Dr. Vanessa Aguiar-Pulido is specialized in computational life sciences and biomedical data science. As part of the Center for Neurogenetics at WCM, her research focuses on understanding the underlying genetic causes of neurological conditions, how these causes can be influenced by environmental factors, and how these findings can be applied to clinical practice. Background and interests include big data analytics, health informatics, ontologies, biomedical data integration, machine learning, data mining, bioinformatics, artificial intelligence, neuroscience, epigenetics and omics in general.

Dr. Fabien Campagne's laboratory focuses on approaches to enable basic and translational research across a range of biological and clinical disciplines. Past contributions include, in collaboration with the Marambaud lab, the discovery of the CALHM1 gene, the first member of a new type of calcium channel expressed in brain (Dreses-Werringloer, Cell, 2008). The laboratory develops open-source bioinformatics tools, including the Goby framework (Campagne, PLOS One, 2013) and GobyWeb (Dorff, PLOS One, 2013), MetaR and the NextflowWorkbench to facilitate the management of increasingly large amounts of sequencing data. In collaboration with the Suthanthrian group, the laboratory leveraged exome sequencing to develop a novel approach to match donor and recipients for kidney transplantation (Mesnard et al, PLOS Comp. Bio 2016). Recent work leverages deep learning approaches to improve the identification of somatic variations in RNA-Seq data.

Dr. Susan Gauthier’s research is focused on the translation of early-stage imaging techniques to explore biological mechanisms at play in multiple sclerosis (MS) with a specific interest in quantification of myelin and inflammation.


Dr. Iman Hajirasouliha is passionate about developing new algorithms, machine learning and deep learning methods, and their applications to genomics, metagenomics, cancer research and biomedical imaging. Some of the current projects in his lab include characterizing human genomes and metagenomes sequenced by exciting new technologies, quantifying cancer evolution, a study of tumor heterogeneity using genomics and digital pathology images, and deep learning classification of embryology imaging. 


The research interests of the Khurana lab fall under the broad categories of genomics, computational biology and systems biology. The decreasing costs of genome sequencing are leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. We develop integrative computational models to understand the relationship between genomic sequence variation and disease. The impact of sequence variants in non-protein-coding regions of the genome is especially less-well-understood. We have developed multiple computational approaches that integrate large-scale data to identify the DNA point mutations and rearrangements in protein-coding genes and non-coding regulatory regions leading to human disease, in particular cancer.

Dr. Ilhami Kovanlikaya is an Associate Professor of Research in the Radiology Department at Weill Cornell Medicine. He is interested in exploring the assessment of brain tumor heterogeneity with quantitative MR-Imaging biomarkers.

Dr. Amy Kuceyeski is an Associate Professor of Mathematics in Radiology and Neuroscience at WCM. Her research interests lie mainly in statistical and mathematical models of the brain's connectivity network, also known as the connectome. She is interested in using machine learning techniques applied to neuroimaging metrics to better understand, diagnose and treat neurological disorders.

The Mason laboratory develops and deploys computational and experimental methodologies to identify the functional genetic elements of the human genome. To do this, they perform research in three principal areas: (1) molecular profiling in patients with extreme phenotypes, including brain malformations, aggressive cancers, and astronauts, (2) creating new biochemical techniques in DNA/RNA sequencing and DNA/RNA base modifications, and (3) the development of bioinformatics and machine-learning models for systems biology and metagenomics.

Dr. Henning Voss research focuses on the development of neuroimaging and modeling tools and their clinical and pre-clinical application. Some of his most relevant topics include:

  • Mathematical modeling of dynamical systems with application to the biomedical sciences

  • Algorithms to model complex spatiotemporal signals

  • MRI techniques to image structure and function of the brain with clinical applications

  • Preclinical functional and neurophysiological MRI

Dr. Fei Wang is an Assistant Professor in Division of Health Informatics, Department of Healthcare Policy and Research, Cornell University. His major research interest is data analytics and its applications in health informatics. His papers have received over 3,700 citations so far with an H-index 33. His paper won best short paper award at ICHI 2016, best student paper at ICDM 2015, best research paper nomination for ICDM 2010, Marco Romani Best paper nomination in AMIA TBI 2014, and his paper was selected into the best paper finalist in SDM 2011 and 2015. Dr. Wang is an action editor of the journal Data Mining and Knowledge Discovery, an associate editor of Journal of Health Informatics Research and Smart Health, and an editorial board member of Pattern Recognition and International Journal of Big Data and Analytics in Healthcare. Dr. Wang is the vice chair of the KDD working group in AMIA.

Dr. Yiye Zhang is an Assistant Professor in Health Informatics at Weill Cornell Medical College. A central theme of her research is to “pave the cowpaths,” where she uses data mining and machine learning algorithms to elicit actual healthcare practice patterns from data source such as electronic health records (EHR), to provide data-driven evidence to inform multiple stakeholders in healthcare. She is especially passionate about data inference from electronic health records (EHR), that captures critical information from patient care, and yet has an extremely complex and dynamic data structure for analysis.

Dr. Yize Zhao is an Assistant Professor from Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research. Her methodology research focuses on the development of statistical methods, in particularly scalable Bayesian approaches to analyze large-scale complex data to achieve feature selection, prediction, data integration and network analysis. She has strong interests in applications on imaging (fMRI, PET and DTI), imaging genetics and omics data, and she recently also starts to work on method development and data analysis for electronic health record data and lifetime science data.