New York, New York, United States
I’m a PhD student at the University at Buffalo, specializing in molecular modeling and computational chemistry. My research centers on RNA-ion interactions, molecular dynamics simulations, and coarse-grained modeling. I’m deeply passionate about leveraging my expertise to tackle impactful challenges, particularly in the fields of computer-aided drug discovery and machine learning applications. My skill set includes proficiency in Python programming, high-performance computing, and tools like GROMACS and OpenMM. Additionally, I have strong experience in scientific writing and collaborative teamwork.
Host: Steve Bonilla, Laboratory of RNA Structural Biology and Biophysics
Developing coarse-grained molecular dynamics models to study RNA condensate structure and ion-mediated interactions under physiological conditions.
1) Designed and synthesized new-class of bioorthogonal probes. 2) Performed comprehensive characterization of kinetics, stability, and in vitro performance of bioorthogonal probes. The work was published in Journal of the American Chemical Society (JACS). 3) Published news article covering the 2022 Nobel Prize awarded to bioorthogonal chemistry. 4) Published review paper summarizing state-of-the-art light-activated bioorthogonal chemistry methodologies. 5) Transitioned to the computational chemistry track after two years to expand technical expertise and pursue higher-impact research
1) Developed a computer-aided drug design pipeline by leveraging MD simulations to generate water-phase conformers, combined with shape- and electrostatic-similarity screening to support hit-to-lead optimization. 2) Built and applied machine learning models to predict bioactivity, integrating cheminformatics descriptors with advanced statistical learning approaches. 3) Recognized as Employee of the Month, nominated by peers for exemplifying Enveda's core values of curiosity, agency, journey, charity, and unity.