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.


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

  1. "A Generalized Hierarchical Nonnegative Tensor Decomposition."
    By J. Vendrow, J. Haddock, and D. Needell.
    IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2022.  [arXiv] [Code]

  1. "Realistic Face Reconstruction from Deep Embeddings."
    By E. Vendrow* and J. Vendrow*.
    NeurIPS Workshop on Privacy in Machine Learning, 2021.  
      *Authors contributed equally.
  1. "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] 
  1. "Neural Nonnegative CP Decomposition for Hierarchical Tensor Analysis."
    By J. Vendrow, J. Haddock, and D. Needell.
    Asilomar Conf. on Signals, Systems and Computers, 2021.
    [Code]  [Poster]  [Slides] 

Journal Publications

  1. "On the Relation of Gene Essentiality to Intron Structure: A Computational and Deep Learning Approach."
    By E. Schonfeld, E. Vendrow, J. Vendrow, and E. Schonfeld.
    Life Science Alliance, 2021.  [Journal] [bioRxiv] [Code] 
  1. "Feature Selection from Lyme Disease Patient Survey Data Using Machine Learning."
    By J. Vendrow, J. Haddock, D. Needell, and L. Johnson.
    Algorithms, 2020.  [Journal] [arXiv] [Code] 
  1. "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]


  1. "Learning low-rank latent mesoscale structures in networks."
    By H. Lyu, Y. Kureh, J. Vendrow, and M. A. Porter.
    arXiv preprint, 2021.  [arXiv] [Code]
  1. "Weakly-Supervised Object Localization using Semi-Supervised Non-Negative 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.
  1. "Learning to predict synchronization of coupled oscillators on heterogeneous graphs."
    By H. Bassi, R. Yim, R. Kodukula, J. Vendrow, C. Zhu, and H. Lyu.
    arXiv preprint, 2020.  [arXiv] [Code]
  1. "Analysis of Legal Documents via Non-negative 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

An implementation of the Fast Nonnegative Least Squares algorithm.
[PyPi] [Code]
Network Dictionary Learning
Learning from and reconstructing networks using MCMC motif sampling and Nonnegative Matrix Factorization.
[PyPi] [Code]
A custom graph/network/multi-weighted network class optimized for scalable sampling and searching algorithms.
[PyPi] [Code]

Class Projects

CS 239 (Quantum Programming): Implementating and Running Quantum Algorithms on Google and IBM Quantum Computers
ECE 247 (Deep Learning): Classifying Movement-Related EEG Data using Neural Networks
[Report] [Code]
ECE 239AS (Reinforcement Learning): Applying Proximal Policy Optimization to OpenAI Environments
[Report] [Poster] [Code]
CS M226 (ML in Bioinformatics): Predicting Synaptic Connections in Drosophila Melanogaster
[Report] [Code]

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
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
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


I also have a twin brother! Check out Edward Vendrow.