Patrick Koller

Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping

Wed, 15 Apr 2026 00:00:00 +0000

Ugur Demir, Philipp M Srivastava, Aggelos K. Katsaggelos, Vicky Kalogera, Santiago L Tapia, Manuel Ballester, Shamal Lalvani, Patrick Koller, Jeff J Andrews, Seth Gossage, Max M Briel, and Elizabeth Teng

BibTeX Citation

@misc{demir2026irregularlysampledtimeseries,
 title={Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping},
 author={Ugur Demir and Philipp M. Srivastava and Aggelos Katsaggelos and Vicky Kalogera and Santiago L. Tapia and Manuel Ballester and Shamal Lalvani and Patrick Koller and Jeff J. Andrews and Seth Gossage and Max M. Briel and Elizabeth Teng},
 year={2026},
 eprint={2604.13604},
 archivePrefix={arXiv},
 primaryClass={astro-ph.SR},
 url={https://arxiv.org/abs/2604.13604},
}

Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks

Mon, 06 Apr 2026 00:00:00 +0000

Manuel Ballester, Santiago Lopez-Tapia, Seth Gossage, Patrick Koller, Philipp M Srivastava, Ugur Demir, Yongseok Jo, Almudena P Marquez, Christoph Würsch, Souvik Chakraborty, Vicky Kalogera, and Aggelos K. Katsaggelos

Abstract

This research introduces a mesh-free approach to solving the fundamental equations of stellar structure using Physics-Informed Neural Networks (PINNs). By enforcing hydrostatic and thermal equilibrium constraints directly within the loss function, we eliminate the need for traditional grid-based discretization used in solvers like MESA.

Our framework incorporates auxiliary neural surrogates for microphysics (opacities and equation of state), allowing the model to learn continuous, differentiable radial profiles across a wide range of stellar masses.

BibTeX Citation

@misc{ballester2026learningstellarstructure,
 title={Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks},
 author={Manuel Ballester and Santiago Lopez-Tapia and Seth Gossage and Patrick Koller and Philipp M. Srivastava and Ugur Demir and Yongseok Jo and Almudena P. Marquez and Christoph Wuersch and Souvik Chakraborty and Vicky Kalogera and Aggelos Katsaggelos},
 year={2026},
 eprint={2604.06255},
 archivePrefix={arXiv},
 primaryClass={astro-ph.SR},
 url={https://arxiv.org/abs/2604.06255},
}

Deep Neural Operators for Detailed Binary Evolution Simulation

Wed, 01 Oct 2025 00:00:00 +0000

Overview

Binary stellar evolution simulations are computationally expensive and form a fundamental component of population synthesis pipelines. This project explores deep neural operators as a way to approximate binary evolution dynamics from initial system parameters such as primary mass, secondary mass, and orbital period.

The goal is to accelerate large-scale astrophysical simulations while preserving the morphology and physical structure of evolutionary tracks. The work was developed during the NVIDIA and NCSA Open Hackathon in collaboration with the POSYDON project and the NSF-Simons AI Institute for the Sky.

The project demonstrated the potential of neural operator models for scalable binary evolution simulation and was selected for presentation at the Open Accelerated Computing Summit 2025.

Size-Informed Representations for Unsupervised Image Clustering

Sun, 14 Sep 2025 00:00:00 +0000

Philipp Rajah Moura Srivastava, Charilaos Apostolidis, Saumya Pailwan, Patrick Koller, and Leandros Stefanopoulos

Accepted and presented at the IEEE ICIP 2025 Satellite Workshop: “Generative AI for World Simulations and Communications & Celebrating 40 Years of Excellence in Education: Honoring Prof. Aggelos Katsaggelos,” Anchorage, Alaska, USA, Sept 14, 2025.

BibTeX Citation

@INPROCEEDINGS{Srivastava_2025_SizeInformedClustering,
 author={Srivastava, Philipp Rajah Moura and Apostolidis, Charilaos and Pailwan, Saumya and Koller, Patrick and Stefanopoulos, Leandros},
 booktitle={2025 IEEE International Conference on Image Processing Workshops (ICIPW)},
 title={Size-Informed Representations for Unsupervised Image Clustering},
 year={2025},
 volume={},
 number={},
 pages={743-748},
 keywords={Visualization;Image coding;Conferences;Semantics;Pipelines;Data models;Image preprocessing;Standards;Unsupervised Image Clustering;SelfSupervised Learning;Size Encoding;Image Preprocessing;Deep Embedded Clustering},
 doi={10.1109/ICIPW68931.2025.11386144}}

Caption-Driven Explainability: Probing CNNs for Bias via CLIP

Mon, 01 Sep 2025 00:00:00 +0000

Patrick Koller, Amil V. Dravid, Prof. Dr. Guido Schuster, and Prof. Dr. Aggelos Katsaggelos

BibTeX Citation

@INPROCEEDINGS{Koller_2025_CaptionXAI,
 author={Koller, Patrick and Dravid, Amil V. and Schuster, Guido M. and Katsaggelos, Aggelos K.},
 booktitle={2025 IEEE International Conference on Image Processing Workshops (ICIPW)},
 title={Caption-Driven Explainability: Probing CNNS for Bias Via Clip},
 year={2025},
 volume={},
 number={},
 pages={663-667},
 keywords={Explainable AI;Computational modeling;Machine vision;Zero shot learning;Surgery;Medical services;Debugging;Predictive models;Robustness;Convolutional neural networks;Multi-Modal Explainability;CLIP;Model Bias Detection;Zero-Shot Learning;Network Surgery},
 doi={10.1109/ICIPW68931.2025.11386015}
}

Trade-that! A quantitative trading engine

Mon, 08 Aug 2022 00:00:00 +0000

Patrick Koller, Florian Merz, and Hannes Badertscher

AI in injection molding

Fri, 18 Feb 2022 00:00:00 +0000

Patrick Koller, Florian Merz, and Hannes Badertscher

Empowering Talent with Powercoders

Thu, 01 Apr 2021 00:00:00 +0000

Powercoders is a nonprofit program that empowers refugees and immigrants by providing intensive training in coding and digital skills, helping them access opportunities in the tech industry.

Through a structured curriculum, participants develop essential programming, web development, and professional skills needed to succeed in a fast-changing digital world. The program combines classroom learning with hands-on projects and often includes internship opportunities, giving participants valuable real-world experience.

As part of my involvement with Powercoders, I had the rewarding opportunity to coach a refugee participant and support their technical and professional development. Through mentorship and guidance, I helped this talent strengthen their skills and successfully secure an internship with one of Switzerland’s largest banks, UBS AG.

Powercoders not only helps close skill gaps but also promotes social integration and economic independence. It creates meaningful pathways for individuals to rebuild their careers while also connecting the tech industry with diverse and motivated talent.