(368m) Data-Driven Discovery of Small Molecules for Immune Modulation Via Machine Learning and High Throughput Screening

My research centers on the intersection of artificial intelligence, machine learning, and small molecule discovery, with a focus on developing advanced methodologies for identifying and designing better molecules. By integrating machine learning algorithms with high throughput screening techniques, I aim to streamline the experimental pipeline and enhance the efficiency of molecule discovery. This approach allows for the precise tuning of molecular properties, making it applicable not only to immuno-modulation but also to a wide range of applications in pharmaceuticals, materials science, and chemical engineering. Upon completing my Ph.D., I aspire to leverage my machine learning expertise to tackle real-world biomedical challenges, serving as a liaison between computational specialists and laboratory scientists. I thrive in cross-functional environments and am passionate about bridging the gap between theoretical insights and practical applications. My goal is to leverage AI and ML to innovate and optimize experimental processes, ultimately contributing to the development of safer, more effective therapeutics and materials. This transferable skill set positions me to work effectively as a data scientist in various industries, where I can apply these advanced techniques to drive experimental research and development.