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Davis, Delimitrou, DiStasio win Sloan fellowships

Assistant professors Damek Davis, Christina Delimitrou and Robert A. DiStasio Jr. have won 2020 Sloan Research Fellowships from the Alfred P. Sloan Foundation. The fellowships support early-career faculty members’ original research and education related to science, technology, mathematics and economics.

Damek Davis

Davis is in the School of Operations Research and Information Engineering and Delimitrou is in the Department of Electrical and Computer Engineering, both in the College of Engineering. DiStasio is in the Department of Chemistry and Chemical Biology in the College of Arts and Sciences.

Christina Delimitrou

They are among the 126 researchers in the United States and Canada who received two-year, $75,000 fellowships to advance their work.

Robert A. DiStasio Jr.

“A Sloan research fellow is someone whose drive, creativity and insight makes them a researcher to watch,” said Adam F. Falk, president of the Sloan Foundation.

Davis studies the mathematics of data science, particularly in the interplay of optimization, signal processing, statistics and machine learning. His research is helping to lay the mathematical foundations of machine learning algorithms, which often succeed in practice although researchers lack a coherent mathematical understanding of why they work and the class of problems one could prove they could solve.

In his work, he employs an eclectic mix of mathematical tools – including variational analysis, stochastic processes and high-dimensional statistics – to design and analyze non-convex optimization methods, which underlie the most-effective machine learning algorithms. Davis seeks to explain why these methods are so good at training machine learning models, which could provide techniques to help practitioners consistently improve models’ performance.

Delimitrou, the John and Norma Balen Sesquicentennial Faculty Fellow, is working on designing the hardware and software techniques that will power future cloud-computing systems. She applies data-driven machine learning principles to large-scale system problems, which can improve their performance, efficiency and security. She has developed several cloud management systems, including Quasar, that leverage ideas from online recommender systems to better manage cloud services’ resources, making them more reliable and efficient.

Her current research involves introducing these learning-based principles to emerging hardware and software trends in cloud computing, such as microservices. Her team is also exploring the potential of automated approaches when cloud systems collaborate with devices, such as swarms of drones used in agriculture or disaster-recovery efforts.

DiStasio’s theoretical chemistry group uses new approaches to understand the non-bonded interactions that occur between molecules. Among other uses, these approaches can help predict the structures and properties of the different forms, or polymorphs, of molecular crystals. DiStasio and his research group work at the intersection of quantum and statistical mechanics, numerical analysis and high-performance computing to explore the vast number of polymorphs resulting from different experimental conditions and time scales.

A better understanding of these polymorphs – which may have different properties from each other and are challenging to predict with existing methods – could lead to more effective and stable pharmaceuticals, in addition to potential applications in energy, the environmental sciences, technology and industry.

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Rebecca Valli