Computer Science Department
School of Engineering & Computer Science (Rogers building)
105 Baylor Ave.
Waco, TX 76798-7356, USA
Email: hamerly at cs dot baylor dot edu
I am an associate professor of computer science at Baylor University in Waco, Texas. I'm also the director of graduate studies for computer science. On this webpage you can find more information about my research, teaching, schedule, publications, funding, and other things.
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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:
- Automatically detecting and classifying leukocoria of the pupil in the eye from natural images. This is for early detection of negative conditions such as Retinoblastoma.
- Unsupervised learning methods (clustering, primarily), and improving algorithms like k-means to be faster, give better-quality results, or to act more intelligently (such as by finding the number of clusters).
- Applying machine learning to the task of optimizing computer program simulation, in the SimPoint project.
- Mixtures of naive Bayes models in unsupervised learning, for the detection of failures in hard drives.
Here is my NSF-funded research in developing a novel curriculum in computational thinking.
|CSI 3334||Data structures and algorithms||Fall 2016,|
|CSI 4144||Competitive learning I/II/III||Fall 2016,|
|CSI 4330||Foundations of computing|
|CSI 4336||Computer science theory||Fall 2016,|
|CSI 5325||Introduction to Machine Learning|
|CSI 5010||Graduate Seminar |
(jointly held with 4010)
Weekly schedule (Fall 2016)
Here is my usual weekly schedule for this semester. If you can't stop by my office during office hours, please email me or just stop by at another time; I am usually glad to meet with students at other times.
Here is my Google calendar that shows when I have scheduled events.
- Geometric methods to accelerate k-means algorithms To appear at SDM 2016, 2016. [pdf, supplementary graphs] , .
- Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings. In BMC Opthamology, 2014. , , , .
- Accelerating Lloyd's algorithm for k-means clustering. Chapter in Partitional Clustering Algorithms (Springer), 2014. [pdf] , .
- A Convolutional Neural Network Approach for Classifying Leukocoria. In proceedings of the 2014 Southwest Symposium on Image Analysis and Interpretation (SSIAI), April, 2014. , , , .
- 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. , , , .
- 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. , , , , , , , , ,
- Accelerated k-means with adaptive distance bounds. In OPT2012: the 5th NIPS Workshop on Optimization for Machine Learning, December, 2012. [pdf] , .
- Computational Thinking: Building a Model Curriculum In ACET Journal of Computer Education and Research, 2012. [pdf] , , , , .
- 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. , , , ,
- 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] , ,
- Making k-means even faster In proceedings of the 2010 SIAM international conference on data mining (SDM 2010), April 2010. [pdf] ,
- Hierarchical Stability-Based Model Selection For Clustering Algorithms In proceedings of the International Conference on Machine Learning and Applications, December 2009. , ,
- 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] , ,
- Cross Binary Simulation Points In Proceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS-2007) March 2007. [pdf] , , , , , ,
- 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] , ,
- Using Machine Learning to Guide Architecture Simulation. Journal of Machine Learning Research, Volume 7, Pages 343-378, 2006. [abstract, pdf] , , , , ,
- 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] , , ,
- SimPoint 3.0: Faster and more flexible program analysis. Journal on Instruction-Level Parallelism (JILP), September, 2005. [pdf] , , , ,
- SimPoint 3.0: Faster and more flexible program analysis. Workshop on Modeling, Benchmarking and Simulation (MoBS), June 2005. [abstract, pdf] , , , ,
- 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. , , , ,
- 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] , , , , ,
- 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] , , , , ,
- Exploring perceptron-based register value prediction. In the second value-prediction and value-based optimization workshop, October 2004. [pdf] , ,
- How to use SimPoint to pick simulation points. In ACM SIGMETRICS Performance Evaluation Review, Volume 31(4), March 2004. [abstract, pdf] , , ,
- Discovering and Exploiting Program Phases. In IEEE Micro: Micro's top picks from computer architecture conferences, November-December 2003 (Vol. 23, No. 6) [pdf]. , , , , ,
- 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]) , ,
- Picking Statistically Valid and Early Simulation Points. In proceedings of the international conference on parallel architectures and compilation techniques (PACT), September 2003. [abstract, pdf] , , ,
- 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] , , , , ,
- 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] , , , ,
- 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]) , ,
- 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:, , , , , , , , , , , .
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.
My research and teaching work has been generously supported by the following:
- National Science Foundation
- Intel Corporation
- Baylor University Young Investigator's Development Program, University Research Council, and Undergraduate Research and Scholarly Achievement programs
Current and former students
- Vaclav Cibur, M.S. 2016 (expected)
- James Boer, M.S. 2016 (expected)
- Petr Ryšavý, M.S. 2015
- Ryan Yan, M.S. expected 2015
- Ryan Henning, M.S. 2014
- Paniz Karbasi, M.S. 2014, working on a Ph.D. in ECE at Baylor
- Li Guo, M.S. 2014
- Pablo Rivas-Perea, postdoc (2012-current)
- Jonathan Drake, M.S. 2013, Undergraduate Scholars Thesis 2011
- Tak-Chien Chiam, M.S. 2012, currently at Amazon
- Hao Guo, M.S. 2012
- George Montanez, M.S. 2011, working on a Ph.D. at Carnegie Mellon
- Winston Ewert, M.S. 2011, now at Google
- Lei Meng, M.S. 2011, working on a Ph.D. at Notre Dame
- Bing Yin, M.S. 2009, currently at Amazon
- Josh Johnston, M.S. 2007
- Yu Feng, M.S. 2006, currently at Microsoft
- I am a member of ACM
- DTAI group at the KU Leuven computer science department
- AI lab at the UCSD computer science department
- Computer science department at CalPoly, San Luis Obispo
- I went to the ACM ICPC world finals as a contestant for UC San Diego in 2000 and 2001.
- I have been a coach for students at UCSD and Baylor.
- I regularly teach Competitive Learning at Baylor, a course on how to do algorithmic problem solving. I developed this course with David Sturgill, and together we have authored more than 300 problems for the course.
- I participated in putting together the following contests:
- I have been a site director for the ICPC South Central USA regional competition in 2010-2013, 2015, 2016.
- I have been a member of the ICPC Live Analytics team at the ICPC world finals since 2011.
- In 2014-2016 I have been part of the ICPC World Finals judge team, with my primary responsibilities being for the dress rehearsal problems.