Eva Dyer

Eva Dyer
evadyer@gatech.edu
Website

Dyer’s research interests lie at the intersection of machine learning, optimization, and neuroscience. Her lab develops computational methods for discovering principles that govern the organization and structure of the brain, as well as methods for integrating multi-modal datasets to reveal the link between neural structure and function.

Assistant Professor
Phone
404-894-4738
Office
UAW 3108
Additional Research
Eva Dyer’s research combines machine learning and neuroscience to understand the brain, its function, and how neural circuits are shaped by disease. Her lab, the Neural Data Science (NerDS) Lab, develops new tools and frameworks for interpreting complex neuroscience datasets and building machine intelligence architectures inspired by the brain. Through a synergistic combination of methods and insights from both fields, Dr. Dyer aims to advance the understanding of neural computation and develop new abstractions of biological organization and function that can be used to create more flexible AI systems.
Research Focus Areas
Google Scholar
https://scholar.google.com/citations?user=Sb_jcHcAAAAJ&hl=en
LinkedIn Related Site

Irfan Essa

Irfan Essa
irfan@cc.gatech.edu
Website

Irfan Essa is a Professor in the School of Interactive Computing and Senior Associate Dean in the College of Computing (CoC), at the Georgia Institute of Technology. Professor Essa works in the areas of Computer Vision, Artificial Intelligence, Machine Learning, Robotics, Computer Graphics, and Social Computing, with potential impact on Content Creation, Analysis and Production (e.g., Computational Photography & Video, Image-based Modeling and Rendering, etc.) Human Computer Interaction, Artificial Intelligence, Computational Behavioral/Social Sciences, and Computational Journalism research.He has published over 150 scholarly articles in leading journals and conference venues on these topics and several of his papers have also won best paper awards. He has been awarded the National Science Foundation CAREER Award and was elected an IEEE Fellow. He has held extended research consulting positions with Disney Research and Google Research and also was an Adjunct Faculty Member at Carnegie Mellon's Robotics Institute. He joined Georgia Tech in 1996 after his earning his Master's (1990), Ph.D. (1994), and holding a research faculty position at the Massachusetts Institute of Technology Media Lab (1988-1996).

Senior Associate Dean; College of Computing
Professor; School of Interactive Computing
Phone
404.894.6856
Additional Research
Healthcare Security; Machine Learning; Mobile & Wireless Communications; Computer Vision and Robotics; Computer Graphics and Animation; Computational Photography and Video; Intelligent and Aware Environments; Digital Special Effects; Computational Journalism; Social Computing
Research Focus Areas

Kamran Paynabar

Kamran Paynabar
kamran.paynabar@isye.gatech.edu
Departmental Bio

Kamran Paynabar is the Fouts Family Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his B.Sc. and M.Sc. in Industrial Engineering from Iran in 2002 and 2004, respectively, and his Ph.D. in Industrial and Operations Engineering from The University of Michigan in 2012. He also holds an M.A. in Statistics from The University of Michigan. His research interests comprise both applied and methodological aspects of machine-learning and statistical modeling integrated with engineering principles. He is a recipient of the INFORMS Data Mining Best Student Paper Award, the Best Application Paper Award from IIE Transactions, the Best QSR refereed paper from INFORMS, and the Best Paper Award from POMS. He has been recognized with the Georgia Tech campus level 2014 CETL/BP Junior Faculty Teaching Excellence Award and the Provost Teaching and Learning Fellowship. He served as the chair of QSR of INFORMS, and the president of QCRE of IISE.

Assistant Professor
Phone
404.385.3141
Office
Groseclose Building, Room 436
Additional Research
High-dimensional data analysis for systems monitoring, diagnostics and prognostics, and statistical and machine learning for complex-structured streaming data including multi-stream signals, images, videos, point clouds and network data with applications ranging from manufacturing including automotive and aerospace to healthcare.
Personal Website