Mehmet Ahsen

Mehmet Ahsen

Assistant Professor of Business Administration

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4024 Business Instructional Facility

515 Gregory Drive

Champaign, IL 61820

217-300-7186

ahsen@illinois.edu

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Listings

Educational Background

  • Ph.D., Biomedical Engineering, University of Texas at Dallas at Dallas, 2015
  • M.S., Electrical and Electronics Engineering, Bilkent University, 2011
  • B.S., Electrical and Electronics Engineering, Middle East Technical University, 2009
  • B.S., Mathematics, Middle East Technical University, 2009

Positions Held

  • Health Innovation Professor, Carle Illinois School of Medicine, 2022-2027
  • Affliate, Carl R. Woese Institute for Genomic Biology at UIUC, 2021 to present
  • Assistant Professor of Business Administration, Business Administration, University of Illinois at Urbana-Champaign, 2019 to present
  • Assistant Professor, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai Hospital, 2017-2019
  • Systems Biology Specialist, IBM, 2015-2017

Recent Publications

  • Creason, A., Haan, D., Dang, K., Chiotti, K., Inkman, M., Lamb, A., Yu, T., Hu, Y., Norman, T., Buchanan, A., Ahsen, M., & others, . (2021). A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery. Cell systems, Cell Press, 12 (8), 827--838.
  • Manica, M., Bunne, C., Mathis, R., Cadow, J., Ahsen, M., Stolovitzky, G., & Mart\'\inez, Mar\'\ia Rodr\'\iguez, . (2021). COSIFER: a Python package for the consensus inference of molecular interaction networks. Bioinformatics, Oxford University Press, 37 (14), 2070--2072.
  • Zhang, Z., Genc, Y., Wang, D., Ahsen, M., & Fan, X. (2021). Effect of ai explanations on human perceptions of patient-facing ai-powered healthcare systems. Journal of Medical Systems, Springer US, 45 (6), 1--10.
  • Roy, S., Kiral, I., Mirmomeni, M., Mummert, T., Braz, A., Tsay, J., Tang, J., Asif, U., Schaffter, T., Ahsen, M., & others, . (2021). Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data. EBioMedicine, Elsevier, 66 103275.
  • Kim, S., Arun, A., Ahsen, M., Vogel, R., & Stolovitzky, G. (2021). The Fermi--Dirac distribution provides a calibrated probabilistic output for binary classifiers. Proceedings of the National Academy of Sciences, National Academy of Sciences, 118 (34).
  • Han, W., Wang, X., Ahsen, M., & Wattal, S. (2021). The Societal Impact of Sharing Economy Platform Self-Regulations?An Empirical Investigation. Information Systems Research, INFORMS.
  • Craig, A., Garcia-Lezana, T., Ruiz de Galarreta, Marina, ., Villacorta-Martin, C., Kozlova, E., Martins-Filho, S., von Felden, J., Ahsen, M., Bresnahan, E., Hernandez-Meza, G., & others, . (2021). Transcriptomic characterization of cancer-testis antigens identifies MAGEA3 as a driver of tumor progression in hepatocellular carcinoma. PLoS genetics, Public Library of Science San Francisco, CA USA, 17 (6), e1009589.
  • Von Felden, J., Garcia-Lezana, T., Dogra, N., Gonzalez-Kozlova, E., Ahsen, M., Craig, A., Gifford, S., Wunsch, B., Smith, J., Kim, S., & others, . (2021). Unannotated small RNA clusters associated with circulating extracellular vesicles detect early stage liver cancer. Gut, BMJ Publishing Group.

Other Publications

Articles

  • Ahsen, M., Vogel, R., & Stolovitzky, G. (2020). R/PY-SUMMA: An R/Python Package for Unsupervised Ensemble Learning for Binary Classification Problems in Bioinformatics. Journal of Computational Biology, Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~….
  • Diaz, J., Ahsen, M., Schaffter, T., Chen, X., Realubit, R., Karan, C., Califano, A., Losic, B., & Stolovitzky, G. (2020). The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies. Elife, eLife Sciences Publications Limited, 9 e52707.
  • Kerns, S., Fachal, L., Dorling, L., Barnett, G., Baran, A., Peterson, D., Hollenberg, M., Hao, K., Narzo, A., Ahsen, M., & others, . (2020). Radiogenomics Consortium Genome-Wide Association Study Meta-analysis of Late Toxicity after Prostate Cancer Radiotherapy JNCI: Journal of the National Cancer Institute.
  • Losic, B., Craig, A., Villacorta-Martin, C., Martins-Filho, S., Akers, N., Chen, X., Ahsen, M., von Felden, J., Labgaa, I., D'Avola, D., & others, . (2020). Intratumoral heterogeneity and clonal evolution in liver cancer. Nature communications, Nature Publishing Group, 11 (1), 1--15.
  • Schaffter, T., Buist, D., Lee, C., Nikulin, Y., Ribli, Dezs\Ho, ., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S., Ahsen, M., & others, . (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA network open, American Medical Association, 3 (3), e200265--e200265.
  • Tanevski, J., Nguyen, T., Truong, B., Karaiskos, N., Ahsen, M., Zhang, X., Shu, C., Xu, K., Liang, X., Hu, Y., & others, . (2020). Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data. Life science alliance, Life Science Alliance, 3 (11).
  • Ahsen, M., & Vidyasagar, M. (2019). An approach to one-bit compressed sensing based on probably approximately correct learning theory. The Journal of Machine Learning Research, JMLR. org, 20 (1), 408--430.
  • Ahsen, M., Chu, Y., Grishin, A., Grishina, G., Stolovitzky, G., Pandey, G., & Bunyanovich, S. (2019). NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers. Nature Scientific Reports.
  • Ahsen, M., Vogel, R., & Stolovitzky, G. (2019). Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions Journal of Machine Learning Research, 20 (166), 1--40.
  • Choobdar, S., Ahsen, M., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., Lin, J., Hescott, B., Hu, X., Mercer, J., & others, . (2019). Assessment of network module identification across complex diseases. Nature Methods, Nature Publishing Group, 16 (9), 843--852.
  • Davis, S., Button-Simons, K., Bensellak, T., Ahsen, M., Checkley, L., Foster, G., Su, X., Moussa, A., Mapiye, D., Khoo, S., & others, . (2019). Leveraging crowdsourcing to accelerate global health solutions. Nature biotechnology, Nature Publishing Group, 37 (8), 848--850.
  • Menden, M., Wang, D., Mason, M., Szalai, B., Bulusu, K., Guan, Y., Yu, T., Kang, J., Jeon, M., Wolfinger, R., Ahsen, M., & others, . (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature communications, Nature Publishing Group, 10 (1), 2674.
  • Murillo, O., Thistlethwaite, W., Rozowsky, J., Subramanian, S., Lucero, R., Shah, N., Jackson, A., Srinivasan, S., Chung, A., Laurent, C., Ahsen, M., & others, . (2019). exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids. Cell, Cell Press, 177 (2), 463--477.
  • Ahsen, M., Ayvaci, M., & Raghunathan, S. (2018). When algorithmic predictions use human-generated data: A bias-aware classification algorithm for breast cancer diagnosis. Information Systems Research, INFORMS, 30 (1), 97--116.
  • Ayvaci, M., Alagoz, O., Ahsen, M., & Burnside, E. (2018). Preference-Sensitive Management of Post-Mammography Decisions in Breast Cancer Diagnosis. Production and Operations Management, 27 (12), 2313--2338.
  • Fourati, S., Talla, A., Mahmoudian, M., Burkhart, J., Klen, R., Henao, R., Yu, T., Ayd\in, Zafer, ., Yeung, K., Ahsen, M., & others, . (2018). A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature communications, Nature Publishing Group, 9 (1), 4418.
  • Pandey, G., Pandey, O., Rogers, A., Ahsen, M., Hoffman, G., Raby, B., Weiss, S., Schadt, E., & Bunyavanich, S. (2018). A nasal brush-based classifier of asthma identified by machine learning analysis of nasal RNA sequence data. Scientific reports, Nature Publishing Group, 8 (1), 8826.
  • Singh, N., Ahsen, M., Challapalli, N., Kim, H., White, M., & Vidyasagar, M. (2018). Inferring genome-wide interaction networks using the phi-mixing coefficient, and applications to lung and breast cancer. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, IEEE, 4 (3), 123--139.
  • Smith, J., Wunsch, B., Dogra, N., Ahsen, M., Lee, K., Yadav, K., Weil, R., Pereira, M., Patel, J., Duch, E., & others, . (2018). Integrated nanoscale deterministic lateral displacement arrays for separation of extracellular vesicles from clinically-relevant volumes of biological samples. Lab on a Chip, Royal Society of Chemistry, 18 (24), 3913--3925.
  • Ahsen, M., & Vidyasagar, M. (2017). Error bounds for compressed sensing algorithms with group sparsity: A unified approach. Applied and Computational Harmonic Analysis, Academic Press, 43 (2), 212--232.
  • Ahsen, M., Boren, T., Singh, N., Misganaw, B., Mutch, D., Moore, K., Backes, F., McCourt, C., Lea, J., Miller, D., & others, . (2017). Sparse feature selection for classification and prediction of metastasis in endometrial cancer. BMC genomics, BioMed Central, 18 (3), 233.
  • Ahsen, M., Challapalli, N., & Vidyasagar, M. (2017). Two new approaches to compressed sensingexhibiting both robust sparse recovery and the grouping effect. The Journal of Machine Learning Research, JMLR. org, 18 (1), 1745--1768.
  • Ayvaci, M., Ahsen, M., Raghunathan, S., & Gharibi, Z. (2017). Timing the Use of Breast Cancer Risk Information in Biopsy Decision-Making. Production and Operations Management, 26 (7), 1333--1358.
  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2016). Analysis of a gene regulatory network model with time delay using the secant condition. IEEE life sciences letters, IEEE, 2 (2), 5--8.
  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). A secant condition for cyclic systems with time delays and its application to gene regulatory networks. IFAC-PapersOnLine, Elsevier, 48 (12), 171--176.
  • Misganaw, B., Ahsen, M., Singh, N., Baggerly, K., Unruh, A., White, M., & Vidyasagar, M. (2015). Optimized Prediction of Extreme Treatment Outcomes in Ovarian Cancer. Cancer Informatics, 14 45--55.
  • Ahsen, M., \"Ozbay, H, ., & Niculescu, S. (2014). On the analysis of a dynamical model representing gene regulatory networks under negative feedback. International Journal of Robust and Nonlinear Control, 24 (11), 1609--1627.
  • Ahsen, M., & Vidyasagar, M. (2013). Mixing coefficients between discrete and real random variables: Computation and properties. IEEE Transactions on Automatic Control, IEEE, 59 (1), 34--47.

Book Chapters

  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2017). Stability and Robustness Analysis of a Class of Cyclic Biological Systems. Time Delay Systems ( pp. 155--168). Springer, Cham.
  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Basic Tools from Systems and Control Theory. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays ( pp. 13--23). Birkh\"auser, Cham.
  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Deterministic ODE-Based Model with Time Delay. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays ( pp. 43--51). Birkh\"auser, Cham.
  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Functions with Negative Schwarzian Derivatives. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays ( pp. 25--42). Birkh\"auser, Cham.
  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Gene Regulatory Networks Under Positive Feedback. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays ( pp. 73--85). Birkh\"auser, Cham.

Books and Monographs

  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Analysis of deterministic cyclic gene regulatory network models with delays. Springer.

Conference Proceedings

  • Ahsen, M., Chun, Y., Grishin, A., Grishina, G., Stolovitzky, G., Pandey, G., & Bunyavanich, S. (2020). NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers. ( pp. 1--13). Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics.
  • Basu, S., Subramanyam, R., & Ahsen, M. (2019). Is a megapixel worth a few thousand words? An Empirical Assessment of Image Sentiments on Philanthropic Crowdfunding Success. Conference on the Digital Economy (CODE) 2019.
  • Lin, Y., Ahsen, M., Shaw, M., & Seshadri, S. (2019). The Impacts of Patients' Sentiment Trajectory Features on Their Willingness to Share in Online Support Groups ICIS ICIS.
  • von Felden, J., Craig, A., Ahsen, M., Labgaa, I., D'Avola, D., Meza, G., Allette, K., Dogra, N., Lezana, T., Tabrizian, P., & others, . (2019). RNA-sequencing of plasma exosomes reveals specific transcriptomic profiles in patients with hepatocellular carcinoma. American Association for Cancer Research.
  • Losic, B., Craig, A., Martins-Filho, S., Villacorta-Martin, C., Akers, N., Chen, X., Ahsen, M., Labgaa, I., D'Avola, D., Lira, S., & others, . (2018). Deciphering the impact of immune editing on liver cancer clonal evolution using immunogenomics. AACR.
  • Craig, A., Villacorta-Martin, C., Labgaa, I., Ahsen, M., Martins-Filho, S., D'AVOLA, D., Stueck, A., Ward, S., Fiel, M., Gunasekaran, G., & others, . (2017). A potential role of cancer testis antigens in hepatocellular carcinoma progression. Hepatology.
  • Challapalli, N., Ahsen, M., & Vidyasagar, M. (2016). Modelling drug response and resistance in cancer: Opportunities and challenges. 2016 IEEE 55th Conference on Decision and Control (CDC) ( pp. 2488--2493).
  • Craig, A., Ahsen, M., Villacorta-Martin, C., Chen, X., Labgaa, I., Stueck, A., D'Avola, D., Ward, S., Fiel, M., Gunasekaran, G., & others, . (2016). Multi-regional integrative genomic analysis reveals intra-tumor heterogeneity in a subset of hepatocellular carcinoma. Hepatology ( vol. 64, pp. 267A--268A).
  • Torlak, F., Ayvaci, M., Ahsen, M., Arce, C., Vazquez, M., & Tanriover, B. (2016). Estimating waiting time for deceased donor renal transplantion in the era of new kidney allocation system. Transplantation proceedings ( 6 ed vol. 48, pp. 1916--1919).
  • Ahsen, M., & Vidyasagar, M. (2015). A PAC learning approach to one-bit compressed sensing. 2015 American Control Conference (ACC) ( pp. 4228--4230).
  • Ahsen, M., & Vidyasagar, M. (2015). An approach to one-bit compressed sensing based on probably approximately correct learning theory. 2015 54th IEEE Conference on Decision and Control (CDC) ( pp. 7377--7379).
  • Ahsen, M., & Vidyasagar, M. (2014). Near-ideal behavior of compressed sensing algorithms. 53rd IEEE Conference on Decision and Control ( pp. 6354--6357).
  • Bulut, E., Ahsen, M., & Szymanski, B. (2014). Opportunistic wireless charging for mobile social and sensor networks. 2014 IEEE Globecom Workshops (GC Wkshps) ( pp. 207--212).
  • Ahsen, M., & Vidyasagar, M. (2013). On the computation of mixing coefficients between discrete-valued random variables. 2013 9th Asian Control Conference (ASCC) ( pp. 1--5).
  • Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2012). Stability analysis of a dynamical model representing gene regulatory networks. ( PART 1 ed vol. 10, pp. 191--196).
  • Ahsen, M., Singh, N., Boren, T., Vidyasagar, M., & White, M. (2012). A new feature selection algorithm for two-class classification problems and application to endometrial cancer. 2012 IEEE 51st IEEE conference on decision and control (CDC) ( pp. 2976--2982).
  • Singh, N., Ahsen, M., Mankala, S., Vidyasagar, M., & White, M. (2012). A novel application of mixing coefficients for reverse-engineering gene interaction networks. 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) ( pp. 1461--1466).
  • Singh, N., Ahsen, M., Mankala, S., Vidyasagar, M., & White, M. (2012). Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) ( pp. 168--171).

Working Papers

  • Han, W., Ahsen, M., Lin, Y., & Zhu, Y. Drivers of the cryptocurrency market - insights from analyzing tweets.
  • Ahsen, M., & Garimella, A. Can Explainable AI Aid Fake News Detection?: A Series of Randomized Controlled Experiments.
  • Ahsen, M., , M., Subramanyam, R., & Raghunathan, S. Contests for Predictive Algorithms: Ensembling,Interdependency, and Optimal Rewards Design.
  • Ahsen, M., Ayvaci, M., & Mookerjee, R. Purchasing Algorithms for Human-Machine Cooperative Decision Making.
  • Ahsen, M., Narang, U., & Lin, Y. Competition or Collaboration? The Impact of Open Science on Healthcare Innovation.
  • Basu, S., Ahsen, M., & Subramanyam, R. Is a megapixel worth a few thousand words? An Empirical Assessment of Image Sentiments on Philanthropic Crowdfunding Success.
  • Lin, Y., Ahsen, M., Shaw, M., & Seshadri, S. The impacts of patients’ sentiment trajectory features on their willingness to share in online support groups.
  • Wu, A., Ahsen, M., & Subramanyam, R. Evidence of Green Washing through Social Media.
  • Wu, A., Garimella, A., Subramanyam, R., & Ahsen, M. Keeping Kids Learning: Online Crowdfunding Communities Respond to the COVID-19 Pandemic.  link >
  • Hao, S., Xu, Y., Mukherjee, U., Seshadri, S., Ahsen, M., Bose, S., Ivanov, A., Souyris, S., & Sridhar, P. Hotspots for Emerging Epidemics: Multi-Task and Transfer Learning over Mobility Networks.

Honors and Awards

  • Best Published Paper Award, Informations Systems Research, 2020 to present
  • RC Evans Data Analytics Fellow, University of Illinois, Gies College of Business, 2020-2021

Grants

  • Can Explainable Artificial Intelligence Aid Fake News Detection?, Illinois Campus Research Board, 2021-2023
  • How to design and operate end-to-end vaccine deployment using social media, adressing supply chain allocation constraints, and utilizing telemedicine?, Jump Arches, 2021-2023
  • How to Facilitate Business Continuity by Addressing Supply Chain Constraints Caused by COVID-19?, Gies College of Business, 2021-2023

Current Courses

  • Business Analytics II (BADM 211) Builds on the foundation from the Business Analytics I (BADM 210), synthesizes concepts through hands-on application and project-based learning. Focuses on data acquisition, organization, analysis and visualization in a business setting. Expanding on the use of statistics in generating basic inferences to predictive modeling Identify opportunities for improving business decisions using data, conduct relevant analysis of the gathered and cleaned data, and finally, interpret and present analysis outcomes to decision makers. Using statistical tools and software applications to identify business problems, acquire relevant data, and generate analytic solutions using advanced analytics techniques and tools for generating insights. Introduces the students to analyzing, learning, and prediction using advanced analytics techniques and tools for generating business insights. This course will provide a practical introduction to various techniques regarding clustering, text mining, classification and decision trees, and time series analysis. Finally, the course will introduce advanced and emerging topics in predictive analytics.

  • Proseminar in Informat Systems (BADM 591) Lectures in topics of current interest not covered by regular course offerings. Subjects are announced in the Class Schedule.

Contact

4024 Business Instructional Facility

515 Gregory Drive

Champaign, IL 61820

217-300-7186

ahsen@illinois.edu

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