CV

A PDF version of my CV is available here. My papers are listed with abstracts on the publications page, and slides and recordings on the talks page.

Education

• Ph.D. in Computer Science, Eindhoven University of Technology (TU/e), Nov 2021 – Dec 2025
  • Thesis: Effective and Efficient Multivariate Similarity Search
  • Supervisors: dr. Odysseas Papapetrou and prof. dr. George Fletcher
• M.Sc. in Data Science and Artificial Intelligence, TU/e, 2019 – 2021 — Cum Laude (GPA 9.1/10)
• B.Sc. in Industrial Engineering, TU/e, 2016 – 2019 — Cum Laude (GPA 8.5/10)

Work experience

• Dec 2025 – present: Postdoctoral Scientist
  • Barcelona Supercomputing Center (BSC), Earth Sciences Department
  • Developing generative AI methods for downscaling atmospheric chemistry simulations — diffusion models, flow-matching, and physics-constrained CNNs with hard constraints such as mass conservation
• Nov 2021 – Dec 2025: Doctoral Researcher
  • Eindhoven University of Technology, Data & AI cluster
  • Scalable multivariate time-series similarity search algorithms; published at VLDB, VLDB Journal, SIGMOD, and ICDE
  • Led work in the EU-funded STELAR project on ML pipelines for agricultural field delineation from satellite imagery
• Dec 2019 – Nov 2021: Freelance Software Engineer
  • Data-driven applications and statistical analyses for clients (Python, Spark, Kafka, TensorFlow, AWS, Angular)
• Jul 2020 – Dec 2020: Data Science Intern
  • BMW Group, Advanced Analytics team (Powertrain), Munich
  • Cloud data infrastructure (AWS, Spark) processing ~150 TB/day; ML-based root-cause analysis of a battery safety issue
• Oct 2018 – Dec 2019: Data Scientist (part-time)
  • Crossyn Automotive, Tilburg
  • Streaming services for real-time processing of automotive sensor data (Kafka, Docker, PostgreSQL, ClickHouse)

Grants

• SURF Compute Call 2025 — Small Compute Grant on Snellius (Dutch national supercomputer), grant no. EINF-13076

Skills

• Deep learning: PyTorch, TensorFlow, diffusion models, flow-matching, physics-informed neural networks, distributed/multi-GPU training
• HPC & infrastructure: SLURM (MareNostrum 5, LUMI, Snellius), Kubernetes, Spark, Kafka, AWS, Docker, MLflow, Weights & Biases
• Programming: Python, SQL, Java, Scala, C++, R

Publications

Neural downscaling of air-quality simulations requires structural correction before spatial refinement

d'Hondt, J., Petetin, H., Pérez García-Pando, C., Jorba, O., Guevara, M. Neural downscaling of air-quality simulations requires structural correction before spatial refinement. Preprint, Research Square (2026), under review at Nature Communications.

Physics-Constrained Deep Learning for Downscaling Atmospheric Chemistry Simulations: The Role of Auxiliary Forcings and Model Architecture

d'Hondt, J. E. and Petetin, H.: Physics-Constrained Deep Learning for Downscaling Atmospheric Chemistry Simulations: The Role of Auxiliary Forcings and Model Architecture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9829.

MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance

Jens E. d'Hondt, Teun Kortekaas, Odysseas Papapetrou, and Themis Palpanas. 2025. MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance. Proc. VLDB Endow. 19, 2, 99–112.

Generative Correlation Manifolds: Generating Synthetic Data with Preserved Higher-Order Correlations

d'Hondt, J.E., Punter, W.R., Papapetrou, O. (2025). Generative Correlation Manifolds: Generating Synthetic Data with Preserved Higher-Order Correlations. arXiv preprint arXiv:2510.21610.

A Structured Study of Multivariate Time-Series Distance Measures

Jens E. d'Hondt, Haojun Li, Fan Yang, Odysseas Papapetrou, and John Paparrizos. 2025. A Structured Study of Multivariate Time-Series Distance Measures. Proc. ACM Manag. Data 3, 3 (SIGMOD), 1–29.

MULISSE: Variable-Length Similarity Search for Multivariate Time Series

Bart Pelok and Jens E. d'Hondt. 2025. MULISSE: Variable-Length Similarity Search for Multivariate Time Series. In Proceedings of the MulTiSA Workshop at the 41st IEEE International Conference on Data Engineering (ICDE 2025), Hong Kong.

A Survey on Time-Series Distance Measures

Paparrizos, J., Li, H., Yang, F., Wu, K., d'Hondt, J.E., Papapetrou, O. (2024). A Survey on Time-Series Distance Measures. arXiv preprint arXiv:2412.20574.

Multivariate Similarity Search - A Call for a New Breed of Similarity Search Algorithms

O. Papapetrou and J. E. d'Hondt, "Multivariate Similarity Search - A Call for a New Breed of Similarity Search Algorithms," 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 2024, pp. 5662-5662.

Beyond the Dimensions: A Structured Evaluation of Multivariate Time Series Distance Measures

J. E. d'Hondt, O. Papapetrou and J. Paparrizos, "Beyond the Dimensions: A Structured Evaluation of Multivariate Time Series Distance Measures," 2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW), Utrecht, Netherlands, 2024, pp. 107–112.

Efficient detection of multivariate correlations with different correlation measures

d'Hondt, J.E., Minartz, K. & Papapetrou, O. Efficient detection of multivariate correlations with different correlation measures. The VLDB Journal 33, 481–505 (2024).

Multivariate correlations discovery in static and streaming data

Koen Minartz, Jens E. d'Hondt, and Odysseas Papapetrou. 2022. Multivariate correlations discovery in static and streaming data. Proc. VLDB Endow. 15, 6 (February 2022), 1266–1278.

Evaluation of computer-tailored motivational messaging in a health promotion context

d'Hondt, J.E., Nuijten, R.C.Y., Van Gorp, P.M.E. (2019). Evaluation of Computer-Tailored Motivational Messaging in a Health Promotion Context. In: Bella, G., Bouquet, P. (eds) Modeling and Using Context. CONTEXT 2019. LNCS 11939, pp. 120–133.

Talks

MulTiSA 2024 - Evaluating Multivariate Distance Measures

May 13, 2024

Talk at MulTiSA workshop at the International Conference on Data Engineering (ICDE), Utrecht, The Netherlands

DSDSD 2023 - Multivariate Correlation Analysis

March 24, 2023

Talk at Dutch Seminar on Data Systems Design (DSDSD), Online

VLDB 2022 - Correlation Detective

September 09, 2022

Talk at VLDB 2022, Sydney, Australia

CONTEXT 2019 - Automated Health Nudges

November 17, 2019

Talk at International Conference on Modeling using Context (CONTEXT 2019), Trento, Italy

Eindhoven Data Science Summit 2019 - Automated Health Nudges

November 12, 2019

Talk at Data Science Summit 2019, Eindhoven, The Netherlands

Academic service

• Reviewer: VLDB 2026 (workshops), VLDB Journal, SIGMOD 2026, MulTiSA 2025 (ICDE workshop), Data Mining and Knowledge Discovery, Journal for Big Data Research
• Publication chair, MulTiSA Workshop on Multivariate Time Series Analysis, ICDE 2024 & 2025
• Co-lecturer, “Big Data Management” (TU/e); supervisor to 8 Master’s students