About

I'm a first-year Ph.D. student in Computer Engineering at the University of Maryland, College Park, working under the supervision of Professor Ming Lin. Previously, I got my B.S. in Computer Engineering from Sharif University of Technology, where I worked with Professor Mahdieh Soleymani, and Professor Mohammad Hossein Rohban.

My research is dedicated to building trustworthy, safety-critical AI systems that people can rely on. Currently, I pursue two complementary lines of work:

  • Behavior-informed trajectory forecasting for autonomous vehicles, where models learn human driving styles to support robust interaction with other road users.
  • Efficiency-versus-safety trade-offs in learning-based algorithms, investigating how to balance resource use and risk management in critical applications.

Together, these efforts aim to advance AI that not only performs well across diverse real-world scenarios but also upholds the rigorous safety guarantees needed for human trust.

Publications

  1. Quantifying and Modeling Driving Styles in Trajectory Forecasting, Laura Zheng*, Hamidreza Yaghoubi*, Tony Wu, Sandeep Thalapanane, Tianyi Zhou, and Ming C. Lin, Accepted at IROS 2025.
  2. Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation, Fahimeh Hosseini Noohdani, Parsa Hosseini, Aryan Yazdan Parast, Hamidreza Yaghoubi, Mahdieh Soleymani Baghshah, CVPR 2024.
  3. Annotation-Free Group Robustness via Loss-Based Resampling, Mahdi Ghaznavi, Hesam Asadollahzadeh, Hamidreza Yaghoubi, Fahimeh Hosseini Noohdani, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah, OOD-CV at ICCV 2023.

Work Experiences

Data Scientist, TAPSI
Tehran, Iran | Feb 2021 - Feb 2023

TAPSI is an Iranian ride‑hailing company similar to Uber, and stands as one of Iran’s largest and most technologically advanced companies. Notable projects I contributed to include:

  • Estimated Time of Arrival (ETA): Investigated and developed traffic prediction models, improving ETA accuracy using techniques inspired by industry best practices.
  • Location Search Engine: Processed raw data and built an in-house search engine with optimized offline and online metrics.
  • GPS Denoising: Applied noise reduction techniques to enhance GPS data quality and reliability for location-based services.
  • Destination Suggestion: Built ML models to recommend destinations based on user ride data.

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