Spring Seminar Series 2024 – Dr. Maria Gorlatova

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

Maria Gorlatova, Ph.D.
Associate Professor
Department of Electrical and Computer Engineering
Duke University, Durham, NC

Date:
03/08/2024

Time:
9:30 am – 10:30 am CST

Location:
San Pedro 1, Weston Conference Center
506 Dolorosa St, San Antonio, TX 78204

Zoom: https://utsa.zoom.us/j/94807623288

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.

NSF AI Spring School

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

NSF AI Spring School WEBSITE

Spring Seminar Series 2024 – Dr. Carol Espy-Wilson

Speech Inversion based on Articulatory Phonology

Carol Espy-Wilson, Ph.D.
Professor
University of Maryland
College Park, MD

Date:
03/01/2024

Time:
11:00 am – 12:00 pm CST

Zoom: https://utsa.zoom.us/j/94807623288

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.

Spring Seminar Series 2024 – Dr. Gonzalo Mateos

Learning with Graphs

Gonzalo Mateos, Ph.D.
Associate Professor
University of Rochester, Rochester, NY

Date:
02/23/2024

Time:
11:00 am – 12:00 pm CST

Location:
San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Zoom: https://utsa.zoom.us/j/94807623288

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.

Spring Seminar Series 2024 – Dr. George Sklivanitis

Toward Secure, Resilient, and AI-Driven Machine-to-Machine Wireless Communications

George Sklivanitis, Ph.D.
Research Associate Professor
Florida Atlantic University
Boca Raton, FL

Date:
02/16/2024

Time:
11:00 am – 12:00 pm CST

Location:
San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
Next-generation (NextG) network systems such as 6G cellular, future versions of WiFi, satellite networks are expected to connect billions of heterogeneous Internet of Thing (IoT) devices along with billions of people, enable machine-to-machine communications, and provide low-latency computational and storage resources on-demand at the edge and in the cloud. Any failure, tampering or degradation in network service can have highly disruptive, if not potentially catastrophic, effects. NextG network systems should be secure, intelligent and support autonomous decision-making. In this talk, I will present our work on model-based algorithms for waveform design for NextG wireless systems that autonomously optimize how to exchange information with any desirable level of interference avoidance allowed by the physics of the medium and hardware limitations. I will explore challenges and opportunities associated with AI-driven spectrum sensing architectures that provide the support for NextG waveform design and optimization. I will also cover data-driven frameworks enabling NextG security at the physical layer with functionalities such as radio fingerprinting for device authentication and dynamic link encryption using wireless channel state information. I will present our implementation work and experiments with software-defined radio platforms and discuss the way forward.

Spring Seminar Series 2024 – Dr. Anna Scaglione

Artificial Intelligence Architectures for Grid Data Analysis and Safe Control of Electric Power Systems

Anna Scaglione, Ph.D.
Professor, IEEE Fellow
Department of Electrical and Computer Engineering
Cornell Tech, New York, NY

Date:
02/09/2024

Time:
11:00 am – 12:00 pm CST

Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
There is a flurry of research activities recently focusing on applications of Artificial Intelligence in electric power systems analysis and operation. It is often motivated by emerging trends that are tied to decarbonizing the grid through the adoption of distributed renewable energy sources and demand response programs that call for methods account for the stochasticity of the net-demand. The vast majority of the research takes AI tools as a black box that internalizes every aspect of the observations, including the physics of the system (namely Ohm’s law) and learns to control the system by being rewarded in choosing good actions. In this talk we discuss how to go a step further. First we introduce Complex Graph Neural Network and show that they are most effective in both inference and reinforcement learning tasks, thanks to their physics inspired feature extraction capabilities. Then we move to introduce the problem of “safe” reinforcement learning, which allows to enforce the physical and safety constraints in the optimal policy.