Mucahit Cevik

150 

Mucahit Cevik, Ph.D.
Assistant Professor
Mechanical and Industrial Engineering
Ryerson University

Data Science Lab

Contact

350 Victoria Street,
Toronto, ON, Canada, M5B 2K3
Phone: x3756
mcevik[at]ryerson[dot]ca

Education

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

Research Interests

Methodologies: Applied Machine Learning, Markov Decision Processes, Reinforcement Learning, Integer Programming
Applications: Healthcare Operations, Medical Decision Making, Radiation Therapy, Transportation

Journal Papers

  1. H. Jahanshahi, M. Cevik, ‘‘SDABT: A Schedule and Dependency-aware Bug Triaging Method’’, Information and Software Technology, 2022 [link].

  2. O. Seker, M. Cevik, M. Bodur, Y. Lee, M. Ruschin, ‘‘Multiobjective optimization approaches for sector duration optimization problem in radiosurgery’’’, INFORMS Journal on Computing, 2022 [link].

  3. H. Jahanshahi, S. Kazmi, M. Cevik, ‘‘Auto Response Generation in Online Medical Chat Services’’, Journal of Halthcare Informatics Research, 2022.

  4. M. Cevik, S. Angco, E. Heydari, H. Jahanshahi, N. Prayogo, ‘‘Active Learning for Multi-way Sensitivity Analysis’’, Journal of Healthcare Informatics Research, 2022.

  5. O. Ozyegen, D. Kabe, M. Cevik, ‘‘Word-level Text Highlighting of Medical Texts for Telehealth Services’’, Artificial Intelligence in Medicine, 2022.

  6. H. Jahanshahi, M. Cevik, J. Navas-Su, A. Basar, A. Gonzales-Torres ‘‘Wayback Machine: Capturing the evolutionary behaviour of the bug dependency graph in open-source software systems’’, Journal of Systems and Software, 2022.

  7. H. Jahanshahi, A. Bozanta, M. Cevik, E. Kavuk, A. Tosun, S. Sonuc, A. Basar, ‘‘A Deep Reinforcement Learning Approach for the Meal Delivery Problem’’, Knowledge-Based Systems, 2022.

  8. S. Mohammadjafari, M. Cevik, A. Basar, ‘‘VARGAN: Variance Enforcing Network Enhanced GAN’’, Applied Intelligence, 2022.

  9. O. Ozyegen, H. Jahanshahi, M. Cevik, B. Bulut, D. Yigit, F. Gonen, A. Basar, ‘‘Classifying multi-level product categories using dynamic masking and transformer models’’, Journal of Data, Information and Management, 2022.

  10. O. Ozyegen, S. Mohammadjafari, M. Cevik, Karim El mokhtari, J. Ethier, A. Basar, ‘‘An empirical study on using CNNs for fast radio signal prediction’’, SN Computer Science, 2022.

  11. C. Kavaklioglu, M. Cevik, ‘‘Scalable Grid-based Approximation Algorithms for POMDPs’’, Concurrency and Computation: Practice and Experience, 2021.

  12. A. Bozanta, M. Cevik, C. Kavaklioglu, E. Kavuk, A. Tosun, S. Sonuc, A. Duranel, A. Basar, ‘‘Courier Routing Optimization for Food Delivery Service Using Reinforcement Learning’’, Computers and Industrial Engineering, 2021.

  13. J. Wang, M. Cevik, M. Bodur, ‘‘On the Impact of Deep Learning-based Time-series Forecasts on Multistage Stochastic Programming Policies’’, INFOR, 2021.

  14. O. Ozyegen, I. Ilic, M. Cevik, ‘‘Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting’’, Applied Intelligence, 2021.

  15. P. Lak, A. Bozanta, C. Kavaklioglu, M. Cevik, A. Basar, M. Petitclerc, G. Wills, ‘‘A replication study on implicit feedback recommender systems with application to the data visualization recommendation’’, Expert Systems, 2021.

  16. E. Kavuk, A. Tosun, M. Cevik, A. Bozanta, S. Sonuc, A. Duranel, M. Tutuncu, A. Basar, ‘‘Order Dispatching for an Ultra-Fast Delivery Service via Deep Reinforcement Learning’’, Applied Intelligence, 2021.

  17. I. Ilic, B. Gorgulu, M. Cevik, M. Baydogan, ‘‘Explainable boosted linear regression for time series forecasting’’, Pattern Recognition, 2021.

  18. J. Wang, M. Cevik, S. Amin, A Parsaee, ‘‘Mixed-integer linear programming models for the paint waste management problem’’, Transportation Research Part E, 2021.

  19. S. Mohammadjafari, O. Ozyegen, M. Cevik, E. Kavurmacioglu, J. Ethier, A. Basar, ‘‘Designing mm-Wave Electromagnetic Engineered Surfaces Using Generative Adversarial Networks’’, Neural Computing and Applications, 2020.

  20. S. Mohammadjafari, S. Roginsky, E. Kavurmacioglu, M. Cevik, J. Ethier, A. Basar, ‘‘Machine Learning-based Radio Coverage Prediction in Urban Environments’’, IEEE TNSM, 2020 [link].

  21. A. Bhowmick, M. Cevik, A. Basar, ‘‘Analyzing Intracranial EEG in Pharmacoresistant Epilepsy Patients Using Hidden Markov Models and Time Series Forecasting Methods’’, SN Computer Science, 2020 [link].

  22. A. Berdyshev, M. Cevik, D. Aleman, H. Nordstrom, S. Riad, Y. Lee, A. Saghal, M. Ruschin, ‘‘Knowledge-based Isocenter Selection in Radiosurgery Planning’’, Medical Physics, 2020 [link].

  23. M. Cevik, D. Aleman, Y. Lee, A. Berdyshev, H. Nordstrom, S. Riad, A. Sahgal, M. Ruschin, “Simultaneous optimization of isocenter locations and sector duration in radiosurgery”, Physics in Medicine & Biology, 2019 [link].

  24. M. Cevik, P. Shirvani Ghomi, D. Aleman, Y. Lee, A. Berdyshev, H. Nordstrom, S. Riad, A. Sahgal, M. Ruschin, “Modeling and comparison of alternative approaches for sector duration optimization in a dedicated radiosurgery system”, Physics in Medicine & Biology, 2018 [link].

  25. M. Cevik, T. Ayer, O. Alagoz, B. Sprague, “Analysis of mammography screening policies under resource constraints”, Production and Operations Management, 2018 [link].

  26. 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, “Comparing CISNET breast cancer models using the maximum clinical incidence reduction methodology”, Medical Decision Making, 2018 [link].

  27. O. Alagoz, M. Ergun, M. Cevik, B. Sprague, D. Fryback, R. Gangnon, J. Hampton, N. Stout, A. Trentham-Dietz, “The University of Wisconsin breast cancer epidemiology simulation model: An update”, Medical Decision Making, 2018 [link].

  28. M. Cevik, M. Ergun, N. Stout, A. Trentham-Dietz, M. Craven, O. Alagoz, “Using active learning for speeding up calibration in simulation models”, Medical Decision Making, 2016 [link].

  29. 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, “Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts”, Annals of Internal Medicine, 2015 [link].

  30. C. Lee, M. Cevik, O. Alagoz, B. Sprague, A. Tosteson, D. Miglioretti, K. Kerlikowske, N. Stout, J. Jarvik, S. Ramsey, C. Lehman, “Comparative effectiveness of combined digital mammography and tomosynthesis screening for women with dense breasts”, Radiology, 2014 [link].

  31. 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, “Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography,” Journal of National Cancer Institute, 2014 [link].

  32. Z. C. Taşkın, and M. Cevik, “Combinatorial Benders Cuts for Decomposing IMRT Fluence Maps Using Rectangular Apertures”, Computers and Operations Research, 40:2178-2186, 2013 [link].

Peer Reviewed Conference Papers

  1. R. Helmeczi, M. Cevik, S. Yildirim, ‘‘A Prompt-based Few-shot learning Approach to Software Conflict Detection ’’, Proceedings of the 32nd Annual International Conference on Computer Science and Software Engineering, 2022.

  2. S. Rafayal, M. Cevik, ‘‘Time series forecasting-based peak shaving for building energy management’’, Proceedings of the 32nd Annual International Conference on Computer Science and Software Engineering, 2022.

  3. N. Prayogo, S. Rafayal, D. Pirayesh Neghab, M. Cevik, ‘‘Partially Observable Markov Chain Models for Evaluating Lung Cancer Screening Policies’’, Proceedings of the 32nd Annual International Conference on Computer Science and Software Engineering, 2022.

  4. S. Rafayal, M. Cevik, D. Kici, ‘‘An empirical study on probabilistic forecasting for predicting city-wide electricity consumption’’, 35th Canadian Conference on Artificial Intelligence, 2022.

  5. G. Malik, M. Cevik, S. Bera, S. Yildirim, D. Parikh, A. Basar, ‘‘Software requirement specific entity extraction using transformer models’’, 35th Canadian Conference on Artificial Intelligence, 2022.

  6. H. Jahanshahi, O. Ozyegen, M. Cevik, B. Bulut, D. Yigit, F. Gonen, A. Basar, ‘‘Text Classification for Predicting Multi-level Product Categories’’, Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, 2021.

  7. S. Kazmi, A. Bozanta, M. Cevik, ‘‘Time Series Forecasting for Patient Arrivals in Online Health Services’’, Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, 2021.

  8. D. Kici, A. Bozanta, M. Cevik, D. Parikh, A. Basar, ‘‘Text classification on software requirements specifications using transformer models’’, Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, 2021.

  9. A. Bozanta, S. Angco, M. Cevik, A. Basar, ‘‘Sentiment Analysis of StockTwits Using Transformer Models’’, Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (6 pages, double column), 2021.

  10. S. Mohammadjafari, M. Thanabalasingam, M. Cevik, A. Basar, ‘‘Using ProtoPNet for Interpretable Alzheimer's Disease Classification’’, Proceedings of the 34th Canadian Conference on Artificial Intelligence (12 pages, single column), 2021.

  11. G. Malik, Y. Khedr, M. Cevik, D. Parikh, A. Basar, ‘‘Named Entity Recognition on Software Requirements Specification Documents’’, Proceedings of the 34th Canadian Conference on Artificial Intelligence (6 pages, single column), 2021

  12. D. Kici, G. Malik, M. Cevik, D. Parikh, A. Basar, ‘‘A BERT-based transfer learning approach to text classification on software requirements specifications’’, Proceedings of the 34th Canadian Conference on Artificial Intelligence (12 pages, single column), 2021.

  13. H. Jahanshahi, K. Chhabra, M. Cevik, A. Basar, ‘‘DABT: A Dependency-aware Bug Triaging Method’’, Proceedings of the EASE 2021, pages 221-230, 2021 [link].

  14. H. Jahanshahi, M. Cevik, A. Basar, ‘‘Moving from Cross-Project Just-In-Time Defect Prediction to Heterogeneous Just-In-Time Defect Prediction: An Extended Replication Study’’, Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering, pages 133-142, 2020 [link].

  15. J. Wang, A. Bhowmick, M. Cevik, A. Basar, ‘‘Deep learning approaches to classify the relevance and sentiment of news articles to the economy’’, Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering, pages 207-216, 2020.

  16. N. Prayogo, M. Cevik, M. Bodur, ‘‘Time Series Sampling for Probabilistic Forecasting’’, Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering, pages 153-162, 2020 [link].

  17. B. Sandikci, M. Cevik, ‘‘Value of MRI and Ultrasound Screening for Breast Cancer in Non-High-Risk Population’’, Proceedings of the Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE), pages 453-467, 2020 [link].

  18. H. Jahanshahi, M. Cevik, A. Basar, ‘‘Predicting the Number of Reported Bugs in a Software Repository’’, Proceedings of the 33rd Canadian Conference on Artificial Intelligence, pages 309-320, 2020.

  19. K. El mokhtari, M. Cevik, A. Basar, ‘‘Using Topic Modelling to Improve Prediction of Financial Report Commentary Classes’’, Proceedings of the 33rd Canadian Conference on Artificial Intelligence, pages 201-207, 2020.

  20. I. Ilic, B. Gorgulu, M. Cevik, ‘‘Augmented Out-of-sample Comparison Method for Time Series Forecasting Techniques’’, Proceedings of the 33rd Canadian Conference on Artificial Intelligence, pages 302-308, 2020 [link].

  21. H. Jahanshahi, D. Jothimani, A. Başar, M. Cevik, “Does chronology matter in JIT defect prediction?: A Partial Replication Study”, Proceedings of the PROMISE’19, pages 90-99, 2019 [link].

Preprints

  1. B. Sandikci, M. Cevik, D. Schacht, “Screening for Breast Cancer: The Role of Supplemental Tests and Breast Density Information”, 2018 [link].

  2. K. Mousavi, M. Bodur, M. Cevik, M. Roorda, ‘‘ADP for Crowdshipping with In-store Customers’’, 2021 [link].

Teaching

Ryerson University

  • DS 8001: Design of Algorithms (Fall 2020, Fall 2021, Fall 2022)

  • DS 8004: Data Mining (Winter 2020, Winter 2021, Winter 2022)

  • DS 8010: Interactive Learning in Decision Process (Winter 2021, Winter 2022)

  • 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)

Links