Professor of Computer Science
Department of Computer Science
5329 Sennott Square
University of Pittsburgh
Pittsburgh, PA 15260
- August 2020. Congrats to Siqi Liu, Yanbing Xue and Patrick Luo for completing their dissertations.
- July 2020. Patrick's paper on group-based active learning with multiple region hierarchies has been accepted to CIKM 2020.
- July 2020. JeongMin's paper on multi-scale RNNs for EHR event prediction has been accepted to AIME 2020.
- June 2020. Three Phd students from the ML group (Patrick, Siqi, and Yanbing) successfully passed their oral dissertation defense the week of 06/22/2020.
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.
Past research projects:
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 in Spring 2021:
Current PhD students
Former PhD students:
- 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)
- Branislav Kveton,
PhD Fall 2006 ( thesis link),
senior research scientist, Google Research, CA
- Tomas Singliar,
PhD Fall 2008 ( thesis link),
senior data scientist, Amazon Research, WA
- Richard Pelikan,
PhD Spring 2011 (
thesis link), Bioinformatics Scientist, Oklahoma Medical Research Foundation
- Michal Valko,
PhD Summer 2011 (
thesis link), research scientist, Deep Mind/INRIA France
- Iyad Batal,
PhD October 2012 (
thesis link), research scientist, Microsoft, CA.
- Saeed Amizadeh, PhD Fall 2013, research scientist, Microsoft Research, WA.
- Quang Nguyen , PhD Spring 2014, ( thesis link) senior data scientist, Intuit Inc
- Eric Heim , PhD Fall 2015, ( thesis link), research scientist, CMU
- Zitao Liu , PhD Summer 2016, ( thesis link), research scientist, TAL Education Group
- CharmGil Hong, PhD Fall 2017, ( thesis link), faculty, Handong University, South Korea
- Siqi Liu, PhD Summer 2020.
- Yanbing Xue , PhD Summer 2020.
- Zhipeng (Patrick) Luo, PhD Summer 2020.
- Lei Wu, 2011-2012, currently Director of Data Science, Ultimate Software Inc
- Hamed Valizadegan, 2010-2013, currently at
NASA Research, CA
- Iyad Batal, 2012-2013, currently at Microsoft, CA
- CharmGil Hong , 2017-2018, currently at Handong University
Past courses taught:
- Yanbing Xue, MS, Fall 2014
- David Krebs, MS, Fall 2011
- Eric Heim, MS, Fall 2010
- Adi Nemlekar, MSc, Summer 2007
- Jose Nunez-Varela, MSc, Spring 2006
- Aaron Cois, MSc, Spring 2006
- Xinghua Lu, MSc, 2004
- Elizabeth Clause, MSc
- Gregory Nilsen, MSc
- CS 1571
Introduction to Artificial Intelligence (Fall 2013, 2012, 2010, 2007, 2006, 2003, 2002, 2001)
- CS 1675
Introduction to Machine Learning - Spring 2019
(past CS 1675 courses taught in
Fall 2018 )
- CS 441 Discrete mathematics for Computer
Science ( Spring 2006, Spring 2005, Fall 2004)
- CS 2710 Foundations of Artificial
Intelligence (past CS 2710 courses taught in
Fall 2017, Fall 2005, Fall 2004)
- CS 3710 Probabilistic Graphical Models
(Advanced Topics in AI) (Fall 2005)
Machine Learning (Spring 2020) (past CS 2750 courses taught in
Spring 2018 ,
Spring 2010 ,
Spring 2007 ,
Spring 2004 ,
- CS 3750 Advanced Topics in Machine Learning- Fall 2020
(past CS 3750 courses taught in
- CS 2740 Knowledge Representation
(Fall 2008, Spring 2007)
CS 2001 Research topics in Computer
Science. Module: Bayesian belief
networks (Fall 2002, Fall 2001)
Last updated by milos on 09/15/16.