About
I am currently a first-year CE Ph.D. student at the University of Maryland, College Park, supervised by 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 interests lie in Machine Learning and Deep Learning. More specifically, I'm interested in developing and optimizing models and algorithms to address real-world challenges. Previously, I conducted research on projects such as Traffic Forecasting and Generalization across different distributions. Currently, I'm researching Trajectory Forecasting based on different personality types.
My research interests lie in Machine Learning and Deep Learning. More specifically, I'm interested in developing and optimizing models and algorithms to address real-world challenges. Previously, I conducted research on projects such as Traffic Forecasting and Generalization across different distributions. Currently, I'm researching Trajectory Forecasting based on different personality types.
Publications
- Quantifying and Modeling Driving Styles in Trajectory Forecasting, Laura Zheng*, Hamidreza Yaghoubi*, Tony Wu, Sandeep Thalapanane, Tianyi Zhou, and Ming C. Lin, Under Review at IROS 2025.
- 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.
- 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
TPASI 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.
Academic Service
ICLR 2025