and Intelligence

Next in our spring seminar series, we welcome Dr. Chen Pan, an Assistant Professor from the University of Texas at San Antonio.

Abstract:
This presentation will present innovative solutions to enhance IoT systems under intermittent energy conditions. First, it will introduce a sparsity-aware data sensing framework for efficient energy management in data acquisition. Then, the presentation will discuss a novel software paradigm for optimizing energy utility in edge devices for transient computing. After that, it will present a self-healing MAC protocol to ensure resilient communication against power interruption. Finally, the presentation will introduce an adaptive deep reinforcement learning-based routing policy for optimizing energy allocation and routing to improve networking performance. These approaches collectively aim to advance IoT infrastructure, emphasizing sustainability, intelligence, and resiliency under power-intermittent scenarios.

You can see the presentation live at the UTSA Main Campus in the Student Union Mesquite room (2.01.24).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/94807623288.

Topic: Through the Lens of Innovation: AI-powered Healthcare Solutions for Enhanced Outcomes and Health Equity

Next in our spring seminar series, we welcome Dr. Nawar Shara; Chief, Research Data Science, Co-Director of the Center of Biostatistics, Informatics and Data Science (CBIDS), at the MedStar Health Research Institute in Washington D.C.

Abstract:
My talk will focus on the power of innovative solution such as AI-powered chatbots and ML-based clinical decision support systems in healthcare and the impactful role they can play. The power of AI coupled with the catalytic role of data in redefining patient experiences and in improving healthcare utilization and patients outcomes. I will demonstrate how the integration of AI and data from different sources is reshaping healthcare paradigms, driving inclusivity, and paving the way for a patient-centric future that hopefully will leave no one behind.

You can see the presentation live at the UTSA Main Campus in the Student Union Mesquite room (2.01.24).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/94807623288.

Topic: Edge and IoT-supported Intelligent Augmented Reality: Promise, Challenges, and Solutions

Next in our spring seminar series, we welcome Dr. Maria Gorlatova, an Associate Professor in the Department of Electrical and Computer Engineering at Duke University.

Abstract:
Mobile augmented reality (AR), which integrates virtual objects with 3D real environments in real time, has been showing outstanding potential in many application domains including education, retail, and healthcare. AR is broadly expected to redefine how we interact with technology and the world around us. Yet current AR falls short of many of the expectations. This talk presents our vision of multi-device, edge computing-supported and Internet-of-Things (IoT)-integrated platforms for next-generation intelligent context-aware AR. I describe shortcomings in modern AR platforms’ spatial and semantic awareness capabilities and identify key gaps that need to be addressed to enable AR to become robust and resource-efficient; I present solutions to some of the challenges, touching on topics in edge computing, machine learning, and resource-efficient simultaneous localization and mapping (SLAM). I also highlight opportunities associated with close integration of AR platforms and their users and describe a set of solutions for robust gaze-based user context awareness in AR. The talk showcases several applications of next-generation context-aware AR, such as AR-based surgical guidance and AR support for mental and behavioral health and well-being.

You can see the presentation live at the UTSA Main Campus in the Student Union Mesquite room (2.01.24).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/94807623288.

Experience the first-ever week-long AI Spring School, proudly sponsored by MATRIX, NSF NAIAD, ATHENA & the UTSA Office of Research!

Connect with leading research scientists and industry experts as you immerse yourself in artificial intelligence’s latest advancements and hands-on experiences. We’ll dive deep into cutting-edge topics on neuro-inspired AI, federated learning, and continual learning for edge. This event is open to anyone who wants to learn and be a part of a vibrant AI community.

Dates: Monday, March 4 – Friday, March 8, 2024
Location: Weston Conference Center, San Pedro 1, UTSA, San Antonio, Texas

Topic: Speech Inversion based on Articulatory Phonology

Next in our spring seminar series, we welcome Dr. Carol Espy-Wilson, a professor from the University of Maryland.

Abstract:
Speech articulation is a complex activity requiring finely timed coordination across the articulators: lips, jaw, tongue, glottis and soft palate. This systematic coordination of the articulators differs between individuals, and across languages and dialects of the same language, accounting for many aspects of foreign accent, speech disorders and speaking style. Visualization of articulatory movements has required expensive and specialized equipment. However, we have developed a Speech Inversion (SI) system based on Articulatory Phonology that can accurately recover articulatory movements directly from the acoustic speech signal using machine learning techniques. In this talk, we will discuss the development of the SI system and how we are using the generated articulatory information for mental health assessment. I will also discuss an autoencoder architecture (a Mirror network) that we developed to explore sensorimotor learning. We show that the Mirror network can be used to control an articulatory synthesizer that takes as input articulatory parameters to generate continuous, natural sounding speech for unseen speakers. Further, we show that the Mirror network can learn in an unsupervised way meaningful articulatory representations with comparable accuracy to the SI system that is trained in a completely supervised fashion.

You can see the presentation live at the UTSA Main Campus in the Student Union Mesquite room (2.01.24).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/94807623288.

Topic: Learning with Graphs

Next in our spring seminar series, we welcome Dr. Gonzalo Mateos, an Associate Professor from the University of Rochester.

Abstract:
This talk is broadly about learning from network data, which arises for instance with applications involving online social media, recommendation systems, transportation, and network neuroscience. By fruitfully exploiting the inductive biases in relational data, graph neural networks (GNNs) have attained unprecedented performance in various machine learning tasks, including node/graph classification, link prediction, and graph generation. To provide additional motivation, I will start with a user-friendly and didactic introduction to graph signal processing. The goal is to establish the foundations and basic concepts that will be useful to introduce graph GNNs in an intuitive way. After discussing architectures and key properties that make GNNs the model of choice when it comes to learning from relational data, I will highlight several success stories of GNN-based learning for Amazon’s recommendation system, Google Maps navigation, antibiotic discovery, and our own work on explainable brain age prediction.

You can see the presentation live at the UTSA Main Campus in the Student Union Mesquite room (2.01.24).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/94807623288.