Reza Rawassizadeh
Associate Professor of Computer Science;
Coordinator of AI and Machine Learning, Web Application Development
- Title Associate Professor of Computer Science;
Coordinator of AI and Machine Learning, Web Application Development - Office 1010 Commonwealth Avenue, 3rd Floor, 320
- Email rezar@bu.edu
- Education Ph.D. University of Vienna
M.S. Vienna University of Technology
B.S. Tehran Azad University
Reza Rawassizadeh holds a Ph.D. of Computer Science from University of Vienna, a Master’s degree from the Vienna University of Technology, and a Bachelor’s degree from Tehran Azad University (Central Branch).
His research interest focuses on ubiquitous technologies including wearables, mobile devices and robots. He has made notable contributions in designing resource efficient, on-device machine learning algorithms to operate on small battery-powered devices such as smartwatches or fitness trackers, without any network requirement.
For several AI-based companies, he serves as the senior or chief scientific advisor.
Reza’s personal homepage is available under this link: https://sites.google.com/view/rezar
Research Interests
- On-device machine learning and artificial intelligence
- Ubiquitous, mobile and wearable computing
- Digital health
- AI Democratization
Scholarly Works
Selected Recent Publications
- Wen, Q., Zeng, X., Zihan, Z., Shuaijin, L., Hosseinzadeh, M., & Rawassizadeh, R. (2025). GradES: Significantly Faster Training in Transformers with Gradient-Based Early Stopping. https://arxiv.org/abs/2509.01842
- Wang, H, Hosseinzadeh, M., & Rawassizadeh, R. (2025). TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization. https://arxiv.org/abs/2509.00914
- Ejimuda, C. U., Longhitano, G., & Rawassizadeh, R. (2025). Analyzing the Resource Utilization of Lambda Functions on Mobile Devices: Case Studies on Kotlin and Swift. IEEE Pervasive Computing 24.2 (2025): 48-51.
- Wen, Q., Kochhar, P., Zeyada, S., Javaheri, T., & Rawassizadeh, R. (2025). From Clicks to Conversations: Evaluating the Effectiveness of Conversational Agents in Statistical Analysis. International Journal of Human–Computer Interaction, 1–18. https://doi.org/10.1080/10447318.2025.2561760
- Lu, H., Alemi, M. and Rawassizadeh, R. (2024) The Impact of Quantization and Pruning on Deep Reinforcement Learning Models. https://arxiv.org/abs/2407.04803
Visit Google Scholar for a full list of scholarly works.
Recent Press Releases and Media Appearances
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