Kanishk Gohil; Current: Ph.D. Candidate, University of Maryland; Desired: Postdoctoral Research Fellow
KANISHK GOHIL,
University of Maryland Abstract Number: 646
Working Group: Meet the Job Seekers
AbstractI am currently a fifth-year Ph.D. candidate at the University of Maryland, College Park. I work with Dr. Akua Asa-Awuku, and I study the effects of chemical composition and particle morphology on the CCN activity of ambient aerosols. My research can be broadly divided into 3 main projects: 1. Chemical classification of ambient aerosols using their Raman spectral signatures with the help of distance-based machine learning algorithms, 2. Development of a generalized hygroscopicity parameter for aerosol with varying physicochemical properties with the help of controlled droplet growth experiments, and 3. Incorporation of the newly devised hygroscopicity parameterization into the Single-Column Atmospheric Model (SCAM; National Center for Atmospheric Research) to analyze the variability in resulting cloud response. During my Ph.D. tenure, I have had the opportunities to do collaborative research at the Army Research Laboratory (ARL), and the University Corporation of Atmospheric Research (UCAR). As a Ph.D. student, I have gained significant hands-on experience in developing different machine learning classification algorithms, operating and analyzing laboratory data from the Continuous Flow Streamwise Thermal Gradient CCN Counter (CFSTGC), Aerodynamic Aerosol Classifier (AAC, Cambustion Ltd.), and Scanning Mobility Particle Sizer (SMPS, TSI Inc.), and operating the Community Earth System Model (CESM, NCAR) on High-Performance Computation systems.
I anticipate defending my dissertation in April 2023 and will be able to begin postdoctoral research anytime in the summer of 2023. I wish to primarily utilize my knowledge and further develop my experience in computational research, statistics, and machine learning. I would like to develop and improve my skills in surrogate modeling especially focusing on improving the prognostic and diagnostic capabilities of large-scale weather/climate models. I currently have no geographical preferences and am willing to work either in the US or internationally.