Deanna Needell

Alums

                                  

Postdoc Alums

  • Jocelyn Chi (2024), UCLA, NSF Postdoc, numerical linear algebra, statistical learning (now at Univ. Colorado Boulder)
  • Halyun Jeong (2024), UCLA, CAM Postdoc, signal processing, machine learning, nonlinear signal recovery (now at SUNY Albany)
  • Michael Murray (2024), UCLA, PIC Postdoc, machine learning, deep learning theory (now at Univ. Bath)
  • Shuang Li (2023), UCLA, CAM Postdoc, signal processing (now at Iowa State)
  • Michael Perlmutter (2023), UCLA, Hedrick Postdoc, signal processing, dimension reduction (now at Boise State)
  • Longxiu Huang (2022), UCLA, CAM Postdoc, numerical linear algebra, learning, approximation (now at Michigan State)
  • Jamie Haddock (2021), UCLA, CAM Postdoc, optimization and learning (now at Harvey Mudd College)
  • Zhishen Leo Huang (2021), UCLA, CAM Postdoc, optimization and sensing (now at Amazon)
  • Hanbaek Lyu (2021), UCLA, Hedrick Postdoc, dynamical systems, distributed algorithms, network data analysis (now at Univ. Wisconsin)
  • Liza Rebrova (2021), UCLA, Postdoc, non-asymptotic random matrix theory (now at Princeton Univ.)
  • Palina Salanevich (2020), UCLA, Postdoc, phase retrieval and learning (now at Utrecht University)
  • Guangliang Chen (2015), postdoctoral researcher (now at San Jose State)

    Student Alums

  • Yotam Yaniv, UCLA, (2024), stochastic optimization, numerical linear algebra (now at Lawrence Berkeley National Lab)
  • Zehan Chao, UCLA, (2023), machine learning, sentiment analysis (now at BDStar)
  • Ben Jarman, UCLA, (2023), numerical linear algebra, machine learning (now at G-Research)
  • Chris Strohmeier, UCLA, (2023), Data completion and learning
  • Will Swartworth, UCLA, (2023), stochastic iterative methods, PSD testing, Dissertation Prize (now at Carnegie Mellon)
  • Xia Li, UCLA, (2022), Distributed optimization (now at Microsoft)
  • Jacob Moorman (2021), UCLA, Stochastic optimization and Graphical systems (now at Google)
  • Denali Molitor (2020), UCLA, Data analysis and learning (now at Google)
  • Anna Ma (2018), Claremont Graduate University, Topic modeling & compressed sensing (now at UC Irvine)
  • Tina Woolf (2017), Claremont Graduate University, Practical Compressed Sensing (now at JPL)
  • Ran Zhao (2015), Claremont Graduate University, Stochastic optimization (now at AIG)

    Summer Research

    • 2024 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
      AI For Health and Criminal Justice









      Team (mentor: Minxin Zhang)
      • Shreya Balaji
      • Jiayue Liu
      • Xiangdi Lin
      • Anshuman Singh
      • Alexandria Tan
      • Kyle Torres

    • 2023 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
      AI For Lyme Disease









      Team (mentor: Lara Kassab)
      • Xinyu Dong
      • Haowen Geng
      • Abby Hultquist
      • Aoxi Li
      • Xiangdi Lin
      • Jingyi Liu
      • Chelsea Nguyen
      • Nika Nia

    • 2022 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
      AI with Community Partners









      Team (mentors: Jocelyn Chi (lead), Will Swartworth)
      • Xiaofu Ding
      • Xinyu Dong
      • Olivia McGough
      • Chenxin Shen
      • Annie Ulichney
      • Ruiyao Xu

    • 2022 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
      AI for Holocaust Studies









      Team (mentors: Joyce Chew, Michael Lindstrom, Michael Perlmutter (lead), Todd Presner (co-PI))
      • Keyi Cheng
      • Leo Lizhou Fan
      • Stefan Inzer
      • Michelle Lee
      • Xiaoxian Hercy Shen
      • Yaxuan Zheng

    • 2021 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
      Data Science for Innocence









      Team (mentors: Joyce Chew, Longxiu Huang (lead), Ben Jarman)
      • Pengyu Li
      • Imani Maliti
      • Christine Tseng
      • Yaxuan Zheng

    • 2020 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
      Data Science for Innocence









      Team (mentors: Denali Molitor, Liza Rebrova)
      • Ryan Budahazy
      • Lu Cheng
      • Andrew Johnson
      • Josh Vendrow
      • Diana Wu

    • 2019 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
      Topic-aware Chatbot








      Team (mentor: Hanbaek Lyu)
      • Yuchen Guo
      • Nicholas Hanoian
      • Zhexiao Lin
      • Nicholas Liskij
      • Jiahao Qu
      • Henry Sojico
      • Yuliang Wang
      • Zhe Xiong
      • Zhenhong Zou

    • 2018 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
      Patterns of Lyme Disease










      Data Completion Team
      • Matthew Patterson, Pomona College
      • Sneha Sambandam, UCLA
      • Joy Song, Tsinghua Univ.
      • Yuanxun Sun, Peking Univ.


      Deep Models Team
      • Mengdi Gao, UC Irvine
      • Eli Sadovnik, UCLA
      • Tyler Will, Michigan State
      • Runyu Zhang, Peking Univ.


      Classification Team
      • Eric Chen, UCLA
      • Rong Huang, UCLA
      • Diyi Liu, Shanhai Jiao Tong Univ.
      • Cathy Wahlenmayer, Gannon Univ.
      • Jiewen Wang, UCLA


      Co-mentors
      • Jamie Haddock, UCLA
      • Denali Molitor, UCLA
      • Anna Ma, UC San Diego

  • 2017 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
    Data Science meets Lyme Disease Data
    • Allyson Cruz, Claremont McKenna College
    • Anna Ma (co-mentor), Claremont Graduate University
    • Kevin Stangl, UCLA

  • 2015 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
    Practical Compressive Signal Processing









    • Mindy Case, UCLA
    • Xiaoyi Gu (NSF RA), UCLA
    • Hao-Jun Michael Shi, UCLA
    • Shenyingying Tu (NSF RA), UCLA

  • 2015 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
    Longitudonal MRI and compressed sensing









    • Samuel Birns, UCLA
    • Bohyun Kim, UC Irvine
    • Stephanie Ku, UCLA
    • Kevin Stangl, UCLA

  • 2014 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
    Practical Compressive Signal Processing










    • Christopher Garnatz, Pomona College
    • Xiaoyi Gu (NSF RA), UCLA
    • Alison Kingman, Harvey Mudd College
    • James LaManna, Pitzer College
    • Shenyingying Tu (NSF RA), UCLA

  • 2014 Research Experience for Undergraduates (REU), Applied Mathematics, UCLA
    Analysis of Neuroscience Data









    • Arpineh Asadoorian, UCLA
    • Christian Ayala, Claremont McKenna College
    • Jessica Nadalin, UC Berkeley
    • Ryan McCarthy (RA), UCLA
    • Susannah Shoemaker, Pomona College

  • 2014 Summer Graduate Research Program, Claremont McKenna College
    Practical Compressive Signal Processing
    • Flora Xu Shu Jing (NSF RA), Claremont Graduate University
    • Tina Woolf (NSF RA), Claremont Graduate University
    • Ran Zhao (NSF RA), Claremont Graduate University

  • 2012 Research in Industrial Projects for Students (RIPS), Institute of Pure and Applied Mathematics, UCLA
    Spectral Clustering and the Attrition Problem









    • Bianca Cung, UCLA
    • Tony Jin, Stanford
    • Juan Carlos Ramirez, Univ. of Mexico
    • Aubrey Thompson (project manager), Univ. of Nebraska

  • 2010 VIGRE VPUE Undergraduate Summer Workshop, Stanford
    Robust Principal Component Analysis
    • Michael Hornstein, Stanford

      Past thesis students

            







            







    • Matthew Aven, Claremont McKenna College (2017), Daily Traffic Flow Pattern Recognition by Spectral Clustering
    • Wei Wu, Scripps College (2017), Paving the Randomized Gauss-Seidel Method
    • Dejun Wan, Claremont McKenna College (2016), The Randomized Kaczmarz Method with Application on Making Macroeconomic Predictions
    • Phillip North, Claremont McKenna College (2015), One-bit compressed sensing with prior information [Best Thesis Award 2015]
    • Jonathon Briskman, Claremont McKenna College, Block Kaczmarz Method with Inequalities
    • Evan Casey, Claremont McKenna College, Scalable Collaborative Filtering Recommendation Algorithms on Apache Spark [Outstanding Thesis Award 2014]
    • Nathan Falk, Claremont McKenna College, Policy Uncertainty and Irreversible Investment in the United States
    • Aparna Sarkar, Pomona College, Compressed Sensing and Human Vision
    • Zachary Siegel, Pomona College, Generative Models and Sparse Coding
    • Nathan Lenssen, Claremont McKenna College (2013), Audio Signal Processing and Forecasting [Best Thesis Award 2013]
    • Morgan Mayer-Jochimsen, Scripps College (2013), Public Healthcare Evaluation Techniques
    • Jing Wen, Pomona College (2013), Spectral Clustering Methods in Finance
    •