Postdoctoral Fellows

Ben Chidester	Advisor: Jian Ma Ben’s research aims to discover connections between bioimaging and genomics through computational image analysis and machine learning. Ben has applied state-of-the-art image analysis tools drawing on advances in deep learning to analyze histological images and relate nuclear and cellular features to genomic data, such as gene expression and mutation, primarily within the context of breast cancer. Ben is working to develop appropriate machine learning tools that can infer causal relationships between phenotype and genotype through such joint image-genomic analysis. Ben is also interested in extending these ideas and image analysis tools to other microscopic imaging modalities that may reveal unique genomic connections. Ben has worked on other image processing problems, such as stereo vision, and is generally interested in image and signal processing and machine learning. Ben received his B.S. in ECE from Carnegie Mellon University in 2009 and his Ph.D. in ECE from the University of Illinois at Urbana-Champaign in 2017.
Jun Ding	Advisor: Ziv Bar-Joseph I obtained my Ph.D. degree at University of Central Florida in Computer Science. During my Ph.D. study, I developed computational methods for Transcription Factor binding and microRNA targeting, which are very important in transcriptional and post-transcriptional gene regulation. As a postdoc in Dr. Ziv Bar-Joseph’s group, I am carrying on my gene regulation study in cell differentiation. I am studying on the genetic regulatory networks which regulate the cell differentiation process using machine learning and statistical models. Google Scholar
Ashok Rajaraman	Advisor: Jian Ma Ashok obtained his doctorate in Mathematics from Simon Fraser University in 2009, under the supervision of Cedric Chauve. His research focuses on the description, classification and solving of problems in combinatorial optimization in problems arising from and relevant to questions in comparative genomics. He also maintains a strong interest in combinatorics, statistical learning and computational complexity. Interests: comparative genomics, cancer genomics Google Scholar
Matt Ruffalo	Advisor: Ziv Bar-Joseph Matt received his B.S. degree in Computer Engineering from Case Western Reserve University in 2009, and remained at Case for graduate school, completing his Ph.D. in Computer Science in July 2015. His Ph.D. work focused on feature construction from omic data, and using these constructed features to improve the performance of machine learning algorithms. He has applied these techniques to the problem of short-read alignment in the context of next-generation sequencing, and then shifted focus to cancer genomics, with emphasis on network-based methods for survival analysis and disease gene prioritization. He is currently interested in using network-based methods for more general problems in the context of semi-supervised learning, and in continuing cancer genomics work in collaboration with researchers at the University of Pittsburgh. Google Scholar
Dechao Tian	Advisor: Jian Ma Dechao received his PhD in Statistics from National University of Singapore in 2015. Before moving to Singapore, he received his B.S. in Mathematics and M.S. in Statistics from Northeast Normal University, China, in 2009 and 2011 respectively. Dechao currently explores potential connections between the 3D chromosomal connectome and transcriptional regulation, aiming to discover interesting principal connections and develop algorithms. For example, he is interested in using latent Graphical models to infer direct transcriptional regulations. Google Scholar
Morgan Wirthlin	Advisor: Andreas Pfenning The primary goal of Morgan’s research is to understand the evolution of complex behaviors through a synthesis of comparative genomics and experimental neurobiology. Morgan received her B.A. in Biological Sciences, with a specialization in Evolution & Ecology, from the University of Chicago in 2009. She earned her Ph.D. in Behavioral Neuroscience from Oregon Health & Science University in 2016. In her dissertation work, she sought to identify the fundamental genomic and molecular properties that characterize brain circuits for vocal learning, the basis for birdsong and human speech. Her efforts culminated in several high-profile publications, and proposed a new model for the evolution of complex vocal behavior. In order to further this work, she is now developing methodologies to interrogate the genomic regulatory elements that drive behavioral gene expression, through a combination of large-scale computational genomic analyses and high-throughput experimental assays of gene expression. She has performed field work in North and South America, and maintains a long-term interest in developing new methods for exploring neurobiological and genomics questions in field settings. She was awarded the inaugural BrainHub postdoctoral fellowship and joined the Computational Biology Department in 2016. Google Scholar
Jing Xiang	Advisor: Seyoung Kim Jing’s research is focused on using machine learning and statistical models to understand the genetic basis of regulatory networks. She is currently using graphical models and statistical learning to study signaling pathways in breast cancer. She received her PhD in 2017 from the Machine Learning Department at Carnegie Mellon University, where she worked on developing a statistical approach for validating transcription factor regulatory relationships using population SNP and expression data. Previously, she did a Master’s degree at the University of British Columbia, Canada in 2010, where she did research on medical imaging for image-guided surgery. Jing received her undergraduate degree in Biomedical Computing from Queen’s University, Canada in 2008.