About Me
I am a fourth year at UCLA double majoring in Computer Science and Mathematics with a minor in Philosophy, and I am a research assistant for the UCLA Computational Applied Math Department. I am advised by Deanna Needell, and my other mentors are
Jamie Haddock
and Hanbaek Lyu.
I am currently interning at Apple on the Human and Object Understanding (HOUr) team within the Systems Intelligence and Machine Learning group (SIML) group, working on developing core vision technologies.
Research
My current research focuses on applications of matrix and tensor factorization to topic modeling, computer vision, and network science. I'm also interested in AI ethics, deep learning and privacy.Conference Publications

"Realistic Face Reconstruction from Deep Embeddings."
By E. Vendrow* and J. Vendrow*.
NeurIPS Workshop on Privacy in Machine Learning, 2021.
[Code] *Authors contributed equally.

"On a Guided Nonnegative Matrix Factorization."
By J Vendrow, J. Haddock, E. Rebrova, and D. Needell
IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2021.
[Proceedings] [arXiv] [Code] [Poster] [Slides]
Journal Publications

"Antibiotic Treatment Response In Persistent Lyme Disease: Why Do Some Patients Improve While Others Do Not?"
By L. Johnson, M. Shapiro, R. Stricker, J. Vendrow, J. Haddock, and D. Needell.
Healthcare, 2020. [Journal]
Preprints

"WeaklySupervised Object Localization using SemiSupervised NonNegative Matrix Factorization."
By E. Sizikova*, J. Vendrow*, R. Grotheer, J. Haddock, L. Kassab, A. Kryshchenko, T. Merkh, M. Rajapaksha, H. V. Vo, C. Wang, K. Leonard, and D. Needell.
Submitted, 2020. *Authors contributed equally.

"Analysis of Legal Documents via Nonnegative Matrix Factorization Methods."
By R. Budahazy, L. Cheng, Y. Huang, A. Johnson, P. Li, J. Vendrow, Z. Wu, D. Molitor, E. Rebrova, and D. Needell.
arXiv preprint, 2021. [arXiv]
Software Development
Class Projects
CS 239 (Quantum Programming): Implementating and Running Quantum Algorithms on Google and IBM Quantum Computers
[Report]
[Report]
Selected Coursework
Computer Science
CS M226: ML for Bioinformatics
CS 239: Quantum Programming
CS 181: Formal Languages and Automata
CS 146: Machine Learning
CS 180: Algorithms and Complexity
CS 111: Operating Systems
CS M226: ML for Bioinformatics
CS 239: Quantum Programming
CS 181: Formal Languages and Automata
CS 146: Machine Learning
CS 180: Algorithms and Complexity
CS 111: Operating Systems
Electrical Engineering
EE 239AS: Reinforcement Learning
EE 247: Neural Nets and Deep Learning
EE 236A: Linear Programming
EE 236B: Convex Optimization
EE 133A: Applied Computing
EE 133B: Optimization
EE 239AS: Reinforcement Learning
EE 247: Neural Nets and Deep Learning
EE 236A: Linear Programming
EE 236B: Convex Optimization
EE 133A: Applied Computing
EE 133B: Optimization
Mathematics
Math 171: Stochastic Processes
Math 170A: Probability Theory I
Math 170B: Probability Theory II
Math 131A: Real Analysis I
Math 131B: Real Analysis II
Math 115A: Linear Algebra
Math 121: Topology
Math 171: Stochastic Processes
Math 170A: Probability Theory I
Math 170B: Probability Theory II
Math 131A: Real Analysis I
Math 131B: Real Analysis II
Math 115A: Linear Algebra
Math 121: Topology