Events for January 2023 – Matrix: The UTSA AI Consortium for Human Well-Being

All Seminars Workshops Summit Past Events

1 event, 3

11:00 am - 12:00 pm

MATRIX Spring Seminar Series – Dr. Jiannan Cai

MATRIX Spring Seminar Series – Dr. Jiannan Cai
February 3, 2023 • 11:00am
Data-driven Approach for Context-aware Human-Robot Collaboration in Future Construction

Jiannan Cai, Ph.D.

Assistant Professor

School of Civil and Environmental Engineering, and Construction Management

The University of Texas at San Antonio

2/3/2023

11 AM CST – 12PM CST

https://utsa.webex.com/utsa/j.php?MTID=m9c9f14f98a0738fb93ecf37e6474e8fd

Construction is labor-intensive, physically demanding, and hazardous work, while the persistent workforce aging and labor shortage further exacerbates the challenges. Construction robotics has been increasingly recognized as a promising solution to relieve human workers from repetitive and physically demanding tasks and thus improving work productivity and workers’ occupational health. However, the unstructured and dynamic workspaces and the diverse and complex construction activities make it extremely difficult to apply industrial robots that are traditionally pre-programmed to conduct a single task in a fixed working environment. Without eliminating construction workers, collaborative robots in construction must be designed to cognitively team up with workers, in order to assist humans in repetitive and physically demanding tasks while leveraging human expertise to handle unexpected and complex situations. In this presentation, Dr. Jiannan Cai will introduce the opportunities and challenges in construction robotics, explain their findings from human-robot collaboration experiments, and discuss their research efforts in developing data-driven approaches by coupling sensing, computer vision, deep learning, and robotics, to promote safe and efficient human-robot collaboration in future construction.
LINK

January 27

January 27, 2023 • 11:00 am - 12:00 pm

MATRIX Spring Seminar Series – Dr. Furong Huang

MATRIX Spring Seminar Series – Dr. Furong Huang
January 27, 2023 • 11:00am
Trustworthy Machine Learning in Complex Environments

Dr. Furong Huang

Assistant Professor of the Department of Computer Science

University of Maryland

1/27/2023

11AM – 12PM CST

https://utsa.webex.com/utsa/j.php?MTID=m9ab3786b04dc901b8cd82f477b43ddc3

Abstract: With the burgeoning use of machine learning models in an assortment of applications, there is a need to rapidly and reliably deploy models in a variety of environments. These trustworthy machine learning models must satisfy certain criteria, namely the ability to: (i) adapt and generalize to previously unseen worlds although trained on data that only represent a subset of the world, (ii) allow for non-iid data, (iii) be resilient to (adversarial) perturbations, and (iv) conform to social norms and make ethical decisions. In this talk, towards trustworthy and generally applicable intelligent systems, I will cover some reinforcement learning algorithms that achieve fast adaptation by guaranteed knowledge transfer, principled methods that measure the vulnerability and improve the robustness of reinforcement learning agents, and ethical models that make fair decisions under distribution shifts.

LINK

February 3

February 3, 2023 • 11:00 am - 12:00 pm

MATRIX Spring Seminar Series – Dr. Jiannan Cai

MATRIX Spring Seminar Series – Dr. Jiannan Cai
February 3, 2023 • 11:00am
Data-driven Approach for Context-aware Human-Robot Collaboration in Future Construction

Jiannan Cai, Ph.D.

Assistant Professor

School of Civil and Environmental Engineering, and Construction Management

The University of Texas at San Antonio

2/3/2023

11 AM CST – 12PM CST

https://utsa.webex.com/utsa/j.php?MTID=m9c9f14f98a0738fb93ecf37e6474e8fd

Construction is labor-intensive, physically demanding, and hazardous work, while the persistent workforce aging and labor shortage further exacerbates the challenges. Construction robotics has been increasingly recognized as a promising solution to relieve human workers from repetitive and physically demanding tasks and thus improving work productivity and workers’ occupational health. However, the unstructured and dynamic workspaces and the diverse and complex construction activities make it extremely difficult to apply industrial robots that are traditionally pre-programmed to conduct a single task in a fixed working environment. Without eliminating construction workers, collaborative robots in construction must be designed to cognitively team up with workers, in order to assist humans in repetitive and physically demanding tasks while leveraging human expertise to handle unexpected and complex situations. In this presentation, Dr. Jiannan Cai will introduce the opportunities and challenges in construction robotics, explain their findings from human-robot collaboration experiments, and discuss their research efforts in developing data-driven approaches by coupling sensing, computer vision, deep learning, and robotics, to promote safe and efficient human-robot collaboration in future construction.
LINK