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Research Engineer at DocuWare AI Hub | PhD (NLP)
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Applied ML/NLP researcher and engineer with 10+ years of industrial and academic R&D experience working on real-world applications in several domains, including healthcare, education, legal, and e-commerce. The primary focus has been on information extraction and text mining, especially in limited data regimes requiring transfer learning, active learning, few-shot, and self-supervised methods.
Universität des Saarlandes
Doctor of Philosophy - PhD, Natural Language Processing
April 1, 2019 – June 1, 2024
National University of Sciences and Technology (NUST)
Bachelor’s Degree, Electrical Engineering
September 1, 2011 – June 1, 2015
DocuWare
Research Engineer
July 1, 2025 – Present
Saarbrücken · On-site
natif.ai
Research Engineer
December 1, 2022 – June 1, 2025
Saarbrücken · On-site
Saarland University
Researcher
June 1, 2022 – November 1, 2022
Saarbrücken, Saarland, Germany · On-site
Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)
Researcher
December 1, 2021 – November 1, 2022
Saarbrücken, Saarland, Germany · On-site
Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)
Junior Researcher
April 1, 2019 – November 1, 2021
Saarbrücken, Saarland, Germany · On-site
Lightscope Spend Analytics by Oxford Informatics
NLP Engineer
October 1, 2018 – March 1, 2019
Brighton, England, United Kingdom · Remote
EDT Software - Discovery Simplified.
Machine Learning Engineer
August 1, 2017 – March 1, 2019
Paddington, Queensland, Australia · Remote
Sellomni Ltd.
Technology Lead
June 1, 2016 – October 1, 2016
Singapore, Singapore · Remote
SeeAlgo
NLP Intern
February 1, 2016 – May 1, 2016
Bothell, Washington, United States · Remote
Fiverr
Machine Learning Engineer
February 1, 2016 – December 1, 2017
https://www.fiverr.com/perfectionin5 · Remote
NUST School of Electrical Engineering & Computer Science
Research Assistant
August 1, 2015 – December 1, 2015
Islamabad, Islāmābād, Pakistan · On-site
Towards Understanding the Role of Graph Structures in Medical Codes Classification
March 1, 2023 – December 1, 2023
M.Sc. thesis Supervisor for Noon Pokaratsiri Goldstein at the Language Science and Technology Department, Saarland University.
DaTa-Pin: Digital Teaching Plug-in
May 1, 2022 – November 1, 2022
As part of the Data-Pin projects, AIden is an AI chatbot assistant for the education domain deployed in Saarland University lectures. (Funding) Stiftung Innovationen in der Hochschullehre
ELRC: European Language Resource Coordination
May 1, 2022 – November 1, 2022
ELRC collects multilingual language data from the EU. Here the goal is to extract pre-training data for Large Language Models (LLM) by mining the web for the LEAM initiative (https://leam.ai). (Funding) European Union (EU) funding code SMART 2019/1083
Low-rank Tensor Completion for Temporal Knowledge Graphs
September 1, 2021 – February 1, 2022
M.Sc. thesis Supervisor for Ioannis Dikeoulias at the Computer Science Department, Saarland University.
CoRA4NLP: Contextual Reasoning and Adaptation for Natural Language Processing
January 1, 2021 – May 1, 2022
CoRA4NLP goal is to develop natural language understanding methods that enable: * Reasoning over broader co- and contexts * Efficient adaptation to novel and/or low resource contexts * Continual adaptation to, and generalization over, evolving contexts (Funding) German Federal Ministry of Education and Research (BMBF) [01IW20010]
Precise4Q: Personalised Medicine by Predictive Modelling in Stroke for better Quality of Life
April 1, 2019 – April 1, 2022
Precise4Q aims to minimize the burden of stroke for the individual and society with personalized data-driven stroke treatment. As part of the consortium, DFKI aims to provide medical information extraction and text mining language technology. (Funding) European Union (EU) Horizon 2020 programme [777107]
DEEPLEE: Deep Learning for end to end Applications in Language Technology
April 1, 2019 – September 1, 2020
Deep Learning and Language Technology project focusing in the following areas: * Modularity in architectures of deep neural networks * Use of external knowledge * Deep neural networks with explanation functionality * Machine teaching strategies for deep neural networks (Funding) German Federal Ministry of Education and Research (BMBF) [01IW17001]
Human-in-the-loop Active Learning for Augmented Review in eDiscovery
March 1, 2018 – March 1, 2019
HITL active learning for eDiscovery aims at identifying relevant documents from a large pool of documents in a continuous manner with a lawyer and a machine learning model. The project goals include: * Document representation * Active learning with uncertainty sampling * Post hoc similarity analysis * Prevalence estimation and sampling
Automatic Seizure Detection
August 1, 2015 – December 1, 2015
The project aims at a machine learning approach for automatically detecting seizure onset for better care and management. A random under-sampling with AdaBoost.M2 (RUSBoost) is investigated here for mitigating class imbalance.
Machine Learning: Regression
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning Summer School
National Taiwan University
June 24, 2026 – Present
Statistical Learning
Stanford Online
June 24, 2026 – Present
Machine Learning Foundations: A Case Study Approach
Coursera Course Certificates
June 24, 2026 – Present
Introduction to R Programming
edX
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background in academic research and industry roles, particularly within European research centers and startups, suggests a strong fit for environments that value innovation, continuous learning, and collaborative problem-solving. Their involvement in funded projects and supervision roles indicates a capacity for independent work and mentorship. The diversity of projects, from AI chatbots for education to eDiscovery and medical text mining, demonstrates a broad interest in applying ML to real-world challenges, which aligns well with a dynamic, impact-driven culture.
Soft Skills & Operational Fit
The candidate's extensive project and research experience, including supervising Master's theses and leading a small team, suggests strong collaboration and leadership potential. Their involvement in diverse projects across various domains (education, legal, medical, finance) indicates adaptability and a proactive approach to problem-solving. The descriptions imply a strong work ethic and ability to manage complex, long-term initiatives.