Carnegie Mellon University's Aaditya Ramdas, assistant professor in the Department of Statistics & Data Science and Machine Learning Department, has received the National Science Foundation's (NSF) Faculty Early Career Development Award for his project, titled "Online Multiple Hypothesis Testing: A Comprehensive Treatment." "Arguably, one of the major hurdles to reproducibility of scientific studies is the cherry picking of results among the vast array of tests run or quantities estimated," Ramdas said. "We need'online' methods to correct for cherry picking, first acknowledging that the problem exists and then designing algorithms that can account and correct for it." According to Ramdas, statistical methods that improve reproducibility in large-scale scientific studies will combat the increasing public distrust in science. The results of this five-year grant could transform how technological and pharmaceutical industries as well as the sciences perform large-scale hypothesis testing.

The team of researchers was composed of Zico Kolter, Shaojie Bai, Devin Wilmott, Mordechai Kornbluth, and Jonathan Mailoa, who won a machine learning Kaggle competition on Predicting Molecular Properties this past September.

The award recognizes researchers that have demonstrated significant impact in research, contribution, leadership, and services, in the areas of data mining, machine learning, and artificial intelligence, and have received their doctoral degree in the past ten years.

The U.S. Centers for Disease Control and Prevention has named Carnegie Mellon University as an Influenza Forecasting Center of Excellence, a five-year designation that includes $3 million in research funding. For four of the past five years, Carnegie Mellon's forecasting efforts have proven the most accurate of all the research groups participating in the CDC's FluSight Network. In addition to expanding CMU's existing forecasting research, the new funding will enable CMU to initiate studies on how to best communicate forecast information to the public and to leaders. It will also support efforts to determine how forecasting techniques might apply to pandemics -- the rare occasions when a truly novel strain of flu is prevalent around the world. Roni Rosenfeld, head of CMU's Machine Learning Department and leader of its epidemic forecasting efforts, said the designation of CMU and the University of Massachusetts at Amherst as the first two CDC flu forecasting centers of excellence marks a coming of age for the epidemic forecasting community.

The Machine Learning Department of the School of Computer Science at Carnegie Mellon University occupies a privileged position in the world of machine learning, in part as the world's only academic Machine Learning Department. The Department has close relationships through shared faculty and active collaboration across the university, especially the Statistics Department and other academic units in the School of Computer Science (Computer Science Department, Language Technologies Institute, Computational Biology Department, and the Robotics Institute). We seek applicants who will thrive in this interdisciplinary setting.

The individual filling this position will be responsible for leading the modernization of our teaching of machine learning, including developing new online and technology-assisted materials to improve educational outcomes and to extend our reach. They will work closely with the department head and other faculty to develop a strategic plan for taking advantage of new online and technology-assisted educational options over the coming decade. They will also be responsible for teaching classes and overseeing aspects of the educational program, e.g., admissions to our Ph.D. and Masters programs and advising undergraduate students minoring in Machine Learning. Candidates should have a Ph.D. with deep expertise in machine learning, and background of demonstrated excellence and dedication to teaching. Candidates must be prepared to teach extensive lecture courses at the advanced undergraduate and graduate level, and also be prepared to work with the existing faculty of the department to establish, improve, and standardize the curriculum.