Parnian Kassraie

Bio

I am a 3rd year Ph.D. student at the department of Computer Science at ETH Zurich, fortunate to be advised by Andreas Krause and Peter Bühlmann. I am a member of ETH Institute for Machine Learning and associated with the ETH AI Center. As of March 2024, I am a research intern at Apple MLR in Paris, hosted by Marco Cuturi. I am interested in statistics, optimization, and theory of Machine Learning, often in the context of sequential decision-making and online learning.

I completed my M.Sc. at ETH, under the supervision of Andreas Krause and Fanny Yang, with a thesis on Contextual Neural Bandits. Before coming to ETH, I did a dual B.Sc. in Electrical Engineering and Computer Science at Sharif University of Technology. My thesis was on Visualizing Adversarial Attacks on Convolutional Neural Networks, which I wrote with Nassir Navab and Federico Tombari at TU Munich.

News

• [26.03.24] Was awarded the ETH Doc.Mobility Fellowship, for a research visit at CMU Statistics with Aaditya Ramdas.
• [04.03.24] Started an internship at Apple MLR, Paris.
• [23.02.24] Giving a talk at ELLIS workshop on Interactive Learning, Oberwolfach Mathematical Research Institute, Feb 27, on Model Selection for Sequential Inference and Optimization. [Slides]
• [09.11.23] Giving a talk at IMS ICSDS, Lisbon Dec 18, on Model Selection for Sequential Inference and Optimization. [Slides]
• [01.11.23] Instance-Dependent Generalization Bounds via OT to appear in JMLR.
• [18.06.23] Serving as the head teaching assistant for Probabilistic Artificial Intelligence.
• [07.09.23] Giving a talk at the Royal Statistical Society Conference 2023 on Anytime Model Selection. [Slides]
• [19.06.23] Giving a talk at the PAC-Bayes Meets Interactive Learning Workshop on Anytime Model Selection in Linear Bandits.
• [01.06.23] Made it 2 years into the PhD.
• [08.05.23] LiBO and HAMBO to be presented at UAI 2023.
• [22.11.22] Attending NeurIPS@Paris. Presenting GNN Bandits.
• [12-16.09.22] Attending Quantifying Uncertainty Workshop @ Simons Institute. Presenting Meta-KeL and some new results.
• [17-23.07.22] Attending ICML 2022, serving as a Session Chair. Presenting Meta-KeL at the main conference, and at ReALML. (visa arrived too late)
• [21.06.22] Invited to Deep Learning Theory Workshop @ Simons Institute. (visa arrived too late)
• [01.06.22] Made it 1 year into the PhD.
• [30.05-03.06.22] Attending SAVI 2022. Presenting Meta-KeL.
• [28-30.03.22] Attending AISTATS 2022. Presenting Neural Contextual Bandits without Regret.
• [22.12.21] Giving a talk at ReACT 2021 on 'No-regret Bayesian Optimization with limited knowledge of the function space'.
• [01.06.21] Started a PhD.

Publications

My work focuses on sample-efficient sequential decision-making in rich and structured domains. To address these problems, I adapt tools from deep learning [NN] and meta-learning/high-dimensional statistics [META].

• All
• META
• NN