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

This Friday (8/26), from 2:20 pm - 3:10 pm, at the Seminar in Advances in Computing, Dr. Forest Agostinelli from UofSC will give an in-person talk entitled “Explainable Artificial Intelligence and the Rubik’s Cube”.

In-Person Meeting Location:

Storey Innovation Center 1400

Live Meeting Link for Virtual Audience:

https://teams.microsoft.com/l/meetup-join/19%3ameeting_Nzc3MThiZTktZDY0Zi00MDQzLWE5YmMtMzhiYjlhZTBiMjE3%40thread.v2/0?context=%7b%22Tid%22%3a%224b2a4b19-d135-420e-8bb2-b1cd238998cc%22%2c%22Oid%22%3a%2218da07c8-8a10-4930-a982-b5863c90ddf4%22%7d 

Talk Abstract: The field of artificial intelligence (AI) has allowed computers to learn to synthesize chemical compounds, fold proteins, and write code. However, these AI algorithms cannot explain the thought processes behind their decisions. Not only is explainable AI important for us to be able to trust it with delicate tasks such as surgery and disaster relief, but it could also help us obtain new insights and discoveries. In this talk, I will present DeepCubeA, an AI algorithm that can solve the Rubik’s cube, and six other puzzles, without human guidance. Next, I will discuss how we are building on this work to create AI algorithms that can both solve puzzles and explain their solutions in a manner that we can understand. Finally, I will discuss how this work relates to problems in the natural sciences.

Speaker's Bio: Forest Agostinelli is an assistant professor at the University of South Carolina. He received his B.S. from the Ohio State University, his M.S. from the University of Michigan, and his Ph.D. from the University of California, Irvine under Professor Pierre Baldi. His group conducts research in the fields of deep learning, reinforcement learning, search, explainability, bioinformatics, and neuroscience. His homepage is located at https://cse.sc.edu/~foresta/.

THESIS DEFENSE

Author : Nishat Tabassum

Advisor : Dr. Casey Cole

Date : July 11, 2022

Time 3:00 pm

Place : Virtual (Teams link below)

Meeting Link:here

Abstract

 Concrete structures have been a major aspect of social infrastructure since the 1700s, so it has been used for centuries. Concrete is used for the durability and support it provides to buildings and bridges. Assessing the state of these structures is important in preserving the longevity of structures and the safety of the public. Detecting cracks in their early stage allows repairs to be made without the need to replace the whole structure, so it reduces the cost. Traditional methods are slowly falling behind as technology advances and an increase in demand for a practical method of crack detection. This study aims to review the practicality of CNN for evaluating damages from cracks autonomously. In addition, many previous methods of crack detection such as traditional manual techniques, image processing techniques, and machine learning methods are discussed. These methods will be investigated to assess the results and effectiveness of each method. Four primary cracks and sixteen secondary cracks of varying depths were chosen to train the CNN model for binary classification of whether a crack is present. A database of images of concrete without cracks was utilized to train the CNN model to recognize the features of images with and without cracks. Multiclass CNN was trained with a dataset of known depths of cracks to predict the severity of damages and cracks. Few studies have been done on depth prediction of cracks, so the aim of this study is to suggest XGBoost of a regression model as an effective method of in-depth prediction. vi Test results show that both the CNN models produced high accuracy in crack identification and damage zone classification. So, it is an effective method that can be used by civil engineers to monitor the well-being of the concrete to reduce labor and increase time efficiency. In addition, the XGBoost of a regression model produced exemplary accuracy in results for predicting the depths of cracks. This demonstrates the possibility of crack depth prediction. Predicting the depths of cracks is important in gaining insight into the health of the structure and can help determine the severity of the cracks and damage to the structure.