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Education, research, publications, and teaching.

Basics

Name Prateek Anand
Role Computer Science Ph.D. Student
Email prateek@cs.ucla.edu
Phone (408) 431-1325
Homepage https://prateekanand2.github.io/
Summary My research interests are in developing novel AI/ML computational methods. Broadly, I am invested in deep generative models, traditional machine learning, and statistical approaches for understanding and generating biological data. Recently, I have been focused on deep generative models for regulatory DNA design. I am also building generative models of human genetic variation for synthetic data and imputation, and exploring ensemble machine learning approaches for imputing rare variants across diverse ancestries.

Education

  • 2024 - 2029

    Los Angeles, CA

    Ph.D.
    UCLA
    Computer Science
    • Advisor: Sriram Sankararaman
    • Major Field: Artificial Intelligence
    • Minor Fields: Data Science Computing and Computational Systems Biology
  • 2024 - 2026

    Los Angeles, CA

    M.S.
    UCLA
    Computer Science
  • 2020 - 2024

    Los Angeles, CA

    B.S.
    UCLA
    Computer Science

Experience

  • 06/2022 - Present
    Graduate Student Researcher
    UCLA Computer Science | Sriram Lab
    Developing discrete diffusion models and improved guidance methods for cell-type-specific enhancer design (under review).
    • Building deep generative models of genetic variation for synthetic data and imputation
    • Developing ensemble machine learning approaches for imputing rare variants across diverse ancestries
    • Creating scalable machine learning/statistical software to understand human genetic architecture
    • Research Intern: Bruins in Genomics Research Program (06/2022 - 08/2022)
  • 06/2023 - 01/2024
    Research Intern
    Stanford University School of Medicine | Curtis Lab
    Early detection of blood cancer through computational modeling and inference.
    • Canary CREST Research Program for Early Cancer Detection (06/2023 - 09/2023)
  • 01/2021 - 04/2022
    Research Assistant
    UCLA Jonsson Comprehensive Cancer Center | Boutros Lab
    Pipeline development for quantifying/locating copy-number aberrations in human cancer samples.

Publications

Preprints

Presentations

Teaching

Skills

Programming languages
Python
C++
Frameworks
Scikit-learn
PyTorch
NumPy
Pandas
Git
Courses
Machine Learning
Artificial Intelligence
Neural Networks and Deep Learning
Big Data Analytics
Software Engineering and Construction
Data Structures
Algorithms and Complexity
Linear Algebra
Statistics and Probability
Optimization
Machine Learning in Genetics
Algorithms in Bioinformatics
Other
Diffusion Models
Generative Models
Probabilistic Models
Hypothesis Testing
Cluster Computing

Awards