rage against the machine learning
personal website of ryan r. curtin


Understand an algorithm. Make it faster.


Ph.D. in Electrical and Computer Engineering
Georgia Institute of Technology, Atlanta, GA
Advisors: Dr. David V. Anderson, Dr. Alexander G. Gray, Dr. Charles L. Isbell, Jr.
completed August 2015

Master of Science in Electrical and Computer Engineering
Georgia Institute of Technology, Atlanta, GA
received May 2009

Bachelor of Science with Highest Honors in Electrical Engineering
Georgia Institute of Technology, Atlanta, GA
received May 2008

relevant and recent publications


professional experience

RelationalAI, Atlanta, GA
Summer 2018 - present
Computer Scientist

At RelationalAI my work consists of developing new accelerated in-database algorithms for machine learning problems, as well as helping design and implement the database system on which these algorithms will be run.

Symantec Corporation, Atlanta, GA
Center for Advanced Machine Learning
Fall 2015 - Summer 2018
Principal Research Scientist

My responsibilities at Symantec fell into roughly three categories:

Georgia Institute of Technology, Atlanta, GA
Fall 2009 - Fall 2015
Graduate Research Assistant

At various times I worked for these four labs:

I was/am also the primary developer and maintainer for mlpack, an open-source scalable C++ machine learning library that is in use by scientists worldwide, currently with over 75k downloads and 100 contributors.

I was also involved as a TA or guest lecturer for multiple courses and groups.

Compuglobalhypermeganet, L.L.C., Atlanta, GA
Spring 2013 - present

I do machine learning consulting and advisement.

Google, Inc., Mountain View, CA
Summer 2010
Software Engineering Intern

I worked with the Similar Pages team to provide improved search results.

Georgia Tech Research Institute, Atlanta, GA
Food Processing Technology Division
Fall 2009 - Spring 2010
Graduate Research Assistant

I applied machine learning techniques for stress detection in broiler chickens.

Georgia Tech Research Institute, Atlanta, GA
Spring 2009 - Fall 2009
Graduate Research Assistant

I investigated techniques for the A-to-D frontend of a radar warning receiver.

Nexidia, Inc., Buckhead, GA
Summer 2007
Research Intern

I created voice synthesizers that can generate missing samples and still be comprehensible.

advising, mentoring, and professional service

Through both Google Summer of Code and the labs I have worked for, I have advised and mentored a number of students.

I have also served in a number of volunteer positions.

full publication list

(in preparation)

(journal publications)

  1. ``User-Friendly Sparse Matrices with Hybrid Storage and Template-Based Expression Optimisation''. C. Sanderson, R.R. Curtin. Submitted to Mathematical and Computational Applications, 2019. [pdf] [code]
  2. ``mlpack 3: a fast, flexible machine learning library''. R.R. Curtin, M. Edel, M. Lozhnikov, Y. Mentekidis, S. Ghaisas, S. Zhang. The Journal of Open Source Software, volume 3, issue 26, pp. 726, 2018. [pdf]
  3. ``Exploiting the structure of furthest neighbor search for fast approximate results''. R.R. Curtin, J. Echauz, A.B. Gardner. Information Systems, 2018. [pdf]
  4. ``gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation''. C. Sanderson, R.R. Curtin. The Journal of Open Source Software, vol. 2, 2017. [pdf]
  5. ``Armadillo: a template-based C++ library for linear algebra''. C. Sanderson, R.R. Curtin. Journal of Open Source Software, vol. 1:26, pp. 1-2, 2016. [pdf
  6. ``Plug-and-play runtime analysis for dual-tree algorithms''. R.R. Curtin, D. Lee, W.B. March, P. Ram. The Journal of Machine Learning Research, vol. 16, p. 3269-3297, 2015. [pdf]
  7. ``Dual-tree fast exact max-kernel search''. R.R. Curtin, P. Ram. Statistical Analysis and Data Mining, vol. 7, issue 4, p. 229-253, 2014. [pdf]
  8. ``mlpack: a scalable C++ machine learning library''. R.R. Curtin, J.R. Cline, N.P. Slagle, W.B. March, P. Ram, N.A. Mehta, A.G. Gray. In The Journal of Machine Learning Research (JMLR), vol. 14, p. 801-805, 2013. [pdf]

(conference and workshop publications)

  1. ``On Coresets for Regularized Loss Minimization''. R.R. Curtin, S. Im, B. Moseley, K. Pruhs, A. Samadian. Submitted to NeurIPS 2019, 2019.
  2. ``On functional aggregate queries with additive inequalities''. M.A. Khamis, R.R. Curtin, B. Moseley, H.Q. Ngo, X.L. Nguyen, D. Olteanu, M. Schleich. Accepted to The 2019 ACM SIGMOD/PODS International Conference on Management of Data, 2019. [pdf]
  3. ``Detecting DGA domains with recurrent neural networks and side information''. R.R. Curtin, A.B. Gardner, S. Grzonkowski, A. Kleymenov, A. Mosquera. Accepted to The 14th International Conference on Availability, Reliability, and Security, 2019.
  4. ``ensmallen: a flexible C++ library for efficient function optimization''. S. Bhardwaj, R.R. Curtin, M. Edel, Y. Mentekidis, C. Sanderson. Proceedings of the Systems for ML Workshop at NeurIPS 2018, 2018. [pdf]
  5. ``A User-Friendly Hybrid Sparse Matrix Class in C++''. C. Sanderson, R.R. Curtin. Proceedings of The 2018 International Congress on Mathematical Software (ICMS 2018), p. 422--430, South Bend, Indiana, 2018. [pdf] [bib] [code]
  6. ``An open source C++ implementation of multi-threaded Gaussian Mixture Models, k-means and expectation maximisation.''. C. Sanderson, R.R. Curtin. Proceedings of the 11th International Conference on Signal Processing and Communication Systems (ICSPCS 2017), p. 1-8, Surfers Paradise, Gold Coast, Australia, 2017. [pdf]
  7. ``pfsuper: simulation-based prognostics to monitor and predict sparse time series''. J. Echauz, A.B. Gardner, R.R. Curtin, N. Vasiloglou, G.J. Vachtsevanos. In Annual Conference of the Prognostics and Health Management Society 2017 (PHM '17), p. 1-9, St. Petersburg, Florida, 2017. [pdf]
  8. ``A dual-tree algorithm for fast k-means clustering with large k'', R.R. Curtin. In Proceedings of the 2017 SIAM International Conference on Data Mining, p. 300-308, Houston, Texas, 2017. [pdf]
  9. ``Fast approximate furthest neighbors with data-dependent candidate selection''. R.R. Curtin, A.B. Gardner. In Similarity Search and Applications 2016 (SISAP 2016), p. 221-235, Tokyo, Japan, 2016. [pdf]
  10. ``Faster dual-tree traversal for nearest neighbor search''. R.R. Curtin. In Similarity Search and Applications, p. 77-89, Glasgow, Scotland, 2015. [pdf]
  11. ``Collaborative filtering via matrix decomposition in mlpack''. S. Agrawal, R.R. Curtin, S. Ghaisas, M.R. Gupta. In ICML 2015 Workshop on Machine Learning Open Source Software, Lille, France, 2015. [pdf]
  12. ``An automatic benchmarking system''. M. Edel, A. Soni, R.R. Curtin. In NIPS 2014 Workshop on Software Engineering for Machine Learning, Montreal, Canada, 2014. [pdf]
  13. ``Classifying broiler chicken condition using audio data''. R.R. Curtin, W. Daley, D.V. Anderson. GlobalSIP 2014 Symposium on Signal Processing Applications Related to Animal Environments, Atlanta, Georgia, 2014. [pdf]
  14. ``Tree-independent dual-tree algorithms''. R.R. Curtin, W.B. March, P. Ram, D.V. Anderson, A.G. Gray, C.L. Isbell, Jr. In Proceedings of The 30th International Conference on Machine Learning (ICML '13), p. 1435-1443, Atlanta, Georgia, 2013. [pdf]
  15. ``Fast exact max-kernel search''. R.R. Curtin, P. Ram, A.G. Gray. In SIAM International Conference on Data Mining (SDM '13), p. 1-9, Austin, Texas, 2013. Nominated for Best Paper Award. [pdf]
  16. ``mlpack: a scalable C++ machine learning library''. R.R. Curtin, J.R. Cline, N.P. Slagle, M.L. Amidon, A.G. Gray. In NIPS 2011 Workshop on Big Learning, Granada, Spain, 2011. [pdf]
  17. ``Learning distances to improve phoneme classification''. R.R. Curtin, N. Vasiloglou, D.V. Anderson. In Proceedings of the 2011 IEEE International Workshop on Machine Learning in Signal Processing (MLSP 2011), p. 1-6, Beijing, China, 2011. [pdf]

(technical reports/other)

  1. ``A generic and fast C++ optimization framework''. R.R. Curtin, S. Bhardwaj, M. Edel, Y. Mentekidis. arXiv preprint arXiv:1711.06581, 2017. [pdf]
  2. ``Designing and building the mlpack open-source machine learning library.''. R.R. Curtin, M. Edel. Submitted to The Fourth International Conference of PUST (ICOPUST 2017)---conference cancelled. 2017. [pdf]
  3. ``Detecting adversarial samples from artifacts''. R. Feinman, R.R. Curtin, S. Shintre, A.B. Gardner. arXiv preprint arXiv:1703.00410, 2017. [pdf]
  4. ``Improving dual-tree algorithms''. Ph.D. thesis, Georgia Institute of Technology, 2015. [pdf]
  5. ``Single-tree GMM training''. R.R. Curtin. Technical report GT-CSE-2015-01, Georgia Institute of Technology, School of Computational Science and Engineering, 2015. [pdf]

references available upon request.


back to index