cv

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Basics

Name ANDREY BRYUTKIN
Label PhD Candidate in Applied Mathematics, MIT
Email bryutkin@mit.edu
Summary Applied mathematician and physicist specializing in stochastic processes, time series analysis, Bayesian inference, and uncertainty quantification. ICML-published researcher with expertise in developing UQ methods for large-scale models and dynamical systems. Production-level Python and HPC experience. Seeking quantitative research roles in systematic strategies where rigorous mathematical modeling meets real-world financial data.

Work

  • 2025.06 - 2025.08
    Research Intern
    MIT–IBM Watson AI Lab
    Cambridge, MA
    • Designed and evaluated uncertainty-aware metrics for Granite Guardian models, including accuracy rejection and out-of-distribution detection; built scalable evaluation pipelines in Python/JAX/PyTorch
    • Implemented gradient-based and representation-based UQ scoring methods; automated experiment tracking, data loading, and benchmarking across diverse datasets
    • Collaborated with research engineers to enhance code reproducibility and performance through vectorization, batched I/O, and profiling optimization
  • 2022.09 - Present
    Academic Assistant
    ETH Zurich
    Remote / Zurich
    • Co-authored lecture materials for ``Waves & Electrodynamics'' course; developed comprehensive problem sets and autograding scripts; streamlined LaTeX production toolchain
  • 2022.02 - 2022.05
    Software Engineer—Optimization & Data Systems
    pick8ship
    Zurich
    • Architected warehouse optimization components and data interfaces; integrated algorithms with production databases and robotics systems
    • Enhanced data sampling and preprocessing pipelines, improving stability of downstream optimization processes
  • 2020.09 - 2022.07
    Teaching Assistant
    ETH Zurich
    Zurich
    • Led exercise sessions (20–40 students), authored solutions, and graded assignments for C++ (2020/2021), Physics II (2020), Mathematical Methods II (2022)
  • 2020.09 - 2020.12
    Research Assistant
    ETH Zurich Particle Physics Group (CERN)
    Zurich
    • Designed and deployed lab monitoring and data management systems using InfluxDB; implemented analytics dashboards for experimental workflows

Volunteer

  • - Present

    Speaker
    German Academic Scholarship Foundation
  • - Present

    Zurich, Switzerland

    Board Member
    Maths & Physics Student Association (ETH)
  • - Present

    Zurich, Switzerland

    Vice President
    Mindphair Job Fair (ETH)

Education

  • 2023.09 - Present

    Cambridge, MA

    PhD
    Massachusetts Institute of Technology (MIT)
    Applied Mathematics
    • Probability
    • Stochastic Processes
    • Statistical Inference
    • Convex & Numerical Optimization
    • Time Series
    • Machine Learning
    • PDEs
  • 2022.10 - 2023.06

    Cambridge, UK

    MASt (Master of Advanced Study — ``Part III'')
    University of Cambridge
    Machine Learning, Probability, Partial Differential Equations
  • 2019.09 - 2022.09

    Zurich, Switzerland

    BSc in Physics, with Distinction
    ETH Zurich
    Physics
    • Exchange Semester: University of Toronto (Fall 2021)

Awards

Publications

Skills

Programming
Python (NumPy, Pandas, JAX, PyTorch, SciPy)
C++17
Julia
MATLAB
LaTeX
SQL
ML/Statistics
Bayesian inference
Monte Carlo methods
time series analysis
signal processing
optimization
graphical models
Systems
Linux
Git
HPC (vectorization, parallel I/O)
experimentation toolchains

Languages

English
fluent
German
fluent
Russian
native

Projects

  • 2024 - 2025
    Canonical Bayesian Linear System Identification
    Research at MIT.
    • Developed gauge-invariant Bayesian identification framework for LTI systems; derived canonicalization under GL($d$) orbits and efficient MCMC/VI estimators
    • Implementation: JAX/NumPyro/BlackJAX; emphasis on numerical stability, Fisher information structure, and priors near unit roots
  • 2024 - Present
    UQ for Molecular Dynamics Emulation
    Collaboration with NVIDIA.
    • Developing uncertainty-aware surrogates for force fields on graphs; adapting transformer operators to physical constraints and OOD shifts
  • 2024 - Present
    UQ for Quantum Field Theory
    Collaboration with Columbia University.
    • Implementing transport-map methods and scalable samplers for lattice models; exploiting conditional sparsity and site ordering for linear-time triangular maps
  • 2022 - 2023
    HAMLET: Graph Transformer Neural Operator for PDEs
    Research at University of Cambridge.
    • Combined diffusion GNNs with Fourier neural operators for inverse problems on graphs; published open-source implementation and benchmarks