Greg Hamerly

a picture of me
Professor and Interim Chair
Hankamer 330.20
Computer Science Department
Baylor University
One Bear Place #97141
Waco, TX 76798-7141, USA
Phone: +1-254-710-3876
Fax: +1-254-710-3889
Email: hamerly at cs dot baylor dot edu

I am a professor of computer science at Baylor University in Waco, Texas. On this webpage you can find more information about my research, teaching, schedule, publications, funding, and other things. Here is my Google Scholar page.

Research

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My research is in machine learning, a sub-field of artificial intelligence. Some of the projects I'm involved in are:

Here is my NSF-funded research in developing a novel curriculum in computational thinking.

Here is my curriculum vitae, here are my publications, and here is my Ph.D. thesis.

Teaching

CourseNameLinks
CSI/STA 2300 Intro to Data Science S21
CSI 3334 Data structures and algorithms S24, F23, S23, S22, F21, S21, F20, S20, F19, S19, F18, F17, F16, F15, F14, F13, F11, S11, F10, S10, F09, S09, F08, S08, F07, S07, F06, S06, F05, F04
CSI 4144 Competitive learning I/II/III F22, S22, F21, S21, F20, S20, F19, S19, F18, F17, S17, F16, S16, F15, S15, F14, S14, F13, S13, F12, S12, S11, F10, S10, F08, S08, F07, S07, F06, S06, F05
CSI 4330 Foundations of computing F24 F12
CSI 5310 (formerly 4336) Computer science theory F24, F22, F21, F20, F19, F18, F17, F16, F15, F14, F13, F12, F11, F10, F09, F08, F07, F06, F05
CSI 5010 Graduate Seminar
(jointly held with 4010) 		F14, F13, F12, F11
CSI 5325 Introduction to Machine Learning S25, S23, S22, S20, S19, S18, S17, S16, S15, S14, S13, S12, S11, S10, S09, S08, S07, S06, S05

Schedule and Office Hours

My office hours for Spring 2025 are:

To make an appointment for a meeting outside of my office hours, please first consult my Outlook Calendar (linked below) and find a reasonable time (within business hours) that I am available. Then send me an Outlook Calendar invitation for meeting, preferably at least a day in advance of when you want to meet. Please schedule only the amount of time you think you'll need (e.g. 10-15 minutes for a student meeting).

Student meetings and office hours are in my office, but could be online if you prefer (you may chat with me via Microsoft Teams). Due to my busy and changing schedule, some office hours may be delayed or cancelled without notice.

Here is my Outlook Calendar that you can view in a separate page. It defaults to a monthly view; you can see more details by selecting a weekly / daily view.

Publications

  1. Shaif Chowdhury, Greg Hamerly, Monica McGarrity. Active Learning Strategy Using Contrastive Learning and K-Means for Aquatic Invasive Species Recognition. In The Maritime Computer Vision Workshop of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024.
  2. Micah Bowman, Ryan McManamay, Alejandro Rodriguez Perez, Greg Hamerly, William Arnold, Eric Steimle, Kurt Kramer, Bryan Norris, David Prangnell, Mike Matthews. Analysis of an optical imaging system prototype for autonomously monitoring zooplankton in an aquaculture facility. Aquacultural Engineering, Volume 104, 2023.
  3. Korn Sooksatra, Greg Hamerly, Pablo Rivas. Is ReLU Adversarially Robust?. In proceedings of the LatinX in AI Workshop at ICML, 2023.
  4. Shaif Chowdhury, Sadia Nasrin Tisha, Monica E. McGarrity, Greg Hamerly. Video-Based Recognition of Aquatic Invasive Species Larvae Using Attention-LSTM Transformer. In proceedings of the International Symposium on Visual Computing, 2023.
  5. Shaif Chowdhury, Greg Hamerly. Recognition of Aquatic Invasive Species Larvae using Autoencoder-based Feature Averaging. In proceedings of the International Symposium on Visual Computing, 2022.
  6. Alibek Zhakubayev, Greg Hamerly. Clustering Faster and Better with Projected Data. In proceedings of the 6th International Conference on Information System and Data Mining, 2022. [pdf]
  7. 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 photographs In Science Advances, 2019. [web, pdf]
  8. Petr Ryšavý, Greg Hamerly. Geometric methods to accelerate k-means algorithms At SDM 2016, 2016. [pdf, supplementary graphs]
  9. 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.
  10. Greg Hamerly, Jonathan Drake. Accelerating Lloyd's algorithm for k-means clustering. Chapter in Partitional Clustering Algorithms (Springer), 2014. [pdf]
  11. 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]
  12. 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]
  13. Katherine Talcott, Elizabeth Shaw, Rebecca Holden, Brandon Taylor, Erich Baker, Greg Hamerly, Alex Kentsis, Shizuo Mukai, Carlos Rodriguez-Galindo, Bryan Shaw Colorimetric 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.
  14. 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]
  15. William A. Booth, Greg Hamerly, David Sturgill, Ivy Hamerly, Todd Buras. Computational Thinking: Building a Model Curriculum In ACET Journal of Computer Education and Research, 2012. [pdf]
  16. 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.
  17. Greg Hamerly, Greg Speegle, Efficient Model Selection for Large-Scale Nearest-Neighbor Data Mining In proceedings of the 2010 British National Conference on Databases (BNCOD 2010), June 2010. [pdf]
  18. Greg Hamerly, Making k-means even faster In proceedings of the 2010 SIAM international conference on data mining (SDM 2010), April 2010. [pdf]
  19. Bing Yin, Greg Hamerly, Hierarchical Stability-Based Model Selection For Clustering Algorithms In proceedings of the International Conference on Machine Learning and Applications, December 2009.
  20. Joshua Johnston, Greg Hamerly, Improving SimPoint accuracy for small simulation budgets with EDCM clustering In proceedings of the Second workshop on Statistical and Machine learning approaches to ARchitectures and compilaTion (SMART '08), January 2008. [pdf]
  21. Erez Perelman, Jeremy Lau, Harish Patil, Aamer Jaleel, Greg Hamerly, Brad Calder, Cross Binary Simulation Points In Proceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS-2007) March 2007. [pdf]
  22. Yu Feng, Greg Hamerly, PG-means: learning the number of clusters in data. In proceedings of the twentieth annual conference on neural information processing systems (NIPS), December 2006. [ps, pdf]
  23. Greg Hamerly, Erez Perelman, Jeremy Lau, Timothy Sherwood, Brad Calder, Using Machine Learning to Guide Architecture Simulation. Journal of Machine Learning Research, Volume 7, Pages 343-378, 2006. [abstract, pdf]
  24. Greg Hamerly, Erez Perelman, Brad Calder, Comparing Multinomial and K-means clustering for SimPoint. In the 2006 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS-2006), 2006. [abstract, pdf]
  25. Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Calder, SimPoint 3.0: Faster and more flexible program analysis. Journal on Instruction-Level Parallelism (JILP), September, 2005. [pdf]
  26. Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Calder, SimPoint 3.0: Faster and more flexible program analysis. Workshop on Modeling, Benchmarking and Simulation (MoBS), June 2005. [abstract, pdf]
  27. Brad Calder, Timothy Sherwood, Greg Hamerly, Erez Perelman, SimPoint: Picking Representative Samples to Guide Simulation. Chapter 7 in the book Performance Evaluation and Benchmarking, edited by Lizy Kurian John and Lieven Eeckhout; published by CRC Press, 2005.
  28. Jeremy Lau, Erez Perelman, Greg Hamerly, Timothy Sherwood, Brad Calder, Motivation for variable length intervals and hierarchical phase behavior. In the 2005 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS-2005), March 2005. [abstract, pdf]
  29. Jeremy Lau, Jack Sampson, Erez Perelman, Greg Hamerly, Brad Calder, The strong correlation between code signatures and performance. In the 2005 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS-2005), March 2005. [abstract, pdf]
  30. John Seng, Greg Hamerly, Exploring perceptron-based register value prediction. In the second value-prediction and value-based optimization workshop, October 2004. [pdf]
  31. Greg Hamerly, Erez Perelman, Brad Calder, How to use SimPoint to pick simulation points. In ACM SIGMETRICS Performance Evaluation Review, Volume 31(4), March 2004. [abstract, pdf]
  32. Tim Sherwood, Erez Perelman, Greg Hamerly, Suleyman Sair, Brad Calder, Discovering and Exploiting Program Phases. In IEEE Micro: Micro's top picks from computer architecture conferences, November-December 2003 (Vol. 23, No. 6) [pdf].
  33. Greg Hamerly, Charles Elkan, Learning the k in k-means. In proceedings of the seventeenth annual conference on neural information processing systems (NIPS), pages 281-288, December 2003. [ps, pdf] (Older UCSD technical report CS2002-0716 [ps])
  34. Erez Perelman, Greg Hamerly, Brad Calder, Picking Statistically Valid and Early Simulation Points. In proceedings of the international conference on parallel architectures and compilation techniques (PACT), September 2003. [abstract, pdf]
  35. Erez Perelman, Greg Hamerly, Michael Van Biesbrouck, Tim Sherwood, Brad Calder, Using SimPoint for Accurate and Efficient Simulation. In proceedings of the international conference on measurement and modeling of computer systems (SIGMETRICS), June 2003. [abstract, pdf]
  36. Tim Sherwood, Erez Perelman, Greg Hamerly, Brad Calder, Automatically characterizing large scale program behavior. In proceedings of the tenth international conference on architectural support for programming languages and operating systems (ASPLOS), October 2002. [abstract, pdf]
  37. Greg Hamerly, Charles Elkan, Alternatives to the k-means algorithm that find better clusterings. In proceedings of the ACM conference on information and knowledge management (CIKM), pages 600-607, November 2002. [ps] (Older UCSD technical report CS2002-0702 [ps])
  38. Greg Hamerly, Charles Elkan, Bayesian approaches to failure prediction for disk drives. In proceedings of the eighteenth international conference on machine learning (ICML), June 2001. [ps]

Here are links to my coauthors and collaborators: Brad Calder, Charles Elkan, Yu Feng, Aamer Jaleel, Jeremy Lau, Harish Patil, Erez Perelman, Jack Sampson, Suleyman Sair, John Seng, Tim Sherwood, Michael Van Biesbrouck.

An upper bound on my Erdős number is 4. One such path is me → Charles Elkan → Russell Greiner → Michael S. O. Molloy → Paul Erdős. Another such path is me → Tim Sherwood → Ömer Eğecioğlu → Charles Ryavec → Paul Erdős.

Thesis

Defended on June 26, 2003. Learning structure and concepts in data through data clustering. [ps, 1.8MB] [pdf, 3.6MB]
Thanks to Tom Stepleton at Sony Japan for catching a typo in one of my equations.

Funding

My research and teaching work has been generously supported by the following:

Current and former students

Current/former Affiliations

Programming contests

Links to other things I've done

Copyright © 2004 Greg Hamerly
Computer Science Department
Baylor University

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