Elham Azizi is a postdoctoral fellow at the Computational & Systems Biology Program at Memorial Sloan Kettering Cancer Center. She will be joining Columbia University as an Assistant Professor of Biomedical Engineering and Herbert & Florence Irving Professor of Cancer Data Research in the Irving Institute for Cancer Dynamics in January 2020. Her research utilizes single-cell genomic technologies combined with statistical machine learning techniques, to characterize interacting cells in the tumor microenvironment as well as their dysregulated gene circuitry. She received a PhD in Bioinformatics from Boston University (2014), an MS degree in Electrical Engineering also from Boston University (2010) and a BS in Electrical Engineering from Sharif University of Technology (2008). She is a recipient of the NIH NCI Pathway to Independence Award, the Tri-Institutional Breakout Prize for Junior Investigators, and an American Cancer Society Postdoctoral Fellowship.
Alexis Battle is an Associate Professor of Biomedical Engineering at Johns Hopkins University, and a 2016 Searle Scholar. Her research group focuses on understanding the impact of genetic variation on the human body, using machine learning and probabilistic methods to analyze large scale genomic data. She is interested in applications to personal genomics, genetics of gene expression, and gene networks in disease, leveraging diverse data to infer more comprehensive models of genetic effects on the cell. She earned her Ph.D. in Computer Science in 2013 from Stanford University, where she also received her Bachelor’s degree in Symbolic Systems in 2003. Alexis spent several years in industry as a manager and member of the technical staff at Google, Inc. She joined Johns Hopkins University in July 2014.
Elodie Ghedin, PhD is Director of the Center for Genomics and Systems Biology at New York University, and Professor of Biology and Global Public Health. Her laboratory uses comparative genomics, evolutionary biology, and systems biology techniques to generate critical insight about host-pathogen interactions. Prof. Ghedin’s research program meets at the interface of molecular parasitology, microbiology, and genomics and focuses on the molecular basis of macroparasite (nematode) adaptation to niches in their human hosts, and microparasite (virus and bacteria) diversity and interaction in transmission and virulence.
Prof. Ghedin received her BS in Biology and PhD in Molecular Parasitology from McGill University (Montreal, Canada). She was named a MacArthur Foundation Fellow (2011), A Kavli Frontier of Science Fellow (2012), and an American Academy of Microbiology Fellow
Anshul Kundaje is an Assistant Professor of Genetics and Computer Science at Stanford University. The Kundaje lab develops interpretable machine learning and deep learning approaches for large-scale integrative analysis of functional genomic data to decode regulatory elements and pathways across diverse cell types and tissues and understand their role in cellular function and disease. Anshul completed his Ph.D. in Computer Science in 2008 from Columbia University. As a postdoc at Stanford and MIT/Broad, he led the integrative analysis efforts for two of the largest functional genomics consortia - The Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Project. Dr. Kundaje is a recipient of the 2016 NIH Director’s New Innovator Award and The 2014 Alfred Sloan Foundation Fellowship. Anshul is also a member of the NIH Director's Advisory Committee for Artificial Intelligence in Biomedical Research.
Professor Joakim Lundeberg heads the Department of Gene Technology, KTH Royal Institute of Technology and focus on molecular technology development. His research group is since May 2010 located at the Science for Life Laboratory (SciLifeLab), a national center for molecular biosciences with focus on health and environmental research. The center combines frontline technical expertise with advanced knowledge of translational medicine and molecular bioscience. The current research focus of JL relates to spatially resolved gene expression studies in situ, Spatial Transcriptomics. RNA-sequencing offers the possibility to analyze the expression of all genes in a sample. However, the spatial information of gene expression is lost. In the pioneering work a method was described that allowed studies of gene expression in tissue sections using RNA-sequencing to uncover transcriptional patterns in situ (Ståhl et al, Science, 2016). The basic concept is remarkably simple; by placing tissue sections on arrayed reverse transcription oligonucleotides with positional barcodes, cDNA for RNA-sequencing can be generated with maintained positional information within the tissue. The quality of the obtained cDNA libraries is as high as with the best protocols for homogenized tissue. Applying this strategy has been demonstrated to work remarkably well and allows visualizing and quantifying the transcriptome in regular histological tissue sections, i.e. tissue domains can be matched to precise gene expression patterns. Furthermore, data driven methods can be applied to discover in an unsupervised manner transcriptomic patterns in space. Such patterns correspond to cell-types, microenvironments, or tissue components that allows for novel avenues of research.
The avalanche of easy-to-create genomics data has impacted almost all areas of medicine and science, from cancer patients and microbial diagnostics to molecular monitoring for astronauts in space. Recent technologies and algorithms from our laboratory and others demonstrate that an integrative, cross-kingdom view of patients (precision metagenomics) holds unprecedented biomedical potential to discern risk, improve diagnostic accuracy, and to map both genetic and epigenetic states around the world and in real-time. Finally, these methods and molecular tools work together to guide the most comprehensive, longitudinal, multi-omic view of human astronaut physiology in the NASA Twins Study, which lay the foundation for future long-duration spaceflight, including sequencing, quantifying, and engineering genomes in space.
Sohrab Shah was appointed to MSK in April 2018 as the inaugural Chief of the Computational Oncology Service and is the incumbent of the Nicholls-Biondi Chair. He received a PhD in computer science from the University of British Columbia in 2008 and was appointed as a Principal Investigator to The British Columbia Cancer Agency and the University of British Columbia in 2010 where he developed the roots of his research program. He is a University of British Columbia Killam laureate and a Susan G. Komen Foundation Scholar. His research focuses on cancer evolution, where he uses integrative approaches involving genomics and computational modeling. He has led major projects including the analysis team of the METABRIC consortium, and has published major works in breast and ovarian cancer genomics, including the first description of mutational evolution in a breast cancer patient (Shah et al. Nature 2009), the first mutational landscape of triple negative breast cancers (Shah et al. Nature 2012) and single cell resolution demonstration of clonal evolution in breast cancer xenografts (Eirew et al. Nature 2015). More recently, he has made seminal contributions to understanding clonal evolution in ovarian cancer and discovered that specific mutational patterns related to foldback inversions in the genomes of ovarian cancers are prognostic in terms of treatment outcomes. Dr. Shah’s recent focus is in deciphering clonal evolution and mutational processes at single cell resolution. His work has been published in Nature, Nature Genetics, Nature Methods, Cell, NEJM, Genome Research, Genome Biology, amongst others.
Dr. Skok's laboratory applies a combination of sophisticated imaging techniques, molecular biology (including chromosome conformation capture) and genetics to investigate the contribution of nuclear organization and long-range interactions in coordinating transcriptional programs during development, and redirecting these in cancer cells. Since starting her lab she has continued to pioneer new applications of 3-D FISH and have set up a highly innovative CRISPR/Cas9 live imaging system. She independently established chromosome conformation capture at NYU. In collaboration with Richard Bonneau’s computational biology group at NYU they developed a method for 4C-seq analysis, 4C-ker. In addition, they developed 4Tran to identify transposable element interaction profiles for individual endogenous retrovirus (ERV) families and integration events specific to particular genomes (Genome Biology). They can now show that TEs participate in both long- and short-range contacts and could potentially be in involved in the regulation of multiple target loci. Thus, they are one of a handful of labs that has expertise in both the experimental and analytical aspects of chromosome folding. Her lab shares its knowledge widely with the scientific community both inside and outside of NYU as demonstrated by their numerous collaborative publications.
Peng Yin works as an Assistant(2010-2014)/Associate(2014-2016)/Full(2016-) Professor of Systems Biology at Harvard Medical School and a Core Faculty Member at Wyss Institute for Biologically Inspired Engineering at Harvard University (2010-). He is co-founder and director of Ultivue, Inc., an early stage company for digital pathology backed by Arch Ventures. He is also co-founder and director of NuProbe Global, a startup for PCR and NGS based molecular diagnostics backed by prestigious venture funds. His research interests lie at the interface of information science, molecular engineering, and biology. The current focus is to engineer information directed self-assembly of nucleic acid (DNA/RNA) structures and devices, and to exploit such systems to do develop applications in nano-fabrication, imaging, sensing, diagnostics, and therapeutics. He is a recipient of a 2010 NIH Director's New Innovator Award, a 2011 NSF CAREER Award, a 2011 DARPA Young Faculty Award, a 2011 ONR Young Investigator Program Award, a 2013 NIH Director's Transformative Research Award, a 2013 NSF Expedition in Computing Award, a 2014 ACS Synthetic Biology Young Investigator Award, 2014/2015 Finalists for Blavatnik National Award for Young Scientists, 2014/2015 World Economic Forum Young Scientist Awards, a 2017 Tulip Award for DNA Computing and Molecular Programming, and a 2018 NIH Director’s PIONEER award. He graduated from Peking University with B.S. in Biochemistry and Molecular Biology and Bachelor of Economics in 1998, and from Duke University with M.S. in Molecular Cancer Biology in 2000 and Ph.D. in Computer Science in 2005, and did his postdoc training at CalTech (2005-2009).