Biography
Mohammad Sadegh Sirjani is a Ph.D. student in Computer Science at the University of Texas at San Antonio. He is a Graduate Research Assistant in the ASIC Lab, advised by Prof. Mimi Xie, and a Teaching Assistant at UT San Antonio. He received his B.Sc. in Computer Engineering from Ferdowsi University of Mashhad and passed his qualifying examination in 2026.
His research focuses on TinyML, edge AI, and intermittent computing for energy-harvesting and resource-constrained devices. He takes a cross-layer approach spanning machine learning and embedded systems to build intelligent devices that run reliably on batteryless hardware, with applications in wearable health sensing and sustainable IoT. His papers appear in GEM, IEEE EMBS BHI, Sustainable Computing, Cluster Computing, and IEEE SaTC.
He is a two-time DAC Young Fellow and won the DAC 2-minute presentation award in both 2025 and 2026. He received the Outstanding Paper Award at GEM 2026 and the Fan Favorite Award at the Draper Data Science Business Plan Competition. He earned an NVIDIA certificate in building agentic AI applications and is a reviewer for GLSVLSI.
News
View all- Paper "CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment" accepted at IEEE EMBS BHI 2026.
- Earned Building Agentic AI Applications with Large Language Models from NVIDIA, hosted by the College of AI, Cyber and Computing at UTSA.
- Won the 2-minute presentation award competition at the 63rd DAC. Watch the presentation video here.
- Awarded the Graduate School Academic Travel Fund to attend the Design Automation Conference (DAC) 2026.
- Paper "Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods" published at the GEM Workshop — The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026).
- Paper "Optimizing Task Scheduling in Fog Computing with Deadline Awareness" published at the IEEE 2nd International Conference on Secure IoT, Assured and Trusted Computing (SATC 2026).
- Paper "Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods" received an Outstanding Paper award at GEM 2026, colocated with ACL 2026.
- Selected as a scholarship recipient for the DAiR3 Data Science Summer School.
- Won the Fan Favorite Award at the Draper Data Science Business Plan Competition 2026.
- Invited to serve as a reviewer for GLSVLSI 2026.
- Awarded the 63rd DAC Young Fellows fellowship.
- Passed the PhD Qualifying Examination. Thank you Dr. Mimi Xie, Dr. Dakai Zhu, and Dr. Wei Wang!
- Started a Research Assistantship at the ASIC Lab, UT San Antonio.
- Paper "QTE-IoT: Q-Learning-Based Task Scheduling Scheme to Enhance Energy Consumption and QoS in IoT Environments" published in Sustainable Computing: Informatics and Systems.
- Paper "Controller Placement in Software-Defined Networks Using Reinforcement Learning and Metaheuristics" published in Cluster Computing.
- Won the 2-minute presentation award competition at the 62nd DAC.
- Awarded the DAC Young Fellows fellowship and attended DAC 2025 as a Young Fellow.
- Awarded the Graduate School Academic Travel Fund to attend the Design Automation Conference (DAC) 2025.
- Started the PhD in Computer Science at UT San Antonio, advised by Dr. Mimi Xie, and joined the ASIC Lab.
Publications
View allCogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment
VenueIEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2026)
Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods
VenueFifth Workshop on Generation, Evaluation and Metrics (GEM 2026), colocated with ACL 2026Outstanding Paper
Optimizing Task Scheduling in Fog Computing with Deadline Awareness
VenueIEEE 2nd International Conference on Secure IoT, Assured and Trusted Computing (SATC 2026)
QTE-IoT: Q-Learning-Based Task Scheduling Scheme to Enhance Energy Consumption and QoS in IoT Environments
VenueSustainable Computing: Informatics and Systems Journal
Controller placement in software-defined networks using reinforcement learning and metaheuristics
VenueCluster Computing: The Journal of Networks, Software Tools and Applications