Julian Candia - Research


  • Computational Biology and Bioinformatics

    My research interests are focused on the development and application of analysis tools to provide new insight into biological processes. In particular, the availability of high-throughput, multiparametric datasets presents unique opportunities--and challenges--for the development of novel cross-disciplinary tools and frameworks. In this context, my goal is to apply my expertise at the crossroads of statistical physics, network science, machine learning, and bioinformatics to contribute innovative ideas to key problems in systems biology.

    My role as Staff Scientist with the Center for Human Immunology, Autoimmunity and Inflammation at the National Institutes of Health (NIH) is to work in collaboration with basic, translational, and clinical research scientists to develop quantitative methods of data analysis to better leverage the potential of new technologies in the biomedical realm with special emphasis in human immunology.

    Previously, I worked as a Postdoctoral Research Scientist with a joint appointment from the Department of Physics at the University of Maryland at College Park (UMCP) and the School of Medicine at the University of Maryland at Baltimore (UMB). At UMCP, I served as liaison with the National Cancer Institute (NCI) within the mission of the Cancer Technology Partnership, aiming to strengthen the collaboration opportunities between lab research at NIH and the expertise of UMCP scientists. At UMB, I was a recipient of an NIH T32 Cancer Biology Training Grant to apply novel data analysis techniques to current research on stem cells and leukemia.

  • Research History and Accomplishments

    In the past, I have worked on a variety of problems involving computational and theoretical modeling of far-from-equilibrium dynamical systems, including the turbulent transport of high-energy cosmic rays in the Galaxy, random walk models on complex network substrates, the irreversible growth of spin systems, processes of opinion formation and spreading in social systems, as well as flocking and swarming in biological systems. I have also worked on data mining and the analysis of large social datasets, which aimed at gaining a heuristic understanding of social dynamics and social interaction patterns.

    For my full list of refereed publications, see the Publications Section.