Curriculum Vitae

Work Experience

  • Research Associate in Machine Learning, University of Cambridge, since 2020
    Development of reinforcement learning approaches for molecular design in 3D
  • Postdoctoral Researcher, University of Cambridge, 2018–2020
    Design of Bayesian deep learning models to accelerate the discovery of novel materials
    Advisor: José Miguel Hernández-Lobato
  • Machine Learning Engineer, Semiconductor Research Corporation, 2018–2020
    Multi-Objective Bayesian optimization method development for design space exploration of integrated circuits

Education

  • Ph.D. Theoretical Chemistry, ETH Zurich, 2018
    Thesis: Error-Controlled Quantum Chemical Exploration of Reaction Networks
    Advisor: Markus Reiher
  • MSc. Interdisciplinary Sciences (with distinction), ETH Zurich, 2015
    Focus: Chemistry and Physics
    Thesis: Bridging the Gap Between Theory and Experiment Using Gaussian Processes, Harvard University.
  • BSc. Interdisciplinary Sciences, ETH Zurich, 2013
    Focus: Chemistry.

Awards and Fellowships

  • Computational Resources, 100,000 GPU hours, EPSRC, 2021–2022
  • Early Postdoc.Mobility fellowship of the Swiss National Science Foundation, 2018–2020
  • Ph.D. Fellowship of the Fonds der Chemischen Industrie, 2015–2017
  • Best Poster Presentation, Swiss Chemical Society Meeting, Fall 2016
  • Master’s degree with distinction, 2015

Talks

  • IBM Research, Zurich (virtual), January 2021
    Reinforcement Learning for Molecular Design Guided by Quantum Chemistry
  • Bjørk Hammer’s Research Group, Aarhus (virtual), December 2020
    Reinforcement Learning for 3D Molecular Design Guided by Quantum Chemistry
  • Machine Learning and AI in (Bio)chemical Engineering, Cambridge, July 2020
    Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
  • Deep structures – Joint workshop by Alan Turing Institute and Finnish Center for Artificial Intelligence, Helsinki, December 2019
    GraphDG: A Generative Model for Molecular Distance Geometry
  • Machine Learning and AI in (Bio)chemical Engineering, Cambridge, July 2019
    Machine Learning for Chemical Reaction Networks
  • Microsoft Research Cambridge AI+Pizza, Cambridge, February 2019
    Exploring Chemical Reaction Networks with Gaussian Processes
  • 53rd Symposium on Theoretical Chemistry, Basel, August 2017
    Automated Exploration of Complex Chemical Reaction Networks
  • Competence Center for Computational Chemistry Meeting, IBM Research, January 2017
    Heuristics-Guided Exploration of Reaction Mechanisms

Skills

  • Mentoring: Supervised two master’s students during their research projects
  • Teaching: Teaching assistant for several lectures including general chemistry, inorganic chemistry, and computer science, since 2013
  • Programming languages: Python and C++
  • Databases: MongoDB, PostgreSQL
  • Administration of high performance computing facility (2015–2018)

Journal and Conference Reviewing