Fatemeh Almodaresi and Yuan Su Receive the Larry S. Davis Doctoral Dissertation Award

Descriptive image for Fatemeh Almodaresi and Yuan Su Receive the Larry S. Davis Doctoral Dissertation Award

Two Ph.D. graduates from the Department of Computer Science were recently honored for the excellence of their research and scholarship at the University of Maryland.

Fatemeh Almodaresi (left in photo) and Yuan Su (right), who graduated with a Ph.D. in computer science in the summer and spring of 2020 respectively, are this year’s recipients of the Larry S. Davis Doctoral Dissertation Award.

The annual award recognizes outstanding doctoral dissertations in the department that convey excellence in their technical depth, significance, potential impact and presentation quality.

The award is named for Larry Davis, a Distinguished University Professor of computer science who served as chair of the department from 1999–2012. Davis was also the founding director of the University of Maryland Institute for Advanced Computer Studies (UMIACS), providing leadership for the institute from 1985–1994.

Both graduate students did the majority of their research in UMIACS-supported centers: Almodaresi in the Center for Bioinformatics and Computational Biology (CBCB) and Su in the Joint Center for Quantum Information and Computer Science (QuICS).

Almodaresi’s UMD dissertation, “Algorithms and Data Structures for Indexing, Querying, and Analyzing Large Collections of Sequencing Data in the Presence or Absence of a Reference,” covers the development of new data structures as well as innovative, practical and efficient solutions to indexing large collections of genomes.

“Fatemeh demonstrates both technical brilliance and the ability to come up with ideas that are both theoretically interesting and of tremendous practical impact,” says Rob Patro, an associate professor of computer science who advised Almodaresi during her doctoral studies.

In recommending her for the dissertation award, Patro noted Almodaresi’s design of a new compacted version of a De Bruijn graph, a data structure that is used in bioinformatics to assemble and analyze genomes. Although similar structures have previously been proposed, Almodaresi’s work greatly improved the practicality and efficiency in important ways, Patro explains. 

Almodaresi also built a tool using her new data structure for the taxonomic assignment of metagenomic read data, a well-studied problem in the field of computational biology. The taxonomic classifier she designed is both more accurate and more efficient—requiring less memory while operating at a similar speed—than two popular tools that are widely-used to accomplish the same task, Patro says.

“I anticipate that much of the theory and methodology Fatemeh develops will have a far-reaching impact both within and beyond the field of genomics,” says Patro. “She is a truly expectational researcher—displaying an intelligence, commitment and real passion at a level that I find to be rare, even among top Ph.D. students.”

Su’s UMD doctoral thesis, “Algorithms for Quantum Simulation: Design, Analysis, Implementation, and Application,” presents a comprehensive view on understanding and optimizing the performance of quantum algorithms for simulating quantum physics.

Through his work in QuICS and other opportunities that included a 2019 Google Ph.D. Fellowship, Su has become one of the leading experts on quantum algorithms for simulating Hamiltonian dynamics, says Andrew Childs, a professor of computer science and co-director of QuICS.

“Yuan was impressively productive during his graduate studies, and his work has been both influential and technically strong,” says Childs, who was Su’s academic adviser during his five years at Maryland. “His doctoral thesis is a tour de force that presents some truly remarkable contributions.”

In recommending Su for the dissertation award, Childs noted some of his research activities in QuICS. This included Su’s collaboration on an extensive study of concrete resource estimates for digital quantum simulation algorithms, with the goal of finding a task that would be infeasible classically, but requires the fewest resources to solve with a quantum computer.

Su subsequently presented a talk on this work at QIP 2018, and a journal paper was published in the Proceedings of the National Academy of Sciences and highlighted in a news piece in Nature Physics.

Su is currently working as a postdoctoral scholar research associate at the California Institute of Technology.

“Yuan is a promising, highly motivated researcher with powerful technical skills and broad knowledge of quantum information,” Childs says. “I’m excited to see what he accomplishes as he moves forward in his scientific career.”


—Story by Maria Herd, UMIACS


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