Curriculum Vitae

Education

• University of Illinois Chicago - Chicago, USA (January 2017 - December 2021)
  PhD in Industrial Engineering and Operations Research, Department of Mechanical and Industrial Engineering

• RheinMain University of Applied Sciences - Wiesbaden, Germany (October 2014 - June 2016)
  Master of Engineering in Media & Communications Technology, Department of Engineering Sciences

• RheinMain University of Applied Sciences - Wiesbaden, Germany (October 2010 - September 2014)
  Bachelor of Engineering in Media Technology, Department of Engineering Sciences

• Savonia University of Applied Sciences - Kuopio, Finland (August 2012 - March 2013)
  Exchange Student in Information Technology, Department of Engineering and Technology

Experience

• Deloitte Center for Process Bionics - Berlin, Germany (February 2022 - Present)
  Senior Consultant

• YAS.life - MAGNUM EST Digital Health GmbH - Berlin, Germany (March 2021 - January 2022)
  Data Scientist
  • Person responsible for all data-related requests
  • Conducted statistical experiments and analyses to provide the business development and product teams with valuable insights for decision-making and acceleration of innovation cycles
  • Maintained and enhanced the business intelligence hub for internal and external reporting
  • Integrated new data sources into ETL processes and defined new KPIs to increase the transparency of processes
  • Created data and analysis concepts in cooperation with insurers and partners
• University of Illinois Chicago - Chicago, USA (January 2017 - December 2021)
  Researcher, Process Mining and Intelligent System Analytics Team
  • Developing novel Sequence Generative Adversarial Network methodologies to overcome conformance checking and generalization assessment issues in process model graphs using Tensorflow evaluated on 60 discovered process models and 15 ground truth systems
  • Designing and implementing novel algorithms to predict upcoming events and time properties in running cases using process model graphs and Deep Learning Networks that outperform the SOTA on all benchmark datasets by at least 5% using Keras and PM4Py
  • Applying concepts to practice to improve KPIs in healthcare with a focus on diabetes and COVID-19 patients in cooperation with the U of I Hospital and Fortune 500 companies
  • Overseeing multiple data science interns and undergraduate research students
• University of Illinois Chicago - Chicago, USA (January 2018 - December 2019)
  Teaching Assistant
  • Professional Development Seminar (Spring 2019)
  • Stochastic Processes and Queuing Models (Fall 2018)
  • Ergonomics and Human Factors in Engineering (Fall 2018)
  • Probability and Statistics for Engineers (Spring 2018, Summer 2018)
  • Taught 10+ lessons as a substitute lecturer
• Dimetis GmbH - Dietzenbach, Germany (June 2016 - December 2016)
  Software Engineer
  • Prototypical development and integration of Machine Learning functionalities to manage Software-Defined Networks and Virtual Network Functions in OSS systems
  • Wrote studies on deployability of Machine Learning and Artificial Intelligence in OSS- and network management environments
  • Data visualization of complex telecommunication and broadcast networks using D3.js
  • Supported preparation of trade-fair appearances
• Dimetis GmbH - Dietzenbach, Germany (October 2014 - December 2015)
  Software Engineering Intern
  • Research on applicability of Machine Learning and Deep Learning in virtualized network environments
  • Research on commercial Software-Defined Networks and Network Function Virtualization
  • Installed and deployed laboratory testbeds to evaluate real-world scenarios of virtual networks
  • Integration and development of software modules to manage Software-Defined Networks
• Dimetis GmbH - Dietzenbach, Germany (February 2014 - June 2015)
  IT Engineering Intern
  • Fundamental research on Network OSS, Software-Defined Networking, and Data Analytics
  • Analyzed feasibility of graph-based network fault management module
  • Wrote market investigations on Software-Defined Networking controller

Additional Experience

• Südwestrundfunk - Mainz, Germany (August 2013 - May 2016)
  Sound Assistant
  • Choice and positioning of microphones for live TV productions
  • Preventive maintenance of sound equipment
• Studio Speyer Media Production - Speyer, Germany (June 2008 - February 2014)
  Co-Founder
  • Produced image and product movies, commercials, music clips, and sound recordings
  • Managed international network of audio-visual media designer, media engineers, and marketing professionals

Skills

• Software Engineering: Python, Java, JavaScript, HTML, Matlab
• Software Frameworks: TensorFlow, Keras, PM4Py, PyTorch, D3.JS, Apache Spark, Apache Cassandra, Django
• Knowledge: Process Mining, Deep Learning, Data Science, Machine Learning
• Analysis Tools: ProM, Arena Simulation, Minitab
• Other Technical Skills: Linux, Windows OS, Microsoft Office, LaTeX
• Languages: German (fluent), English (fluent), Finnish (basic), French (basic), Spanish (basic)

Publications

Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments

Julian Theis and Houshang Darabi. (2019). "Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments." Proc. ACM Hum.-Comput. Interact. 3, EICS, Article 13 (June 2019), 16 pages. DOI: https://doi.org/10.1145/3331155

Process Mining of Programmable Logic Controllers: Input/Output Event Logs

J. Theis, I. Mokhtarian and H. Darabi, "Process Mining of Programmable Logic Controllers: Input/Output Event Logs," 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada, 2019, pp. 216-221. doi: 10.1109/COASE.2019.8842900

Decay Replay Mining to Predict Next Process Events

Theis, Julian, and Houshang Darabi. "Decay Replay Mining to Predict Next Process Events." IEEE Access 7 (2019): 119787-119803.

Adversarial System Variant Approximation to Quantify Process Model Generalization

J. Theis and H. Darabi, "Adversarial System Variant Approximation to Quantify Process Model Generalization," in IEEE Access, vol. 8, pp. 194410-194427, 2020, doi: 10.1109/ACCESS.2020.3033450.

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture

J. Theis, W. L. Galanter, A. D. Boyd and H. Darabi, "Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 1, pp. 388-399, Jan. 2022, doi: 10.1109/JBHI.2021.3092969.

Process Mining Model to Predict Mortality in Paralytic Ileus Patients

J. Theis and H. Darabi, "Masking Neural Networks Using Reachability Graphs to Predict Process Events," 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), 2021, pp. 1-6, doi: 10.1109/ICCSI53130.2021.9736237.

On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization

Theis, J., Mokhtarian, I., Darabi, H. (2022). On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization. In: Munoz-Gama, J., Lu, X. (eds) Process Mining Workshops. ICPM 2021. Lecture Notes in Business Information Processing, vol 433. Springer, Cham. https://doi.org/10.1007/978-3-030-98581-3_21

Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models

Galanter, William, Jorge Mario Rodriguez-Fernandez, Kevin Chow, Samuel Harford, Karl M. Kochendorfer, Maryam Pishgar, Julian Theis, John Zulueta, and Houshang Darabi. "Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models." BMC Medical Informatics and Decision Making 21, no. 1 (2021): 1-18.

Masking Neural Networks Using Reachability Graphs to Predict Process Events

J. Theis and H. Darabi, "Masking Neural Networks Using Reachability Graphs to Predict Process Events," 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), 2021, pp. 1-6, doi: 10.1109/ICCSI53130.2021.9736237.

Prediction of Unplanned 30-Day Readmission for ICU Patients with Heart Failure

M. Pishgar, J. Theis, M. Del Rios, A. Ardati, H. Anahideh, and H. Darabi, "Prediction of unplanned 30-day readmission for ICU patients with heart failure," BMC Medical Informatics and Decision Making 22, Article number: 117 (2022). doi: https://doi.org/10.1186/s12911-022-01857-y

A Process Mining- Deep Learning Approach to Predict Survival in a Cohort of Hospitalized COVID‐19 Patients

M. Pishgar, S. Harford, J. Theis, W. Galanter, J. M. Rodríguez-Fernández, L. H Chaisson, Y. Zhang, A. Trotter, K. M. Kochendorfer, A. Boppana, and H. Darabi, "A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients," BMC Medical Informatics and Decision Making 22, Article number: 194 (2022). doi: https://doi.org/10.1186/s12911-022-01934-2

Improving Predictive Process Monitoring Through Reachability Graph-Based Masking of Neural Networks

J. Theis and H. Darabi, "Improving Predictive Process Monitoring Through Reachability Graph-Based Masking of Neural Networks," in IEEE Transactions on Computational Social Systems, 2022, doi: 10.1109/TCSS.2022.3220262.

Academic Projects

• Investigation and Implementation of a Prototype for User Behavior in Large IoT Networks Based on Artificial Intelligence (December 2015 - June 2016)
  Master Thesis, RheinMain University of Applied Sciences via Dimetis GmbH and Viridis Networks Inc.
  • Fundamental research on Machine Learning and Deep Learning to leverage advantages of Software-Defined Networking and Network Function Virtualization using IoT user behavior data
  • Applied algorithms to cluster (k-means, hierarchical clustering, expectation-maximization) and classify (Naive Bayes, k-NN, AdaBoost, Support Vector Machines, Neural Networks) user behavior data from simulated IoT environments
  • Forecasted time series user data using Support Vector Regression, Genetic algorithms, and Neural Networks
  • Integrated algorithms prototypically into umbrella management system to manage IoT and telecommunication networks
• License Plate Character Recognition Using MATLAB and Tesseract (January 2015 - March 2015)
  Research Project, RheinMain University of Applied Sciences
  • Developed a Matlab-based application which recognizes license plate characters from video sequences
  • Elaborated teaching material and produced the corresponding video lectures for a chapter of the MOOC “Modelling and Simulation Using Matlab” which had more than 50,000 enrollments
• Analytics Application Development to Optimize and Expand Existing Fault Management Features for Conventional and Software-Defined Networks (July 2014 - September 2014)
  Bachelor Thesis, RheinMain University of Applied Sciences via Dimetis GmbH
  • Elaborated graph-based concept to optimize the existing fault management of Dimetis’ BOSS Link Manager
  • Interconnected BOSS Link Manager to open source Software-Defined Networking controller
  • Developed a prototype which analyzed SNMP fault data, Software-Defined Networking controller information and internal event logs in real-time to expand existing fault management features
  • Successful demonstration of the prototype at the MPLS & SDN Summit 2016 in Paris, France
• MOOC Production: Modelling and Simulation Using MATLAB (September 2013 - February 2014)
  Course Project, RheinMain University of Applied Sciences
  • Head of video production of MOOC “Modelling and Simulation Using Matlab” which had 50,000+ enrollments
  • Assessed and evaluated course material of five contributing professors

Talks

Masking Neural Networks Using Reachability Graphs to Predict Process Events

December 20, 2021

Talk at 2021 International Conference on Cyber-physical Social Intelligence, Beijing, China

On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization

November 01, 2021

Talk at 6th International Workshop on Process Querying, Manipulation, and Intelligence held in conjunction with the 3rd International Conference on Process Mining, Eindhoven, Netherlands

Introduction to Entrepreneurship

October 15, 2019

Talk at Engineering Success Initiative of the University of Illinois at Chicago, Chicago, USA

Process Mining of Programmable Logic Controllers: Input/Output Event Logs

August 23, 2019

Talk at 2019 IEEE 15th International Conference on Automation Science and Engineering, Vancouver, Canada

Hands-On: Artificial Intelligence

July 18, 2019

Workshop at University of Illinois at Chicago Summer Bridge Program for NSF S-STEM Scholars, Chicago, Illinois

Behavioral Petri Net Mining and Automated Analysis for Human Computer Interaction Recommendations in Multi-Application Environments

June 20, 2019

Talk at 11th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, Valencia, Spain

PyData Meetup - Process Mining

May 06, 2019

Talk at PyData Chicago, Chicago, Illinois

Neural Networks and Deep Learning

April 03, 2019

Seminar at University of Illinois at Chicago - School of Public Health, Chicago, Illinois

Artificial Intelligence Around Us

March 12, 2019

Seminar at Institute of Industrial and Systems Engineers, Chicago, Illinois

Hands-On: Artificial Intelligence

July 19, 2018

Workshop at University of Illinois at Chicago Summer Bridge Program for NSF S-STEM Scholars, Chicago, Illinois

Regression and Classification Algorithms

November 28, 2017

Workshop at Data Science I (IE594), University of Illinois, Chicago, Illinois

Machine Learning Using Tensorflow

November 21, 2017

Workshop at Data Science I (IE594), University of Illinois, Chicago, Illinois

Deep Learning as an Integral Part of Future Umbrella Management Systems for IoT Platforms

March 14, 2017

Talk at Media Symposium organized by FKTG and RheinMain University of Applied Sciences, Wiesbaden, Germany

Secure Socket Layer Virtual Private Networks

June 18, 2015

Talk at Secure Networking, RheinMain University of Applied Sciences, Rüsselsheim, Germany

Software-defined Networking

November 05, 2014

Talk at Multimedia Networking, RheinMain University of Applied Sciences, Wiesbaden, Germany

Awards and Funds

• UIC Research Open Access Article Publishing Fund (October 2020)
• UIC Graduate College - Student Presenter Award (August 2019)
• UIC Research Open Access Article Publishing Fund (August 2019)
• IEEE Robotics and Automation Society Travel Grant (June 2019)
• Master of Engineering Degree with Distinction (June 2016)
• IABM Engineering Student Award (September 2013)

Volunteering and Leadership

• Reviewer, 30th European Conference on Information Systems (2021)
• Reviewer, International Conference on Cyber-physical Social Intelligence (2021)
• Corona Self-Service Test Registration, #WirVsVirus Hackathon of the German Federal Government in response to the COVID-19 pandemic (ranked 105 / 1,498; deployed in Viersen, Germany) (March 2020)
• Reviewer, IEEE Transactions on Automation Science and Engineering (2018 - Present)
• Reviewer, IEEE International Conference on Automation Science and Engineering (2018)
• Reviewer, International Journal of Modelling and Simulation (2019)
• Examination Board Student Representative, RheinMain University of Applied Sciences (May 2015 - April 2016)
• Head of Video Production & Course Material Evaluator, Modelling and Simulation Using Matlab MOOC with more than 50,000 enrollments (September 2013 - March 2015)

Memberships

• IEEE Member (February 2019 - Present)
• IEEE Young Professionals (February 2019 - Present)
• IEEE Task Force on Process Mining (January 2020 - Present)
• Association of Global Intelligent Science and Technology (January 2020 - Present)
• IEEE Robotics and Automation Society (May 2019 - December 2019)
• Erasmus Student Network - Kuopio International Student Association (August 2012 - March 2013)