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Iman Nodozi

Email: inodozi[at]ucsc[dot]edu

Hello! I am Iman, a Ph.D. candidate in the Department of Electrical and Computer Engineering at UC Santa Cruz. My research interests are Stochastic Control, Hybrid Systems, and Convex Optimization. I am working under the supervision of Abhishek Halder.

I enjoy hiking, watching movies, playing piano, surfing, and running in my free time.

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2024
03/15:    I successfully passed my PhD defense: Measure-valued Proximal Recursions for Learning and Control
01/15:    New manuscript: Solution of the Probabilistic Lambert Problem: Connections with Optimal Mass Transport, Schrödinger Bridge and Reaction-Diffusion PDEs
2023
12/12:    Our paper Proximal Mean Field Learning in Shallow Neural Networks is accepted in Transactions on Machine Learning Research
11/23:    Our papers Neural Schrödinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly is accepted in IEEE Transactions on Control Systems Technology
09/13:    New manuscript: Wasserstein Consensus ADMM
07/26:    New manuscript: Neural Schrödinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly
07/11:    Our papers Optimal Mass Transport over the Euler Equation and A Controlled Mean Field Model for Chiplet Population Dynamics are accepted in 2023 CDC.
05/31:    I presented our work on " A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly" in ACC 2023 , paper here
05/05:    I won the 2023-24 Baskin School of Engineering Dissertation Year Fellowship! .
04/07:    I co-chaired the 5th NorCal Control Workshop and presented our work: "Wasserstein consensus ADMM" . My colleague Alexis Teter also gave a talk on our work "Solution of the Probabilistic Lambert Problem" .
03/31:    New manuscript: Optimal Mass Transport over the Euler Equation
03/17:    New manuscript: A Controlled Mean Field Model for Chiplet Population Dynamics
01/19:    Our paper A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly is accepted in 2023 ACC
2022
10/25:    New manuscript: Proximal Mean Field Learning in Shallow Neural Networks
09/15:    Prof. Abhishek Halder presented our work A Distributed Algorithm for Measure-valued Optimization with Additive Objective in the Invited Session "Optimal Transport in Networks and Systems" at MTNS 2022
09/12:    I started an internship at Onsemi as an applications engineer for Fall 2022.
08/21:    New manuscript: A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly
07/15:    Our papers Schrödinger Meets Kuramoto via Feynman-Kac: Minimum Effort Distribution Steering for Noisy Nonuniform Kuramoto Oscillators and A Mixed Integer Approach for the Solution of Hybrid Model Predictive Control Problems are accepted in 2022 CDC
06/06:    Our invited paper A Distributed Algorithm for Measure-valued Optimization with Additive Objective is accepted in 2022 Mathematical Theory of Networks and Systems
06/03:    Delivered the talk Neural Schrödinger Bridge for Minimum Effort Self-assembly in the 4th NorCal control workshop
03/31:    New manuscript: A Mixed Integer Approach for the Solution of Hybrid Model Predictive Control Problems
02/18:    New manuscript: Schrödinger Meets Kuramoto via Feynman-Kac: Minimum Effort Distribution Steering for Noisy Nonuniform Kuramoto Oscillators
02/17:    New manuscript: A Distributed Algorithm for Measure-valued Optimization with Additive Objective
02/09:    I passed the Ph.D. Qualifying Exam: Measure-valued Proximal Recursions for Learning and Control


Past News