Topic: Data-driven Approach for Context-aware Human-Robot Collaboration in Future Construction

Next in our spring seminar series, we welcome Dr. Jiannan Cai, Assistant Professor, School of Civil and Environmental Engineering, and Construction Management at the The University of Texas at San Antonio.

Abstract:
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.

Those who cannot attend in-person are welcome to tune in virtually through Webex: https://utsa.webex.com/utsa/j.php?MTID=m9c9f14f98a0738fb93ecf37e6474e8fd.

Topic: Trustworthy Machine Learning in Complex Environments

Next in our spring seminar series, we welcome Dr. Furong Huang, Assistant Professor of the Department of Computer Science, University of Maryland.

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.

Those who cannot attend in-person are welcome to tune in virtually through Webex: https://utsa.webex.com/utsa/j.php?MTID=m9ab3786b04dc901b8cd82f477b43ddc3.

Topic: Responsible AI: How do we get there?

Next in our fall seminar series, we welcome Dr. Mohamed Amer, Robust AI.

Abstract:
Machine learning models are getting larger and more complex. While machine learning provides a powerful set of tools, the models fail unexpectedly, harbor biases, and are poorly understood. Nonetheless, they are deployed at scale. A machine learning-driven system requires extensive engineering & computational resources, and massive amounts of data. Currently, these resources are only available to big players, governments & corporations, and there is a lack of tools or processes to enable auditing, compliance, and evaluation of such complex systems. The models are sometimes governed by a set of rules defined by the developers, which are often subjective and not well thought out. This talk will cover a wide variety of topics around responsible AI highlighting deep learning current challenges, vulnerabilities, and possible ways to mitigate them.

Those who cannot attend in-person are welcome to tune in virtually through Webex: https://utsa.webex.com/utsa/j.php?MTID=m66e32a039daa62769074b57712455b0c.

Topic: Computational Methods for Analysis of Functional Brain Images

Next in our fall seminar series, we welcome Dr. Ananth Grama, Purdue University.

Abstract:
Rapid advances in neuroimaging have resulted in large repositories of images with high temporal and spatial resolutions. This has motivated complex connectomic analyses aimed at understanding representation and processing of stimuli . Such analyses require novel computational tools that significantly extend the state-of-the-art in machine learning and data science. These studies have far-reaching implications for the fields of precision psychiatry, behavioural analysis, and neurodegeneration, in addition to applications in AR/VR and advanced human interfaces.
In this talk, I will discuss recent work in our group on understanding neuronal response to naturalistic visual stimulus — I present two methods based on archetypal analysis and deep learning to: (a) find interpretable representations of fMRI response, (b) predict objects in visual frames, and (c) reconstruct visual inputs. I will also briefly describe computational methods to characterize individual-level uniqueness of functional connectomes. I will present three methods based on matrix sampling, graph alignment, and linear algebraic techniques to accurately identify the identity of individuals, and the nature of the cognitive task being performed.  I will conclude my talk by highlighting the immense significance and challenges posed by problems in the field of neuroimage analyses.

Those who cannot attend in-person are welcome to tune in virtually through Webex: https://utsa.webex.com/utsa/j.php?MTID=m0dd130eaf6ddc33055fb62e2e1c06923.