About Me

I graduated from UC Berkeley with a Ph.D. in Electrical Engineering and Computer Science in December 2024. Before starting grad school, I spent three years at MIT Lincoln Laboratory doing radar and wireless communications signal processing. My research while at Berkeley spanned ML for the physical layer of wireless communications, ML using wireless signals, and most recently generative modeling for data-scarce domains. I am passionate about taking tools from across domains and applying them in new and creative ways to solve real problems. I am unafraid to tackle challenging problems and acquire new skills to develop solutions. Outside of research, I love cooking, rock climbing, and running with my Mini Australian Shepherd, Vesemir.

I am currently looking for work, interested particularly in projects which combine machine learning with perception and prediction of the world. I can be contacted via LinkedIn.

More on my research and teaching.

Publications

A. Sahai, J. Sanz, V. Subramanian, C. Tran, and K. Vodrahalli. 2019. Learning to Communicate with Limited Co-design. In 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE Press, 184–191. doi: 10.1109/ALLERTON.2019.8919749.

A. Sahai, J. Sanz, V. Subramanian, C. Tran and K. Vodrahalli, "Blind Interactive Learning of Modulation Schemes: Multi-Agent Cooperation Without Co-Design," in IEEE Access, vol. 8, pp. 63790-63820, 2020, doi: 10.1109/ACCESS.2020.2984218.

A. Abedi, J. Sanz, and A. Sahai, “Automatic Calibration in Crowd-Sourced Network of Spectrum Sensors,” in Proceedings of the 22nd ACM Workshop on Hot Topics in Networks, ser. HotNets ’23, Cambridge, MA, USA: Association for Computing Machinery, 2023, pp. 157–164. doi: 10.1145/3626111.3628187.

J. Sanz, A. Abedi, and A. Sahai, “Automatic Indoor-Outdoor Detection Using Signals of Opportunity,” in 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), May 2024. doi: 10.1109/DySPAN60163.2024.10632851.

A. Abedi, J. Sanz, and A. Sahai, “Using Signals of Opportunity to Establish Trust in Distributed Spectrum Monitoring Systems,” in 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2024, pp. 33–38. doi: 10.1109/DySPAN60163.2024.10632745.

A. Abedi, J. Sanz, M. Zheleva, and A. Sahai. 2024. From Foe to Friend: The Surprising Turn of Mega Constellations in Radio Astronomy. In Proceedings of the 23rd ACM Workshop on Hot Topics in Networks (HotNets '24). Association for Computing Machinery, New York, NY, USA, 10–16. doi: 10.1145/3696348.3696863.

J. Sanz, “Lightly Supervised Machine Learning for Wireless Signals,” phdthesis, EECS Department, University of California, Berkeley, 2024. Available Online.