Students & Research

Donghoon Lee - Gerstein lab (entered Fall 2014)
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Donghoon’s research has primarily focused on cancer genomics. He works on developing computational methods that integrate genomic, transcriptomic, and epigenomic signals from various next-generation functional sequencing assays. Using a comprehensive set of functional elements and an accurate regulatory network, he works on interpreting non-coding mutations and gene regulation. In particular, he is interested in studying the roles of epigenetics and chromatin structure on transcriptional and splicing regulation in cancer. Using integrative approach, he plans to decipher the hidden messages buried within the epigenetic landscape.

Xiaotong Li - Gerstein lab/ - Pusztai lab (entered Fall 2014)
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Xiaotong is interested in next-generation sequencing data analysis, with a major focus on breast cancer. Currently she is working on whole genome sequencing analysis on inflammatory breast cancer, and heterogeneity analysis.

 
Ryan Powles - Zhao lab/ - Pusztai lab (entered Fall 2014)
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Ryan applies network-level analysis tools and clonal evolution methods to sequencing data to characterize resistance in breast cancer to subtype-specific targeted therapies.

Jun Zhao - Kluger lab/ - Flavell lab (entered Fall 2014)
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Jun’s research focuses on the analysis of high-throughput sequencing data, RNA-seq, CRISPR, CLIP-Seq, etc. The goal is to help understand the genetic mechanisms of immune-related biological processes.

 
Daniel Chawla - Kleinstein lab (entered Fall 2015)
Dingjue Ji - Zhao lab (entered Fall 2015)

Frank is working on spatio-temporal transcriptomic data of brain. He is interested in developing new methods to identify the expression pattern of specific genes related to brain development. The new approaches are expected to help to understand the mechanisms of brain diseases like ASD.

Kevin Lopez - Brandt lab (entered Fall 2015)
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Kevin works on image classification using deep learning and convolutional neural networks. He is also working on expanding the capabilities of Yale Image Finder by applying deep learning methods to classify images from PubMed publications.

Quan Zhou - Kleinstein lab (entered Fall 2015)
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Julian works with high-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) data, especially in the context of B cell-mediated autoimmune diseases. He is interested in developing novel methods for inferring B cell lineages that help shed light on their developmental pathways.

David Chang - Brandt lab/ - Zhao lab (entered Fall 2016)
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David is looking at large drug perturbation and gene expression datasets to explore efficient drug discovery methods that can improve the outcomes of immunotherapy treatments. The idea is to computationally characterize drugs by their capacity to modulate the neoepitope landscape of cancer cell lines, and to predict their effects on patient outcomes when used in conjunction with immunotherapy treatments.

Hussein Mohsen - Gerstein lab (entered Fall 2016)
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Hussein’s main research interests are machine learning and cancer genomics. He is working on projects that leverage genomics big data to explore variation patterns in cancer. In particular, he is interested in developing machine and deep learning methods that study the underlying interaction between somatic and germline genetic variations in pan-cancer tumor types.

Nir Neumark - Kaminski lab/ - Coifman lab (entered Fall 2016)
Jay Stanley - Krishnaswamy lab (entered Fall 2016)

Jay studies learned representations of biological data, in particular graph representations and their processing.

Jiawei Wang - Zhao lab (entered Fall 2016)

Jiawei’s research interest lies in imaging genetics and mental diseases. He is working on gene expression analysis to help discover the etiology of PTSD and graphical models to study brain functional and structural network. 

Zhaolong Yu - Zhao lab (entered Fall 2016)
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Zhaolong’s current research focus is patient outcomes prediction based on multi-omics data. He is particularly interested in developing machine learning algorithms to better predict cancer patient outcomes.

Evan Cudone (entered Fall 2017)
Jeffrey Fisk - Townsend Lab (entered Fall 2017)
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Nick (Jeffrey) works on constructing phylogenetic trees to answer questions about cancer development, evolution, and the selective pressure treatment induces on the system. He also works on developing and implementing methods to optimize general phylogenetic experimental design.

Jiahao Gao - Gerstein lab (entered Fall 2017)

Jiahao focuses on the analysis of high throughput sequencing data, with special interest in ChIP-seq and other functional genomics technologies. He is currently working on denoising the ChIP-seq binding sites in order to improve the de novo inference of transcription factor binding motifs.

Scott Gigante - Krishnaswamy lab (entered Fall 2017)
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Scott’s research is focused the development of methods in deep learning and graph signal processing to understand the structure of single-cell genomics data, especially single-cell RNA sequencing data. He is particularly interested in how to make the most of many high-resolution noisy measurements, and how manifold learning techniques can facilitate this understanding.

Pranav Kantroo - Wagner lab/ - Machta lab (entered Fall 2017)
Tianxiao Li - Gerstein lab (entered Fall 2017)

Tianxiao is interested in applying novel machine learning techniques to inference of gene regulatory networks and 3D genomics. He is now working on developing novel methods to associate 3D genomics structures with gene regulatory elements.

Wei Liu - Zhao lab (entered Fall 2017)
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Wei’s research is mainly focused on understanding genetic architecture and mechanisms of complex diseases. She is now working in developing statistical methods investigating disease-associated genes using multi-omics data including genetics, epigenetics and transcriptomics data. Wei is also interested in population genetics and causal inference.

Rihao Qu - Kluger lab/ - Flavell lab (entered Fall 2017)

Rihao’s research interests include high-throughput sequencing analysis and immunogenomics. He is working on developing computational and statistical methods to process high-dimensional single cell sequencing data and help understand the genetic mechanisms underlying immune pathways.

Yixuan Ye - Zhao lab (entered Fall 2017)
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Yixuan research focuses on the genetic risk prediction for chronic diseases and cancer. She is currently working on exploring the interaction between gene, lifestyle and diseases. She is also interested in developing new methods to improve genetic prediction accuracy and the interpretation of polygenic risk score. 

Geyu Zhou - Zhao lab (entered Fall 2017)

Geyu’s research interest is statistical genomics and genetics. He is experienced in gene expression data analysis. He is currently working on developing statistical methods to compute polygenic risk score.

Edel Aron - Kleinstein lab (entered Fall 2018)