Daniel joined the Greene Lab as a Postdoctoral Researcher in June 2016, after receiving his PhD in _Biological & Medical Informatics_ at UCSF. He uses hetnets — networks with muliple types of nodes and relationship — to integrate and learn from the past several decades of biomedical research. All of Daniel's research is entirely open: openly licensed and immediately available.
Check out his GitHub to follow his recent work. Daniel also performs many data science projects designed to make science more open and hence productive. [Website, Email (for support questions or feedback on Daniel's research, please first consider public venues — such as GitHub Issues — before email)]
Qiwen is Postdoctoral Researcher at the University of Pennsylvania Perelman School of Medicine. She received her PhD in Bioinformatics from North Carolina State University in 2016, where she studied translational regulation in plant genomes. She developed machine learning and statistical approaches based on high-throughput sequencing datasets to identify regulatory elements that affect transcription and translation. Qiwen joined the Greene Lab in 2017. Currently, her research interests focus on integration of different types of high-throughput sequencing data to identify meaningful biological patterns, key transcriptional regulators related to drug addiction and chromatin-regulated alternative splicing using computational approaches.
Jaclyn joined the Greene Lab as a Postdoctoral Researcher in May 2016. She is currently interested in integrating large amounts of publicly available data and utilizing unsupervised machine learning to answer big outstanding in the fields of rheumatology, autoimmunity, and in rare diseases in particular. She works in close collaboration with clinicians and biologists who are experts in these disorders and she is currently appointed to Penn Rheumatology’s T32. Before coming to Penn, Jaclyn received her PhD from the Molecular and Cellular Biology program at Dartmouth where she studied the rare autoimmune disease systemic sclerosis with Michael Whitfield and where she was awarded the John H. Copenhaver, Jr. and William H. Thomas, MD 1952 Fellowship. [Email, Website]
Brett graduated in 2011 with a B.S. in Computer Science from Boston College. After graduation he worked as a technology consultant doing information management and data science at large financial institutions. He co-founded Wymsee, an entertainment technology company in 2012. He is a Ph.D. student in Penn's Genomics and Computational Biology program. He is a member of both the Greene and Moore labs. His research centers on using deep learning-based methods to more precisely define phenotypes from large-scale data repositories, e.g. those contained in clinical records.
David is a Ph.D. student in the Greene Lab through Penn’s Genomics and Computational Biology (GCB) PhD Program. He graduated from University of Maryland Baltimore County in 2015 with a B.S. in Computer Science and minor in Bioinformatics. He joined the lab in summer 2017 and has been using a natural language processing (NLP) toolkit called snorkel to extract biological relationships from biomedical literature.
Greg is a Ph.D. candidate in the Greene Lab through Penn's Genomics and Computational Biology (GCB) PhD program. Greg graduated from The College of New Jersey in 2011 with a B.S. in Biology and from Saint Joseph's University in 2014 with an M.S. in Biology. He joined the lab in 2015 at Dartmouth and transferred with Casey to Penn. At Penn, he was awarded a T32 Training Grant. He has published papers on supervised methods to detect glioblastoma tumors with NF1 inactivation, and unsupervised deep learning methods to aggregate pan-cancer gene expression features. His goal is to improve outcomes for cancer patients through the development of methods that help unravel the complexity of cancer.
Michael is an undergraduate at Penn working in the Greene Lab. He will graduate in May 2019 with majors in Physics and Biophysics, as well as minors in Math and Statistics, and he hopes to submatriculate in a simultaneous Master's degree in Physics. He started working in the Greene Lab with Daniel in the summer of 2017, and has been developing tools for graph traversal within hetnets. Michael is also a student in the Vagelos Scholars Program in Molecular Life Science, an intensive research-focused undergraduate program through the Chemistry Department.