Welcome to the website of the RTG: Cross-Training in Statistics and Computer Science project. This project is generously funded by the National Science Foundation, under award DMS-1547433.

PEOPLE

• Genevera Allen, Assistant Professor, Department of Statistics.
• Kathy Ensor, Professor, Department of Statistics.
• Chris Jermaine, Professor, Department of Computer Science.
• Lydia Kavraki, Professor, Department of Computer Science.
• Marek Kimmel, Professor, Department of Statistics.
• Matt Koslovsky, Post-doc, Department of Statistics.
• Risa Myers, Lecturer, Department of Computer Science.
• Luay Nakhleh, Professor, Department of Computer Science. (PI)
• David Scott, Professor, Department of Statistics.
• Devika Subramanian, Professor, Department of Computer Science.
• Marina Vannucci, Professor, Department of Statistics. (PI)

ACTIVITIES

• STAT/COMP 496/696: RTG: Cross-training in Data Science.
  In the Fall 2016 offering, 19 PhD students and 1 post-doctoral fellow attended the course and gave presentations:
  • Mohamed Abdelrahman (CS), Beidi Chen (CS), Lee Chen (CS), Neel Desai (STAT), Peng Du (CS), Xian Fan (CS), Kelly Geyer (STAT), Jeong Hwan Kook (STAT), Terence Liff (STAT), Yinsen Miao (STAT), Nathan Osborne (STAT), Elin Shaddox (STAT), Ryan Spring (CS), Elizabeth Sweeney (STAT), Yaxuan Wang (CS), Ryan Warnick (STAT), Dingqiao Wen (CS), Robert Weylandt (STAT), Hamim Zafar (CS), Jiafan Zhu (CS).
• The 2018 Rice Undergraduate Data Science Summer Program (RUDSSP).
Through funding from this RTG award and generous support from TwoSigma, NASA, and the Provost's office, we were able to recruit eleven (11) undergraduates in Summer 2018. A major change in the program (over the Summer 2017 program) was the involvement of Dr. Risa Myers, a Data Science Lecturer in the Computer Science Department, as the person in charge of the program. Dr. Myers met with the teams individually on a weekly basis, and arranged for access to computing resources at Rice, among many other efforts. The following is a listing of the projects and assigned students and faculty members.
• Project: Simulating, inferring, and scaling up single nucleotide variants in single-cell genomic data.
  • Undergrads: Austin Liu, Noushin Quazi, and Minghao Yan
  • Grads: Hamim Zafar
  • Faculty member: Luay Nakhleh
• Project: Predicting sensorimotor disturbances with spacelift (a NASA project).
  • Undergrads: Kyran Adams and James Warner
  • Grads: Kelly Geyer
  • Postdoc mentor: Matt Koslovsky
• Project: Space mice study reproducibility (a NASA project).
  • Undergrads: Lynn Zhao and Adam Strathman
  • Grads: Nathan Osborne
  • Mentor: Dan Bourgeois
• Project: Deception networks of news articles.
  • Undergrads: Wei-Lin Hsiao, Diksha Gupta, and Soo Bin Park
  • Grads: Nathan Osborne
  • Mentor: Kelly Geyer
• The 2017 Rice Undergraduate Data Science Summer Program (RUDSSP).
This RTG award allowed us to recruit six (6) undergraduates in Summer 2017. Furthermore, through generous support from TwoSigma ($25,000) and the Rice offices of Provost Marie Lynn Miranda and Vice Presidet for Finance Kathy Collins, we were able to recruit ten (10) more undergraduates. We created the RUDSSP program where each of the sixteen undergraduates was assigned to a research project and worked closely with a graduate student. The following is a listing of the projects and assigned students and faculty members.
• Project: Evolutionary diversity across genomes.
  • Undergrads: Chabrielle Allen, Travis Benedict, Peter Dulworth
  • Grads: R.A.L. Elworth
  • Faculty member(s): Luay Nakhleh
• Project: Hierarchical graphical methods and network features.
  • Undergrads: Dessy Akinfenwa and Ami Sheth
  • Grads: Elin Shaddox
  • Faculty member(s): Marina Vannucci
• Project: Leveraging randomized machine learning algorithms.
  • Undergrads: Jay Ryu
  • Grads: Ryan Spring
  • Faculty member(s): Anshumali Shrivastava
• Project: Digital text forensics.
  • Undergrads: Arjoon Srikanth and Hao Wang
  • Grads: Kelly Geyer
  • Faculty member(s): Marina Vannucci
• Project: Applied finance.
  • Undergrads: William Guo
  • Grads: Michael Weylandt
  • Faculty member(s): Kathy Ensor
• Project: Simulating single-cell DNA evolution.
  • Undergrads: Mustafa El-Gamal
  • Grads: Hamim Zafar
  • Faculty member(s): Luay Nakhleh
• Project: Scalable deep learning.
  • Undergrads: Emily Braverman, Peter Jalbert, and Xincheng Tan
  • Grads: Ryan Spring
  • Faculty member(s): Anshumali Shrivastava
• Project: Optimization heuristics for learning hidden Markov models.
  • Undergrads: Jan Li
  • Grads: R.A.L. Elworth
  • Faculty member(s): Luay Nakhleh
• Project: Implementation of in-browser Python 3.
  • Undergrads: Sophia Jefferson
  • Grads: N/A
  • Faculty member(s): Scott Rixner
• Project: Parallelizing the FreshBreeze Kiva hardware simulator with Habanero Java's Actor model.
  • Undergrads: Justin Fan
  • Grads: N/A
  • Faculty member(s): Vivek Sarkar

LECTURES

PUBLICATIONS

• A. Aghazadeh, R. Spring, D. LeJeune, G. Dasarathy, A. Shrivastava, and R. G. Baraniuk, "MISSION: Ultra Large-Scale Feature Selection using Count-Sketches." Proceedings of the International Conference on Machine Learning 2018.
• E. Shaddox, C.B. Peterson, F.C. Stingo, N. Hanania, C. Cruickshank-Quinn, K. Kechris, R. Bowler, and M. Vannucci, "Bayesian inference of networks across multiple sample groups and data types." Biostatistics, invited revision 2018.
• R.A.L. Elworth, C. Allen, T. Benedict, P. Dulworth, and L. Nakhleh, "DGEN: A test statistic for detection of general introgression scenarios." Proceedings of the Workshop on Algorithms in Bioinformatics. Helsinki, Finland, 2018.
• R.A.L. Elworth, C. Allen, T. Benedict, P. Dulworth, and L. Nakhleh, "ALPHA: A toolkit for automated local phylogenomic analyses." Bioinformatics, 2018 (in Press).
• R. Spring and A. Shrivastava, "Scalable estimation via LSH Samplers." Workshop Track for International conference on Learning Representation 2017.
• R. Spring and A. Shrivastava, "Scalable and Sustainable Deep Learning via Randomized Hashing." Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 445-454. Halifax, Nova Scotia, Canada, 2017.
• R.A.L. Elworth and L. Nakhleh, "Inferring Local Genealogies on Closely Related Genomes." Proceedings of RECOMB Comparative Genomics, 213-231. Barcelona, Spain, 2017.

LINKS

• The Data Science Initiative at Rice University.