Our paper (S. Liu, A. Wright, M. Hauskrecht) on anomaly detection for Rule-count Time series was accepted for publication in Artificial Intelligence in Medicine journal.
Our paper (P. Luo and M. Hauskrecht) on the decision tree-driven active group learning approach was accepted to ECML 2018.
Our P. Luo and M. Hauskrecht. Hierarchical Active Learning with Group Proportion Feedback, will be presented at IJCAI 2018.
Our paper: Z. Liu, Y. Yan, and M. Hauskrecht. A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic, was accepted to SIGIR 2018
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 work is in machine learning, data mining, probabilistic modeling and their applications in biomedical informatics.
The main focus of my current research is the development of modern machine learning technologies for analysis of
high dimensional time-series data in electronic health record (EHR) data and their application in
clinical decision support and clinical alerting.
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)
