Education

• Ph.D., Industrial and Systems Engineering, UW-Madison, 2016
• M.S., Industrial Engineering, Bogazici University, 2011
• B.S., Industrial Engineering, Bogazici University, 2009

Research Interests

• Methodologies: Applied Machine Learning, Reinforcement Learning, Stochastic Modeling and Simulation, Integer Programming
• Applications: Healthcare, Transportation, Software Engineering

Journal Papers

1. Multistage stochastic fractionated intensity modulated radiation therapy planning
   M. Bodur , M. Cevik, A. Cire, M. Ruschin, J. Wang
   Computers and Operations Research (2023)
2. Sequence labeling for disambiguating medical abbreviations
   M. Cevik, S. Mohammadjafari, M. Myers, S. Yildirim
   Journal of Healthcare Informatics Research (2023)
3. ADPTriage: Approximate dynamic programming for bug triage
   H. Jahanshahi, M. Cevik, K. Mousavi, A. Basar
   IEEE Transactions on Software Engineering (2023)
4. Improved alpha-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planning
   S. Mohammadjafari, M. Cevik, A. Basar
   Applied Intelligence (2023)
5. A multiobjective constrained POMDP model for breast cancer screening
   R. Helmeczi, C. Kavaklioglu, M. Cevik, D. Pirayesh Neghab
   Operational Research (2023)
6. Linear programming-based solution methods for constrained POMDPs
   R. Helmeczi, C. Kavaklioglu, M. Cevik
   Applied Intelligence (2023)
7. SDABT: A schedule and dependency-aware bug triaging method
   H. Jahanshahi, M. Cevik
   Information and Software Technology (2022)
8. Multiobjective optimization approaches for sector duration optimization problem in radiosurgery [link]
   O. Seker, M. Cevik, M. Bodur, Y. Lee, M. Ruschin
   INFORMS Journal on Computing (2022)
9. Auto response generation in online medical chat services
   H. Jahanshahi, S. Kazmi, M. Cevik
   Journal of Healthcare Informatics Research (2022)
10. Active learning for multi-way sensitivity analysis with application to disease screening modeling
    M. Cevik, S. Angco, E. Heydari, H. Jahanshahi, N. Prayogo
    Journal of Healthcare Informatics Research (2022)
11. Word-level text highlighting of medical texts for telehealth services
    O. Ozyegen, D. Kabe, M. Cevik
    Artificial Intelligence in Medicine (2022)
12. Wayback machine: Capturing the evolutionary behaviour of the bug dependency graph in open-source software systems
    H. Jahanshahi, M. Cevik, J. Navas-Su, A. Basar, A. Gonzales-Torres
    Journal of Systems and Software (2022)
13. A deep reinforcement learning approach for the meal delivery problem
    H. Jahanshahi, A. Bozanta, M. Cevik, E. Kavuk, A. Tosun, S. Sonuc, A. Basar
    Knowledge-Based Systems (2022)
14. VARGAN: Variance enforcing network enhanced GAN
    S. Mohammadjafari, M. Cevik, A. Basar
    Applied Intelligence (2022)
15. Classifying multi-level product categories using dynamic masking and transformer models
    O. Ozyegen, H. Jahanshahi, M. Cevik, B. Bulut, D. Yigit, F. Gonen, A. Basar
    Journal of Data, Information and Management (2022)
16. An empirical study on using CNNs for fast radio signal prediction
    O. Ozyegen, S. Mohammadjafari, M. Cevik, Karim El mokhtari, J. Ethier, A. Basar
    SN Computer Science (2022)
17. Scalable grid-based approximation algorithms for POMDPs
    C. Kavaklioglu, M. Cevik
    Concurrency and Computation: Practice and Experience (2021)
18. Courier routing optimization for food delivery service using reinforcement learning
    A. Bozanta, M. Cevik, C. Kavaklioglu, E. Kavuk, A. Tosun, S. Sonuc, A. Duranel, A. Basar
    Computers and Industrial Engineering (2021)
19. On the impact of deep learning-based time-series forecasts on multistage stochastic programming policies
    J. Wang, M. Cevik, M. Bodur
    INFOR: Information Systems and Operational Research (2021)
20. Evaluation of local explanation methods for multivariate time series forecasting
    O. Ozyegen, I. Ilic, M. Cevik
    Applied Intelligence (2021)
21. A replication study on implicit feedback recommender systems with application to the data visualization recommendation
    P. Lak, A. Bozanta, C. Kavaklioglu, M. Cevik, A. Basar, M. Petitclerc, G. Wills
    Expert Systems (2021)
22. Order dispatching for an ultra-fast delivery service via deep reinforcement learning
    E. Kavuk, A. Tosun, M. Cevik, A. Bozanta, S. Sonuc, A. Duranel, M. Tutuncu, A. Basar
    Applied Intelligence (2021)
23. Explainable boosted linear regression for time series forecasting
    I. Ilic, B. Gorgulu, M. Cevik, M. Baydogan
    Pattern Recognition (2021)
24. Mixed-integer linear programming models for the paint waste management problem
    J. Wang, M. Cevik, S. Amin, A Parsaee
    Transportation Research Part E (2021)
25. Designing mm-wave electromagnetic engineered surfaces using generative adversarial networks
    S. Mohammadjafari, O. Ozyegen, M. Cevik, E. Kavurmacioglu, J. Ethier, A. Basar
    Neural Computing and Applications (2020)
26. Machine learning-based radio coverage prediction in urban environments [link]
    S. Mohammadjafari, S. Roginsky, E. Kavurmacioglu, M. Cevik, J. Ethier, A. Basar
    IEEE Transactions on Network and Service Management (2020)
27. Analyzing intracranial EEG in pharmacoresistant epilepsy patients using hidden Markov models and time series forecasting methods [link]
    A. Bhowmick, M. Cevik, A. Basar
    SN Computer Science (2020)
28. Knowledge-based isocenter selection in radiosurgery planning [link]
    A. Berdyshev, M. Cevik, D. Aleman, H. Nordstrom, S. Riad, Y. Lee, A. Saghal, M. Ruschin
    Medical Physics (2020)
29. Simultaneous optimization of isocenter locations and sector duration in radiosurgery [link]
    M. Cevik, D. Aleman, Y. Lee, A. Berdyshev, H. Nordstrom, S. Riad, A. Sahgal, M. Ruschin
    Physics in Medicine & Biology (2019)
30. Modeling and comparison of alternative approaches for sector duration optimization in a dedicated radiosurgery system [link]
    M. Cevik, P. Shirvani Ghomi, D. Aleman, Y. Lee, A. Berdyshev, H. Nordstrom, S. Riad, A. Sahgal, M. Ruschin
    Physics in Medicine & Biology (2018)
31. Analysis of mammography screening policies under resource constraints [link]
    M. Cevik, T. Ayer, O. Alagoz, B. Sprague
    Production and Operations Management (2018)
32. Comparing CISNET breast cancer models using the maximum clinical incidence reduction methodology [link]
    J. van den Broek, N. van Ravesteyn, J. Mandelblatt, M. Cevik, C. Schechter, S. Lee, H. Huang, Y. Li, D. Munoz, S. Plevritis, H. De Koning, N. Stout, M. van Ballegooijen
    Medical Decision Making (2018)
33. The University of Wisconsin breast cancer epidemiology simulation model: An update [link]
    O. Alagoz, M. Ergun, M. Cevik, B. Sprague, D. Fryback, R. Gangnon, J. Hampton, N. Stout, A. Trentham-Dietz
    Medical Decision Making (2018)
34. Using active learning for speeding up calibration in simulation models [link]
    M. Cevik, M. Ergun, N. Stout, A. Trentham-Dietz, M. Craven, O. Alagoz
    Medical Decision Making (2016)
35. Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts [link]
    B. Sprague, N. Stout, C. Schechter, N. van Ravestayn, M. Cevik, O. Alagoz, C. Lee, J. van den Broek, D. Miglioretti, J. Mandelblatt, H. De Koning, K. Kerlikowske, C. Lehman, A. Tosteson
    Annals of Internal Medicine (2015)
36. Comparative effectiveness of combined digital mammography and tomosynthesis screening for women with dense breasts [link]
    C. Lee, M. Cevik, O. Alagoz, B. Sprague, A. Tosteson, D. Miglioretti, K. Kerlikowske, N. Stout, J. Jarvik, S. Ramsey, C. Lehman
    Radiology (2014)
37. Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography [link]
    N. Stout, S. Lee, C. Schechter, K. Kerlikowske, O. Alagoz, D. Berry, D. Buist, M. Cevik, G. Chisholm, H. De Koning, H. Huang, R. Hubbard, D. Miglioretti, M. Munsell, A. Trentham-Dietz, N. van Ravesteyn, A. Tosteson, J. Mandelblatt
    Journal of National Cancer Institute (2014)
38. Combinatorial Benders cuts for decomposing IMRT fluence maps using rectangular apertures [link]
    Z. C. Taşkın, M. Cevik
    Computers and Operations Research (2013)

Peer Reviewed Conference Papers

1. Few-shot learning approaches to software requirement quality prediction
   S. Yildirim, M. Cevik, A. Basar
   Proceedings of the 33rd Annual International Conference on Computer Science and Software Engineering (2023)
2. Data augmentation for conflict and duplicate detection in software engineering sentence pairs
   G. Malik, M. Cevik, A. Basar
   Proceedings of the 33rd Annual International Conference on Computer Science and Software Engineering (2023)
3. Few-shot learning approaches to essay scoring
   R. Helmeczi, S. Yildirim, M. Cevik, S. Lee
   Proceedings of the 36th Canadian Conference on AI (2023)
4. An empirical study on vagueness detection in privacy policy texts
   G. Malik, S. Yildirim, M. Cevik, A. Basar
   Proceedings of the 36th Canadian Conference on AI (2023)
5. Identifying the factors influencing IPO underpricing using explainable machine learning techniques
   D. Pirayesh Neghab, M. Cevik, A. Basar
   Proceedings of the 36th Canadian Conference on AI (2023)
6. A prompt-based few-shot learning approach to software conflict detection
   R. Helmeczi, M. Cevik, S. Yildirim
   Proceedings of the 32nd Annual International Conference on Computer Science and Software Engineering (2022)
7. Time series forecasting-based peak shaving for building energy management
   S. Rafayal, M. Cevik
   Proceedings of the 32nd Annual International Conference on Computer Science and Software Engineering (2022)
8. Partially observable Markov chain models for evaluating lung cancer screening policies
   N. Prayogo, S. Rafayal, D. Pirayesh Neghab, M. Cevik
   Proceedings of the 32nd Annual International Conference on Computer Science and Software Engineering (2022)
9. An empirical study on probabilistic forecasting for predicting city-wide electricity consumption
   S. Rafayal, M. Cevik, D. Kici
   Proceedings of the 35th Canadian Conference on AI (2022)
10. Software requirement specific entity extraction using transformer models
    G. Malik, M. Cevik, S. Bera, S. Yildirim, D. Parikh, A. Basar
    Proceedings of the 35th Canadian Conference on AI (2022)
11. Text classification for predicting multi-level product categories
    H. Jahanshahi, O. Ozyegen, M. Cevik, B. Bulut, D. Yigit, F. Gonen, A. Basar
    Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering (2021)
12. Time series forecasting for patient arrivals in online health services
    S. Kazmi, A. Bozanta, M. Cevik
    Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering (2021)
13. Text classification on software requirements specifications using transformer models
    D. Kici, A. Bozanta, M. Cevik, D. Parikh, A. Basar
    Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering (2021)
14. Sentiment analysis of stockTwits using transformer models
    A. Bozanta, S. Angco, M. Cevik, A. Basar
    Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (2021)
15. Using ProtoPNet for interpretable Alzheimer’s disease classification
    S. Mohammadjafari, M. Thanabalasingam, M. Cevik, A. Basar
    Proceedings of the 34th Canadian Conference on AI (2021)
16. Named entity recognition on software requirements specification documents
    G. Malik, Y. Khedr, M. Cevik, D. Parikh, A. Basar
    Proceedings of the 34th Canadian Conference on AI (2021)
17. A BERT-based transfer learning approach to text classification on software requirements specifications
    D. Kici, G. Malik, M. Cevik, D. Parikh, A. Basar
    Proceedings of the 34th Canadian Conference on AI (2021)
18. DABT: A dependency-aware bug triaging method [link]
    H. Jahanshahi, K. Chhabra, M. Cevik, A. Basar
    Proceedings of the EASE 2021 (2021)
19. Moving from cross-project just-in-time defect prediction to heterogeneous just-in-time defect prediction: An extended replication study [link]
    H. Jahanshahi, M. Cevik, A. Basar
    Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering (2020)
20. Deep learning approaches to classify the relevance and sentiment of news articles to the economy
    J. Wang, A. Bhowmick, M. Cevik, A. Basar
    Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering (2020).
21. Time series sampling for probabilistic forecasting [link]
    N. Prayogo, M. Cevik, M. Bodur
    Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering (2020)
22. Value of MRI and ultrasound screening for breast cancer in non-high-risk population [link]
    B. Sandikci, M. Cevik
    Proceedings of the Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE) (2020).
23. Predicting the number of reported bugs in a software repository
    H. Jahanshahi, M. Cevik, A. Basar
    Proceedings of the 33rd Canadian Conference on AI (2020)
24. Using topic modelling to improve prediction of financial report commentary classes
    K. El mokhtari, M. Cevik, A. Basar
    Proceedings of the 33rd Canadian Conference on AI, pages 201-207 (2020)
25. Augmented out-of-sample comparison method for time series forecasting techniques [link]
    I. Ilic, B. Gorgulu, M. Cevik
    Proceedings of the 33rd Canadian Conference on AI, pages 302-308 (2020)
26. Does chronology matter in JIT defect prediction?: A Partial Replication Study [link]
    H. Jahanshahi, D. Jothimani, A. Başar, M. Cevik
    Proceedings of the PROMISE’19, pages 90-99 (2019)

Preprints

• Screening for breast cancer: The role of supplemental tests and breast density information [link]
  B. Sandikci, M. Cevik, D. Schacht
  Working paper (2018)

Teaching

• Toronto Metropolitan University
  • DS 8001: Design of Algorithms (Fall 2020, Fall 2021, Fall 2022, Fall 2023)
  • DS 8004: Data Mining (Winter 2020, Winter 2021, Winter 2022, Winter 2023)
  • DS 8010: Interactive Learning in Decision Process (Winter 2021, Winter 2022, Winter 2023)
  • DS 8002: Machine Learning (Fall 2019)
  • IND 405: Introduction to Data Analytics (Fall 2019)
  • IND 300: Introduction to Management (Winter 2019)
  • IND 708: Information Systems (Fall 2018)
• University of Toronto
  • MIE 1605: Stochastic Process (Fall 2017)
• University of Wisconsin - Madison
  • ISyE 620: Simulation Modeling and Analysis (Summer 2014)