Education

• Ph.D., University of California San Diego (2003)
• MS, University of California San Diego (2001)
• BS, California Polytechnic State University (1999)

Contact Info:

Email: Greg_Hamerly@baylor.edu

Phone: (254) 710-6846

Academic Specialization:

Development & Application of Algorithms in Machine Learning & Data Clustering

Google Scholar

Research Interests

• Machine Learning
• Unsupervised Learning
• Efficient Learning

Recent Classes

• CSI 4336 Introduction to Computation theory
• CSI 5325 Introduction to Machine Learning

Research Focus

Dr. Hamerly's research focuses on machine learning, efficient and robust clustering, detecting photographic symptoms of the disease, quantification of invasive freshwater species, text mining and critical analysis, collaborative learning, and competitive programming.

Media

• Look Here: Phone App Checks Photos For Eye Disease
• Eye cancer in children? Baylor profs develop an app for that

Projects

• Eye Disease Symptom Detection
• Fast and Robust Data Clustering
• Mining Text for Critical Analysis
• Collaborative Learning and Competitive Programming

Selected Publications

1. Micheal C. Munson, Devon L. Plewman, Katelyn M. Baumer, Ryan Henning, Collin T. Zahler, Alexander T. Kietzman, Alexandra A. Beard, Shizuo Mukai, Lisa Diller, Greg Hamerly, Bryan F. Shaw. Autonomous early detection of eye disease in childhood photographsin Science Advances, 2019. [web, pdf]
2. Petr Ryšavý, Greg Hamerly. Geometric methods to accelerate k-means algorithmsat SDM 2016, 2016. [pdf, supplementary graphs]
3. Pablo Rivas-Perea, Erich Baker, Greg Hamerly, Bryan F Shaw. Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings.In BMC Opthamology, 2014.
4. Greg Hamerly, Jonathan Drake. Accelerating Lloyd's algorithm for k-means clustering.Chapter in Partitional Clustering Algorithms (Springer), 2014. [pdf]
5. Ryan Henning, Pablo Rivas-Perea, Bryan Shaw, Greg Hamerly. A Convolutional Neural Network Approach for Classifying Leukocoria. In proceedings of the 2014 Southwest Symposium on Image Analysis and Interpretation (SSIAI), April, 2014. [pdf]
6. Pablo Rivas-Perea, Ryan Henning, Bryan Shaw, Greg Hamerly. Finding the Smallest Circle Containing the Iris in the Denoised Wavelet Domain. In proceedings of the 2014 Southwest Symposium on Image Analysis and Interpretation (SSIAI), April, 2014. [pdf]
7. Katherine Talcott, Elizabeth Shaw, Rebecca Holden, Brandon Taylor, Erich Baker, Greg Hamerly, Alex Kentsis, Shizuo Mukai, Carlos Rodriguez-Galindo, Bryan ShawColorimetric Image Analysis in Detection of Leukocoria from Retinoblastoma in Snapshots Taken by Standard Digital Photography. Meeting Abstract. In Investigative Ophthalmology & Visual Science June 2013. Volume 54, Issue 15, Page 1584.
8. Jonathan Drake, Greg Hamerly. Accelerated k-means with adaptive distance bounds.In OPT2012: the 5th NIPS Workshop on Optimization for Machine Learning, December, 2012. [pdf]
9. William A. Booth, Greg Hamerly, David Sturgill, Ivy Hamerly, Todd Buras. Computational Thinking: Building a Model Curriculumin ACET Journal of Computer Education and Research, 2012. [pdf]
10. Greg Hamerly, Erez Perelman, Timothy Sherwood, Brad Calder, Representative Sampling Using SimPoint.Chapter 10 in the book Processor and System-on-Chip Simulation, edited by Rainer Leupers and Olivier Temam; published by Springer, 2010.
11. Greg Hamerly, Greg Speegle, Efficient Model Selection for Large-Scale Nearest-Neighbor Data MiningIn proceedings of the 2010 British National Conference on Databases (BNCOD 2010), June 2010. [pdf]
12. Greg Hamerly, Making k-means even fasterin proceedings of the 2010 SIAM international conference on data mining (SDM 2010), April 2010. [pdf]