Fatemeh Ghoreishi
(she/her/hers)
Assistant Professor
Research interests
- Machine learning
- Artificial intelligence
- Robotics
- Bayesian statistics
- Design under uncertainty
- Stochastic optimization
Education
- PhD in Mechanical Engineering, Texas A&M University
- MSc in Mechanical Engineering, Texas A&M University
- MSc in Biomedical Engineering, Iran University of Science and Technology
- BS in Mechanical Engineering, University of Tehran — Iran
Biography
Seyede Fatemeh Ghoreishi is an assistant professor in the Khoury College of Computer Sciences and the College of Engineering at Northeastern University, based in Boston.
Ghoreishi teaches machine learning, as well as probabilities and statistics. Her research focuses on machine learning and Bayesian statistics for design and decision-making under uncertainty, and she has given talks and led workshops on these subjects at IEEE conferences, Virginia Tech, Georgia Tech, Penn State University, the University of Southern California, the University of Wisconsin-Madison, and the University of California San Diego. She also co-chaired the 2021 American Control Conference.
Before joining Northeastern in 2021, Ghoreishi was a postdoctoral research fellow at the University of Maryland’s Institute for Systems Research. During her time there, she was selected for the Rising Stars in Mechanical Engineering in 2019 (at the University of Texas at Austin) and 2020 (at the University of California, Berkeley).
Recent publications
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Bayesian Optimization for Design of Multi-Actuator Soft Catheter Robots
Citation: S. F. Ghoreishi, R. D. Sochol, D. Gandhi, A. Krieger and M. Fuge, "Bayesian Optimization for Design of Multi-Actuator Soft Catheter Robots," in IEEE Transactions on Medical Robotics and Bionics, vol. 3, no. 3, pp. 725-737, Aug. 2021, doi: 10.1109/TMRB.2021.3098119. -
Bayesian surrogate learning for uncertainty analysis of coupled multidisciplinary systems
Citation: Ghoreishi, Seyede Fatemeh & Imani, Mahdi. (2021). Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems. Journal of Computing and Information Science in Engineering. 10.1115/1.4049994. -
Scalable inverse reinforcement learning through multifidelity Bayesian optimization
Citation: M. Imani and S. F. Ghoreishi, "Scalable Inverse Reinforcement Learning Through Multifidelity Bayesian Optimization," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3051012.
