Kishan KC

RIT Computer Science Ph.D. Student
kk3671_at_rit_dot_edu

I am a second year PhD student in Computing and Information Science at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT) . I am a Graduate Research Assistant under the supervision of Prof. Anne Haake and Prof. Rui Li. My research interest is to develop computational methods to provide insights into underlying biological phenomena that are critical to understanding phenotypes in health and diseases. Specifically, I am currently working to integrate heterogeneous information such as interaction networks, expression profiles, transcription factor binding sites, gene sequences, functional annotations from gene ontology, metabolic pathways, etc. to derive functional insights about genes or proteins.


I am interested in learning low-dimensional vector representation for each gene or protein, that canonically represents topological patterns in interaction networks and various information associated with that gene, which can be plugged into off-the-shelf machine learning methods for diverse functional tasks: gene function prediction, gene ontology reconstruction, and genetic interaction prediction.


Research interests: Heterogeneous Data Integration, Network Representation Learning, Deep Learning, Computational Biology





Timeline

February 2018 Attended New Deep Learning Techniques Workshop at IPAM, UCLA.
May 2017 Defended the PhD Research Potential Assessment.
August 2016 Joined Rochester Institute of Technology as PhD Student in Computing and Information Sciences.

Academic Projects

Gene Network Embedding Developed a deep learning framework to integrate gene expression data with topological properties of gene interaction networks that models their relative importance in gene interaction prediction. This framework learns a lower-dimensional representations for a gene, which is used to predict its interactions with other genes.
Reconstruction of Gene Regulatory Networks with Ensemble SVM Developed an ensemble of Support Vector machine that identifies the regulatory relationships between transcription factors and target genes. Framing the reconstruction as feature selection problem, we followed recursive feature elimination approach to identify the candidate transcription factors that are significant in expression of a target gene.


Non-Academic Projects

Agricultural Data Integration and Analysis Major Qualifying project to predict the amount of crop yield based on different environmental factors like temperature, rainfall.
3D CAD Viewer with HTML5 A real world particle and container simulator in HTML5 plus collaborate by sharing via web application.
Customer Management and Behavior Analysis Application A web application to store and analyze data about customers and visualize trends of customer visits and preferences of foods.
Online Book Reservation System A web application to assist students with details about books, their availability and reservation.
Water Jug Puzzle solver A quick implementation of Water Jug Puzzle Solver for different number of water jugs using Parallel Java Library.
Floyd–Warshall algorithm in OpenMP A parallel implementation of Floyd–Warshall algorithm to solve All pairs shortest path problem using OpenMP.
Next Word Predictor A predictive text model to predict the likelihood of next word to appear in a sentence based on n-gram model.
Tourist Visit to Nepal A shiny application to visualize tourism data.