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Hershraj Niranjani

hershraj [at] berkeley.edu

San Francisco, CA

Hi, I'm Hershraj. I'm a third-year EECS undergrad at UC Berkeley. I'll soon be working alongside a legendary team as a Member of Technical Staff on the intersection of AI and video.


Previously, I worked on graph algorithms and agentic systems at Virio. Prior to that, I interned as a machine learning engineer at Property Finder, where I worked on deep learning models for real estate price forecasting.


At BAIR, my research focused on LLM-guided program synthesis and interpretable ML, especially on 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, AI agents, or pizza. Feel free to reach out.

LinkedIn GitHub

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 25% GPA (HKN)
  • The Residency at Arcadia House (Summer 2024)
  • Moorpark College [Dual Enrollement] (2019—2023)
    • Honors (Top 25% GPA)

Research

  • LLM misalignment with Professor Phan Xuan Tan.
  • LLM Routing with Professor Ebrahim Bhagheri.
  • 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.
    • In collaboration with Leo Nagel.
  • 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

  • UCS1 (Tutor): CS 170 (Fall 2025)
  • Academic Intern: CS 61A (Spring 2024)

Computer Science Mentors (CSM)

  • Senior Mentor: CS 70 (Fall 2025)
  • Junior Mentor: CS 61A, CS 70 (Spring 2025)
  • 5.0/5.0 average student rating

Coursework

Fall 2025

(Enrolled, withdrew mid-semester)

  • EECS 229A: Information Theory and Coding (Graduate)
  • COMPSCI 270: Combinatorial Algorithms (Graduate)
  • COMPSCI 189: Machine Learning
  • COMPSCI 370: Teaching Computer Science

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

  • (Incoming) Member of Technical Staff — Stealth Startup Starting Feb 2026, San Francisco
    • Working on something really awesome at the intersection of AI and video.
  • Founding Software Engineer — Virio Sep—Nov 2025, San Francisco
    • Designed and implemented graph algorithms for mining and analyzing social network data at scale.
    • Built ML systems for identifying high-quality content and posts.
    • Built multi-agentic deep-research systems for marketing and content creation.
  • Machine Learning Engineering Intern — Property Finder May—Aug 2025, Dubai
    • Built and deployed deep learning models for forecasting Dubai real estate prices across 5M+ listings using a custom transformer architecture; achieved 9% 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 — LexARI Aug—Oct 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.

I will note that I have done all kinds of on-and-off paid consulting work for startups, small businesses, and other organizations, but I won't bother listing those here.