I am a full-stack data scientist with a strong foundation in software engineering and experience building end-to-end data and AI products. I earned my PhD from TU/e, where my research focused on optimizing notification delivery strategies to improve user experience using artificial intelligence and large-scale software systems.

I hold both a bachelor’s and a master’s degree in computer software engineering, with my master’s specializing in data science.

My industry experience spans the full product lifecycle, from early ideation and research to production systems used in real-world environments. I have worked across roles, including full-stack developer, software engineer, data engineer, machine learning engineer, data scientist, team lead, and CTO, in organizations ranging from startups and scale-ups to large multinational companies.

I am deeply passionate about research and development, exploring new ideas, and translating them into practical solutions. My research and applied work has focused on several machine learning areas, including predictive modeling, graph mining, causal inference, and multivariate time-series analysis.

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Work Experience

Alstom, Ridderkerk, Netherlands

As a full-stack data scientist at Alstom, I apply machine learning, big data analytics, and software engineering techniques to create, develop, and maintain smart data-driven solutions, drive customer value, and satisfy the requirements of our customers at the fleet support center.

I lead our machine learning and data-driven products focused on:

• Predictive maintenance
• Preventive maintenance
• Corrective maintenance
• Scalable analytical platform for fleet monitoring

GameBus, Eindhoven, Netherlands

During my PhD at Eindhoven University of Technology (TU/e), I was an active member of the GameBus team, an open-access GDPR-oriented gamified mHealth platform that also acts as a data repository.

• As a PhD candidate with software, data & ML engineering skills within our team of 5-10 (PhDs, researchers, student assistants, and developers) we continuously designed and developed new enhancements to GameBus both at the feature and architecture level.
• Led the ETL & design and development of data streams from smartwatches for continuous data collection and integration with data visualization solutions. I also composed advanced queries to visualize derived metrics from complex physiological data structures.
• Upgraded the infrastructure’s underlying data model for designing surveys and managing external data-providing devices such as smartwatches.
• Led the design and development of an alerting mechanism using a dynamic interoperable pseudo-programming language that accumulates/processes data streams and generates notifications based on stakeholder-defined rules.
• Established our independent docker registry to facilitate our containerized microservice architecture.

Yekom Consulting Engineers, Tehran, Iran

Yekom is a leading multidisciplinary consultancy private firm with 50 years of experience, 400 staff, and about 1000 local and international projects completed so far. Yekom is among the top 5 Iranian consultancy firms in irrigation, water, and environments. I designed, implemented, and maintained:

• Intranet portal
• Warehouse management
• Quality control document management
• And, payroll management

applications (also including ETL, and data engineering) while at Yekom alongside a technical team of 5.

I also supervised interns and led the onboarding and training of our software developers.

Nullatech AI

Nullatech was a SaaS provider based on big data analysis and machine learning algorithms. We provided analytic and prediction services on massive financial data, customer behavior, fraud detection, and social media mining.

I was heavily involved in designing and developing our algorithm and leading our corresponding team of 5-10 (including data engineers, data scientists, and back-end and front-end developers). Besides, I led the technical aspect of work including data and ML engineering (ETL, AutoML, MLOps, DevOps).

TechCrunch Battlefield

We were invited as a top-pick FinTech startup to present our work at the venue in Berlin, Germany in 2018.

TechStars Accelerator

We were qualified for the TechStars accelerator startup acceleration program, in London, UK, 2018.

IFIA Award

International Federation of Inventors’ Associations (IFIA) Award for novel AI algorithm design and innovative solution to financial market prediction, Tehran, Iran, 2017.

Startup Istanbul

Nullatech reached the top 15 finalist startups among 25000 teams from +135 countries, Istanbul, Turkey, 2017.

Entrepreneur In Residence

Our journey started at the Tivan Entrepreneurship Club, then we moved to Samsung AUT Tech, and lastly to Zavié where our IP ultimately merged with another company and the founding team of Nullatech AI exited.

Freelancer

Designed and implemented various software applications, both individually and within teams of 2-10 developers, including:

• 5 commercial mobile applications,
• 10 e-commerce, company, and personal websites,
• 10 enterprise Windows desktop applications,
• Server configuration and administration of most such projects some with >10k daily active users.
• Advised startups and business owners to build or expand their software development teams.

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Education

Design with Data and AI for Optimized Experience Sampling

Eindhoven University of Technology (TU/e)

During my PhD, as a member of the STRAP (Self TRAcking for Prevention and diagnosis of heart disease, an NWO-funded project) consortium I offer my data science and software engineering expertise, and I have been doing research about data-driven and smart adaptive systems that improve acceptance, adherence, and quality of data collection, especially in the context of the Experience Sampling Method. Accordingly, I have done numerous user studies with participants at TU/e and in collaboration with institutions such as Utrecht University, GGz Centraal, and Wageningen University.

You can read my dissertation titled “From simulation to reality and back again: A Hybrid Approach to Optimize the Compliance and Tailor for Validity of Responses in ESM Studies” below:

You can also watch my doctoral defense:

FOSS contributions

During my PhD, I have created the following open-source projects:

Master’s Student Supervision

I have closely supervised 3 master’s students Lars Giling, Sven Bormans, and Catarina Dias de Oliveira who worked on “data-enabled design for cardiac telemonitoring”, “adaptive, context-sensitive smartwatch user interfaces”, and “human digital twins for diabetes management” topics respectively. Below you can find our related publications:

• Lars Giling
  • A Case Study of Data-Enabled Design for Cardiac Telemonitoring | Proceedings of the European Conference on Cognitive Ergonomics 2023 (acm.org)
• Sven Bormans & Kang Ling
  • Comparative Evaluation of Touch-Based Input Techniques for Experience Sampling on Smartwatches (acm.org)
• Catarina Dias de Oliveira
  • Exploring the Feasibility of Data-Driven Emotion Modeling for Human Digital Twins | Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments (acm.org)

Publications

Below you can find my core publications related to my PhD research:

• Experiencer: An Open-Source Context-Sensitive Wearable Experience Sampling Tool | SpringerLink
• Simulating Participant Behavior in Experience Sampling Method Research | Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (acm.org)
• Evaluation of Code Generation for Simulating Participant Behavior in Experience Sampling Method by Iterative In-Context Learning of a Large Language Model | Proceedings of the ACM on Human-Computer Interaction
• IJERPH | Free Full-Text | Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn Devices (mdpi.com)

For the full list of my publications please visit my Google Scholar page.

Data Science

Iran University of Science and Technology (IUST)

The title of my master’s thesis is “Action Extraction from Social Network Graphs Using Causal Structures to Increase Applicability of Extracted Actions”. You can find its cover page and abstract here.

Teaching Assistant

During my master’s, I was an active member of the data engineering lab and I taught “Advanced databases” and “Search engines and web mining” for 2 consecutive semesters.

Publications

Below you can find my publications w.r.t my research during my Mater’s program:

• Actionable knowledge discovery from social networks using causal structures of structural features – IOS Press
• Extracting Actionable Knowledge From Social Networks Using Structural Features | IEEE Journals & Magazine | IEEE Xplore

For the full list of my publications please visit my Google Scholar page.

Computer Software Engineering

Urmia University

My bachelor end project title is “Location-based Cross-platform Search Engine Application for Local Businesses and Consumers”. You can find its cover page and abstract here.

Certificate of Appreciation

for devoted work at the 7th International Conference on Information and Knowledge Technology, Urmia University, May 26-28, 2015.