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Hershraj Niranjani

Researcher | MLE | SWE

hershraj [at] berkeley.edu

Berkeley, CA

Hi, I'm Hershraj. I'm a third-year EECS undergrad at UC Berkeley, focusing on machine learning and algorithms. Last summer, I was a machine learning engineering intern at Property Finder, where I worked on deep learning models for real estate price forecasting.

At BAIR, my research centers on LLM-guided program synthesis and interpretable ML, with a focus on grammar-based modeling and navigating large combinatorial search spaces. I've also worked on time series forecasting and physics-informed models.

I'm always up for a good conversation — whether it's about information theory, model design, or pizza. Feel free to reach out.

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Projects

  • FlowZero: RL-powered Flow Free solver built on Monte-Carlo Graph Search with expert iteration.
    • Also includes a stochastic puzzle generator and a SAT-based oracle for stress-testing & benchmarking.
  • DreamPy: Python rewrite of the Dreamcoder framework.
  • ThrowdownTV: Low-latency RTMP live video streaming platform (React, Express, Rust, Typescript).
  • Formify: Computer vision tool to convert photos of tables into accurate CSV files.

Education

  • University of California, Berkeley (2023—2027)
    • Bachelor's of Science, Electrical Engineering and Computer Science
    • Top 35% GPA (HKN)
  • The Residency at Arcadia House (Summer 2024)
  • Moorpark College [Dual Enrollement] (2019—2023)
    • Honors (Top 25% GPA)

Research

  • LLM-guided program synthesis at BAIR under Matteo Guarrera & Carlo Bosio.
    • Won the EECS Evergreen Undergraduate Research Award for this work.
  • Computationally verifying Berry-Esseen rates for sparse random d-regular graphs.
  • Multihorizon timeseries forecasting via deep learning and attention.
  • Physics-informed neural networks for PDE solvers.
  • Prior work on neural cost computation during decision-making at the Wilbrecht Lab.

Teaching

Course Staff

  • CS 170: Efficient Algorithms and Intractable Problems - Tutor [UCS1] (Fall 2025)
  • CS 61A: The Structure and Interpretation of Computer Programs - Academic Intern (Spring 2024)

Computer Science Mentors (CSM)

  • Senior Mentor: CS 70: Discrete Mathematics and Probability Theory (Fall 2025)
  • Junior Mentor: CS 61A: The Structure and Interpretation of Computer Programs (Spring 2025)
  • Junior Mentor: CS 70: Discrete Mathematics and Probability Theory (Spring 2025)
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Coursework

Fall 2025 (current)

  • ELENG 229A: Information Theory and Coding (Graduate)
  • COMPSCI 270: Combinatorial Algorithms and Data Structures (Graduate)
  • COMPSCI 189/289A: Machine Learning
  • ASAMST 128AC: Muslims in America

Spring 2025

  • EECS 126: Probability and Random Processes
  • PHYSICS 137A: Quantum Mechanics
  • COMPSCI 61C: Great Ideas of Computer Architecture (Machine Structures)
  • COMPSCI 198: Directed Group Studies for Advanced Undergraduates

Fall 2024

  • COMPSCI 170: Efficient Algorithms and Intractable Problems
  • EECS 127/227AT: Optimization Models in Engineering (Convex Optimization)
  • PHILOS R1B: Reading and Composition Through Philosophy

Summer 2024

  • COMPSCI 70: Discrete Mathematics and Probability Theory

Spring 2024

  • COMPSCI 61B: Data Structures
  • COMPSCI 197: Field Study
  • EECS 16B: Designing Information Devices and Systems II
  • PHYSICS 7B: Thermodynamics, Electricity, and Magnetism

Fall 2023

  • COMPSCI 61A: The Structure and Interpretation of Computer Programs
  • COMPSCI 195: Social Implications of Computer Technology
  • EECS 16A: Designing Information Devices and Systems I
  • SOCIOL 110: Organizations and Social Institutions

Moorpark College Dual Enrollment (2019—2023)

  • MATH M21: Discrete Mathematics
  • CNSE M84: Ethical Hacking
  • MATH M25C: Multivariable Calculus
  • PHYS M20A: Mechanics of Solids and Fluids
  • PHYS M20B: Thermodynamics, Electricity, and Magnetism
  • PHYS M20C: Light Optics, Wave Motion, and Modern Physics
  • CS M125: Programming Concepts and Methodology

Industry

  • Machine Learning Engineering Intern — Property Finder Summer 2025, Dubai
    • Built and deployed deep learning models for forecasting Dubai real estate prices across 5M+ listings using a custom transformer architecture; achieved 11% MAPE over a 6-month horizon.
    • Won “Best Tech” at the 2025 internal hackathon for an Mixture-of-Experts ensemble with 5% MAPE on listing price predictions.
    • Explored interpretability via Kalman filters and physics-inspired modeling (SINDy).
  • Software Engineering Intern — Stealth Startup Summer 2024, Remote
    • Built custom database infrastructure and internal APIs to support scalable backend systems.
    • Designed and implemented backend services in TypeScript, enabling structured data querying, caching, and analytics.
    • Worked closely with the founding team on backend architecture and product iteration.
  • Software Engineering Intern — LexARI Fall 2023, Berkeley
    • Developed core infrastructure for “ChatGPT for legal documents” using retrieval-augmented generation (RAG) on court cases and contracts.
    • Integrated PGVector and built semantic search pipelines to boost document relevance and Q&A performance.
    • Created robust scraping tools and containerized testing infrastructure using Docker; deployed services to Azure.