Michael A. Nalisnik
Email: mnalisnik at emory dot edu
Office: PAIS, 566
Address: 36 Eagle Row, 5th Floor South, Atlanta, GA 30322
Office: PAIS, 566
Address: 36 Eagle Row, 5th Floor South, Atlanta, GA 30322
About me
I'm a Ph.D candidate in the Computer Science and Informatics program at Emory University, working in the Cooper Lab for Computational Biology My research interests include scalable machine learning and data mining, gigapixel image analysis and high performance computing. Other areas of interest include operating systems, computer architecture, embedded systems and real-time systems. Prior to beginning my studies at Emory, I worked developing software and hardware for over 17 years in various industries such as; document processing, wireless test equipment and automated toll collection.Education
- Ph.D. Computer Science & Informatics, Emory University, Expected 2016
- M.S. Computer Science (Data Science), Emory University, 2016
- M.S. Computer Science (Systems / Image Processing), Montclair State University, 1997
- B.S. Computer Science, Kean University, 1992
Publications
Journal / Conference
L.A. Cooper, J. Kong, D.A. Gutman, W.D. Dunn, M. Nalisnik,
and D.J. Brat, Novel genotype-phenotype associations in human
cancers enabled by advanced molecular platforms and computational
analysis of whole slide images. Laboratory Investigation, 2015.
M. Nalisnik, D.A. Gutman, J. Kong and L.A. Cooper,
An Interactive Learning Framework for Scalable Classification of
Pathology Images. 2015 IEEE International Conference on Big Data
(IEEE BigData 2015). 2015.
T. Kurc, X. Qi, D. Wang, F. Wang, G. Teodoro, L.A. Cooper,
M. Nalisnik, L. Yang, J. Saltz and D.J. Foran,
Scalable Analysis of Big Pathology Image Data Cohorts using
Efficient Methods and High-Performance Computing Strategies.
BMC Bioinformatics, 2015.
Abstract / Poster
P. Widener, M. Nalisnik, S. Cholleti, S. Agravat
and J. Saltz, Enabling high-throughput feature generation and
analysis, HP-MICCAI/MICCAI-DCI, 2011.
M. Nalisnik, W.D. Dunn, C. Vaughn, L.A. Cooper and
D.A. Gutman, Scalable Visualization of Billions of Histological
Objects using the Cancer Digital Slide Archive. The Cancer
Genome Atlas' 4th Annual Scientific Symposium, 2015.
J. S. Cordova, M. Nalisnik, Z. Liang, D. Brat,
C. G. Hadjipanayis, J. J. Olson, H.G. Shu, C.A. Holder, H.Shim and
L.A. Cooper, Integrating Histology with MR Spectroscopic Imaging
Using Digital Whole-Slide Image Analysis. World Molecular Imaging
Congress, 2015.
Talks
BMI Academic Exchange
Big Learning on Visual Data: Applications in Pathology
Department of Biomedical Informatics, Emory University, October 2014
Big Learning on Visual Data: Applications in Pathology
Department of Biomedical Informatics, Emory University, October 2014
1st Computational Pathology Workshop
An Interactive Learning Framework for Scalable Classification of Pathology Images
ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2015), September 2015
An Interactive Learning Framework for Scalable Classification of Pathology Images
ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2015), September 2015
Complex Big Data Applications Session
An Interactive Learning Framework for Scalable Classification of Pathology Images
IEEE International Conference on Big Data (IEEE BigData 2015), October 2015
An Interactive Learning Framework for Scalable Classification of Pathology Images
IEEE International Conference on Big Data (IEEE BigData 2015), October 2015
Teaching
- CS 170 - Introduction to Computer Science I
- CS 255 - Computer Organization / Assembly Language Programming