About Me

I am happy to annouce I will be joining Stanford in the Autumn quarter to pursue my PhD in Electrical Engineering!

I am Vidhata Jayaraman, a fourth year undergraduate student at the University of Illinois Urbana-Champaign (UIUC) pursuing a dual degree in Computer Engineering and Mathematics. I am currently doing research under Professors Lav R. Varshney and Rayadurgam Srikant at UIUC in machine learning theory (with a particular focus on information theoretic methods). I am also being co-advised by both professors for my senior thesis on “Information-theoretic limits of Knowledge Distillation”. I am planning to pursuing a Ph.D. in machine learning theory/statistics. I also have a passion for music as I played piano for 14 years and trombone for 9 years (both until age 18).

Academic Interests

My main research interests lie in Machine Learning Theory, High-Dimensional Statistics, Information Theory, Optimal Transport, and Optimization/Control. My main goals lie in doing statistical and geometric analyses on modern high-dimensional systems to explain their behavior, motivate algorithmic improvements, and find ulitmate performance bounds. I am currently doing work on deriving information-theoretic bounds on knowledge distillation particularly in LLMs, analyzing visual processing in VLMs, analyzing the two-stage retrieval process in Transformer models, and improving federated learning with multimodal data with Argonne National Laboratory.

Academic Coursework

course denotes graduate-level

Spring 2026 Courses:

• Optimal Controls (ECE 553)
• Partial Differential Equations (MATH 442)
• Cryptography (CS 407)
• Senior Thesis

Current Courses:

• Information Theory (ECE 563)
• Probability and Measure (STAT 553)
• Deep Generative Models (ECE 498/598)
• Complex Variables (MATH 448)
• Senior Research

Past courses:

• Math:
  • (Honors) Real Analysis
  • (Honors) Abstract Algebra
  • Graph Theory
  • Probability Theory
  • (Honors) Linear Algebra
  • Differential Equations
  • Algebraic Topology
• Applied Math:
  • Optimization
  • Random Processes
  • Machine Learning
  • Deep Learning for Computer Vision
  • Quantum Information Theory
  • Algorithms & Models of Computation
  • Analog and Digital Signal Processing