Tulane Home Tulane Shield logo linking to site home page
Home / Evangelos Triantaphyllou, PhD
Evangelos Triantaphyllou, PhD
Evangelos Triantaphyllou, Ph.D.

Education

  • Dual PhD in Industrial Engineering and Operations Research (a branch of applied mathmatics), Penn State University
  • MS, Computer Science, Penn State University
  • Dual MS in Environment and Operations Research, Penn State University

Biography

Dr. Triantaphyllou’s Dual PhD dissertation was on machine learning and data mining by using optimization approaches.  This is one of the earliest dissertations on machine learning and data mining when at that time (in the 1980s) the concept of data science was unknown to most people.  He has a strong mathematical modeling and computer science background.  His multi-disciplinary education and training has enabled him to be able to communicate complex concepts to people of different backgrounds which might not be trained in mathematical modeling, computer science or engineering.  

Since the spring of 2020 he is an Adjunct Associate Professor at Tulane’s School of Medicine, Department of Medicine, Section of Hematology / Medical Oncology.  Since January of 2005 Dr. Triantaphyllou is also a tenured full professor of Computer Science at Louisiana State University (for more information please check his personal webpage at LSU at http://www.csc.lsu.edu/trianta ).  Before his appointment as a Computer Science professor, Dr. Triantaphyllou had served as an assistant / associate / full professor of Industrial Engineering for 15 years at the same university (LSU).  He is also a former associate dean of engineering at LSU.

Before he joined LSU, he had worked at Kansas State University (1990-1993) as an assistant professor.  During that time, he had also taught at the Command and General Staff School at the United States Army Combined Arms Center at Fort Leavenworth, Kansas.  As a graduate student at Penn State, he worked as a computer consultant at Penn State’s Computer Center.

Professor Triantaphyllou evaluates research and fellowship proposals for the National Research Council of the U.S. National Academies every year since the middle of the 1990s.  He serves / has served at the editorial boards of some of the most prominent journals in his field.  He has received numerous awards and distinctions for excellence in research and academics including the prestigious 1998 research award by the Operations Research Division of the IIE.  More details can be found at http://www.csc.lsu.edu/trianta

Professor Triantaphyllou is the sole author of two books and co-editor of two books.  His seminal book entitled “Multi-Criteria Decision Making Methods: A Comparative Study” (Kluwer Academic Publishers, now under Springer) is highly referenced (more than 2,700 citations in May 2020) by people who work in multi-criteria decision making / multi-criteria decision analysis.

He is best known for his work on decision making, especially, on how to select the best alternative among a finite number of competing alternatives.  This is a ubiquitous problem with applications in many domains, including in health care when one considers problems such as how to select the best treatment or screening option among multiple alternative choices.  His work in decision making spans more than 35 years; since the time he was a graduate student at Penn State University in the middle of 1980s.  His work on data mining examines important problems on data monotonicity properties and on the complexity of classifiers inferred from training data.  Currently, he is applying his expertise in decision making and data mining in medical decision making.

  • A special area of interest is that of shared decision making.  This area of decision making is in the interface of multi-criteria decision making / analysis and the management of medical treatments.  Advances in medicine and health care have created multiple treatments for many diseases and conditions.  However, choosing the best treatment for a given case can be a complex problem.  The application of one treatment may influence the way other treatments are applied in the future or even it may exclude the use of other treatments in the future.  The connection of treatments to different adverse effects, the way individual patients perceive such adverse effects and the stochastic nature of the way diseases respond to treatments make this problem to be a unique one in the decision making filed.  Dr. Triantaphyllou and his associates are creating decision aids for deciding which treatment is the best and how patient preferences may be incorporated in the decision making process.  This is also done for deciding the best screening procedure for identifying if certain diseases and conditions are present.
     
  • Another area of research interest is that of computer-aided medical diagnosis.  Dr. Triantaphyllou and his associates are developing methods that capitalize on the availability of large amounts of data to design cost-efficient diagnostic systems.  Such systems consider data in a step-wise manner that aims at reaching a diagnostic decision by first using easily obtainable data and if a decision is not made with high confidence, then to gradually increase the inclusion of data that are more expensive to obtain.  A related problem is how to optimally balance false-positive, false-negative, and undecided cases when using data for diagnostic purposes.  The problem of overdiagnosis is pertinent here as well.
     
  • A third area of interest related to medicine is in the design of new types of data mining methods for medical applications.  Such methods are based on certain properties that may be present in the training data.  Of primary interest is the property of data monotonicity which can significantly benefit the data mining process even if this property is only partially present in the data.

Publications

Select Peer-reviewed Publications
  1. Triantaphyllou, E, and J Yanase, (2020). How to Identify and Treat Data Inconsistencies When Eliciting Health-State Utility Values for Patient-Centered Decision Making, AI in Medicine, accepted for publication, May 12, 2020. DOI: pending. Available on Research Gate at: https://www.researchgate.net/profile/Evangelos_Triantaphyllou
     
  2. Kujawski, E, E Triantaphyllou, and J Yanase, (2019). Additive Multicriteria Decision Analysis Models: Misleading Aids for Life-Critical Shared Decision Making, Medical Decision Making, Vol. 39, No. 4, pp. 437-449, May 1, 2019. DOI: https://doi.org/10.1177%2F0272989X19844740
     
  3. Yanase, J, and E Triantaphyllou, (2019). A systematic survey of computer-aided diagnosis in medicine: Past and present developments, Expert Systems with Applications, Vol. 138, page 112921, December 2019. DOI: https://doi.org/10.1016/j.eswa.2019.112821
     
  4. Yanase, J, and E Triantaphyllou, (2019). The seven key challenges for the future of computer-aided diagnosis in medicine, International Journal of Medical Informatics, Vol. 129, pp. 413-422, September 2019. DOI: https://doi.org/10.1016/j.ijmedinf.2019.06.017
     
  5. Yanase, J, and E Triantaphyllou, (2019). How to Identify and Deal with Numerical Inconsistencies in Elicited Health State Utilities, Value in Health, ISPOR, Vol. 22, page S329, May 2019. DOI: DOI: https://doi.org/10.1016/j.jval.2019.04.1608
     
  6. Pham, H.N.A., and E. Triantaphyllou, (2009), "An Application of a New Meta-Heuristic for Optimizing the Classification Accuracy When Analyzing Some Medical Datasets," Expert Systems with Applications, Vol. 36, No. 5, pp. 9240-9249.
     
  7. Kovalerchuk, B., E. Triantaphyllou, J.F. Ruiz, V.I. Torvik, and E. Vityaev, (2000), "The Reliability Issue of Computer-Aided Breast Cancer Diagnosis," Comps. & Biomedical Research, Vol. 33, No. 4, pp. 296-313.
     
  8. Kovalerchuk, B., E. Triantaphyllou, J.F. Ruiz, and J. Clayton, (1997), "Fuzzy Logic in Computer-Aided Breast Cancer Diagnosis: Analysis of Lobulation," AI in Medicine, No. 11, pp. 75-85.
Published Books:
  1. Multi-Criteria Decision Making Methods: A Comparative Study, by E. Triantaphyllou, a monograph, Kluwer Acad. Publishers, Applied Optimization Series, 2000, 320 pages.
     
  2. Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques, E. Triantaphyllou and G. Felici (Eds.), Springer, Massive Computing Series, 796 pages, Spring of 2006.
     
  3. Mining of Enterprise Data: Theory and Applications, T.W. Liao and E. Triantaphyllou (Eds.), World Scientific, Operations Research and Computers Series, 620 pages, Spring of 2007.
     
  4. Data Mining and Knowledge Discovery Via a Logic-Based Approach, by E. Triantaphyllou, a monograph, Springer, Massive Computing Series, 420 pages, Fall of 2010.

 

Link to Professor Triantaphyllou’s profile on Google’s Scholar: https://scholar.google.com/citations?user=kZDreZsAAAAJ&hl=en&oi=ao

Link to Professor Triantaphyllou’s profile on Research Gate: https://www.researchgate.net/profile/Evangelos_Triantaphyllou

Link to Professor Triantaphyllou’s personal webpage at LSU: http://www.csc.lsu.edu/trianta