Mohammad Sadegh Sirjani
Ph.D. Student in Computer Science
University of Texas at San Antonio
IoT, Tiny AI, and Edge Computing Researcher
Biography
Mohammad Sadegh Sirjani is a Ph.D. student in Computer Science at the University of Texas at San Antonio (UT San Antonio), where he conducts research in the ASIC Laboratory under the supervision of Professor Mimi Xie. His focus lies in Tiny AI, intermittent computing, energy harvesting, and low-power IoT systems, particularly for real-world applications in healthcare and embedded intelligence.
He completed his B.Sc. in Computer Engineering at Ferdowsi University of Mashhad and has worked as a research assistant and software engineer in both Iran and the U.S. His experience spans backend development, CI/CD pipelines, RESTful APIs, and cloud-native technologies including Docker, Kubernetes, and NGINX.
His academic output includes peer-reviewed publications on fog computing, SDN controller placement, and secure IoT systems. He was selected as a DAC Young Fellow and won first place in the 2-Minute Video Presentation Contest at DAC 2025 for his impactful research in energy-aware AI systems.
In addition to his research, he serves as a teaching assistant for various undergraduate courses such as Operating Systems, Cloud Computing, and Compiler Design, supporting students in mastering core computer science topics.
Outside of academia, Sadegh enjoys reading classic literature, learning new languages. He brings curiosity, discipline, and creativity to both his technical and personal pursuits.
Education
Ph.D., Computer Science, University of Texas at San Antonio, USA
January 2025 - Present
B.Sc., Computer Engineering, Ferdowsi University of Mashhad, Iran
September 2018 - February 2024
Research Interests
Computing
Research Experience
ASIC Laboratory, University of Texas at San Antonio, USA
Current research focus on Tiny AI, Edge Computing, and IoT applications.
Software Quality Laboratory, Ferdowsi University of Mashhad, Iran
- Software analysis and enhancement methods
- Java application execution tracing
- Microservices transition tools
Web Technology Laboratory, Ferdowsi University of Mashhad, Iran
- Psychological data collection systems
- LLM-powered client reporting
- Web API development
Publications
Journal Articles
Q-Learning-Based Task Scheduling Scheme to Enhance Energy Consumption and QoS in IoT Environments
Conference Papers
Optimizing Task Scheduling in Fog Computing with Deadline Awareness
Data mining and cloud computing for customer pattern analysis
SecVanet: provably secure authentication protocol for sending emergency events in VANET
Teaching Experience
Fall 2025 Fundamentals of Operating Systems UT San Antonio
Summer 2025 Data Science UT San Antonio
Summer 2025 Computer Organization UT San Antonio
Spring 2025 Fundamentals of Operating Systems UT San Antonio
Spring 2024 Fundamentals of Cloud Computing Ferdowsi University of Mashhad
Fall 2023 Principles of Compiler Design Ferdowsi University of Mashhad
Fall 2023 Fundamentals of Data Mining Ferdowsi University of Mashhad
News
September 2025 SDN controller placement paper published in Cluster Computing Journal.
June 2025 Won first place at DAC 2025 for energy-efficient AI healthcare research presentation.
June 2025 Presented Tiny AI research at DAC 2025 and networked with industry leaders.
May 2025 Research on reinforcement learning for SDN accepted at Cluster Computing Journal.
January 2025 Began Ph.D. in Computer Science at UT San Antonio, focusing on IoT and Edge AI systems.
Awards and Honors
June 2025 First place winner at DAC 2025 Video Presentation Contest for energy-efficient AI research.
June 2025 Selected as DAC Young Fellow for outstanding contributions to design automation.