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Keegan Kang
SUTD Faculty Fellow
Home / People / Faculty / Keegan Kang

Education

I completed my PhD at Cornell University in 2017.  Before my PhD, I did a four year integrated masters BSc MMORSE degree at the University of Warwick, and graduated with First Class Honours in 2012.

Research Interests

My research interests are in LSH algorithms, machine learning, statistical theory, and educational pedagogy. I am currently exploring using Monte Carlo methods to complement random projections and other LSH algorithms in conjunction with machine learning algorithms to increase their predictive power without incurring a large speed tradeoff. I am also constantly seeking ways to improve the teaching of courses to undergraduates.

Research Projects

I am currently involved in the following projects.

  • Improving Random Projections and LSH Schemes with Statistical Techniques {PI} (MOE2018-T2-2-013)
    This is joint and ongoing work with co-PIs Wong Wei Pin and Sergey Kushnarev, and collaborators Karthyek Murthy and Bob Durrant. This work is funded by the Singapore Ministry of Education’s Tier 2 AcRF grant.
  • “Big Data” and Theoretical Calculations Aided Molecular Design of Fluorophores: from Trial-and-Error to Molecular Engineering {co-PI} (IDG31800104)
    This is joint and ongoing work with PI Liu Xiaogang, and co-PIs Richmond Lee, and Michinao Hashimoto. This work is funded by a grant from the SUTD-MIT International Design Centre.
  • Identifying bottlenecks in teaching and learning mathematics at university {PI}
    This is joint and ongoing work with co-PIs Wong Wei Pin, Nachamma Sockalingam, Sergey Kushnarev, and Tan Da Yang in understanding the difficulties that students face when transitioning to university mathematics, in order to develop a mathematics course which caters to their needs. This work is funded in part by the SUTD Faculty Early Career Award.
  • Autograder for R Code
    I have been involved in developing an autograder for R code since becoming a TA for Statistical Computing in Spring 2016 at Cornell University. The autograder is written in such a way that a TA with minimal programming experience should be able to use the autograder to grade code.

Publications

  1. Keegan Kang, Wong Wei Pin, Sergey Kushnarev, and Haikal Yeo. “Improving Locality Sensitive Hashing Algorithms with Maximum Likelihood Estimators” (submitted)
  2. Keegan Kang. “Correlations Between Random Projections and the Bivariate Normal” (submitted)
  3. Keegan Kang, Wong Wei Pin (2018). “Improving Sign Random Projections with Extra Information“. In Proceedings of the 35th International Conference of Machine Learning volume 80 of Proceedings of Machine Learning Research, Stockholm, Sweden, Jul 10-15, 2018, pp. 2484-2492.
  4. Keegan Kang (2017). “Random Projections with Bayesian Priors“. In Natural Language Processing and Chinese Computing – 6th CCF International Conference, NLPCC 2017, Dalian, China, November 8-12, 2017, Proceedings, pp. 170-182.
  5. Keegan Kang (2017). “Using the Multivariate Normal to Improve Random Projections“. In Intelligent Data Engineering and Automated Learning – IDEAL 2017: 18th International Conference, Guilin, China, October 30 – November 1, 2017, Proceedings, pp. 397-405.
  6. Keegan Kang and Giles Hooker (2017). “Control variates as a variance reduction technique for random projections“. In Pattern Recognition Applications and Methods – 6th International Conference, ICPRAM 2017, Porto, Portugal, February 24-26, 2017, Revised Selected Papers, pp.1-20.
  7. Keegan Kang and Giles Hooker (2017). “Random projections with control variates“. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, Volume 1: ICPRAM, pp. 138-147.
  8. Keegan Kang and Giles Hooker (2016). “Improving the Recovery of Principal Components with Semi-Deterministic Random Projections“. In 2016 Annual Conference on Information Science and Systems, CISS 2016, Princeton, NJ, USA, March 16-18, 2016, pp. 596-601.
  9. Keegan Kang and Giles Hooker (2016). “Block Correlated Deterministic Random Projections”. 5th Annual International Conference On Computational Mathematics, Computational Geometry and Statistics.
  10. Ben Athiwaratkun and Keegan Kang (2015). “Feature Representation in Convolutional Neural Networks“, arXiv preprint arXiv:1507.02313.

Teaching

  • 10.004 Advanced Math II, co-course lead (Trimester 3, 2019)
  • 10.007 Modelling the Systems World, cohort instructor (Trimester 1 2018, Trimester 1 2019)
  • 10.004 Advanced Math II retake class (Trimester 3, 2017)
  • 10.004 Advanced Math II, cohort instructor (Trimester 3 2017, Trimester 3 2018)

Teaching reviews can be found here.

Service and Outreach Activities

  • Science Mentorship Programme (SMP) 2019, where I have mentored four groups (February 2019 – August 2019) with project titles below.1. Glenda Chong Rui Ting and Tan Wee Le and Ryan Tan Zi Lin and Keegan Kang. “New b-bit minwise hashing
    Won 3M Best Project Award in the category Computer Science / Mathematics

    2. Wang Hengyue and Kuo Hsin Wei and Ryan Nathaniel Thesman and Keegan Kang. “Improving b-bit Minwise Hashing with Addition of Standard Vectors

    3. Kuai En Kai, Ethan and Krithikh Gopalakrishnan and Tan Jun Wei and Keegan Kang. “Improving Simple and Efficient Minwise Hashing with Extra Information

    4. Leow May Gwen Veronica and Carol Gan Jianing and Caithlin Ho and Keegan Kang. “Comparison of Different Hashing Algorithms Using Permutations with Binary Data

  • I conduct the SUTD Academy course: “(Statistical) Reading and Writing for the 21st Century”
  • SkillsFuture Festival Activity: “Don’t Trust Numbers” (June, 2018)

Honours and Awards

  • SMP Outstanding Mentor Award (2019)
  • SUTD Faculty Early Career Award (2017)
  • Outstanding Graduate Teaching Assistant (2017)
  • Giving to Warwick student prize (2011)
  • Giving to Statistics student prize (2011)
  • Warwick Advantage gold award (2011)

Miscellaneous Links

  • When I was an undergraduate student, I wrote a few revision guides and scribed lecture notes for some mathematics and statistics courses, which can be found here. They may still be of some use.
  • I once appeared on the BBC.
2020-02-03T22:24:10+00:00