Kishan KC

Ph.D. Candidate

Golisano College of Computing and
Information Sciences (GCCIS)

Rochester Institute of Technology (RIT)

About me


I am a third year Ph.D. 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 focuses on developing computational methods to provide insights into underlying biological phenomena that are critical to understanding phenotypes in health and diseases. My research interests lie at the intersection of machine learning and network science, with a current emphasis on graph representation learning, graph neural networks and their application to computational biology.


Specifically, I am interested in developing deep learning model based on Graph Neural networks to integrate heterogeneous information such as topological landscape of interaction networks (multiple functional association networks, gene interaction networks, PPI networks), and additional information about genes (expression profiles, transcription factor binding sites, gene sequences, functional annotations from gene ontology, metabolic pathways, etc.) to derive functional insights about genes or proteins.


Research interests
Graph Representation Learning, Graph Neural Networks, Heterogeneous Data Integration, Computational Biology




Recent news

Nov 2018 Attended Biological Data Science and presented poster at Cold Spring Harbor Laboratory (CSHL).
Oct 2018 My application for PyTorch Scholarship Challenge from Facebook on Udacity was accepted.
Sep 2018 Our paper "GNE: A deep learning framework for gene network inference by aggregating biological information" was accepted at APBC 2019.
Sep 2018 Our paper "Sparse Covariance Modeling in High Dimensions with Gaussian Processes" was accepted at NIPS 2018.
Jul 2018 Poster entitled "Learning topology-preserving embeddings for gene interaction networks" was accepted for presentation at ECCB 2018 Poster Track.
Feb 2018 Presented poster "Network-based Learning to Infer Genetic Interaction Networks" in New Deep Learning Techniques Workshop at IPAM, UCLA.
Feb 2018 Attended New Deep Learning Techniques Workshop at IPAM, UCLA.
May 2017 Defended the PhD Research Potential Assessment.
Apr 2017 Presented poster "Reconstruction of Gene Regulatory Networks with Ensemble SVM" at GCCIS Research Showcase, RIT.
Aug 2016 Joined Rochester Institute of Technology as PhD Student in Computing and Information Sciences.


Publications

GNE: A deep learning framework for gene network inference by aggregating biological information
Kishan KC, Rui Li, Feng Cui, Qi Yu, Anne Haake
BMC Systems Biology/APBC 2019 (to appear)


Sparse Covariance Modeling in High Dimensions with Gaussian Processes
Rui Li, Kishan KC, Feng Cui, Justin Domke, Anne Haake
NIPS 2018 (Spotlight presentation) (to appear)


Learning topology-preserving embedding for gene interaction networks
Kishan KC, Rui Li, Feng Cui, Anne Haake
ECCB 2018 (Poster Track)

Projects


Gene Network Embedding

Reconstruction of Gene Regulatory Networks with Ensemble SVM

Multiplayer Web Checker

Knowledge Graph, a search engine

Other Projects