Every year, the US National Science Foundation awards five year grants to outstanding tenure-track professors. These CAREER awards are among the most prestigious for young faculty in the sciences. Receiving such an award represents recognition from the leaders of the community both of the outstanding work conducted thus far by the professor and the opportunity for that professor to have a profound impact on the future of the community.
It is with that knowledge that SigOpt is thrilled to congratulate our friend Roman Garnett for his recently being named an NSF CAREER award recipient. Roman has been a prominent contributor to the Bayesian optimization and active search community since his time as a graduate student at Oxford. In 2015, he returned to his alma mater, Washington University in St. Louis, as a faculty member in the department of computer science and engineering.
Roman’s work in Bayesian learning and efficient search has been heavily cited, including by SigOpt’s research team; we regularly look to Roman as an author with both a strong technical understanding and an ability to discuss topics on a variety of levels and in a variety of contexts. He has graciously contributed to our blog, helped us organize social events and encouraged his own students to join us as interns.
Comments from Roman and his students
We asked for comments from members of Roman’s lab. One of Roman’s current students, soon to be Dr. Gustavo Malkomes, commented “More than a well-deserved award, the fruit of hard work and excellent ideas, Roman’s vision for the field will have a tremendous impact on developing novel scientific practices.” Fellow Ph. D. candidate Shali Jiang responded “Roman has this vision of ‘Machine Learning for Scientific Discovery’, and I have been fortunate enough to work in this fascinating field under his guidance. This award is well deserved and about time! I’m excited to continue working with him and striving to have a broader impact.”
When asked to describe the ultimate goal of his active machine learning research, Roman responded “Humanity is at the tipping point of a data revolution, and our ability to collect and store information will likely outpace our capacity to extract useful knowledge from data. Active machine learning provides a solution to this dilemma: we adaptively design expensive experiments guided by statistical models of the underlying process to make the most-effective use of limited resources.”
In addition to his numerous research contributions and involvement to the community (for example, he is on the organizing committee for the 2019 NeurIPS conference), Roman has a transformational vision for his classroom. As part of his award, he is developing a new course in sequential decision making: “Active learning can make a significant and transformative impact across science and engineering, and we believe that current undergraduates in these areas will increasingly adopt these tools. We will design novel course materials to introduce concepts from active learning into the undergraduate science and engineering curriculum across Washington University in St. Louis.”
Congratulations, Roman, on this latest achievement, and for your persistent commitment to excellence!