Surfing

Short Bio

PhD student at University of California, Irvine (UCI), advised by Stephan Mandt. Expert programming knowledge with 10+ years experience across a diverse range of languages and tasks including a long history of successful machine learning projects. Research in the intersection of Computer Science and Mathematics, currently focused on Deep Generative Models, Neural Data Compression, and the application of Machine Learning to Climate Science.

  • October 2020 - December 2022
    Research Assistant, TU Kaiserslautern1, Machine Learning Group
    Conducted various research of use in chemical process engineering and beyond. Developed a new tensor completion framework to make predictions for sparse tabular data and style-transfer methods for time series. Ongoing collaboration, including as invited speaker at a Dagstuhl seminar.
  • October 2019 - May 2020
    Research Assistant, German Research Center for Artificial Intelligence (DFKI)
    Developed an evolutionary algorithm to optimize the topology and hyperparameters of convolutional networks. Designed a front and back end providing 50+ users intuitive access to the local GPU computation cluster.
  • September 2018 - Current
    Student / Teaching Assistant, TU Kaiserslautern1 / UC Irvine
    Supported 1000+ students across 10+ courses in various roles as supervisor, mentor, advisor, educator, and examiner. Topics include probability theory, statistics, scientific computing, programming, machine learning, and more.
  • 1 Since 2023: RPTU in Kaiserslautern
  • Python
    Expert [7+ years] − Machine Learning Research, Data Analysis, Visualization, Hackathons, Coding Competitions, Tool and Application Development, Educator. PyTorch, TensorFlow, Sklearn, Django, etc.
  • R
    Expert [3+ years] − Statistical Analysis, Data Analysis, Educator.
  • MATLAB
    Expert [3+ years] − Optimization, Numerical Methods, Scientific Computing, Educator.
  • Java
    Expert [5+ years] − App and Game Development, Distributed Computing, Algorithm Design, Educator.
  • C / C++
    Advanced [2+ years] − Software Development, Algorithms and Data Structures.
  • HTML / CSS / JavaScript
    Advanced [2+ years] − Full-Stack Development with Python/Django Back-End.
  • Others
    Git, CUDA, Slurm, Docker, Kubernetes, SQL, Google/Microsoft Office Suite, VBA, Latex, UML, etc.
  • German
    Fluent
  • English
    Fluent
  • French
    B2 / Advanced Mid − 7+ years of instruction, student exchange with Ermont, France.
  • Spanish
    A2 / Intermediate High − 2+ years of instruction.
  • Swedish
    A2 / Intermediate Mid − 1+ years of instruction, semester abroad at Lund University, Sweden.
  • Sports
    Snowboarding, Surfing, Volleyball, Football
  • Culture
    Travel, Cooking, Winemaking

Research Interests

Networks

Deep Generative Models

such as Variational Autoencoders, Diffusion Models, their applications, and how to improve their inference.

Save-file

Neural Data Compression

beats traditional codecs in compression rate. I aim to make them more efficient, for example through fast Reverse Channel Coding.

Science

Machine Learning
and Science

such as Chemical Process Engineering and Climate Science, in particular Climate Modeling and Cloud Microphysics.

Publications

    • NeurIPS
    • Conference
    • Best Paper
    ClimSim: A Large Multi-Scale Dataset For Hybrid Physics-ML Climate Emulation
    Sungduk Yu, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus Will et al.
    Neural Information Processing Systems 2023 (Outstanding Paper Award; top 0.05% of submissions)
  2. Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring.
    • NeurIPS
    • Workshop
    Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds
    Justus Will, Andrea Jenney, Kara Lamb, Michael Pritchard et al. and Stephan Mandt
    Machine Learning and the Physical Sciences Workshop - Neural Information Processing Systems 2023
  4. Thorough analysis of local droplet-level interactions is crucial to better understand the microphysical processes in clouds and their effect on the global climate. High-accuracy simulations of relevant droplet size distributions from Large Eddy Simulations (LES) of bin microphysics challenge current analysis techniques due to their high dimensionality involving three spatial dimensions, time, and a continuous range of droplet sizes. Utilizing the compact latent representations from Variational Autoencoders (VAEs), we produce novel and intuitive visualizations for the organization of droplet sizes and their evolution over time beyond what is possible with clustering techniques. This greatly improves interpretation and allows us to examine aerosol-cloud interactions by contrasting simulations with different aerosol concentrations. We find that the evolution of the droplet spectrum is similar across aerosol levels but occurs at different paces. This similarity suggests that precipitation initiation processes are alike despite variations in onset times.

Selected Projects

Project 1

Time Series Style Transfer

In applications to time series, style transfer can enhance realism in simulation data, improving performance on downstream tasks when high-quality training data is limited. Our transformer-based approach disentangles content and style in the latent space of a stylized autoencoder enabling fast and effective stylization. CODE MORE
Project 2

Detecting Faulty Concrete

Non-intrusive early detection of cracks in concrete is a crucial task in construction and maintenance. In collaboration with the Fraunhofer Institute for Industrial Mathematics (ITWM), we developed a computer vision framework that allows for fast and fully automated localization based on 3D CT scans, focusing on reliability and trustworthiness. CODE MORE
Project 3

Orthogonal Inductive Tensor Completion

Predicting missing entries in sparse tabular data is a key technique, for example in recommender systems. With the assumption that complex effects are dominated by interactions between small sets of explanatory variables, we can make efficient predictions for high-dimensional data from limited observations. CODE MORE
Project 4

ConvNEAT

Hyperparameter tuning of modern neural architectures is essential for final model performance and usually requires expert knowledge. We developed evolutionary algorithms that automatically find and tune viable architectures and demonstrate that they perform on par in image classification tasks. CODE MORE
Project 5

Walk Flow

In this Hackathon project, we developed tools that support effective planning of better pedestrian infrastructure and tourism services. To this end, we leverage machine learning and data analysis techniques to extract actionable insights from a large dataset of pedestrian flow. CODE MORE
Project 6

GPU Allocation UI

Based on Kubernetes and Docker, this repository provides users an intuitive graphical user interface to allocate, run, and prototype on the local GPU cluster. The web-based UI is fully integrated and accessible through the appropriate intranet endpoints. CODE MORE
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