I currently run Scapeflow, an edtech startup that uses technology to emulate what tutors do one-on-one in the classroom. We leverage the power of data visualization and analytics to enable effective mastery learning at scale.Previously I worked at a business analytics company, Collective[i], and developed a decision support system based on machine learning and data mining techniques. I created product roadmap, wrote business use cases, designed and tested algorithms for analyses, and collaborated with visualization experts to create prototypes.I also conducted a project in the Machine Learning Lab at Columbia University, in which I developed successfully a new efficient algorithm to store and compare spatiotemporal data from ~1 million mobile devices. My research in astrophysics aims to understand how galaxies form and evolve, through high-performance computing. I design and run numerical models of the Universe, analyze terabytes of hierarchical data, and make detailed comparisons with observations to test various hypotheses.I have co-authored 20 articles in top academic journals, which have received 1200+ combined citations. Results of my work have been exhibited at the American Museum of Natural History and at Liberty Science Center.I have extensive experience in coding (Python/Django, C/C++, Javascript, R, React Native, HTML/CSS), running large-scale simulations on massively parallel systems, and performing complex data analysis and visualization.