My Computer Science and Engineering Department

The iCAS Lab Ph.D. Students, Lily Lamb and MohammadReza Mohammadi, have received the Best Paper Award at the IEEE COINS 2025 Conference.

Their paper, which is part of a project funded by the NSF CAREER program, explores how to enable real-time perception in autonomous systems under real-world conditions. "MultiModal Vision at the Edge: Toward Low-Latency Perception for Autonomous Systems", by Lily Lamb, Mohammadreza Mohammadi, and Ramtin Zand.

Abstract: The paper introduces an extended YOLOv8 framework that fuses RGB, LiDAR-based depth, and grayscale inputs using early, mid, and late fusion strategies, optimized for edge AI platforms. To support real-time processing, the team developed a parallelized depth generation pipeline that transforms sparse LiDAR point clouds into dense maps. Evaluated on the KITTI dataset and benchmarked across Raspberry Pi 5, Jetson Nano, and Google Coral Edge TPU, the models reveal the trade-offs between accuracy and latency, where late fusion delivers the highest accuracy (mAP@50: 92.7%), and early fusion achieves the lowest latency.

A see-in-the-dark monitoring and navigational system intended to improve safety in underground mines is being developed by a University of South Carolina computer science and engineering team in collaboration with researchers in India.

Sanjib Sur, an associate professor in the Molinaroli College of Engineering and Computing, is helping design a system that uses millimeter-wave technology to provide real-time monitoring in underground mines. The three-year project is co-funded by the National Science Foundation and India’s Department of Science and Technology. Read full story here.

The medical school  partnered last year with the Molinaroli College of Engineering and Computing to launch the AI in Medicine Extracurricular Track, an interdisciplinary pilot program with eight lectures — the first of which is required for all medical students — intended to give first- through third-year medical students a better understanding of how AI works and how it can be used in health care. The inaugural cohort of 10 students has completed its first year and a second cohort will be selected this summer.

Homayoun Valafar, chair of the computer science and engineering department and director of USC’s AI Institute, is co-directing the AI in Medicine initiative with Leonardo Bonilha, a neurology professor and the medical school’s senior associate dean for research. Valafar likens AI to a device like others used in medicine such as stethoscopes and handheld ultrasound. Read the full article here.

The Department of Computer Science and Engineering (CSE) at the University of South Carolina invites applications for the position of Department Chair. This is a leadership role for an individual with a strong vision for the future of computer science and engineering education and research, as well as a deep commitment to faculty development and student success.

The Department Chair will serve as the academic and administrative leader of the department, providing strategic direction, fostering collaboration, and supporting a vibrant community of scholars, educators, and students. The Chair will guide the department in defining and expanding areas of core strengths, promoting interdisciplinary sponsored research, and ensuring the highest standards of academic excellence. More details and application here.

The iCAS Lab Ph.D. students, Md Hasibul Amin and MohammadReza Mohammadi have received the Best Poster Award at the GLSCLSI 2025 Conference—a premier venue for disseminating research in VLSI, devices, and system-level design. Earlier this year, this work also earned second place at the CSE Research Symposium.

This is a collaborative work between Dr. Ramtin Zand's lab and Dr. Jason Bakos on a project funded by the National Science Foundation (NSF).

"CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems", by  Md Hasibul Amin, MohammadReza Mohammadi, Jason Bakos, and Ramtin Zand. 

Abstract: This work introduces the CrossNAS framework, an automated approach for exploring a vast, multidimensional search space that spans various design abstraction layers—circuits, architecture, and systems—to optimize the deployment of machine learning workloads on analog processing-in-memory (PIM) systems. CrossNAS leverages the single-path one-shot weight-sharing strategy combined with the evolutionary search for the first time in the context of PIM system mapping and optimization. CrossNAS sets a new benchmark for PIM neural architecture search (NAS), outperforming previous methods in both accuracy and energy efficiency while maintaining comparable or shorter search times.

We are proud to announce that Dr. Christian O'Reilly has received an NSF CAREER Award for his project titiled "Multiscale Model-Driven Analysis of the Brain and its Disorders." He is looking forward to kicking off this project and raising the prominence of Computational Neuroscience at the College and at USC.

For the second year in a row, iCAS Lab has won the First Place Award in the University Demonstration competition at the DAC, The Chips to Systems Conference, recognized as the premier global event for chips to systems!

This year’s award-winning project was "PiCASo: Pi-Integrated Conversational AI for Social-Robot with. Optimized LLMS" led by Mahsa Ardakani and co-presented by herself and Jinendra Malekar, with contributions from Peyton Chandarana — all PhD students at the iCAS Lab directed by Dr. Ramtin Zand.

PiCASo is a fully integrated, voice-to-voice Conversational AI system that runs 𝘦𝘯𝘵𝘪𝘳𝘦𝘭𝘺 on a Raspberry Pi 5. It combines:

• Whisper for speech recognition
• Quantized LLMs for question-answering
• Piper for text-to-speech synthesis

Mahsa and Jinendra applied various post-training quantization (PTQ) strategies — including W2A8, W4A8, W6A8, W8A8 — for Gemma-2B, LLaMA-3B, Phi-3B, and LLaMA-8B, and utilized quantization-aware trained (QAT) BitNet models with Ternary weights and 8-bit activations to enable real-time, offline inference on resource-constrained hardware. This makes PiCASo ideal for education, accessibility, and other embedded AI applications.

Our work on LLM quantization was also presented at the 2025 IEEE/CVF Computer Vision and Pattern Recognition (CVPR) conference, and the paper is now available in the CVPR 2025 proceedings. "LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi". repo

Congratulations to the following CSE recipients of the ASPIRE-AI awards.

• Francisco Leon Oyala (PI, Pharamacology) and Agostinelli (Co-I), “ASPIRE-AI: Towards Affordable HIV Treatment: Discovering Cheaper Synthesis Paths for HIV Drugs with Artificial Intelligence”
• Agostinelli (PI) and Fu (Co-I), "Efficient Quantum Algorithm Compilation with Machine Learning and Heuristic Search"
• Hu (PI), "Generative AI for Data-driven Design of Sulfur Cathode Materials for Next-Generation Batteries"
• Srivastava (PI), "SafeGenChat: A Novel and General Approach for Building Risk-Aware Collaborative Assistants for Trust-sensitive Applications "
• Valafar (Co-I), "AI-Powered Modeling of Cytoplasmic Microviscosity as a Function of RNA Composition"
• Zand and Valafar (Co-I's), "Decoding Alzheimer’s Disease and Advancing AI in Biomedical Research"

In addition, one of our students, Vandana Srivastava, who is now affiliated with the University Libraries, has received funding as the PI.

We congratulate our faculty who received the following research awards:

• Christian O'Reilly, "Neonatal autonomic nervous system dysfunction as a predictor of autism spectrum disorder in preterm infants", (NIMH)/NIH, and "Cholinergic regulation of amygdalar circuits in emotional memory", (NIMH)/NIH.
• Biplav Srivastava,  "Demonstrating a Compact Foundation Model for Planning-Like(PL) Tasks for Next Generation Trusted Applications", NSF.

I am excited to share an opportunity for high school students to explore the cutting-edge intersection of artificial intelligence and scientific discovery at the 2025 South Carolina AI4Science Summer Camp, hosted by the University of South Carolina’s Molinaroli College of Engineering and Computing. Following the resounding success of last year’s camp, we are thrilled to invite your talented students to join us for an inspiring and transformative experience.

• Dates: July 21–August 1, 2025 (2 weeks)
• Format: Hybrid, with the first day (July 21) and last day (August 1) onsite at USC’s Columbia campus, and 10 days of engaging online tutorials and project assistance via Zoom.
• Target Audience: South Carolina high school students eager to explore AI and its applications in science.
• Organizers: Led by Professors Jianjun Hu (Computer Science), Ming Hu (Mechanical Engineering), Sophya Garashchuk (Chemistry), Paul Sagona (Research Computing), and Krystal Dubose (Director of Enrollment Management, CEC).

Last year’s camp, supported by the National Science Foundation and USC, was a tremendous success, empowering students with hands-on AI skills and sparking enthusiasm for STEM careers. Participants engaged in interactive workshops, developed AI-driven projects, and connected with USC faculty and peers, all while exploring real-world applications like computational chemistry and engineering design.

• Innovative Learning: Students will learn AI fundamentals and apply them to scientific challenges, gaining skills in coding, data analysis, and machine learning.
• Career Exploration: Exposure to AI’s role in fields like physics, chemistry, and engineering prepares students for future STEM opportunities.
• USC Experience: Onsite days offer a taste of college life, including campus tours and interactions with faculty and researchers.
• Inclusivity: The camp is designed to broaden participation, welcoming students from diverse backgrounds across South Carolina.

We would like to congratulate graduate student Reza Tavasoli, advised by Sanjib Sur and Srihari Nelakuditi, for winning the SPARC Graduate Research Grant Award.

The following CSE undergraduate students won awards at the Discover USC event.

• Dallas Wade, Computer Science, Sophomore. Effects of Tiered Noise Reduction Techniques on Contour Detection. Under the direction of Dr. Christian O'Reilly, CSE.
• Cayden Scruggs, Computer Science, Senior. Automated Yeast Colony Detection and Classification Using Machine Learning. Under the direction of Dr. Tad Dallas, Department of Biological Sciences.

Ph.D. students, from Dr. Sanjib Sur's lab,  Zhuangzhuang Gu, Hem Regmi, and Reza Tavasoli, and Ryan Titus (USC Med Student, presenter) won the second place award at 2025 Discover USC Medical Scholar poster session.

This year the students in the Senior Capstone course developed 51 apps. There are:

• 32 web applications using technologies such as Django, node, express, Angular, react, Vue, firebase, AWS.
• 14 Android or iOS mobile apps using technologies such as Android Studio, Flutter, React Native, XCode, Firebase.
• 5 desktop games using technologies such as Unity, GameMaker, Godot.

You can view their video demos here. If you want a custom-made app, let us know!

Three computer science and engineering undergraduates have teamed up to create an artificial intelligence (AI) toolkit designed to help students study more efficiently. Junior Arshia Eslami and seniors Julia Grove and Matthew Linder launched the toolkit on their website, JAMAi, in late March. 

These classmates, now also friends, discovered they complemented each other’s talents after working together inside and outside of the classroom. Eslami and Linder met while interning together at Swampfox Technologies in Columbia, while Eslami and Grove met during a hackathon, a programming competition for improving or building new software programs. Eslami introduced Linder and Grove, and the three decided to merge their strengths to create one product that would help their fellow students.

Read the full article here.

What began as a student project in Ramtin Zand’s course in 2022 on neuromorphic computing—an area of computer science that designs artificial intelligence systems inspired by the human brain—has rapidly grown into an award-winning University of South Carolina research initiative with real-world applications in education and AI.

Zand, an assistant professor in the Molinaroli College of Engineering and Computing’s Department of Computer Science and Engineering and principal investigator of the Intelligent Circuits, Architecture and Systems (iCAS) Lab, calls the journey of this facial expression recognition project a prime example of “classroom to research—and beyond.”

Read the rest of the story here.