Program Directors

Maja Bucan, PhD

Maja Bucan, PhD

Professor, Genetics
Associate Dean, Postdoctoral Research Training
Director, Biomedical Postdoctoral Programs (BPP)

Maja Bucan is a Professor in the Department of Genetics at the University of Pennsylvania, formerly the Chair of the graduate group in Genomics and Computational Biology, and currently the Director of Penn Biomedical Postdoctoral Program. Dr. Bucan's research interests are in the area of genomics and bioinformatics of complex neurodevelopmental and psychiatric disorders. Dr. Bucan received her Ph.D. from the University of Belgrade, Yugoslavia (research performed at the European Molecular Research Laboratories, Heidelberg, Germany). After her postdoctoral training at the Imperial Cancer Research Fund in London, UK and at the Wistar Institute in Philadelphia, she joined the departments of Psychiatry and Genetics at the University of Pennsylvania in 1990.

Dr. Bucan helped Dr. Spielman to found the Genomics and Computational Biology graduate group in 2001. She has trained over 50 undergraduate students, 5 graduate students and 18 postdoctoral researchers, with 5 former traineescurrently in professorship positions.

Junhyong Kim, PhD

Junhyong Kim, PhD

Chair and Patricia M. Williams Term Endowed Professor
Department of Biology
Adjunct Professor, Department of Computer and Information Science
Co-Director, Penn Program in Single Cell Biology

Junhyong Kim has been a computational biologist for 35 years with his first publication involving an algorithm for tRNA folding in 1984. He has also worked in experimental genomics and single cell biology for 20 years. He received his PhD in 1992 and held the position of Assistant Professor and Associate Professor with tenure at Yale University from 1994-2002. In 2002, he moved to Penn with appointments in Department of Biology, Department of Computer and Information Sciences, and the GCB program. He has been the Co-Director of the Penn Genome Frontiers Institute from 2006-2014. He is currently the Co-Director with trainer J. Eberwine of Penn Program in Single Cell Biology and also the Co-Director of the NHGRI Center of Excellence in Genomic Sciences, Center for Sub-Cellular Genomics (again with trainer J. Eberwine). He has trained or is training 18 undergraduates, 19 PhD students, and 17 postdoctoral fellows. Of the 30 past trainees, 10 occupy professorships in research-oriented universities, 10 have scientific positions in the biomedical industry, 2 are in science administration and 1 is in postdoctoral fellow status. He has previously served as the Director of Graduate Studies (Ecology and Evolutionary Biology) and the Founding Director of the Computational Biology and Bioinformatics Track at Yale University. He has also taught courses in computational biology, statistical genomics, and mathematical modeling for 30 years.

Current Trainees

Rachael Aubin

PhD Candidate, Bioengineering
Mentor: Pablo Camara, PhD

Description of the trainee/scholar's research project and progress:
My research focuses on deciphering the mechanisms for the tumorigenesis, progression, and relapse of childhood ependymoma. Little is known about the molecular pathways and events that underlie this pediatric brain cancer and there are no effective chemotherapies. To remedy this lack of knowledge, my project aims to uncover the cellular composition, tumorigenic pathways, and molecular dynamics of this devastating disease. This will require utilizing multi-institutional pediatric brain tumor banks, single-nuclei RNA-sequencing (snRNA-seq) technologies, and mathematical approaches. The long-term goal of my research is to identify molecular targets and motivate functional studies that will lead to therapies for this disease.

Ben Auerbach

PhD Candidate, Genomics and Computational Biology
Mentors: Garret FitzGerald, MD, FRS and Mingyao Li, PhD

Description of the trainee/scholar's research project and progress:

I am interested in developing machine learning and statistical methods to study single-cell and circadian biology. Currently, I am developing an algorithm to predict circadian phases in cells from single-cell RNA-Seq. I ultimately hope to apply this approach to study single-cell transcriptional timing and how it can go awry in disease.


Angela Huang

PhD Candidate, Computer and Information Science
Mentor: Junhyong Kim, PhD

Description of the trainee/scholar's research project and progress:

Under the supervision of Junhyong Kim, my current research project involves modeling the geometric structure underlying single-cell RNA-sequencing data. We are particularly interested in modeling developmental data, but have explored the structure of fixed time-point data as well. I am currently developing unsupervised clustering algorithms that leverage this underlying structure in the data to identify the various cell populations within an organism.

Diego Espinoza

MD-PhD Candidate, Immunology
Mentor: Amit Bar-Or, MD, FRCP, FAAN, FANA

Description of the trainee/scholar's research project and progress:

For my thesis project, I will be using CITE-seq to profile the thoracic duct immune compartment in patients with multiple sclerosis and healthy controls to identify disease-related signatures. As part of this project I aim to place these findings in the context of other immune compartments within patients (such as the CSF and blood) and additionally leverage/develop computational tools to better understand the immunopathophysiology of multiple sclerosis.


Jessica Lam

MD-PhD Candidate, Genomics and Computational Biology
Mentor: Gerd Blobel, MD, PhD

Description of the trainee/scholar's research project and progress:

My research involves studying how the genome is structurally organized by investigating the role of architectural protein YY1 in forming chromatin loops. My first aim is to generate and analyze Hi-C data to test its necessity in forming various architectural features. My second aim is to utilize machine learning methods to delineate the genetic and epigenetic context of YY1-mediated loops.


David Nicholson

PhD Candidate, Genomics and Computational Biology
Mentor: Casey Greene, PhD

Description of the trainee/scholar's research project and progress:

My thesis project involves working with a publicly available resource called Hetionet, which is a heterogeneous network that contains pharmacological and biological information. This network depicts information in the form of nodes and edges of different types: nodes represent biological and pharmacological entities and edges represent relationships each entity may share with itself or another. This network has been used for important tasks such as drug repurposing and the discovery of novel disease-gene associations. In principle, the network can be used to predict edges between any type of biomedical entity included in the network. Despite the network's usefulness, majority of the data comes from human-curated databases. These databases, although open, take a significant amount of time to update. Curators must read many scientific papers and extract important information and key conclusions. Given the pace of publishing and level of funding for databases, these databases cannot remain current. The proposed solution for this bottleneck is my first aim, which is designing a system to quickly scan and extract important information and key conclusions from Pubmed abstracts (Manuscript Pending). My second aim is to construct a novel method that will combine deep learning and Hetionet to make novel biological discoveries via predicting new edges between entities.

Ashley Robbins

PhD Candidate, Neuroscience
Mentor: Beverly Davidson, PhD

Description of the trainee/scholar's research project and progress:

The majority of genes implicated in neurodegenerative disorders are widely expressed yet trigger dysfunction in only a subset of neural cell populations. My research focuses on investigating this differential disease vulnerability in the context of a progressive degenerative disorder that primarily affects the cerebellum, a region of the central nervous system that is important for voluntary movement and motor coordination. My thesis project aims to characterize cell-type-specific differential gene expression across the cerebellum that could contribute to regional and cell-type vulnerability. In order to achieve this I am constructing a single-cell resolution, spatiotemporal transcriptome map of the cerebellum across normal aging and disease progression, in a murine disease model using high-throughput single-nuclei RNA sequencing (snRNA-seq). The long-term goal of this project is to gain insights into the cell-type contribution to disease pathology and identify novel therapeutic targets for in vivo validation.

Alexa Woodword, MS

PhD Candidate, Epidemiology
Mentor: Jason Moore, PhD

Description of the trainee/scholar's research project and progress:

My research interests span genetic epidemiology and bioinformatics and center around capturing and characterizing genetic and other types of heterogeneity in complex diseases. My current project focuses on using rule-based machine learning to improve our understanding of genetic and epigenetic heterogeneity in IDH-WT glioblastoma. The goals of my research are to use interpretable machine learning methods on population level data to identify clinically relevant  features or combinations of features that can inform potential biomarkers, targets for therapies, or other advancements in precision medicine.