CV

Sidharth Kannan

Machine learning researcher focusing on agents for science and engineering

sid.kannan11@gmail.com
408-466-5904
California, , US

Summary

Research Engineer at ChipAgents building efficient agents for electronic design automation.

Education

  • B.S. in Computer Science and B.S. in Physics
    March 2026
    University of California, Santa Barbara
    GPA: 3.94, High Honors

Work Experience

  • Research Engineer
    March 2026 - Present
    ChipAgents
    Building efficient machine learning agents for electronic design automation.
    • Built domain-specific datasets for semiconductor tapeout and post-training of GLM 5.2.
    • Shipped an agentic analog mixed-signal design verification toolchain used by tier-one semiconductor companies.
    • Reduced token use by 40% through model routing, programmatic tool calling, and caching optimizations.
  • Computational Physics Fellow
    June 2025 - March 2026
    Los Alamos National Laboratory
    Developed computational models for relativistic electron scattering and fusion-reactor simulations.
    • Developed codes for modeling relativistic electron scattering in dilute gases.
    • Built flow-based models to super-resolve observables in particle-in-cell simulations.
  • Undergraduate Research Assistant
    October 2022 - Present
    University of California, Santa Barbara
    Conducted research in generative machine learning, scientific computing, and probabilistic hardware.
    • Built flow-based generative models for representation learning of cosmological fields.
    • Built the first hardware implementation of a higher-order Ising machine.
    • Increased device capacity by 20% through graph-clustering algorithms for resource sharing.
  • Research Intern
    June 2024 - December 2024
    LIGO Laboratory
    Developed graph-neural-network surrogate models for interferometer simulation.
    • Achieved up to 800x speedups over traditional numerical methods with minimal loss of fidelity.
    • Improved thermal-aberration simulation by comparing frequency- and spatial-domain methods.
  • Flight Software Intern
    June 2023 - September 2023
    Astranis Space Technologies
    Built satellite telemetry analysis tools and a GNSS receiver interface.
  • Firmware Engineering Intern
    June 2022 - September 2022
    TenaFe, Inc.
    Developed a software framework and simulation modules for SSD controller verification.

Skills

Machine Learning

  • PyTorch
  • JAX
  • Slime
  • Harness Engineering
  • Flow Matching
  • Weights & Biases

Scientific Computing

  • NumPy
  • SciPy
  • Distributed Training (DDP)
  • SLURM

Chip Design

  • FPGA
  • Vivado
  • SystemVerilog
  • SPICE
  • Agents for Chip Design

Publications

  • Lambda-CFM: Scale-Aware Representation Learning for Cosmology with Flow Matching
    2025
    Machine Learning: Science and Technology
    In press.
  • Graph Neural Networks for Interferometer Simulation
    2025
    NeurIPS 2025 Workshop on AI for Science
  • All-to-all Reconfigurability with Sparse and Higher-Order Ising Machines
    2024
    Nature Communications
    Equal contribution.
  • The Case for Data Centre Hyperloops
    2024
    51st ACM/IEEE International Symposium on Computer Architecture

Presentations

  • Lambda-CFM: Scale-Aware Representation Learning for Cosmology with Conditional Flow Matching
    2025
    Simons Foundation, Learning the Universe Collaboration
    Invited talk
  • Self-Consistent Relativistic Electron Scattering for X-Ray Diagnostics
    2025
    Kavli Institute for Theoretical Physics, Undergraduate Research Symposium
    Santa Barbara, CA
  • Statistical Mechanics of Machine Learning
    2025
    NSF Institute for Artificial Intelligence and Fundamental Interactions Reading Group
  • Graph Neural Networks for Interferometer Simulations
    2024
    Kavli Institute for Theoretical Physics, Undergraduate Research Symposium
    Santa Barbara, CA

Teaching

  • Undergraduate Learning Assistant
    2023
    University of California, Santa Barbara
    Role: Teaching assistant
    Intro to Scientific Computing, Electrostatics, Data Structures and Algorithms I & II, and Discrete Mathematics.
  • CMPTG CS 5: Statistical Physics and Neural Networks
    2025
    University of California, Santa Barbara
    Role: Course instructor
    Ten-week course connecting neural-network theory and statistical physics.
  • PHYS CS 5: Non-equilibrium Statistical Mechanics
    2026
    University of California, Santa Barbara
    Role: Course instructor
    Ten-week course on stochastic processes, Brownian motion, kinetic theory, and open quantum systems.

Portfolio

  • Mini-GPT
    2026
    Machine learning
    A GPT-2-style language model trained on TinyStories.
  • Fermiacc
    2026
    Ai for science
    AI agents for autonomous theory generation and validation in high-energy physics.