Advisor: Hosein Mohimani
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.
Alex (Qi) Song
Advisor: Ziv Bar-Joseph
Alex is currently a postdoc working with Dr. Ziv Bar-Joseph. His research primarily focuses on modeling regulatory events using computational methods and machine learning. Particularly, he is interested in applying machine learning to build systematic understanding from sparse and noisy single cell omics data. His broad interest also includes developing novel data science tools for genomics in general.
Prior to joining the Bar-Joseph group, Alex was a postdoctoral fellow at Brigham and Women's Hospital in Dr. Kimberly Glass' lab, with joint appointment at Harvard Medical School. Before that, he received his Ph.D. in Bioinformatics at Virginia Tech in 2019, under the supervision of Dr. Song Li.
In his spare time, Alex likes to spend time doing gym workouts and hanging out in the wild. He believes the best ideas are often sparked when one is not doing science.
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.