SAVE THE DATE — MATRIX OPEN HOUSE

Please Save the Date for MATRIX AI Consortium Open House.

Date: 11/05/2021

Time: 3:00PM – 5:00PM CST

More details to come!

MATRIX Fall Seminar Series – Dr. Matei Ciocarlie

An Intelligent Mind for an Intelligent Hand: Sensor, Mechanism and Policy Design for Versatile Manipulators

Dr. Matei Ciocarlie

Columbia University

Friday, October 29, 2021

11 AM – 12 PM CST

https://utsa.zoom.us/j/98953233499

Abstract: What is the key to success in dexterous manipulation: the mechanical structure of the hand, the rich tactile and proprioceptive data it can collect, analysis and planning in the brain, or perhaps all of these? Believing these areas are deeply intertwined, we are pursuing them simultaneously. From a mechanism design perspective, I will present our work on optimizing the hand transmission hardware along with the grasping policy using joint optimization algorithms, an approach we refer to as “Hardware as Policy”. From a sensing perspective, I will present our optics-based tactile finger, providing accurate touch information over a multi-curved three-dimensional surface with no blind spots. Finally, from a motor learning perspective, I will talk about ongoing work on learning tactile-based policies for in-hand manipulation.

MATRIX Fall Seminar Series – Dr. Claire Walton

Control Theory and Deep Learning: Synergistic Partners in Theory and Applications

Dr. Claire Walton

University of Texas San Antonio

Friday, October 22, 2021

11 AM – 12 PM CST

https://utsa.zoom.us/j/98953233499

Abstract: Recent research has revealed significant connections between deep learning infrastructures such as neural networks and the modelling and control of complex dynamic systems.  Dynamic systems describe applications from self-driving cars to energy conservation to SpaceX rockets. Control of these systems brings up many challenges, such as nonlinearity, uncertainty, high dimensionality, and real-time decision-making demands. Application of neural nets to dynamic system control problems is an exciting current research area, which has already provided promising examples. However, perhaps a more surprising direction of impact has been the application of dynamic systems insights to deep learning itself. Equivalencies have been found between neural networks and the optimal control of dynamic systems, suggesting the computational tools of the latter can be used for the former. This has opened the door to many exciting possibilities driven by these equivalencies, such as the use of computational control algorithms to train neural networks, and development of network architectures developed from discretized control and dynamics models. This talk will provide a survey of these topics and of the cross-fertilization underway between control theory and deep learning.

AI & Quantum Computing Symposium

Artificial Intelligence (AI) and Quantum Computing are transformational technologies that will fundamentally change how we observe, interpret, and interact with the world of data that surrounds us. This symposium will focus on the state of the art in AI and Quantum Computing, touch upon the convergence of these two transformational technologies, and provide insights on future R&D opportunities from thought leaders in the field.

There will be a mix of individual speakers and panels. Due to ongoing health concerns, in-person is limited and will be by invitation only.

Register in advance for this event!

MATRIX Fall Seminar Series – Dr. Farinaz Koushanfar

Safe and Robust Machine Learning for Real Systems

Dr. Farinaz Koushanfar

University of California San Diego

Friday, October 15, 2021

11 AM – 12 PM CST

https://utsa.zoom.us/j/98953233499

We are at the CUSP of the fourth industrial revolution empowered by machine learning and application automation: seamlessly connecting people, data, and computing machines. Such intelligent technologies however bring numerous potential security vulnerabilities and threats that might severely compromise their safety. In this talk, I present our work in providing end-to-end solutions for practicable robust machine learning based upon co-design and optimization of ML, data, hardware and software. The goal is to characterize the potential ML attack surface, identify and address the nefarious threats in real-time, and devise novel solutions to tackle these problems. In particular, I will show how our work marks the first set of metrics, as well as real-time resource-efficient solutions for machine learning vulnerability characterization, adversarial attacks, and data poisoning.  I conclude by briefly discussing the challenges and opportunities moving forward.

MATRIX Fall Seminar Series – Dr. Jeffrey L. Krichmar

Flexible Path Planning and Navigation in Humans, Rats, and Robots

Dr. Jeffrey L. Krichmar

University of California, Irvine

Friday, October 8, 2021

11 AM – 12 PM CST

https://utsa.zoom.us/j/98953233499

Flexible path planning is necessary for reaching goals and adapting when conditions change. We introduce a biologically plausible path planning model that learns its environment, rapidly adapts to change, and plans efficient routes to goals. Because the algorithm is suitable for spike-based neuromorphic hardware, it has the potential of realizing orders of magnitude gains in power efficiency and computational gains through parallelization. In the first part of the talk, I will introduce the algorithm and show its effectiveness in autonomous robot navigation. In the second part of the talk, I will explore how this biologically inspired path planning algorithm can explain navigation strategies in humans and in rodents. I suggest that mental effort may be the reason we observe variability in human navigation. I also show that the model activity resembles neural correlates of hippocampal preplay and vicarious trial and error (VTE) observed when rodents are navigating mazes. This variation and flexibility may be beneficial for autonomous agents during information gathering and for robotic swarms.