Program Directors
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
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.
Trainers
Research interests
Dr. Agarwal's research interests include foundational questions in machine learning, applications of machine learning in the life sciences, and connections between machine learning and other disciplines such as economics, operations research, and psychology.
Research interests
Dr. Berger's research focuses on regulation of the nuclear genome in mammals and model organisms. The long strands of nuclear DNA are associated with packaging proteins, called histones, into a structure known as chromatin, akin to the way thread is organized around a spool. We are particularly interested in changes in this chromatin structure via chemical modification of the histone proteins, and how attachment of certain chemical groups onto the histones leads to altered chromatin function. These targeted structural changes are conceptually like the unraveling of the thread to reach specific, buried sections. We are also fascinated by functional changes in chromatin, caused by these histone modifications, that persist through cell division from one cell into two daughter cells; these persistent, or epigenetic, changes are of particular interest because they are key to normal and abnormal growth: they occur during organismal development into multicellular tissues and organs, and are typically disrupted during abnormal reversal of tissue specialization and growth control as in cancer, as well as during aging of cells and individuals.
Research Interests
Dr. Bonasio's laboratory studies the molecular mechanisms of epigenetic memory, which are key to a number of biological processes, including embryonic development, cancer, stem cell pluripotency, and brain function.
Research interests
Dr. Brown's research focuses on genetics of gene expression. His laboratory is interested in identifying and experimentally characterizing functional human non-coding sequence variation.
Research interests
Dr. Davidson's work lies in fundamental computer science as it is applied to biomedicine. Her research interests center around information modeling and management, database systems, distributed systems, and bioinformatics. Within bioinformatics, Susan's group focuses on models and systems for data integration and exchange, representation and management of incomplete and semi-structured information, provenance tracking and management, and scientific workflows. Susan has also been instrumental in establishing degree programs in bioinformatics and computational biology at the undergraduate, master's, and doctoral levels.
Research interests
Dr. Devoto's main research interest is the application and development of statistical genetics methods to identify genes responsible for human disorders or underlying susceptibility to complex genetic traits. Researchers in Dr. Devoto's group use a variety of computational approaches to analyze large-scale genotyping and sequencing data for disease gene-mapping in families and patients with different genetic disorders.
Research interests
Dr. Diskin's research interests are focused on translational genomics in childhood cancers. Her research program is inherently multi-disciplinary in that it couples integrative computational analyses of large scale data such as next generation sequencing, single nucleotide polymorphism (SNP) genotyping, mRNA, miRNA and lincRNA expression, epigenetic profiling, and DNA copy number data, with rigorous experimental validation.
Research interests
Dr. Eberwine is the Elmer Holmes Bobst Professor of Systems Pharmacology and Translational Therapeutics at the University of Pennsylvania. He pioneered single cell PCR, the aRNA amplification protocol, and coined the phrase "expression profile" to describe the relative abundances of RNAs. Dr. Eberwine's research combines cutting edge optical technologies with molecular biology to solve genomic and neuroscience problems. Dr. Eberwine's work has highlighted the kinetics of translation in neuronal dendrites, pioneered the concept of cytoplasmic RNA splicing and illuminated the role of RNA populations in establishing and maintaining cellular phenotype. Dr. Eberwine is an inventor on over 170 patent applications and was elected to the National Academy of Inventors in 2014. He also serves on the NIH Multi-Council Working Group (MCWG), which is charged with advising the NIH Institutes on the implementation and progress of the US Brain Initiative. Dr. Eberwine originated and directed the Cold Spring Harbor Summer Course formerly entitled "Cloning of Neural Genes" and now called "Advanced Techniques in Neuroscience". In 2012, he developed and Co-Directed the first Cold Spring Harbor Course on "Single Cell Techniques".
Research interests
Dr. Garcia was recruited in 2012 from Princeton Univ. as the Presidential Assoc. Prof. of Biochemistry and Molecular Biophysics (BMB), and Faculty Director of the Quantitative Proteomics Resource Core at the Perelman School of Medicine. He is a member of 4 BGS graduate groups. Currently, he is the BMB Grad. Admissions Chair and Vice Chair of the BMB graduate group. His group is developing novel mass spectrometry-based approaches and computation for interrogating protein modifications, especially those involved in epigenetic mechanisms. His work has resulted in over 180 publications. He serves on the Board of Directors for the U.S. Human Proteome Organization (HUPO), and serves on the ASMS Nominating committee, ASMS Diversity and Outreach committee and the ASMS Asilomar Conference committee. He was also elected to the HUPO governing Council (Western Region Representative) in 2016. He has collaborated or consulted for Genentech, Eli Lilly, Pfizer, GSK, Amgen, Constellation, Abbvie, BMS, and Novartis. The Garcia Lab has a Technology Alliance Partnership with Thermo Scientific, and they have also named him as a Thought Leader in the proteomics field.
Research interests
Dr. Greene's research seeks to is to transform how we understand complex biological systems by developing and applying computational algorithms that effectively model processes by integrating multiple types of big data from diverse experiments. This allows researchers to infer the key contextual information required to interpret such data, and facilitates both the computationally driven asking and answering of basic science and translational research questions.
Research interests
Dr. Gregory's lab focuses on the use of high-throughput sequencing to study RNA secondary structure and RNA-protein interactions globally, as well as mechanisms and regulation of RNA silencing pathways.
Research interests
Dr. Grice's research focuses on genomic and metagenomic approaches to understand cutaneous host-microbe interactions in health and disease.
Research interests
Dr. Ives is the Department Chair and Adani President's Distinguished Professor of Computer and Information Science at the University of Pennsylvania. He is a co-founder of Blackfynn, Inc., a company focused on enabling life sciences research and discovery through data integration. Zack's research interests include data integration and sharing, managing "big data," sensor networks, and data provenance and authoritativeness. He is a recipient of the NSF CAREER award, and an alumnus of the DARPA Computer Science Study Panel and Information Science and Technology advisory panel. He has also been awarded the Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching. He is a co-author of the textbook Principles of Data Integration, and has received an ICDE 2013 ten-year Most Influential Paper award as well as the 2017 SWSA Ten-Year Award at the International Semantic Web Conference. He has been an Associate Editor for Proceedings of the VLDB Endowment (2014) and a Program Co-Chair (2015) and Group Leader (2018) for the ACM SIGMOD conference.
Research interests
Dr. Kannan's research spans several subfields in algorithms. In his work on massive data set algorithms, he explores what can be computed efficiently, and what is not computable. He is also interested in program checking, a paradigm for ensuring the correctness of a program by observing its behavior at run-time, and in algorithmic problems in computational biology, particularly the problem of reconstructing the evolutionary history of a set of species from phenotypic and molecular sequence observations.
Research interests
Dr. Lee's research interests focus on developing statistical, probabilistic and computational methods for genetic and genomic data analysis, bioinformatics and computational biology. He has developed improved linkage and association-analysis methods for mapping genes for complex human diseases. He has recently been developing statistical methods for analysis of microarray time course gene expression data, methods for linking high-throughput genomic data such as microarray gene expression data and array CGH data to censored survival data, and methods for inferences of genetic networks.
Research interests
Dr. Li's research seeks to use statistical and computational approaches to understand cellular heterogeneity in human-disease-relevant tissues, to characterize gene expression diversity across cell types, to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell transcriptomics studies, and to translate these findings to the clinics.
Research interests
Dr. Minn's laboratory is interested in gene programs and signaling pathways discovered through unbiased high-dimensional data analysis that regulate cancer metastasis and its resistance to either conventional treatment or immune therapies. In particular, they focus on 1) how stromal cells orchestrate cancer therapy resistance, inflammation, and tumor progression, and 2) how tumor cells regulate an immune suppressive microenvironment to influence response to immunotherapies such as immune checkpoint blockade.
Research interests
Dr. Moore is the Edward Rose Professor of Informatics and Director of the Penn Institute for Biomedical Informatics. He also serves as Senior Associate Dean for Informatics and Director of the Division of Informatics in the Department of Biostatistics and Epidemiology. He has a PhD in Human Genetics and an MS in Applied Statistics from the University of Michigan. He leads an active NIH-funded research program focused on the development of artificial intelligence and machine learning algorithms for the analysis of complex biomedical data with a focus on genomic medicine. He is an elected fellow of the American Association for the Advancement of Science (AAAS), an elected fellow of the American College of Medical Informatics (ACMI), and was selected as a Kavli fellow of the National Academy of Sciences. He also serves as Editor-in-Chief of the journal BioData Mining. Dr. Moore has trained more than 50 undergraduate, 15 graduate, and 10 postdoctoral students and previously served as the founding director of an interdisciplinary graduate training program in Quantitative Biomedical Sciences. He also served as PI of an NCI R25 postdoctoral training program that was successfully renewed. He has considerable experience mentoring early stage investigators including many that have been funded by NIH K awards.
Research interests
Dr. Murray's research aims to understand how the genome orchestrates animal development at single cell resolution. This process is regulated in large part by transcription factors and signaling pathways whose function is conserved from humans to invertebrates. Misregulation of developmental gene expression is a feature of cancer and other diseases, and mutations regulatory elements are common in human genetic diseases.
Research interests
Dr. Nathanson was dually trained in Internal Medicine and Clinical Genetics, and so has been practicing Genomic Medicine for her entire career. She has a well-funded NIH-funded research program in the inherited and somatic genetics/genomics of cancer. She has been elected to the American Society of Clinical Investigation and American Association of Physicians. Dr. Nathanson has trained over 25 undergraduates, graduate students, medical residents, and post-doctoral fellows. She co-led the Cancer Biology (Cellular and Molecular Biology) introductory course for three years, and has served on multiple preliminary and thesis committees. She directed the Medical Genetics rotation in Adult Genetics, and the Internal Medicine resident rotation in Medical Genetics for over 10 years. She serves on the Residency Committee for Medical Genetics, on the admissions committee for the MD-PhD program and as co-PI for the Medical Genetics Research Training Grant.
Research interests
Dr. Cremins's lab investigates the link between three-dimensional organization of genomes and the establishment and maintenance of cellular function. They employ molecular Chromosome-Conformation-Capture technologies and high-throughput sequencing to create high-resolution 3-D genome architecture maps, as well as developing and applying computational tools to (1) create 3-D models of chromatin and (2) integrate 3-D architecture maps with genome-wide maps of epigenetic modifications.
Research interests
Dr. Raj is an Associate Professor of Bioengineering and a pioneer in single molecule in situ technology. He joined Penn in 2010 and he is the recipient of Burroughs-Wellcome Career Award and the NIH Director’s New Innovator Award. His research interests are in single cell biology, systems biology, and molecular basis of cell function. In particular, his lab has developed many computational tools for molecular imaging and mathematical models of cell dynamics.
Research interests
Dr. Ritchie is currently a Professor of Genetics at the Perelman School of Medicine. Marylyn is a statistical and computational geneticist with extensive experience in all aspects of genetic epidemiology and translational bioinformatics as it relates to human genomics. She also has expertise in conducting genome- and phenome-wide association studies, using next-generation sequencing techniques, integrating multiple omics datasets, and developing data visualization approaches. Dr. Richie joined University of Pennsylvania in December 2017 after holding a faculty position at Penn State and leading the Geisinger’s Biomedical and Translational Informatics Program.
Research interests
Dr. Spinner's research interests include human genetics, notch signaling in human disease, alagille syndrome, biliary atresia, SNP array analysis, copy number variation, human disease gene identification by mapping deletions, Ring Chromosome 14, Ring Chromosome 20, genome wide association studies, next-generation sequencing, and chromosomal analysis.
Research interests
Dr. Tishkoff's laboratory uses a highly interdisciplinary approach (integrating field work in Africa, empirical and computational analyses) to study human population genetics, human evolution, the genetic basis of adaptation, and genetic and environmental factors influencing variable anthropometric (including skin pigmentation), cardiovascular, metabolic, and immune-related traits.
Research interests
Dr. Ungar's research group develops scalable machine learning and text mining methods, including clustering, feature selection, and semi-supervised and multi-task learning for natural language, psychology, and medical research. Example projects include spectral learning of language models, multi-view learning for gene expression and MRI data, and mining social media to better understand personality and well-being.
Research interests
Dr. Voight's lab develops and utilizes statistical genetics, computational biology, and population genetics-based approaches to understand the biological underpinnings and evolutionary history of human traits and complex disease.
Research interests
Dr. Zhang's research focuses on statistical analysis of genomic data especially for complex trait genetics. She is an expert in scan statistics and has written extensively on methods for detecting genomic aberrations.