Dr. Krzysztof “Krys” Cios is professor and chair of Computer Science and directs the CIOS Lab: Data Mining and Biomedical Informatics. His research interests are in the areas of machine learning and data mining.
He was advisor of 17 doctoral students, who are now professors at the US, Australian, Vietnamese and Thai universities, work at companies such as Google, Amazon Robotics, General Motors, NASA and Proctor and Gamble.
He published 3 books and over 200 articles.
Industry Expertise (2)
Areas of Expertise (5)
Fellow of the IEEE (professional)
“for contributions to data mining and machine learning”
Fellow of the American Institute for Medical and Biological Engineering (professional)
For outstanding contributions to the application of computer science to the analysis of biological data.
Norbert Wiener Outstanding Paper Award (professional)
Cios KJ and Liu N. 1995. An algorithm which learns multiple covers via integer linear programming, Part I - the CLILP2 algorithm. Kybernetes
Neurocomputing Best Paper Award (professional)
Arciniegas JI, Eltimsahy AH and Cios KJ. 1997. Neural networks based adaptive control of flexible robotic arms. Neurocomputing
Fulbright Senior Scholar Award (professional)
“Computer Detection of Heart Disease from SPECT Images”, National Institute of Cardiology, Warsaw, Poland
Member of the Kosciuszko Foundation Collegium of Eminent Scientists (professional)
For outstanding achievements and contributions to the scientific community.
Fellow of the Asia-Pacific Artificial Intelligence Association (professional)
For outstanding contributions to artificial intelligence.
Polish Academy of Sciences: D.Sc. 1999
University of Toledo: M.B.A. 1994
AGH University of Science and Technology: Ph.D., Computer Science 1984
AGH University of Science and Technology: M.S., Electrical Engineering 1973
- European Acedemy of Sciences and Arts, Member
- Polish Academy of Arts and Sciences, Foreign Member
Media Appearances (2)
With Amazon on its way, Virginia colleges prepare to deal with a nationwide problem: hiring computer science professors
Richmond Times-Dispatch print
“It is an unfulfilled need,” said Krys Cios, the chairman of Virginia Commonwealth University’s computer science department. “Everyone is looking for employees with computer science skills.”
VCU virtual reality lab opens to awe of crowd
Richmond Times-Dispatch print
“Everybody’s using computer science. We can’t live without computers,” Cios said. “This just creates more opportunities for students and gets them more prepared.”
Selected Articles (3)
Self-organizing Feature Maps Identify Proteins Critical to Learning in a mouse model of Down SyndromePLoS One
Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.
RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density EstimatesIEEE Transactions on Knowledge and Data Engineering
A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse nearest neighbor counts as an estimate of observation density. Clustering is performed using a DBSCAN-like approach based on k nearest neighbor graph traversals through dense observations. RNN-DBSCAN is preferable to the popular density-based clustering algorithm DBSCAN in two aspects. First, problem complexity is reduced to the use of a single parameter (choice of k nearest neighbors), and second, an improved ability for handling large variations in cluster density (heterogeneous density). The superiority of RNN-DBSCAN is demonstrated on several artificial and real-world datasets with respect to prior work on reverse nearest neighbor based clustering approaches (RECORD, IS-DBSCAN, and ISB-DBSCAN) along with DBSCAN and OPTICS. Each of these clustering approaches is described by a common graph-based interpretation wherein clusters of dense observations are defined as connected components, along with a discussion on their computational complexity. Heuristics for RNN-DBSCAN parameter selection are presented, and the effects of k on RNN-DBSCAN clusterings discussed. Additionally, with respect to scalability, an approximate version of RNN-DBSCAN is presented leveraging an existing approximate k nearest neighbor technique.
Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records.Hindawi
Management of hyperglycemia in hospitalized patients has a significant bearing on outcome, in terms of both morbidity and mortality. However, there are few national assessments of diabetes care during hospitalization which could serve as a baseline for change. This analysis of a large clinical database (74 million unique encounters corresponding to 17 million unique patients) was undertaken to provide such an assessment and to find future directions which might lead to improvements in patient safety. Almost 70,000 inpatient diabetes encounters were identified with sufficient detail for analysis. Multivariable logistic regression was used to fit the relationship between the measurement of HbA1c and early readmission while controlling for covariates such as demographics, severity and type of the disease, and type of admission. Results show that the measurement of HbA1c was performed infrequently (18.4%) in the inpatient setting. The statistical model suggests that the relationship between the probability of readmission and the HbA1c measurement depends on the primary diagnosis. The data suggest further that the greater attention to diabetes reflected in HbA1c determination may improve patient outcomes and lower cost of inpatient care.