My research spans theory and practice of statistical inference and machine learning:
Reproducibility in science, leveraging state-of-the-art techniques in machine learning and selective inference to make new replicable discoveries.
Prediction with confidence, by developing methodologies for distribution-free uncertainty estimation that work with any complex predictive algorithm, such as neural nets and random forests.
Equitable treatment, designing tools that can be wrapped around any recommendation system to produce unbiased measures of prediction uncertainty.
Theory and practice of neural nets, by proposing and analyzing multi-layer convolutional sparse coding techniques and studying the stability of convolutional neural networks.
I also work on inverse problems, developing highly effective priors and algorithms for solving general image reconstruction problems. The super-resolution technology I invented together with Dr. Peyman Milanfar is being used in Google's flagship products (Pixel 2/XL Phones, Google Clips, Google+, and Motion Stills), increasing the quality of billions of images and bringing significant bandwidth savings.
Keywords—machine learning, deep learning, deep generative models, scientific reproducibility, selective inference, false discovery rate, knockoffs, uncertainty estimation, fairness, sparse representations, convolutional sparse coding, dictionary learning, image processing, inverse problems.
I am an assistant professor in the Departments of Electrical Engineering and of Computer Science at the Technion—Israel Institute of Technology. Before that, I was a postdoctoral scholar in the Department of Statistics at Stanford University, advised by Prof. Emmanuel Candès. I earned my Ph.D. and M.Sc. degrees in 2017 from the Department of Electrical Engineering at the Technion—Israel Institute of Technology, under the supervision of Prof. Michael Elad. Prior to that, in 2012, I received my B.Sc. from the same department.
In 2017, I constructed with Prof. Elad a Massive Open Online Course (MOOC) on the theory and practice of sparse representations, under the edX platform.
I am a recipient of the 2015 Zeff Fellowship, the 2017 Andrew and Erna Finci Viterbi Fellowship, the 2017 Irwin and Joan Jacobs Fellowship, the 2018–2020 Zuckerman Postdoctoral Fellowship, the 2018–2020 ISEF Postdoctoral Fellowship, the 2018–2020 Viterbi Fellowship for nurturing future faculty members, Technion, the 2019–2020 Koret Postdoctoral Scholarship, Stanford University, and the 2021-2022 Leaders in Science and Technology Career Advancement Chair (CAC), Technion. I was awarded the 2020 SIAG/IS Early Career Prize. I was awarded the 2020 Sheila Samson Prime Minister’s Prize for Global Innovation in Smart Mobility and Alternative Fuels for Transportation. Additionally, I recently received the prestigious 2021-2022 Alon scholarship.
S. Feldman, S. Bates, and Y. Romano, Improving Conditional Coverage via Orthogonal Quantile Regression, 2021. Code.
Y. Romano, R. F. Barber, C. Sabatti and E. J. Candès, With Malice Toward None: Assessing Uncertainty via Equalized Coverage, Harvard Data Science Review, 2020. Project website. Code.