The iCAS Lab, directed by Dr. Ramtin Zand, has just published an outreach audiobook on the science communication channel, SciPod! 

The audiobook highlights a recently awarded NSF CAREER project: "Heterogeneous Neuromorphic and Edge Computing Systems for Realtime Machine Learning Technologies." Tailored for a general audience, it emphasizes the potential broader impacts of the research, particularly in real-time sign language translation.

The audiobook is available on all major streaming services. We invite you to listen and reach out to Dr. Zand at ramtin@cse.sc.edu if you are interested in collaborating to advance real-time AI and ML technologies for assistive applications and beyond! The iCAS Lab is also looking to recruit undergraduate research assistants for this exciting project!

Link to Audiobook: Dr Ramtin Zand | A Hybrid System for Real-Time Sign Language Translation • scipod.global

Computer science doctoral candidate Aakriti Adhikari is making this technology a reality through her dissertation. With next generation wireless networks looking to merge high-speed data connectivity with sensing, Adhikari’s work focuses on reconfiguring existing 5G and beyond at-home networking devices with sensing capabilities to enable healthcare applications. Read full article here.

Computer Science and Engineering Assistant Professor Forest Agostinelli has received a nearly $350,000, three-year National Science Foundation grant to study the use of heuristic (experimental) search and machine learning to solve complex pathfinding problems. Agostinelli’s project is expected to advance artificial intelligence (AI) techniques that solve problems faster or find approximate solutions. Read full article here.

We are proud to report the following new research awards received by our faculty:

• Csilla Farkas, "Department of Defense Cyber Scholarship Program 2024-2025", funded by Department of Defense (DOD)
• Forest Agostinelli, "Scalable Learning in Heuristic Search", funded by National Science Foundation (NSF)
• Amit Sheth, "Enhancing the Security and Mitigating Bias in Vision Language Models to Combat Hateful Image Generation and Detoxify Hateful Images", funded by National Science Foundation (NSF)

Professor Vignesh Narayanan from the Artificial Intelligence Institute of the University of South Carolina recently started an NSF-funded research project that aims to understand the vulnerabilities of learning-based controllers and enhance an understanding of possible cyberattacks.  Read full article here.

Assistant Professor of Computer Science and Engineering Ramtin Zand has received a National Science Foundation (NSF) CAREER Award to study computing systems for real-time machine learning technologies. Zand’s five-year, nearly $600,000 project is expected to produce smaller, faster and smarter machine learning computing systems for use in real-world applications without reliance on large servers. Read full article here.

Forest Agostinelli gave a Master Class at the 17th International Symposium on Combinatorial Search (SoCS) on Deep Learning, Reinforcement Learning, and Heuristic Search. The one-hour talk covered his research group’s advancements in the combination of machine learning and heuristic search and its application to problems such as the Rubik’s cube, quantum algorithm compilation, and reaction mechanism prediction.

A Better Assessment provides a quick history of Dr. Agostinelli's work.

The iCAS lab, directed by Dr. Ramtin Zand, won the first-place award at the 2024 University Demonstration at DAC, The Chips to Systems Conference for the project titled "HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI."

Demo by: Brendan Reidy and Peyton Chandarana, Ph.D. Research Assistants

Project Description: With the rise of tiny IoT devices powered by machine learning (ML), many researchers have directed their focus toward compressing models to fit on tiny edge devices. Recent works have achieved remarkable success in compressing ML models for object detection and image classification on microcontrollers with small memory, e.g., 512kB SRAM. However, there remain many challenges prohibiting the deployment of ML systems that require high-resolution images. Due to fundamental limits in memory capacity for tiny IoT devices, it may be physically impossible to store large images without external hardware. To this end, we propose a high-resolution image scaling system for edge ML, called HiRISE, which is equipped with selective region-of-interest (ROI) capability leveraging analog in-sensor image scaling. Our methodology not only significantly reduces the peak memory requirements, but also achieves up to 17.7x reduction in data transfer and energy consumption.

Paper Authors: Brendan Reidy, Sepehr Tabrizchi, MohammadReza Mohammadi, Shaahin Angizi, Arman Roohi, and Ramtin Zand

Dr. Ramtin Zand (PI) and Dr. Jason Bakos (Co-PI) have been awarded an NSF grant for their project "Cross-Layer Design Automation for In-Memory Analog Computing."

Jamshidi received the Most Influential Paper Award in April at the 19th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) in Lisbon, Portugal. Jamshidi’s paper, “Autonomic Resource Provision for Cloud-based Software,” was submitted, accepted and published just prior to earning his Ph.D. from Dublin City University in Ireland in 2014. It was presented at the 2014 SEAMS Conference in India. See original post for details.