MS in Computer Science, Department of Computer Science, Slovak Technical University, Bratislava, Slovakia, May 1988.
PhD in Computer Science, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, August 1997.
My research interest is in the following areas: machine learning; time series modeling; and, planning and optimization in the presence of uncertainty. My research work is motivated and driven mostly by biomedical informatics problems and applications. More specifically, my current work focuses on the development of modern machine learning technologies for analysis of high dimensional time-series data in electronic health record (EHR), patient state models, and their applications to real-time clinical monitoring and alerting, real-time patient assessment, real-time adverse event detection and outcome prediction. Finally, my most recent research is on control and optimization of complex biological and clinical processes.
Machine learning methods for high-throughput bioinformatics data. Statistical methods for identification of hidden regulatory pathways
from gene expression data. Algorithms for preprocessing and analysis of high-throughput MS proteomic and genomic data and for biomarker discovery. Methods for protein ID in whole sample proteomics using prior knowledge
Current research funding:
NIH/NIGMS R01GM088224. R01 Real-time detection of deviations in clinical care in ICU data streams (Principal Investigator)
NIH/NLM R01LM011966. Improving Clinical Decision Support Reliability Using Anomaly Detection Methods (PI: Adam Wright, Partners Healthcare, Milos serves as a UPitt PI)
NIH/NLM. Learning Electronic Medical Record. (PI: Shyam Visweswaran, Milos is a co-investigator)
Teaching/ Advising
Teaching in Spring 2021:
CS 1675 Introduction to Machine Learning (UG) (the course material is now available in Canvas)
CS 2750 Machine Learning (G) (the course material is now available in Canvas)
