In-Person Meeting Location:
Storey Innovation Center 1400
 
Live Meeting Link for Virtual Audience

Abstract:
Aviation is growing rapidly, and the need for reliable and robust wireless signaling for communications, navigation, and surveillance is growing accordingly. As with all communication systems, the physical layer (PHY) forms the foundation—higher-layer operation is irrelevant if the PHY fails. In this talk, we briefly describe the growth in aviation and in related areas of wireless communications, some nascent applications and national/international programs aimed to support these applications, and then turn our attention to the PHY, where we describe some of the unique challenges of aviation networking, focusing on the air-ground and air-air channels themselves. These elements of the communication system can be rapidly time-varying, distorting, and lossy, hence quantification of channel effects is critical to enable design of effective PHY techniques to ameliorate them. We show example results from a prior and recently-completed NASA projects that illustrate some of these challenges. The talk concludes with a summary and identification of some key future work.

Speaker's Bio:
David W. Matolak received the B.S. degree from The Pennsylvania State University, M.S. degree from The University of Massachusetts, and Ph.D. degree from The University of Virginia, all in electrical engineering. He has over 25 years’ experience in communication system research, development, and deployment, with industry, government institutions, and academia, including AT&T Bell Labs, L3 Communication Systems, MITRE, and Lockheed Martin. He has over 250 publications and nine patents. He was a professor at Ohio University (1999-2012), and since 2012 has been a professor at the University of South Carolina. He has been Associate Editor for several IEEE journals, and has delivered several dozen invited presentations at a variety of international venues. His research interests are radio channel modeling, communication techniques for non-stationary fading channels, and secure and covert communications. Prof. Matolak is a Fellow of the IEEE, a member of standards groups in RTCA and ITU, and a member of Eta Kappa Nu, Sigma Xi, Tau Beta Pi, URSI, ASEE, and AIAA.
 

Virtual Meeting Link

Abstract:

Emerging transportation technology is expected to revolutionize the future transportation ecosystem. My research aims to understand the social implications of these emerging technologies in transportation and develop optimal interventions to achieve desirable outcomes. In this talk, I will present two topics: to design optimal controls for autonomous vehicles, and to introduce physics-informed deep learning into classical transportation problems. In the first topic, I will talk about how I address the technological challenges of vehicle automation leveraging the core concepts of game theory, control, and machine learning. In the second topic, a novel framework - physics-informed deep learning - will be introduced and applied to traffic state estimation and uncertainty quantification.

Speaker's Bio: 

Dr. Xuan (Sharon) Di is an Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University in the City of New York and serves on a committee for the Smart Cities Center in the Data Science Institute. Prior to joining Columbia, she was a Postdoctoral Research Fellow at the University of Michigan Transportation Research Institute (UMTRI). She received her Ph.D. degree from the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities in 2014. Dr. Di received a number of awards including NSF CAREER, the Transportation Data Analytics Contest Winner from Transportation Research Board (TRB), the Dafermos Best Paper Award Honorable Mention from the TRB Network Modeling Committee, Outstanding Presentation Award from INFORMS, and the Best Paper Award and Best Graduate Student Scholarship from North-Central Section Institute of Transportation Engineers (ITE). She also serves as the reviewer for a number of journals, including IEEE ITS, Transportation Science, Transportation Research Part B/C/D, European Journal of Operational Research, Networks and Spatial Economics, and Transportation.

Dr. Di directs the DitecT (Data and innovative technology-driven Transportation) Lab @ Columbia University. Her research lies at the intersection of game theory, dynamic control, and machine learning. She is specialized in emerging transportation systems optimization and control, shared mobility modeling, and data-driven urban mobility analysis. Details about DitecT Lab and Prof. Sharon Di’s research can be found in the following link: http://sharondi-columbia.wixsite.com/ditectlab

This Friday (10/7), from 2:20 pm - 3:10 pm, at the Seminar in Advances in Computing, Dr. Vaishak Belle from the University of Edinburgh will give a virtual talk.

Virtual Meeting Link

Abstract:
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concerns itself with questions about the expressiveness of formal languages for capturing knowledge about the world, together with proof systems for reasoning from such knowledge bases. The learning camp attempts to generalize from examples about partial descriptions about the world. In AI, historically, these camps have loosely divided the development of the field, but advances in cross-over areas such as statistical relational learning, neuro-symbolic systems, and high-level control have illustrated that the dichotomy is not very constructive, and perhaps even ill-formed. In this talk, we briefly survey work that provides further evidence for the connections between logic and learning. Our narrative is structured in terms of three strands: logic versus learning, machine learning for logic, and logic for machine learning, but with ample overlap.

Speaker's Bio: Please see https://vaishakbelle.com/about/ for a brief bio.

The Office of the Vice President for Research is honored to welcome UofSC faculty researchers Forest Agostinelli, Orgul Ozturk, Jane Roberts and Bryant Walker Smith, as panelists for AI: Game Changer or Game Over?

Event Overview

The rapid proliferation of artificial intelligence in the 21st century is both promising and fraught, and for good reason—for decades, popular culture has envisioned how this futuristic technology might serve or even destroy humanity. From Rosey the sassy robot maid in the Jetsons to the sinister HAL 9000 computer in Stanley Kubrick’s 2001: A Space Odyssey, and including seemingly endless depictions in-between those extremes, our art has anticipated both helpful, symbiotic relationships and destructive confrontations between biological humanity and human-created intelligent technologies. But now that AI is here with us, what is the reality? How are artificial intelligences serving humanity today and how will their roles evolve tomorrow? What pitfalls come with the benefits of using AI? How do we harness the power of AI without becoming dangerously over-reliant?

These are some of the questions the Office of the Vice President for Research will invite the university community to explore on Thursday, October 6, 2022, when we convene a panel of university faculty experts to discuss their insights on the ethics and implications of artificial intelligence.

In-Person Meeting Location:

Storey Innovation Center 1400

Live Meeting Link for Virtual Audience:

Abstract: 

Speaker's Bio:

Since July 2022 Dr Amitava Das has been working as a Research Associate Professor at The Artificial Intelligence Institute (AIISC), University of South Carolina, USA. Earlier he set up an industry research lab from scratch Wipro AI – an industry research lab, in Bangalore, India, and is still associated with Wipro AI as an Advisory Scientist. At Wipro, he has led several academic collaborations – such as with IIT Patna, IIIT Delhi, UT Austin, AI institute at the University of South Carolina, and Cambridge University. He has started a joint PhD program, a unique setup for industry practitioners to earn their doctoral degrees while working full-time in the industry. 

Earlier he spent a stint (a semester) as an Associate Professor in the Department of Computer Science & Engineering at Mahindra University, Hyderabad. Mahindra University is an Indo-French collaborative institute – a collaboration between Tech Mahinda, and Centrale Supélec, France. Before that, he worked at the Indian Institute of Information Technology Sri City, Andhra Pradesh, India from June 2015-June to 2018 as an Assistant Professor in the Computer Science department. From July 2017- July 2018, he was associated with the Indian School of Business (ISB), Hyderabad as a visiting scientist at the Srini Raju Centre for IT and The Networked Economy (SRITNE).

He has experienced two academic postdocs: in Europe and in the USA. In the USA he worked as a Research Scientist in the Human Language Technologies (HiLT) lab at the University of North Texas, USA. During the summer of 2014, he worked at James Pennebaker’s lab, at the University of Texas-Austin as an Invited Researcher. He spent one year working as a European Research Consortium for Informatics and Mathematics (ERCIM) Post-Doctoral Fellow at the Norwegian University of Science and Technology (NTNU), Norway during 2012-2013.

He did work very briefly with Samsung Research India, Bangalore during the first half of 2013 as a Chief Engineer.

He has obtained PhD (Engineering) from Jadavpur University, India. During my doctoral study, he worked for an Indo-Japan collaborative project entitled “Sentiment Analysis where AI meets Psychology” with the Tokyo Institute of Technology, Japan.

Bio: Dr. Pushpak Bhattacharyya is a Professor of Computer Science and Engineering at IIT Bombay. He has done extensive research in Natural Language Processing and Machine Learning. Some of his noteworthy contributions are IndoWordnet, Eye Tracking assisted NLP, Low Resource MT, Multimodal multitasked multilingual sentiment and emotion analysis, and Knowledge Graph-Deep Learning Synergy in Information Extraction and Question Answering. He has published close to 400 research papers, has authored/co-authored 6 books including a textbook on machine translation, and has guided more than 350 students for their Ph.D., master's, and Undergraduate thesis. Prof. Bhattacharyya is a Fellow of the National Academy of Engineering, Abdul Kalam National Fellow, Distinguished Alumnus of IIT Kharagpur, past Director of IIT Patna, and past President of ACL. http://www.cse.iitb.ac.in/~pb

Abstract: In this talk, we describe our long-standing work on sentiment and emotion analysis, and also the use of NLP for mental health monitoring. The last mentioned is a stigma that society prefers to keep under cover, but with hazardous consequences. We describe our contribution to the techniques of sentiment and emotion analysisoften multimodal, multitasking, and multilingual. Such techniques have also proven useful in monitoring mental distress and providing positivity and hope through NLP agents. The work reported is the result of efforts of generations of students, and has found a place in top journals and conferences.

In-Person Meeting Location:

Storey Innovation Center 1400

Live Meeting Link for Virtual Audience:

Abstract: 

AI services are known to have unstable behavior when subjected to changes in data, models or users. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI work with humans. The current approach of assessing AI services in a black box setting, where the consumer does not have access to the AI’s source code or training data, is limited. The consumer has to rely on the AI developer’s documentation and trust that the system has been build as stated. Further, if the AI consumer reuses the service to build other services which they sell to their customers, the consumer is at the risk of the service providers (both data and model providers).

In this talk, I will cover chatbots (collaborative assistants), the problem of trust in this context and how one may make them more trustable. We will cover software testing, AI robustness, randomized control trial and the idea of rating AI based on their behavior. I will highlight some of our work, present key results and discuss ongoing work.

Speaker's Bio:

Biplav Srivastava is a Professor of Computer Science at the AI Institute at the University of South Carolina. Previously, he was at IBM for nearly two decades in the roles of a Research Scientist, Distinguished Data Scientist and Master Inventor. Biplav is  an ACM Distinguished Scientist, AAAI Senior Member, IEEE Senior Member and AAAS Leshner Fellow for Public Engagement on AI (2020-2021). His focus is on promoting goal-oriented, ethical, human-machine collaboration via natural interfaces using  domain and user models, learning and planning. Biplav has been working in AI trust for the last 3 years pursuing ideas in AI testing,

rating, randomized control and adversarial learning. He applies these techniques in areas of social as well as commercial relevance with particular attention to issues of developing countries  (e.g., transportation, water, health and governance). Biplav’s work has lead to many science firsts and high-impact commercial innovations ($B+), 190+ papers and 60+ US patents issued,

and awards for papers, demos and hacks. He has interacted with commercial customers, universities and governments, been on multilateral bodies, and assisted business leaders on technical issues.

More details about him are at: https://sites.google.com/site/biplavsrivastava/

Women in Computing will be hosting its first meeting of the Fall semester at 6pm next Monday, in Room 2277 at the Story Innovation Center! Women in Computing is open to all majors and everyone – all genders and majors is welcome! Please see the following message for details.

Virtual Meeting Link

Abstract: 

For the past few years, we have been intensively collaborating with mathematicians from Oxford and the University of Sydney. As a product of this collaboration, we have successfully demonstrated that analysing and interpreting the outputs of (graph) neural networks -- trained on carefully chosen problems of interest -- can directly assist mathematicians with proving difficult theorems and conjecturing new approaches to long-standing open problems. Specifically, using our method we have independently derived novel top-tier mathematical results in areas as diverse as representation theory and knot theory.

By doing so, we present AI as the mathematician's "pocket calculator of the 21st century". The significance of our result has been recognised by the journal Nature, where our work featured on the cover page.

In this talk, I aim to tell you all about our findings, from a personal perspective. Expect key details of our modelling work + an account of how it felt to interact with top mathematicians.

Speaker's Bio: Please see https://petar-v.com/ for a short bio.

In-Person Meeting Location:

Storey Innovation Center 1400

Live Meeting Link for Virtual Audience

Talk Abstract: Artificial intelligence and deep learning are increasingly transforming all scientific disciplines with their superior capability to learn to detect patterns from large amount of data and to learn predictive models from data without relying upon theory or deep mechanistic understanding, with their capability to build generative models for inverse design of materials and molecules and with the models to generate synthetic data. In this talk, we present our research focusing on using deep learning and machine learning to discover and model the patterns in and relationships of structures and functions in materials and molecules and how to exploit such learned dark/implicit knowledge in deep learning based generative design of novel materials, graph neural network based materials property prediction, and deep learning based crystal structure prediction of inorganic materials. Considering that the number of inorganic materials discovered so far (~250,000) by humanity is only a tiny portion of the almost infinite chemical design space, our AI based data-driven computational materials discovery has the potential to transform the conventional trial-and-error approaches in materials discovery.

Speaker's Bio: Dr. Jianjun Hu is currently a Full Professor of computer science at the Department of Computer Science and Engineering, University of South Carolina, Columbia SC. He was associate professor from 2013 to 2022 and assistant professor from 2007 to 2013 at the same department. Dr. Hu received his B.S. and M.S. degrees of Mechanical Engineering in 1995 and 1998 respectively from Wuhan University of Technology, China. He received the Ph.D. degree of Computer Science in 2004 from Michigan State University in the area of machine learning and evolutionary computation, under the supervision of Professor Erik Goodman. He then worked as Postdoctoral Fellow at Purdue University with Prof. Daisuke Kihara and University of Southern California with Prof. Xianghong Zhou from 2004 to 2007 in the area of bioinformatics. Dr. Hu’s main research has focused on machine learning, deep learning, evolutionary computation and their applications in materials informatics, bioinformatics, engineering design, and intelligent manufacturing. His works have been published in PNAS, Advanced Science, Nature npj Computational Materials, Patterns (Cell Press), Evolutionary Computation Journal, Journal of physical chemistry, Scientific Report, and so on with a total of more than 200 journal and conference papers (H-index 35 with > 4200 citations). Currently, his main research is focused on utilizing deep learning to discover the relationship of structures and functions in materials, molecules, and proteins and exploit the learned implicit knowledge for generative design of transformative new materials, drugs, and proteins. His work and research lab info can be found online at: http://mleg.cse.sc.edu/publication