I am a computer scientist. I like problems with impact and solve them using methods from a variety of fields, from software engineering/programming language technologies to AI/machine learning/data mining (I believe in opportunities in data) and qualitative methods (I believe in the people and stories beyond the numbers).
I have started a new adventure working for startups, helping them shape and implement their vision through AI and data-driven analytics. Currently, I am the Lead Data Scientist at EquitySim. Founded in 2016 and based in San Francisco, EquitySim envisions a merit-based hiring marketplace where employers find the best candidates to fulfill their missions. The company's online simulation-based recruiting platform identifies top talent for financial services firms providing an objective and holistic data-driven approach powered by artificial intelligence. Candidates from over 220 universities worldwide use EquitySim to go beyond the resume and demonstrate their strengths and true potential, allowing employers to identify top talent from a wider, more diverse pool of candidates not found in traditional recruiting.
In the very recent past, I had been with IBM as a Research Staff Member at T.J. Watson Research Center in New York.
My work involved helping developers work with web APIs as part of the API Harmony project. See our team blog for some interesting updates on that work!
I have a PhD from McGill University. My PhD thesis is on summarizing source code fragments. Imagine you have something like thumbnails for code snippets, like Google Images but for code snippets? That was what I did. I made use a variety of approaches (machine learning, optimization algorithms, program analysis) and research methods (quantitatitive and qualitative), building a summarization algorithm at the end that was motivated by ideally what a human would do to summarize a code snippet. Part of this research was awarded an ACM Distinguished Paper Award.