[[{"fid":"669","view_mode":"embedded_left","fields":{"format":"embedded_left","field_file_image_alt_text[und][0][value]":"Alp Kucukelbir","field_file_image_title_text[und][0][value]":"","field_file_caption_credit[und][0][value]":"","field_file_caption_credit[und][0][format]":"full_html"},"type":"media","link_text":null,"attributes":{"alt":"Alp Kucukelbir","height":250,"width":250,"class":"media-element file-embedded-left"}}]]Machine learning is changing the way we do science. We want to study large datasets to shed light onto natural processes. (How do proteins function? How does the brain work?) To this end, we need tools to rapidly and iteratively explore hidden patterns in data. However, using machine learning to discover such patterns requires enormous effort and cross-disciplinary expertise. My goal is to develop easy-to-use machine learning tools that empower scientists to gain new insights from data. This requires research in automated algorithms, scalable software, and robust machine learning. These are the building blocks of effective machine learning tools.
Alp is a postdoctoral research scientist at the Data Science Institute and the department of Computer Science at Columbia University. He works with David Blei on developing scalable and robust machine learning tools. He collaborates with Andrew Gelman on the Stan probabilistic programming system. Alp received his Ph.D. from Yale University, where he was awarded the Becton prize for best thesis in engineering and applied science. He holds a B.A.Sc. from the University of Toronto.