My Computer Science and Engineering Department

We congratulate the following Computer Science/Engineering undergraduate students for receiving a Magellan Scholar Award for Spring 2022.

• Ian McDowell and Rahul Bulusu with the project "Drone-based Multimodal Surveying for Outdoor 5G Millimeter-Wave Picocell Placement"
• Cassidy Carter for "Detection and discovery of structural motifs in crystal materials using quotient graphs"
• James Seekings for "Attention Tracking for Intuitive Robot Tutors using Neuromorphic Computing"
• Edward Sitar for "5G Millimeter-Wave Enabled At-Home Human Silhouette Estimation"
• Corinne Smith for "A network of UAV deployable sensor packages for monitoring hydraulic parameters during severe weather events"
• Lexington Whalen for "The Wordification Project"

We are to announce the following research awards received by CSE department faculty:

Dr. Yan Tong received an award from Clemson University/SCRA for the project "Modernizing South Carolina Manufacturing Assets to Enable Industry 4.0"

Dr. Chin-Tser Huang received an award from Clemson/USDOT for the project "A Machine Learning-Assisted Framework for Determination of Performance Degradation Causes and Selection of Channel Switching Strategy in Vehicular Networks"

Dr. Casey Cole received an award from the US Department of Education (USDE) for the project "University of South Carolina – Transition to Teaching Residency"

Collaborative project between the AI Institute, College of Nursing, and Prisma Health develops virtual assistant to facilitate mental health care.

CSE alumnus De’Aira Bryant is featured in the following Amazon Science article "How De’Aira Bryant found her path into robotics":

The computer scientist recently finished her second internship at Amazon, where she worked on a new way to estimate the human expression on faces in images.

Dr. Jijun Tang has received a research award for the project "Algorithmic Analysis of Cell Slide Images Using Machine Learning" from the National Institute of General Medical Sciences (NIGMS)/NIH.

Dr. Homay Valafar has received the following research awards:

• “South Carolina IDeA Networks of Biomedical Research Excellence (SC INBRE) - Bioinformatics Core (BIPP) - Year 2 of 5”  from the National Institute of General Medical Sciences (NIGMS)/NIH
• “Targeting important behaviors for weight loss through the use of social gaming and points: The Social Pounds Off Digitally (Social POD) study” from the National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK)/NIH.

Dr. Amit Sheth has received the following research awards:

• "RII Track 2 FEC: Enabling Factory to Factory (F2F) Networking for Future Manufacturing" from the National Science Foundation (NSF)
• "Actionable Sensemaking Tools for Curating and Authenticating Information in the Presence of Misinformation during Crises" from Ohio State University (OSU)/NSF
• "Improving mosquito control methodologies through development of technology-driven traps and artificial intelligence guided detection of mosquito breeding habitats" from the National Institute of Allergy & Infectious Diseases.

We would like to welcome Dr. Vignesh Narayanan as a new Assistant Professor to our Computer Science and Engineering department. He received his Ph.D. in Electrical Engineering from Missouri University of Science and Technology (2017), a M.Tech.in Control Systems from the National Institute of Technology, Kurukshetra, India (2014) and a B.Tech. Electrical and Electronics Engineering, SASTRA University, India, (2012).

His research is in the areas of dynamical systems and networks, data science and learning theory, and computational neuroscience. Visit his homepage to learn more.

We welcome Dr. Christian O’Reilly as a new Assistant Professor to our Computer Science and Engineering department. Dr. O'Reilly received his B.Ing (electrical eng.; 2007), his M.Sc.A. (biomedical eng.; 2011), and his Ph.D. (biomedical eng.; 2012) from the École Polytechnique de Montréal.

His main interests are related to better understanding the brain across spatial and temporal scales in order to address complex neurodevelopmental issues such as autism and other neurodevelopmental disorders. The methods he uses include analytical techniques (e.g., EEG source reconstruction, functional connectivity) and modeling (e.g., point neurons, morphologically-detailed neurons, neural masses), as well as the combination of these two approaches through Bayesian model-driven analyses. He is further interested in novel ways to empower the study of neuroscience through AI and to empower AI through biologically inspired neural networks

The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials. However, lack of user-friendly materials informatics web servers has severely constrained the wide adoption of such tools in the daily practice of materials screening, tinkering, and design space exploration by materials scientists. Herein Dr. Jianjun Hu's team developed http://materialsatlas.org, a web based materials informatics toolbox for materials discovery, which includes a variety of routinely needed tools for exploratory materials discovery, including materials composition and structure check (e.g. for neutrality, electronegativity balance, dynamic stability, Pauling rules), materials property prediction (e.g. band gap, elastic moduli, hardness, thermal conductivity), and search for hypothetical materials, and fool-proof machine learning of user-specified datasets.  These user-friendly tools can be freely accessed at http://materialsatlas.org. This materials informatics apps will greatly ease the pains in exploratory materials discovery. 

Dr. Amit Sheth has received the following funding awards:

• National Institute on Deafness & Other Communication Disorders (NIDCD)/NIH for "AI/ML-Readiness for Neuroimaging of Language (NIH Supplement)"
• SC Research Authority (SCRA) for "Enabling Factory to Factory (F2F) Networking for Future Manufacturing across South Carolina"

Dr. Amit Sheth has received a research award from Wipro Limited for his project "Percuro".

Dr. Biplav Srivastava has received a research awarard from Tantiv4 Inc. for his project "Analysis of Power IoT data for management of infrastructure". 

Dr. Jianjun Hu of the Machine Learning and Evolutionary Laboratory (MLEG) has received a project award titled "Deep Learning Accelerated Inverse Design of Lab-Scale Energy Efficient Heterojunctions for Wide-Bandgap Devices" which is funded by National Science Foundation (NSF) with his collaborators from Department of Mechanical Engineering, USC including Prof. Ming Hu, Prof. Chen Li, Prof. Dongkyu Lee.

This work will extend the great success of deep learning in computer vision and natural language processing into data driven materials inverse design which may dramatically speed up conventional materials discovery processes.

Dr. Pooyan Jamshidi has received an NSF research award for his project on "Causal Performance Debugging for Highly-Configurable Systems" and another NSF research award for a joint project with colleagues in the Math department on "Mathematical Foundation of Data Science at University of South Carolina."

Abstract (Casual Debugging)

Software performance is critical for most software systems to achieve scale and limit operating costs and energy consumption. As modern software systems, such as big data and machine-learning systems, are increasingly built by composing many reusable infrastructure components and deployed on distributed and heterogeneous hardware, developers have powerful tools and abstractions at their fingertips, and as a result face immense configuration complexity... The project is intended to initiate a paradigm shift in today's testing and debugging methodology for complex, highly configurable systems, thereby positively impacting a broad range of industrial sectors relying on complex, highly configurable systems. Specifically, the project contributes to substantial energy savings and reduced carbon emissions, especially for the many big-data and machine-learning systems that operate at a massive scale. Finally, the research is providing valuable training for involved students from diverse backgrounds in research and generating high-quality researchers and practitioners for society.

Abstract (Data Science at UofSC)

This Research Training Group (RTG) project is a joint effort of Mathematics, Statistics, Computer Science and Engineering. It aims to develop a multi-tier Research Training Program at the University of South Carolina (UofSC) designed to prepare the future workforce in a multidisciplinary paradigm of modern data science. The education and training models will leverage knowledge and experience already existing among the faculty and bring in new talent to foster mathematical data science expertise and research portfolios through a vertical integration of post-doctoral research associates, graduate students, undergraduate students, and advanced high school students.

Dr. Amit Sheth has received an NSF research award for his project titled  "Advancing Neuro-symbolic AI with Deep Knowledge-infused Learning."

Abstract

The first wave of AI termed symbolic AI, focused on explicit knowledge. The current second wave of AI is termed statistical AI. The deep learning techniques have been able to exploit large amounts of data and massive computational power to improve upon human levels of performance in narrowly defined tasks. Separately, knowledge graphs emerged as a powerful tool to capture and exploit an extensive amount and variety of explicit knowledge to make algorithms better understand the content, and enable the next generation of data processing, such as in semantic search. Now, we herald towards the third wave of AI built on what is termed as the neuro-symbolic approach that combines the strengths of statistical and symbolic AI. Combining the respective powers and benefits of using knowledge graphs and deep learning is particularly attractive. This has led to the development of an approach we have called knowledge-infused (deep) learning. This project will advance the currently limited forms of combining the knowledge graphs and deep learning, called shallow and semi-diffusion, with a more advanced form called deep-infusion, that will support stronger interleaving of more variety of knowledge at different levels of abstraction with layers in a deep learning architecture.