Welcome toMichael Psenka

The centralized location for everything Michael Psenka that no one asked for.

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About

Research

Industry & Projects

Music

About Me

I work on mathematical approaches to pioneering deep learning algorithms.

I am a fourth year PhD student in EECS/BAIR at UC Berkeley, advised by Prof. Aditi Krishnapriyan, and I have worked with Prof. Yi Ma, Prof. Pieter Abbeel and Prof. Shankar Sastry. I did my undergrad in pure math at Princeton. With my math background, I engineer new deep learning architectures and algorithms that not only have pretty theory, but yield some surprising benefits, especially in how these models can almost tune themselves.

The easier the model is to deploy and work with, the happier I am.

Education

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University of California, Berkeley | MS/PhD in EECS

Focus in AI. GPA: 4.0.

2021-2026
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Princeton University | A.B. in Mathematics

Certificates (minors) in CS and Applied Math. GPA: 3.6. Involvements: Princeton Pianist Ensemble, Math Club, Princeton Data Science, Princeton ACM, HackPrinceton participant.

2017 - 2021

Awards

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Peter A. Greenberg '77 Memorial Prize for Mathematics

June 2020

Awarded for solving an open problem in spectral geometry with three classmates.

"Awarded for outstanding accomplishments in Mathematics by juniors".
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Manfred Pyka Memorial Prize for Physics

June 2018

Performed exceptionally well in an experimental first-year physics-major sequence, which introduced modern topics very quickly.

"Given to outstanding Physics undergraduates who have shown excellence in course work and promise in independent research".
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First Place, HackPrinceton

April 2018

Won first place for A.I.D.A.N., a chatbot that allows you to interact with your data via advanced statistical and machine learning tools.

Best overall project at the HackPrinceton hackathon.

I focus on: mathematical approaches to AI.

I apply mathematical techniques to develop innovative deep learning architectures and algorithms, focusing on practical advancements that bridge theory and application. Click the arrow to learn more about my work. ...


Publications & Workshop Presentations

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Learning a Diffusion Model Policy from Rewards via Q-Score Matching.
Michael Psenka*, Alejandro Escontrela*, Pieter Abbeel, Yi Ma.
ICML, July 2024.
Paper Code
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Representation Learning via Manifold Flattening and Reconstruction.
Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma.
JMLR, May 2024.
Paper Code
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Role of Uncertainty in Anticipatory Trajectory Prediction for a Ping-Pong Playing Robot
Nima Rahmanian, Michael Gupta, Renzo Soatto, Srisai Nachuri, Michael Psenka, Yi Ma, Shankar Sastry.
Nov. 2023.
Paper
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Pursuit of a discriminative representation for multiple subspaces via sequential games.
Druv Pai, Michael Psenka, Chih-Yuan Chiu, Manxi Wu, Edgar Dobriban, Yi Ma.
Journal of the Franklin Institute, April 2023.
Paper Code
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CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction
Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Michael Psenka, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Xiaojun Yuan, Heung Yeung Shum, Yi Ma.
Entropy Journal, March 2022.
Paper Code
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Second-order optimization for tensors with fixed tensor-train rank.
Michael Psenka, Nicolas Boumal.
NeurIPS OPT 2020.
Paper Code
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Reconstruction Without Registration.
Michael Psenka, Tolga Birdal, Leonidas Guibas.
IROS geometric methods workshop 2020.
Paper Code
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A Proof of The Triangular Ashbaugh-Benguria-Payne-Pólya-Weinberger Inequality.
Ryan Arbon*, Mohammed Mannan*, Michael Psenka*, Seyoon Ragavan*.
Journal of Spectral Theory, Sept. 2022.
Paper

Employment

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Co-Head Instructor @ UC Berkeley

Jun - Aug, 2022
  • Organized and taught lectures for CS 70, an undergraduate class for discrete math and probability theory.
  • Link to class page.
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Undergrad Researcher @ Stanford University

Jun - Aug, 2020
  • Worked with Dr. Tolga Birdal on a novel approach to multi-view reconstruction in computer vision that bypasses pairwise view registration.
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Undergrad Researcher @ Princeton University

Jun - Sept, 2019
  • Successfully developed a state-of-the-art method for computing analytic Hessians and second order optimization over tensor train manifolds.
  • Undergraduate research funded by the National Science Foundation through award DMS-1719558.
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Software Developer @ Moovila

June - Aug 2018, '17, '16
  • Developed a machine learning algorithm for workplace analytics.
  • Mathematically modeled collision avoidance in network analysis animation.
  • Worked through a patent application for proprietary software.
  • Worked on improving the search engine for quicker and more robust search results.
  • Denormalized relational database to NoSQL, maximizing data access efficiency and cost-efficiency

Projects

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my VS Code extension

Bookmarks Table of Contents Generator
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my academic PDF tool

citelink
  • Changes all bibliographic links in an academic PDF into direct URL's straight to the paper.
  • Saves a lot of time for related work reading of papers.
  • Link to Github repo.
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pip package for manifold linearization

flatnet
  • Easy to use package for automatically building neural networks that flatten data manifolds.
  • Built from research on this paper.
  • Link to repo.
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some UI/UX tools

michaelpsenka.io
  • The stuff I wrote for this website! If you like anything here, you may find it in this project.
  • Link to repo.

My Music

I'm a classically trained pianist with some experience playing jazz. Recently, I started tinkering around with making music in a digital workstation. Here, I'll upload anything that becomes a finished product.

I'm also a member of the Princeton Pianist Ensemble! My most recent performance was a duet version of Fly Me To The Moon for a virtual concert: Link to video. You can find more from the ensemble here.

1. Hurricane

Drum sample: Incredible Bongo Band - Apache

2. Deadly

Drum sample: Kanye West - Black Skinhead
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