We are proud to announce that Dr. Ramtin Zand has received an NSF CAREER award for his research on "Heterogeneous Neuromorphic and Edge Computing Systems for Realtime Machine Learning Technologies". 

This project aims to harness the combined capabilities of neuromorphic and edge computing to forge a heterogeneous machine learning system. Its primary goal is to enable computer vision and language models on resource- and energy-constrained devices at an unprecedented scale. It focuses on several key aspects: (1) developing hybrid models that merge the energy efficiency, temporal sparsity, and spatiotemporal processing of spiking neural networks with the global processing of transformer models for complex large-scale computer vision tasks, (2) creating a methodology to deploy large language models on edge devices by employing system-level innovations such as computational graph modifications, custom kernels, and mathematical refactoring, (3) designing a flexible edge artificial intelligence (AI) accelerator to overcome hardware limitations hindering real-time implementation of large transformer models at the edge, (4) seamlessly integrating a heterogeneous system of mobile processors, edge AI accelerators, and neuromorphic hardware for a comprehensive end-to-end solution. Throughout the project, rigorous investigation delves into critical trade-offs between bandwidth, accuracy, performance, and energy consumption.

Also see this article about Dr. Zand and his research.

The paper titled  "Expressive and Flexible Simulation of Information Spread Strategies in Social Networks Using Planning," by Bharath Muppasani, Vignesh Narayanan, Biplav Srivastava, Michael N. Huhns, has been selected for the Best Demo Award at AAAI-24. AAAI is a top AI conference and was held over the past week.

The work enables detailed simulations of opinion evolution and strategic interventions using planning. Designed to enhance human-AI collaboration, the framework supports the creation of strategies that facilitate a deeper understanding and informed engagement with the opinion evolution in networks. It was selected from 30 demos, which themselves were selected from a pool of 97 submissions. You can read the poster and watch the video presentation.

The following papers written by our AI Institute members were accepted for presentation at the 2023 Conference on Empirical Methods in Natural Language Processing:

• Counter Turing Test (CT^2): AI-Generated Text Detection is Not as Easy as You May Think - Introducing AI Detectability Index (ADI). Megha Chakraborty, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Shreya Gautam, Tanay Kumar, Krish Sharma, Niyar R Barman, Chandan Gupta, Vinija Jain, Aman Chadha, Amit P. Sheth, Amitava Das.
• The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations. Vipula Rawte, Swagata Chakroborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit P. Sheth, Amitava Das.
• FACTIFY3M: A benchmark for multimodal fact verification with explainability through 5W Question-Answering. Megha Chakraborty, Khushbu Pahwa, Anku Rani, Shreyas Chatterjee, Dwip Dalal, Harshit Dave, Ritvik G, Preethi Gurumurthy, Adarsh Ashok Mahor, Samahriti Mukherjee, Aditya Pakala, Ishan Paul, Janvita Reddy, Arghya Sarkar, Kinjal Sensharma, Aman Chadha, Amit P. Sheth, Amitava Das.

The acceptance of these papers at EMNLP, a leading conference in NLP, is a testament to the high quality of research being conducted at the AI Institute. The papers address important and challenging problems in NLP, and their findings have the potential to significantly advance the state of the art in this field.

The University of South Carolina was just awarded $1.1M with a National Science Foundation MRI grant to purchase a High-Performance Computing cluster (HPC) for boosting AI enabled science, engineering, and education in South Carolina. This grant will be led by the PI Prof. Ming Hu (Mechanical engineering), two Computer Science Co-PIs (Prof. Jianjun Hu and Prof. Forest Agostinelli) and additional two Co-PIs (Prof. Sophya Garashchuk of chemistry, and Sagona, Paul of Div. IT).

The new HPC instrument (with both new GPU and CPU servers) will be hosted at USC but will be made accessible to students of more than 10 regional universities such as Claflin University, Furman University, Francis Marion University, Costal Carolina University, College of Charleston, Charleston Southern University, Winthrop University, Presbyterian College, Benedict College, USC Beaufort and etc. It will promote research in diverse fields such as materials science, physics, chemistry, engineering, computer science, bioinformatics, health science and humanities, all enhanced by the HPC, big data and AI tools. The project team will also organize training workshops for AI-enabled scientific research and engineering innovation, education programs for undergraduate students, and summer camps for high school students in the coming years. More information will be posted on the project website at http://ai4science.sc.edu.

Prof. Jianjun Hu, director of the Machine Learning and Evolution Lab and his collaborators Prof Ming Hu (PI) from USC Mechanical engineering and Prof. Christopher Wolverton (Co-PI) of Northwestern University have just acquired a NSF grant on generating a modern phonon database and developing machine learning prediction, analysis, and visualization tools for data driven materials discovery, which will speed up research and design of novel thermoelectrics, superconductors, photovoltaics, superionic conductors.

Phonon Database Generation, Analysis, and Visualization for Data Driven Materials Discovery

Material databases and their related computing infrastructures have become the major cornerstone of current data driven and artificial intelligence (AI) based materials discovery. However, among the rich material properties of interest to the materials community, few databases have comprehensively included phonon properties, which are at the center of materials science and are related to diverse functionalities such as thermoelectrics, superconductors, photovoltaics, superionic conductors, etc. This project meets these urgent needs to generate a comprehensive phonon database along with analysis, visualization, navigation, and visualization tools, combined with multi-channel infrastructure-community communication and feedback. The phonon database will become an excellent complement to the currently widely used material databases. Developing such an infrastructure will be beneficial for all areas of materials science and engineering, accelerating the prediction, design, and synthesis of novel materials with various emerging applications in modern science and technology. The project will promote the engagement of underrepresented and minority students in research, equip engineering students with interdisciplinary expertise and frontier knowledge crucial to their future careers, and fulfill the mission to prepare a high-quality workforce for science, technology, and engineering. The project will also develop new course materials for undergraduate and graduate computational materials science courses.

We congratulate our faculty members that have received research awards. They are:

• Dr. Christian O'Reilly for receiving funds from NIH-NIMH on the project titled "The Role of Autonomic Regulation of Attention in the Emergence of ASD"
• Dr. Jason Bakos for receiving funds from NSF on the project titled "Collaborative Research: SHF: Small: Sub-millisecond Topological Feature Extractor for High-Rate Machine Learning"
• Dr. Homayoun Valafar for receiving funds from Prisma Health-Upstate on the project titled "Analysis of Patient Glycomic Profiles in Search for Breast Cancer Signatures Using Machine Learning Approach"
• Dr. Jianjun Hu for receiving funds from EPSCoR/IDeA/SC Commission on the project titled "GEAR CRP: Deep learning reinforced high-resolution semiconductor radiation detector for real- time medical imaging"
• Dr. Micheal Huhns for receiving funds from University of Maryland/ARLIS/DOD on the project titled "Information Competition Simulator"
• Mr. Lexington Wahlen for receiving a NASA South Carolina Space Grant Consortium STEM Outreach Award for the project titled "Wordification: A New Way of Teaching English Spelling Patterns"
• Mr. Musa Azeem for receiving a NASA South Carolina Space Grant Consortium STEM Outreach Award for the project titled "Unobtrusive and User-Friendly Acquisition of Multi-sensor Data from Wearable Smartwatch Technology"

As artificial intelligence (AI) and machine learning continue to transform patient care, Computer Science and Engineering Assistant Professor Forest Agostinelli is currently pursuing research on predicting outcomes for PAD patients.

PAD is a growing concern among aging populations and usually caused by a blockage where arteries in the legs are calcified, which is the build-up of calcium-containing plaque in the artery. The risk of developing PAD is increased by smoking, an unhealthy diet, diabetes and high blood pressure. 

Read full article here.

We are proud to announce that Dr Qi Zhang has been awarded an NSF CAREER Award for his project titled "Identifying and Exploiting Multi-Agent Symmetries". The project's abstract:

It is widely believed by scientists that our universe follows certain symmetry patterns and principles, which lead to profound implications such as conservation laws. Artificial intelligence (AI) can and has already benefited tremendously from exploiting these symmetries. This project seeks to identify and exploit symmetries that are prevalent in cooperative AI tasks, where a group of multiple autonomous sequential decision makers, or agents, plan and learn to maximize their combined benefit. As an example, consider the application of adaptive traffic signal control, where each intersection can be modeled as an agent controlling its traffic signal in a way that adapts to real-time traffic conditions to reduce congestion. There exist certain symmetries when the topology of the road network is regular, e.g., as a 4-connected grid, and the road condition is uniform. When done properly, such multi-agent symmetries can be identified and exploited to greatly improve the efficiency and effectiveness of the current solutions to cooperative AI. This project also integrates the proposed research into an array of education initiatives, playing key roles in the curriculum development and undergraduate research experiences at the PI's university, as well as outreach activities that bridge academia with industry practitioners and community stakeholders.

You can read more about his research in this article from USC news.

ChatGPT has disrupted the narrative around AI and fired everyone’s imagination. Just like iPhone disrupted the market for mobile phones, Google did for search, Tesla did for cars, and Watson did for question-answering (with Jeopardy!), ChatGPT has people at every level of education spectrum trying it out for applications ranging from scientific articles to real-estate to law and business exams to programming, and much more. But technologies are not accepted by just its perfect performance but also a socio-technical ecosystem. For example, a car must drive properly but the legal, education, and standards framework allow a user to trust the enabling environment and confidently drive their vehicle on the roads. Similarly, conventional or new application domains alike, the adoption of chatbots were already hindered by the lack of a supportive socio-technical environment. With easy access of LLM-based tools like ChatGPT, the risk of harm will only increase unless other pillars are quickly built. To benefit society from the potential of LLM based technologies, the path forward is not to scuttle LLM-based tools but to increase investment and augment necessary other pillars for the technologies’ safe and trusted usage for the society.

Read the full article by Dr. Biplav Srivastava, or his online recording.