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Agustinus Kristiadi

About

Agustinus Kristiadi is a postdoctoral fellow at the Vector Institute, working primarily with Alán Aspuru-Guzik and Pascal Poupart. He obtained his PhD from the University of Tuebingen in Germany, advised by Philipp Hennig and Matthias Hein. His research interests are in probabilistic inference with large-scale neural networks, decision-making under uncertainty, and their applications in broader scientific fields such as chemistry. His work has been recognized in the form of best PhD thesis award and multiple spotlight papers from flagship machine learning conferences. His contributions to the scientific society includementoring underrepresented students in Canada under the IBET PhD Project and actively contributing to the open-source community.

Interests

Experience

Vector Institute

Postdoctoral Fellow, Feb 2023 -

With Alán Aspuru-Guzik and Pascal Poupart.

University of Tuebingen

PhD, Computer Science, Jun 2019 - Jan 2023

With Philipp Hennig and Matthias Hein.

University of Bonn

MSc, Computer Science, Apr 2017 - Apr 2019

With Asja Fischer and Jens Lehmann.

GDP Venture

Software Engineer, Apr 2013 - Dec 2015

Universitas Atma Jaya Yogyakarta

BEng, Software Engineering, Aug 2009 - Jan 2013

Awards

Best PhD thesis

German Research Foundation's Theoretical Foundations of Deep Learning program, 2023

Spotlight paper (top 4%)

NeurIPS 2023

Spotlight paper (top 3%)

NeurIPS 2021

Long-talk paper (top 6%)

UAI 2021

Best reviewer (top 10%)

ICML 2021

Skills

Languages

Editor Stack

Latest Posts

Selected Works

  • A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
    Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alan Aspuru-Guzik, Geoff Pleiss
  • Uncertainty-Guided Optimization on Large Language Model Search Trees
    Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi
  • The Geometry of Neural Nets' Parameter Spaces Under Reparametrization
    Agustinus Kristiadi, Felix Dangel, Philipp Hennig
    NeurIPS 2023

    Spotlight

    Paper

  • Laplace Redux - Effortless Bayesian Deep Learning
    Erik Daxberger*, Agustinus Kristiadi*, Alexander Immer*, Runa Eschenhagen*, Matthias Bauer, Philipp Hennig
    NeurIPS 2021

    Paper

    Github

  • Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
    Agustinus Kristiadi, Matthias Hein, Philipp Hennig
    ICML 2020

    Paper