Deepak Pathak
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Hi, I'm

Deepak Pathak

Machine Learning Engineer & AI Researcher at DFKI

Machine Learning Engineer and AI Researcher at DFKI working on multimodal ML, earth observation, privacy auditing, and production ML systems.

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About

I work across ML research and production engineering, with a focus on systems that need to be technically sound and operationally reliable.

At DFKI, my work includes multimodal crop-yield prediction from satellite, weather, and geospatial data; privacy auditing for ML models and LLMs in regulated settings; and MLOps infrastructure for reproducible, deployable workflows.

The work has led to publications in remote sensing and machine learning venues, along with production pipelines used with partners in Germany. Before DFKI, I spent four years at IBM building distributed telecom systems, and that engineering background still shapes how I approach projects.

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Experience

AI Researcher

DFKI — SmartCut

Coordinate DFKI-side execution for SmartCut, working with project partners while building Kedro pipelines for harvester-data harmonization and Earth Observation acquisition. Develop Apache Airflow DAGs for raw-data monitoring and automated pipeline execution for field operations.

AI Researcher

DFKI — MissionKI

Developed privacy auditing workflows for classification models and LLMs using membership inference and model inversion evaluations. Automated MLflow-based testing and reporting for healthcare AI compliance, and supported domain-specific LLM fine-tuning and privacy evaluation.

AI Researcher

DFKI — Yield Consortium

Developed multimodal crop-yield prediction systems across satellite, weather, soil, and geospatial features. Built preprocessing pipelines, Dash dashboards, and MLflow/GitLab CI/CD/Docker workflows used with partners in Germany.

Machine Learning Research Intern

Miele & Cie. KG

Built deep metric, contrastive, and self-supervised vision models for fine-grained classification, using PyTorch Lightning and Azure Databricks for scalable training.

Student Research Assistant

virtUOS, University of Osnabrück

Contributed to the SIDDATA digital assistant by supporting backend infrastructure and neural recommender integrations.

Working Student — Software Developer

Aitech Concept UG

Implemented TensorFlow object detection models and Django applications for real-time tracking from surveillance feeds.

Application Developer

IBM

Developed distributed Java EE services in a telecom SOA environment and built Oracle BPM workflows deployed on Oracle WebLogic Server. Also implemented Spring Boot and Twilio automation for incident flagging to speed operational response.

Education

M.Sc., Cognitive Science

Osnabrück University

Specialization in machine learning, computer vision, and data science.

B.Tech, Electronics Engineering

HBTI Kanpur (UPTU)

Technical Skills

Frameworks, platforms, and methods I use most often

Machine Learning

PyTorch
expert
Hugging Face
advanced
TensorFlow
intermediate
Multimodal Learning
expert
LLM Fine-tuning
advanced

MLOps & Pipelines

Python
expert
Kedro
expert
MLflow
expert
Apache Airflow
advanced
Docker
advanced

Cloud & DevOps

GitLab CI/CD
advanced
AWS SageMaker
advanced
Azure Databricks
intermediate
Linux
advanced
SQL
advanced

Remote Sensing & Trustworthy AI

Sentinel-2
expert
Earth Observation
expert
Computer Vision
advanced
Privacy Auditing
advanced
Selected Publications
M. Miranda, D. Pathak, P. Helber, B. Bischke, H. Najjar, F. Mena, C. Sanchez, A. Pai, D. Arenas, M. Valdenegro Toro, M. Charfuelan, M. Nuske, A. Dengel (2026). YieldSAT: A multimodal benchmark dataset for high-resolution crop yield prediction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026.
Project DOI
F. Mena, D. Pathak, H. Najjar, C. Sanchez, P. Helber, B. Bischke, P. Habelitz, M. Miranda, J. Siddamsetty, M. Nuske, M. Charfuelan, D. Arenas, M. Vollmer, A. Dengel (2025). Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction. Remote Sensing of Environment, Vol. 318, 2025.
DOI
H. Najjar, D. Pathak, M. Nuske, A. Dengel (2025). Intrinsic explainability of multimodal learning for crop yield simulation. Computers and Electronics in Agriculture, Vol. 239, 2025.
DOI
M. Miranda, D. Pathak, M. Nuske, A. Dengel (2024). Multi-modal fusion methods with local neighborhood information for crop yield prediction at field and subfield levels. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2024.
DOI
C. Sanchez, D. Pathak, M. Miranda, M. Charfuelan, P. Helber, M. Nuske, B. Bischke, P. Habelitz, N. Rahman, F. Mena, H. Najjar, J. Siddamsetty, D. Arenas, M. Vollmer, A. Dengel (2023). Influence of data cleaning techniques on sub-field yield predictions. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2023.
DOI
D. Pathak, M. Miranda, F. Mena, C. Sanchez, P. Helber, B. Bischke, P. Habelitz, H. Najjar, J. Siddamsetty, D. Arenas, M. Vollmer, M. Charfuelan, M. Nuske, A. Dengel (2023). Predicting crop yield with machine learning: An extensive analysis of input modalities and models on a field and sub-field level. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2023.
DOI

Contact

Contact and profiles

Based in

DFKI GmbH

Kaiserslautern, Germany

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Reach me

Email

deepak_kumar.pathak@dfki.de

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