Shuni Li

Shuni Li

PhD Student

UC Berkeley

Biography

I’m a 5th year PhD student in the Statistics department at UC Berkeley, advised by Haiyan Huang. My research interests lie at the intersection of statistics machine learning and biological/medical research. I’m currently developing gene-gene interaction networks along a pseudotime trajectory utilizing single-cell RNA sequence data. Some of my past research projects are primarily centered around employing deep learning advancements to guide the design of biological sequences and chemical materials.

Interests

  • Statistical Machine Learning
  • Biological Sequence Optimization and Design
  • Computational Biology

Education

  • PhD in Statistics, 2018-2024

    UC Berkeley

  • B.A. in Mathematics and Computer Science, 2014-2018

    Macalester College

Publications

(2023). Population-based heteropolymer design to mimic protein mixtures. In Nature.

PDF Code

(2023). DeepRHP: A Hybrid Variational Autoencoder for Designing Random Heteropolymers as Protein Mimics. In AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE).

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(2019). The Set Splittability Problem. In The Australasian Journal of Combinatorics.

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(2019). Scalable M-Channel Critically Sampled Filter Banks for Graph Signals. In IEEE Transactions on Signal Processing.

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Teaching and Professional Experiences

 
 
 
 
 

ML Engineer Intern

XtalPi

Aug 2021 – Nov 2021
 
 
 
 
 

Research Assistant

NERSC Lawrence Berkeley National Laboratory

May 2020 – Aug 2020
 
 
 
 
 

Graduate Student Instructor

UC Berkeley

Jan 2019 – Present

Taught the discussion sections of the following classes:

  • Spring 2021: STAT 151A Linear Modelling: Theory and Applications
  • Spring 2020: STAT 152 Sampling Surveys; section material
  • Fall 2019: STAT 135 Concepts of Statistics
  • Spring 2019: STAT 133 Concepts in Computing with Data
 
 
 
 
 

Preceptor

Macalester

Sep 2016 – Dec 2017

TAed the following classes:

  • Fall 2017: Combinatorics
  • Fall 2017: Intro to Data Science
  • Spring 2017: Computational Linear Algebra
  • Fall 2016: Linear Algebra

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