University of Chicago

Reviewers: 150

44th in USA

Reviews: 1,398

47th in USA

Merit: 4,165

46th in USA

Openness: 8.1

32nd in USA

Journal Editors at University of Chicago

Reviewers from University of Chicago

  • Reviewer

    Ian M McDonough

    I received a B.S. in Cognitive Science and had a specialization in Computing from UCLA in 2006. In 2007, I served as a laboratory manager at UC Irvine. As a graduate student at the University of Chicago, I received the APF/COGDOP Graduate Student Research Scholarship in Psychology and the APA Dissertation Research Award. After obtaining my PhD in Cognitive Psychology with a minor in Computational Neuroscience in 2011, I joined the Park Aging Mind Laboratory as a postdoctoral researcher. In 2015 I was hired as an Assistant Professor in the Psychology Department at The University of Alabama.

  • Reviewer

    John Fung

    I have more than 30 years of experience in the field of organ transplantation, including liver, kidney, pancreas, islet and intestinal transplantation. My longstanding research interest in transplantation immunology, immunosuppression therapy and liver related immunology is associated with more than 1,000 articles and book chapters. I serve on the editorial board for several medical journals and was the former editor-in-chief for Liver Transplantation

    Currently I am the Director of the University of Chicago Medicine Transplantation Institute.

  • Reviewer

    Yogesh Vinod Kolwadkar MD,MRCS,MCh,MS

    Experienced Orthopaedic Surgeon with a demonstrated history of working in three different continents. Skilled in Advance Arthroscopic Surgeries, Complex Fracture Care, Computer Assisted & Revision Total Joints including Anterior Hip Replacement, and Clinical Research.

  • Reviewer

    Atif Khan

    I a computational scientist with primary interest in the data mining opportunities at the intersection of data science, computation, and biology.

    Throughout my career, I have tried to define my research interests by the demands of health care and how they could be satisfied by the modern computing approaches. I am interested doing research in data-intensive biology, where I can build tools and approaches that can directly address a variety of biological problems. My interest lies in understanding the rich contextual information associated with most data sets in a variety of real-world domains and using it to infer complex patterns. I am using combination of modern analytical and machine learning skills for extracting the rich contextual information associated with complex data sets. In particular, I am developing bioinformatics strategies for mapping and understanding the interplay of genetic and environmental mechanisms of human disease. I am connecting the dots all the way from climate observations to disease prevalence with overarching goal to infer the causes and consequences of environmental insult on human health and formulate testable hypotheses.