Research Interests

Machine Learning, Data Mining, Computational Biology, Health Sciences


  • Ph.D. in Computer Science, University of Virginia, 2014-present
  • M.S in Computer Science, University of Virginia, 2012-2014 (GPA : 3.8)
  • B.E in Computer Engineering, University of Pune (India) , 2008-2012 (73% – Distinction)

Relevant Papers

  • R. Singh, Jack Lanchantin, Arshdeep Sekhon, and Yanjun Qi, “Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin”. NIPS (to appear). [arXiv] (2017)
  • R. Singh, Arshdeep Sekhon, Kamran Kowsari, Jack Lanchantin, Beilun Wang, and Yanjun Qi, “GaKCo: a Fast GApped k-mer string Kernel using COunting”. ECML-PKDD. [arXiv] (2017)
  • R. Singh, Jack Lanchantin, Gabriel Robins, and Yanjun Qi. “Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction”. IEEE/ACM Transactions on Computational Biology and Bioinformatics. (BIOKDD) [paper](2016)
  • R. Singh, Jack Lanchantin, Gabriel Robins, and Yanjun Qi. “DeepChrome:  Deep-learning for predicting gene expression from histone modifications”. Bioinformatics. (ECCB). [paper](2016)
  • R. Singh and Yanjun Qi. “Character based String Kernels for Bio-Entity Relation Detection”. ACL BioNLP Workshop. [paper](2016)
  • R. Singh, Cem Kuscu, Aaron Quinlan, Yanjun Qi, and Mazhar Adli.”Cas9-chromatin binding information enables more accurate CRISPR off-target prediction”. Nucleic Acid Research. [paper](2015)

Other Publications

Work Experience

  • Research Intern at Microsoft Research New England (Summer, 2017)

Teaching Experience (TA)

  • Fall 2015: Machine Learning (Graduate Level)
  • Spring 2013: Theory of Computation (Undergraduate Level)
  • Fall 2012: Algorithms (Undergraduate Level)

Technical Skills

  • Hardware/Software : UNIX/Linux/Mac, Windows
  • Languages : Python, C/C++, Bash script, LaTeX, R, MATLAB

Relevant Coursework

  • Advanced Deep Learning (Fall, 2017)
  • Large Scale Machine Learning (Spring, 2015)
  • Optimization (Fall, 2015)
  • Machine Learning and Data Mining in Practice for Biomedicine (Spring, 2014)
  • Machine Learning (Fall, 2013)
  • Statistics, Bioinformatics and Protein Structure (Spring, 2013)
  • Theory of Computation (Fall, 2012)

Awards and Honors

  • Grace Hopper Celebration of Women in Computing Student Scholarship – 2017 (Anita Borg Institute)
  • Graduate Student Award for Outstanding Research 2016-2017 (Department of Computer Science, UVA)
  • First Prize in Podium Presentation – 2017 (13th Annual UVA Engineering Research Symposium)
  • Travel Fellowship ECCB – 2016 (International Society of Computational Biology)
  • L. William Ballard Fellowship – 2015 (School of Engineering and Applied Sciences, University of Virginia)
  • Chief of Army Staff Best Outgoing Student Award – 2012 (Army Institute of Technology, University of Pune)
  • TATA Merit Scholarship Award – 2010 (Army Institute of Technology, University of Pune)


Graduate Society of Women Engineers (GradSWE) @ University of Virginia

Participated in:

  • Undergraduate Mentorship Program (An undergraduate SWE member is paired with a graduate student mentor)
  • Professional panel discussion for High School Visitation with SWE (Offered perspective about STEM research to high school girls)

Download CV (PDF)