Curriculum Vitae

for Andreas P. Mentzelopoulos

Basics

Name Andreas P. Mentzelopoulos
Email ament@mit.edu
Url https://mentzelopoulos.github.io
Summary Machine Learning Scientist at Wayfair, MIT PhD, interested in deep learning, time-series forecasting, generative-AI

Work

  • 2025.10 - Present
    Machine Learning Scientist
    Wayfair
    Marketing & Measurement Science
  • 2024.06 - 2024.08
    Intern for Quantitative Tranding Strategies
    Citic Securities CLSA
    Development of Quantitative Trading Strategies
    • Market-neutral statistical arbitrage strategies using deep-learning.
    • Back-testing in alternative markets.
    • Software development and optimization.
  • 2023.06 - 2023.08
    Engineering Development Intern
    MathWorks
    Design & Quality Engineering for Simscape Fluids and Multibody products
    • Test Suite deveoplment for Gas, Moist Air, and Isothermal Liquid libraries.
    • Design and deployment of hydraulic and control components for the customer-facing forklift example (2024a).
    • Test suites for 10 customer-facing simscape models (Quadcopter, Forklift, ...).
  • 2020.06 - 2020.08
    Engineering Intern
    American Bureau of Shipping
    Engineering review and classification of offshore structures and equipment
    • Full engineering review of pressure vessels per ASME Section VIII, Div 1.
    • Development of allowable chemical cargo lists for 12 chemical tankers per IBC Code.
    • Classification of BP Mad-Dog phase 2 offshore rig.
    • Strategy development for maritime digitalization.

Education

  • 2020 - 2025

    Cambridge, MA, USA

    Doctor of Philosophy (PhD)
    Massachusetts Institute of Technology
    • PhD Mechanical Engineering and Computation (2025)
    • SM Mechanical Engineering (2022)
    • Minor in Finance
  • 2016 - 2020

    Ann Arbor, MI, USA

    Bachelor's of Science in Engineering (BSE)
    University of Michigan
    • BSE Mechanical Engineering (2020)
    • BSE Naval Architecture and Marine Engineering (2020)
    • Minor in Mathematics

Awards

Skills

Deep Learning
Time Series Forecasting
Transformers, LSTM
Generative Modeling (VAE, Diffusion)
Diffusion Models
Pytorch
Programming Languages
Python
MATLAB
C++ (familiar)
Julia (familiar)
Mathematics & Statistics
Probability Theory
Stochastic Processes
Differential Equations
Numerical Methods
Quantitative Finance
Statistical Arbitrage
Derivatives Pricing
Valuations

Languages

English
Fluent
Greek
Native
German
Familiar (Goethe Zertifikat B1)

Projects

  • 2023 - Present
    VIVformer
    Transformer based network for 2D time series reconstruction and forecasting
    • Time-series recosnrtuction
    • Time-series forecasting
  • 2023 - Present
    LOBSTgER
    Learning Oceanic Bioecological Systems Through gEnerative Representations
    • Diffusion model for underwater image generation
    • Latent Diffusion for high resolution underwtaer image synthesis