Peter Bailis, Assistant Professor of Computer Science, spoke with us on Thursday about his research in post-database data intensive systems, his advice on how to navigate the wide variety in the field of computer science, and the importance of incorporating feedback into the decision-making process as much as possible.
Professor Bailis’s current research investigates questions of what the future of "big data" will look like. More specifically, Dr. Bailis' work focuses on data systems beyond databases, how companies and individuals can make better use of the vast amounts of data collected from their products, and the intersection between the rise of machine learning and the future of systems. He also teaches at the undergraduate level about database design and use in applications, and at the graduate level about database management systems and data-intensive systems through the lens of original research.
Before becoming a member of the Stanford DAWN Project, the Future Data Systems Group, and the Stanford InfoLab, he was a graduate student at UC Berkeley. While there, he also worked on problems surrounding data management and data-intensive computing, and spoke to us about how each problem gives rise to 10 more equally compelling problems, explaining to us that he prefers a depth-first approach to choosing what problems to solve next.
When asked about how to stay engaged in the work he does and decide when to dive into something new, Peter stressed the importance of remaining curious and designing avenues for feedback throughout one's life. He emphasized incorporating feedback facilitates better assessments over the course of your life about what's working and what's not in terms of professional, academic, or even personal trajectories.
To learn more about Peter Bailis, visit his website at www.bailis.org.
Written by Pratyusha Javangula