Education

• Ph.D in Computer Science, University of Oregon, 2017
• M.S. in Information Technology, Sharif University of Technology, 2010
• B.S. in Software Engineering, Shahid Beheshti University, 2007

Work experience

• Aug. 2022 - present: Assistant Professor
  • University of Illinois Chicago
• Aug. 2020 - June 2022: Assistant Professor
  • University of North Caroline, Charlotte
• Apr. 2017 - Aug. 2020: Postdoctoral Research Associate
  • University of Massachusetts Amherst
• Sep. 2011 - Mar 2017: Graduate Employee
  • University of Oregon
• Apr. 2010 - Aug. 2011: Software Engineer
  • Maharan Engineering Group - Tehran, Iran
• Sep. 2005 - Sep. 2007: Founder/Project Manager
  • Sepidan System Idea - Tehran, Iran
• Jun. 2004 - Apr. 2005: Software Developer
  • Eimaa Telecommunication Inc. - Tehran, Iran
• Jun. 2003 - May. 2004: Linux System Developer
  • Maharan Engineering Group. Tehran, Iran

Publications

• D. Wesego, P. Rooshenas , Adversary Aware Optimization for Robust Defense, In Advances in Neural Information Processing Systems 39 (NeurIPS), 2025.
• S. Riazi, P. Rooshenas , LLM-Driven Feedback for Enhancing Conceptual Design Learning in Database Systems Courses, Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE), 2025.
• D. Wesego, P. Rooshenas , Score-Based Multimodal Autoencoders. Transactions on Machine Learning Research. 2024.
• A. Pokkunuru, P. Rooshenas, T. Strauss, A. Abhishek, T. Khan, Improved Training of Physics-informed Neural Networks using Energy-Based priors: A Study on Electrical Impedance Tomography, In Proceedings of the 11th International Conference on Learning Representations (ICLR), 2023.
• S. Bhattacharyya, P. Rooshenas, S. Naskar, S. Sun, M. Iyyer, and A. McCallum, Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4528–4537. Association for Computational Linguistics (ACL), 2021.
• MA. Torkamani, S. Shankar, P. Rooshenas, and Phillip Wallis, Differential Equation Units: Learning Functional Forms of Activation Functions from Data, In Proc. of The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
• P. Rooshenas, Dongxu Zhang, Gopal Sharma, and Andrew McCallum, Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks, In Advances in Neural Information Processing Systems 32 (NeurIPS), 2019.
• P. Rooshenas, A. Kamath, and Andrew McCallum, Training Structured Prediction Energy Networks with Indirect Supervision, In Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL), 2018.
• P. Rooshenas and D. Lowd, Discriminative Structure Learning of Arithmetic Circuits, In Proc. of 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
• D. Lowd and P. Rooshenas, The Libra Toolkit for Probabilistic Models, Journal of Machine Learning Research (JMLR), 16:2459-2463, 2015.
• P. Rooshenas and D. Lowd, Learning Sum-Product Networks with Direct andIndirect Variable Interactions, In Proc. of the Thirty-First International Conference on Machine Learning (ICML), 2014.
• P. Rooshenas and D. Lowd, Learning Tractable Graphical Models Using Mixture of Arithmetic Circuits, Late-Breaking Developments in the Field of Artificial Intelligence, Presented at the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2013.
• D. Lowd, P. Rooshenas, Learning Markov Networks with Arithmetic Circuits, In Proc. of The Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2013.
• P. Rooshenas, H. R. Rabiee, A. Movaghar. M. Y. Naderi. Reducing Data Transmission in Wireless Sensor Networks Using Principal Component Analysis. In Proc. of The Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2010.

Teaching

• Fall 2025: “Generative AI” @ University of Illinois Chicago
• Fall 2024: “Advanced Machine Learning” @ University of Illinois Chicago
• Spring 2023, Fall 2023, Spring 2025, Spring 2026: “Intro to Machine Learning” @ University of Illinois Chicago
• Fall 2022: “Deep Generative Models” @ University of Illinois Chicago
• Spring 2021, Fall 2021, Spring, 2022: “Machine Learning” @ University of North Carolina, Charlotte.
• Fall 2020: “Advanced Machine Learning” @ University of North Carolina, Charlotte.
• Fall 2018: “ML Seminar” @ University of Massachusetts Amherst.
• Fall 2017: “AI/ML Seminar” @ University of Massachusetts Amherst.

Professional Service

• Reviewer: Journal of Machine Learning Research - Software Track.
• Program Committee/Reviewer: ICLR’23, Neurips’23.
• Program Committee/Reviewer: ICML’22, NeurIPS’22.
• Program Committee/Reviewer: ICLR’21, ICML’21.
• Workshop Co-chair for Tractable Probabilistic Methods, TPM 2021, at UAI’21.
• Program Committee/Reviewer: AAAI’20, ICLR’20, IJCAI’20, ICML’20, NeurIPS’20.
• Program Committee/Reviewer: AAAI’19, ICLR’19, ICML’19, IJCAI’19, NAACL’19, NeurIPS’19.
• Program Committee/Reviewer: IJCAI’18, TPM’18, LND4IR, NIPS’18.
• Program Committee: ICML’17 Workshop on Deep Structured Prediction.
• Reviewer: Machine Learning Journal - Springer, International Journal of Approximate Reasoning, ICML 17.
• Reviewer: ICML’16, UAI’16, NIPS’16.
• Program Committee, IJCAI’15.
• Reviewer: AAAI’15.
• Reviewer: NIPS’14.