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Albert Cheu
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Albert Cheu is a PhD student in the Algorithms and Theory program at Northeastern University’s Khoury College of Computer Sciences, advised by Professors Jonathan Ullman and Ravi Sundaram. The Brooklyn, New York native is a graduate of New York University, where he received his bachelor’s degree. Albert’s research focuses on theoretical computer science, and he is a member of the Algorithms & Theory group. He aspires to make novel contributions to data privacy research.
Introductory computer science classes ask students to come up with efficient ways to perform an operation on data. More challenging problems arise when randomness is a part of the task, either as noise in the input or as a constraint on the output; the projects I have worked on are in this vein.
Personal data is being generated minute by minute and corporate entities aggregate it. Statistics thereof are useful to the aggregator; on the other hand, they may violate the privacy of contributors. Prof. Ullman and I work with a particular definition of data privacy and we study techniques to balance the tradeoff between the two objectives.
In theoretical computer science, one can conjure up all kinds of problems; the real challenge is finding a well-motivated one and defining it rigorously. This art of finding the beginning of a maze may not be as celebrated as getting to the end, but it is one that requires much imagination.
I simply wish to be the best learner and researcher that I can be.
While it has a focus on applied research, the School of Engineering at New York University also has a small but solid set of theory professors. They were part of the reason I joined the PhD program at Northeastern University.
I grew up in New York City and attended Stuyvesant High School. Living in a cosmopolitan area allowed me to appreciate diversity.
Albert Cheu is a PhD student in the Algorithms and Theory program at Northeastern University’s Khoury College of Computer Sciences, advised by Professors Jonathan Ullman and Ravi Sundaram. The Brooklyn, New York native is a graduate of New York University, where he received his bachelor’s degree. Albert’s research focuses on theoretical computer science, and he is a member of the Algorithms & Theory group. He aspires to make novel contributions to data privacy research.
Introductory computer science classes ask students to come up with efficient ways to perform an operation on data. More challenging problems arise when randomness is a part of the task, either as noise in the input or as a constraint on the output; the projects I have worked on are in this vein.
Personal data is being generated minute by minute and corporate entities aggregate it. Statistics thereof are useful to the aggregator; on the other hand, they may violate the privacy of contributors. Prof. Ullman and I work with a particular definition of data privacy and we study techniques to balance the tradeoff between the two objectives.
In theoretical computer science, one can conjure up all kinds of problems; the real challenge is finding a well-motivated one and defining it rigorously. This art of finding the beginning of a maze may not be as celebrated as getting to the end, but it is one that requires much imagination.
I simply wish to be the best learner and researcher that I can be.
While it has a focus on applied research, the School of Engineering at New York University also has a small but solid set of theory professors. They were part of the reason I joined the PhD program at Northeastern University.
I grew up in New York City and attended Stuyvesant High School. Living in a cosmopolitan area allowed me to appreciate diversity.