$$ \newcommand{\dint}{\mathrm{d}} \newcommand{\vphi}{\boldsymbol{\phi}} \newcommand{\vpi}{\boldsymbol{\pi}} \newcommand{\vpsi}{\boldsymbol{\psi}} \newcommand{\vomg}{\boldsymbol{\omega}} \newcommand{\vsigma}{\boldsymbol{\sigma}} \newcommand{\vzeta}{\boldsymbol{\zeta}} \renewcommand{\vx}{\mathbf{x}} \renewcommand{\vy}{\mathbf{y}} \renewcommand{\vz}{\mathbf{z}} \renewcommand{\vh}{\mathbf{h}} \renewcommand{\b}{\mathbf} \renewcommand{\vec}{\mathrm{vec}} \newcommand{\vecemph}{\mathrm{vec}} \newcommand{\mvn}{\mathcal{MN}} \newcommand{\G}{\mathcal{G}} \newcommand{\M}{\mathcal{M}} \newcommand{\N}{\mathcal{N}} \newcommand{\S}{\mathcal{S}} \newcommand{\I}{\mathcal{I}} \newcommand{\diag}[1]{\mathrm{diag}(#1)} \newcommand{\diagemph}[1]{\mathrm{diag}(#1)} \newcommand{\tr}[1]{\text{tr}(#1)} \renewcommand{\C}{\mathbb{C}} \renewcommand{\R}{\mathbb{R}} \renewcommand{\E}{\mathbb{E}} \newcommand{\D}{\mathcal{D}} \newcommand{\inner}[1]{\langle #1 \rangle} \newcommand{\innerbig}[1]{\left \langle #1 \right \rangle} \newcommand{\abs}[1]{\lvert #1 \rvert} \newcommand{\norm}[1]{\lVert #1 \rVert} \newcommand{\two}{\mathrm{II}} \newcommand{\GL}{\mathrm{GL}} \newcommand{\Id}{\mathrm{Id}} \newcommand{\grad}[1]{\mathrm{grad} \, #1} \newcommand{\gradat}[2]{\mathrm{grad} \, #1 \, \vert_{#2}} \newcommand{\Hess}[1]{\mathrm{Hess} \, #1} \newcommand{\T}{\text{T}} \newcommand{\dim}[1]{\mathrm{dim} \, #1} \newcommand{\partder}[2]{\frac{\partial #1}{\partial #2}} \newcommand{\rank}[1]{\mathrm{rank} \, #1} \newcommand{\inv}1 \newcommand{\map}{\text{MAP}} \newcommand{\L}{\mathcal{L}} \DeclareMathOperator*{\argmax}{arg\,max} \DeclareMathOperator*{\argmin}{arg\,min} $$

Agustinus Kristiadi

Postdoc at the Vector Institute, Toronto

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 sciences such as chemistry. His work has been recognized in the form of best PhD thesis award and multiple spotlight papers from top machine learning conferences. His contributions to the scientific society include mentoring underrepresented students in Canada under the IBET PhD Project and actively contributing to open-source software.

Email

agustinus[at]kristia.de

Google Scholar

https://scholar.google.com/citations?user=_1qe2mYAAAAJ&hl=en

LinkedIn

https://www.linkedin.com/in/agustinus-kristiadi/

Github

https://github.com/wiseodd

Twitter

https://twitter.com/akristiadi7

Interests

Probabilistic inference

Decision-making under uncertainty

Foundation models

AI for science

Experience

Vector Institute, Toronto

Postdoctoral Fellow, 2023-

With Alan Aspuru-Guzik and Pascal Poupart

University of Tübingen

Research Assistant, 2019-2023

With Philipp Hennig and Matthias Hein

University of Bonn

Part-Time Research Assistant, 2017-2019

With Asja Fischer and Jens Lehmann

Universitas Atma Jaya Yogyakarta

Research Assistant, 2016-2017

Education

University of Tübingen, Germany

Ph.D., Computer Science, 2019-2023

Topic: Bayesian Deep Learning

Thesis: Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence

Advisors: Philipp Hennig and Matthias Hein

Grade: Magna Cum Laude

University of Bonn, Germany

M.Sc., Computer Science, 2017-2019

Focus: Theoretical Computer Science, Intelligent Systems

Thesis: Predictive Uncertainty Quantification with Compound Density Networks

Advisors: Asja Fischer and Jens Lehmann

Grade: 1.1 (3.9 GPA equivalent)

Universitas Atma Jaya Yogyakarta, Indonesia

B.Eng., Informatics Engineering, 2009-2013

Focus: Numerics, Soft Computing

Thesis: Parallel Particle Swarm Optimization for Image Segmentation

GPA: 3.9

Awards

Best PhD thesis award from DFG's Theoretical Foundations of Deep Learning program

Spotlight paper at NeurIPS 2023 (top 4%)

Spotlight paper at NeurIPS 2021 (top 3%)

Long-talk paper at UAI 2021 (top 6%)

Best reviewer at ICML 2021 (top 10%)