Two UC San Diego engineering professors named 2017 Sloan Research Fellows

San Diego, Calif., Feb. 21, 2017 -- Two engineering professors from the University of California Jacobs School of Engineering, computer science professor Daniel M. Kane and electrical engineering professor Siavash Mirarab, have each received $60,000 in the form of a 2017 Sloan Research Fellowship. Kane and Mirarab are among six early-career faculty members from UC San Diego to receive this prestigious fellowship.

The foundation is selective in choosing young professors whose achievements mark them as the nation’s future leaders in science and technology. “The large number of Sloan Research Fellowships awarded this year to our faculty is a powerful confirmation of the quality of our university, scholars and research enterprise,” said UC San Diego Chancellor Pradeep K. Khosla. “I congratulate these young scholars on their achievements and I look forward to seeing them pave the way for our next generation of leaders.”

Daniel M. Kane

Assistant Professor, Department of Computer Science and Engineering

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Daniel M. Kane

Kane will use funds from the Sloan Research Fellowship to further his research on two main topics: distribution learning and the analysis of low-degree polynomials. He aims to delve further into classic questions in statistics that have recently drawn the attention of the computer-science theory community – in many cases, he says, motivated by an effort to improve machine-learning algorithms. “I hope to improve upon my recent work on robust learning of high-dimensional distributions,” Kane said. “These may include expanding the collection of families that it can deal with, while decreasing the sample complexity and final error.” He also has projects in the works involving learning and testing problems related to binary product distributions and Beyesian nets.

To date, Kane has pursued a wide range of research topics in computational geometry, steaming algorithms, dimensionality reduction, cryptography, quantum computing, data structures and pseudorandomness.

Going forward, he aims to go beyond his prior work on polynomial threshold functions and distribution learning. Kane is hoping to improve the parameters of some of the existing decomposition results to produce better pseudorandom generators. He will also tackle other problems applied to quantum computation (the Aaronson-Ambainis conjecture) and learning theory (the Chow parameters problem).

Siavash Mirarab

Assistant Professor, Department of Electrical and Computer Engineering

Faculty member, Center for Microbiome Innovation

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Siavash Mirarab

Mirarab received a Sloan Research Fellowship to develop better and faster algorithms that can reconstruct the evolutionary past. His lab will collaborate with biologists to apply these algorithms to genome-scale large datasets to shed light on downstream questions that can range from the evolution of song learning in birds to parental retention of the eggs in plants. 

“My interest is understanding evolution through computational techniques,” Mirarab said. “Reconstructing the evolutionary past is important in many areas of biology, but reconstructing the past is only possible by using mathematical techniques that seek to answer a seemingly simple question: what reconstruction of the evolutionary history is most consistent with properties that define present-day organisms?”

Mirarab’s previous work has focused on the evolution of species through millions of years. He will use funds from this fellowship to branch out to a new direction he calls “the evolution within.” This involves studying how the evolutionary history of entities that evolve within humans could be best modeled, and then developing algorithms to study them. Entities that evolve within the human body include the microbiome, viruses, repeats in the human genome, and many others. Other examples include studying the methods of reconstructing the transmission of HIV in a social/sexual network, or developing methods to study the content of a microbiome sample based on its evolutionary past.

“Better understanding the evolution within will have an immediate impact on the downstream biomedical applications that range from vaccine design to controlling outbreaks of infectious disease. From a different perspective, methods developed for understanding complex evolutionary processes may also prove useful for understanding other types of big data,” Mirarab said. 

Media Contacts

Doug Ramsey
Jacobs School of Engineering

Ioana Patringenaru
Jacobs School of Engineering

Liezel Labios
Jacobs School of Engineering

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