Hadi Daneshmand
About
Dr. Hadi Daneshmand works on trustworthy AI, focusing on the reliability and explainability of AI models. His research lies at the intersection of theoretical and applied machine learning. He leverages advanced tools from mathematical programming, probability theory, and mathematical physics to characterize the strengths and vulnerabilities of AI methods, develop reliable AI algorithms, and explain the underlying mechanisms of influential AI models. Dr. Hadi Daneshmand joined the University of Virginia in December 2024 as an Assistant Professor of Computer Science. Before his appointment, he was a Postdoctoral Researcher at the Foundations of Data Science Institute (FODSI), jointly hosted by MIT and Boston University, and INRIA Paris, where he worked under the mentorship of Professor Francis Bach. He earned his Ph.D. in Computer Science from the Machine Learning Institute at ETH Zurich.
Research Performance Summary
First Recorded Paper
Estimating diffusion network structures: Recovery conditions, sample complexity & soft-thresholding algorithm
Year: 2014
Citations: 147
Venue: International conference on machine learning
Latest Recorded Paper
Data Generation without Function Estimation
Year: 2025
Citations: 0
Venue: arXiv preprint arXiv:2507.08239
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.