About me

I'm currently Senior AI Scientist at Silo AI, specializing in machine learning for industrial process control, predictive maintenance, and anomaly detection.

I obtained my M.Sc. in Engineering Physics from KTH Royal Institute of Technology in 2014. I spent the spring semester of 2013 at EPFL, Lausanne. My studies focused on mathematics and statistics and I wrote my master thesis on derivative pricing at ORC Group.

I obtained my Ph.D. in Applied and Computational Mathematics from KTH Royal Institute of Technology in 2019. I worked on mean-field type games with Professor Boualem Djehiche; we developed new models for the movement of crowds in congested areas.

I was a Postdoctoral Research Associate at Princeton University, in the Operations Research and Financial Engineering (ORFE) department 2020-2021. I studied differential graphon games and their applications in epidemiology with Professor René Carmona and his group.

My research interests include mean field games, mean-field type control theory, network games, learning in games, stochastic models for crowd dynamics, machine learning methods for FBSDEs and FBPDEs, GAN training.

Daniel lewis
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Daniel lewis

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Resume


Education

  1. KTH Royal Institute of Technology

    2014 — 2019
    Ph.D. Applied and Computational Mathematics

    Ph.D. thesis: "Topics in the Mean-Field Type Approach to Pedestrian Crowd Modeling and Conventions" (2019)

    Selected courses: Statistical Inference, Stochastic Finance, Optimal Control and Filtering, Sequential Monte Carlo Methods, Convex Optimization with Engineering Applications, Numerical Linear Algebra, Computational Methods for Stochastic Differential Equations

  2. KTH Royal Institute of Technology

    2009 — 2014
    M.Sc. Engineering Physics

    M.Sc. thesis: "The SVI Implied Volatility Model and Its Calibration" (2014)
    B.Sc. thesis: "Sound Wave Simulation in Computer Games" (2012)

    Selected courses: Probability Theory, Measure Theory, Martingales and Stochastic Integrals, Risk Management, Portfolio Theory, Applied Numerical Methods, Programming in Python, Object-Oriented Program Construction

  3. EPFL École polytechnique fédérale de Lausanne

    2013
    Erasmus Exchange Semester, Mathematics and Engineering

    Semester project on percolation theory for the square lattice.

    Selected courses: Martingales in Financial Mathematics, Signal Processing for Communications, Introduction to Differentiable Manifolds

Experience

  1. Silo AI

    June 2023 - Ongoing
    Expert Lead

    Supporting expert staff well-being, development, and growth.


    Sep 2022 - Ongoing
    Senior AI Scientist

    AI solutions for predictive maintenance, industrial process control & supply chain management.


    Nov 2021 - Sep 2022
    AI Scientist

  2. Princeton University

    Feb 2021 - Jul 2021
    Instructor

    Lecturing the undergraduate course Fundamentals of Statistics. Topics include confidence intervals, hypothesis testing, and regression.


    Mar 2020 - Jul 2021
    Postdoctoral Research Associate

    Research focused on mathematical models for large games in economical sciences, biology, and epidemiology. Worked towards building understanding of the underlying mechanisms behind macroscopic patterns and behavior with game based models. Multiple projects in parallel with colleagues from the department, developing mathematical theory and machine learning based numerical methods.

  3. ORC Group

    Feb 2014 — Sep 2014
    Quantitative Analyst Intern

    Developed fast numerical methods for calibrating the SVI implied volatility model to market data. Designed a calibration algorithm, based on the SVI model's inherent properties and direct search methods from optimization theory, that was later featured in Orc Group's trading application product Orc Trader.

Portfolio

Blog [UNDER CONSTRUCTION]

Machine learning

Alpha Fold

Reflections on ...

Machine learning

Silo Flow

Data driven predictive maintenance in practice.

Machine learning

Stable diffusion

Reflections on Rombach et al. High-Resolution Image Synthesis with Latent Diffusion Models (2022).

Contact

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