This fall, CHSI for the first time called for diverse pilot projects within immunology and computational biology to award a maximum of $20,000 each in direct cost funding. Emphasis was placed on transdisciplinary partnerships in cross-cutting research areas, including Antibody Dynamics, Cellular Effector Function, Quantitative Immunology, Immunology, Inflammation, Immunotherapy, and Comparative Immunology. Eligibility extended to faculty of all ranks and post-doctoral/clinical fellows, with specific criteria outlined for each group. Get to know the three (3) awardees of the CHSI Pilot this year.
Ronald J. Bouch, PhD
Postdoctoral Associate, Department of Surgery
Functional characterization of influenza-specific memory NK cells in the lung.
Dr. Bouch works in the lab of Keith Reeves at the Division of Innate and Comparative Immunology, CHSI, investigating the anti-viral and cytotoxic function of NK cells, with a specific focus on innate-lymphoid immunobiology in chronic lung disease and acute viral infection. His proposed studies will evaluate the memory capacity and functional role of human lrNK cells against influenza by comparing their cytotoxic and antiviral roles to those of adaptive circulating NK cells.
Elliott D. SoRelle, PhD
Postdoctoral Fellow, Department of Molecular Genomics and Microbiology
Defining Epstein-Barr virus immune reservoirs and neuroinflammatory features in multiple sclerosis.
A multidisciplinary scientist with extensive experience in single-cell and bioinformatic studies of viral oncology and immunology, Dr. SoRelle’s studies in Micah Luftig’s Lab focus on host-pathogen interactions, computational modeling, quantitative microscopy, and image processing. His proposed experiments aim to address the knowledge gap regarding what EBV+ cells and virus-driven immune responses occur in MS neuroinflammatory lesions.
Yuxia Xie, PhD
Postdoctoral Research Associate, Department of Biostatistics and Bioinformatics
TEMPO: Prediction of Tumor Immune Microenvironment subtypes using H&E image data alone with AI-enhanced cross-modal transfer learning from genomic data.
Working in Jichun Xie’s lab within the Division of Integrative Genomics, Dr. Xie’s research interests mainly include data analysis, machine learning, bioinformatics, and deep learning. She proposes to use H&E image data, which is the standard of care, to classify TIME subtypes and predict patient outcomes by using transfer learning on deep neural networks trained on paired H&E genomic datasets.