Presenters

Zishen Wan is a PhD student at Georgia Tech, advised by Profs. Arijit Raychowdhury and Tushar Krishna. His research focuses on computer architecture and VLSI, with an emphasis on cross-layer co-designing systems, architectures, and solid-state hardware for autonomous machines and neuro-symbolic AI, to advance next-generation embodied and agentic applications. His work has received Best Paper Awards at DAC, CAL, and SRC JUMP2.0, First Place in DAC PhD Forum, First Place in ACM Student Research Competition, and IEEE Micro Top Picks honorable mention. He was selected as 2023 ML and Systems Rising Star and 2024 Cyber-Physical Systems Rising Star.

Joshua Viszlai is a PhD student at the University of Chicago advised by Fred Chong. His research aims to bridge the gap between current quantum devices and fault-tolerant quantum computing. His work has addressed a range of architectural questions in surface codes, quantum LDPC codes, neutral atom arrays, and decoding.

Ubaid Bakhtiar is a Ph.D. candidate in Electrical and Computer Engineering at the University of Maryland, College Park, and a member of the Computer Architecture and Systems Lab (CASL) advised by Dr. Bahar Asgari. His doctoral research, “Architecting Efficiency: Data Scheduling, Dynamic Reconfiguration, and Multi-Tenancy for Sparse Acceleration,” focuses on optimizing resource utilization in accelerators for scientific computing and AI applications. His broader goal is to design architectures that make large-scale computing fundamentally more power and resource-efficient. Ubaid has published in top-tier venues such as MICRO and DAC, and aspires to expand his work in a full-time role in industry, developing next-generation accelerator architectures for intelligent and efficient computing systems.

Md Sadik Awal is a PhD candidate in Electrical and Computer Engineering at Florida International University, USA. His research focuses on hardware security, side-channel analysis, system design, embedded systems, and signal processing. Integrating measurement-driven modeling, machine learning, and architectural co-design, his work advances the development of secure and high-performance computing systems. Sadik has collaborated with industry to design side-channel countermeasures for advanced semiconductor packaging and has published in leading IEEE and ACM venues. He earned his BSc in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology in 2021.

Toluwanimi (Toluwa) Odemuyiwa is a PhD Candidate at the University of California, Davis, where she is advised by Prof. John Owens. Prior to Davis, she completed a masters degree in electrical engineering at San Jose State University while concurrently working as a full-time design verification engineer at Microsemi Corporation. Mentored by Prof. Birsen Sirkeci, her MSEE thesis focused on using machine learning to classify wireless radio signals. She obtained her BASc (Engineering Science) from the University of Toronto in 2017 working on using electroencephalography signals as a modality for biometric verification, advised by Prof. Dimitrios Hatzinakos. Her current research interests lie in algorithmic, software, and hardware techniques for finding novel ways to handle sparse data and computations as the need for high performance computing continues to grow in the age of Big Data. She is particularly interested in the ways software and hardware can be co-designed to produce novel solutions.