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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Optimal Detection for Bayesian Attack Graphs Under Uncertainty in Monitoring and Reimaging
Citation: Armita Kazeminajafabadi, Seyede Fatemeh Ghoreishi, Mahdi Imani. (2024). Optimal Detection for Bayesian Attack Graphs Under Uncertainty in Monitoring and Reimaging ACC, 3927-3934. https://doi.org/10.23919/ACC60939.2024.10644873 -
Dynamic Sensor Selection for Efficient Monitoring of Coupled Multidisciplinary Systems
Citation: Negar Asadi, Seyede Fatemeh Ghoreishi. (2024). Dynamic Sensor Selection for Efficient Monitoring of Coupled Multidisciplinary Systems J. Comput. Inf. Sci. Eng., 24. https://doi.org/10.1115/1.4065607 -
Physics-Informed Particle-Based Reinforcement Learning for Autonomy in Signalized Intersections
Citation: Mehrnoosh Emamifar, Seyede Fatemeh Ghoreishi. (2024). Physics-Informed Particle-Based Reinforcement Learning for Autonomy in Signalized Intersections Int. J. Intell. Transp. Syst. Res., 22, 416-430. https://doi.org/10.1007/s13177-024-00407-2 -
Optimal Inference of Hidden Markov Models Through Expert-Acquired Data
Citation: Amirhossein Ravari, Seyede Fatemeh Ghoreishi, Mahdi Imani. (2024). Optimal Inference of Hidden Markov Models Through Expert-Acquired Data IEEE Trans. Artif. Intell., 5, 3985-4000. https://doi.org/10.1109/TAI.2024.3358261 -
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. -
Two-Stage Bayesian Optimization for Scalable Inference in State-Space Models
Citation: M. Imani and S. F. Ghoreishi, "Two-Stage Bayesian Optimization for Scalable Inference in State-Space Models," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3069172. -
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. -
Sequential Information-Theoretic and Reification-Based Approach for Querying Multi-Information Sources
Citation: Ghoreishi, S.F., Thomison, W.D. and Allaire, D.L., 2019. Sequential Information-Theoretic and Reification-Based Approach for Querying Multi-Information Sources. Journal of Aerospace Information Systems, 16(12), pp.575-587. -
MFBO-SSM: Multi-fidelity Bayesian optimization for fast inference in state-space models
Citation: Imani, M., Ghoreishi, S. F., Allaire, D., & Braga-Neto, U. M. (2019). MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7858-7865. https://doi.org/10.1609/aaai.v33i01.33017858 -
Efficient use of multiple information sources in material design
Citation: Ghoreishi, Seyede Fatemeh and Molkeri, Abhilash and Arroyave, Raymundo and Allaire, Douglas and Srivastava, Ankit, Efficient Use of Multiple Information Sources in Material Design (June 20, 2019). Available at SSRN: https://ssrn.com/abstract=3406949 or http://dx.doi.org/10.2139/ssrn.3406949 -
Adaptive dimensionality reduction for fast sequential optimization with gaussian processes
Citation: Ghoreishi, S.F., Friedman, S. and Allaire, D.L., 2019. Adaptive dimensionality reduction for fast sequential optimization with gaussian processes. Journal of Mechanical Design -
Bayesian control of large MDPs with unknown dynamics in data-poor environments
Citation: Mahdi Imani, Seyede Fatemeh Ghoreishi, and Ulisses M. Braga-Neto. 2018. Bayesian control of large MDPs with unknown dynamics in data-poor environments. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, 8157–8167. -
Multi-information source constrained Bayesian optimization
Citation: Ghoreishi, S.F., Allaire, D. Multi-information source constrained Bayesian optimization. Struct Multidisc Optim 59, 977–991 (2019). https://doi.org/10.1007/s00158-018-2115-z
