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Sham Kakade

  • Adjunct Associate Professor

Specializations

Artificial intelligence, machine learning, optimization.

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                    [post_content] => [caption id="attachment_2293" align="alignleft" width="173"] Maryam Fazel[/caption]

[caption id="attachment_11240" align="alignleft" width="152"] Sham Kakade[/caption]

UW Electrical Engineering (UW EE) Professor Maryam Fazel and Paul G. Allen School of Computer Science & Engineering Professor Sham Kakade are co-directors on a three-year $1.5 million award from the National Science Foundation (NSF) Transdisciplinary Research in Principles of Data Science (TRIPODS) program to develop new algorithmic tools that will advance the state of the art in data science. The funding supports the researchers’ project, entitled “Algorithms for Data Science: Complexity, Scalability, and Robustness.”

TRIPODS was designed to engage members of the theoretical computer science, mathematics and statistics communities in developing the theoretical foundations of data science to promote data-driven discovery. Kakade and Fazel’s proposal aims to produce a common language and set of design principles to guide the development of new algorithmic tools that will automate the process of extracting robust insights from vast troves of data.

“Modern data science challenges transcend the ideas of any single discipline, which is what makes this work so exciting,” Kakade said. “With the growing availability of large datasets and increasing computational resources, we need more robust algorithmic tools to address contemporary data science challenges — and we believe a unifying approach is needed to overcome those challenges, accelerate the pace of scientific discovery and generate solutions to real-world problems.”

The UW proposal is one of 12 projects that will receive TRIPODS grants. These awards represent the NSF’s first major investment in “Harnessing the Data Revolution.” This research focus is considered one of the “10 big ideas” critical for future investment.

“These new TRIPODS projects will help build the theoretical foundations of data science that will enable continued data-driven discovery and breakthroughs across all fields of science and engineering,” said Jim Kurose, assistant director for Computer and Information Science and Engineering (CISE) at NSF, in a press release.

Not only will the researchers develop the language for data-driven discoveries, they also present a strong educational impact of the work, aiming to train students and scholars to be well-versed in data science and incorporating appropriate theoretical ideas into a data science curriculum.

“Our project is unique in that it places mathematical optimization theory at the heart of this endeavor, bridging across computer science, mathematics, and statistics,” said Fazel. “The project covers both high-impact interdisciplinary research and institutional activities such as workshops and boot camps to train students with novel techniques from all three disciplines. Ultimately, the project could serve as a springboard for building a full-fledged NSF institute in Phase II of the program.”

Fazel’s research spans optimization, machine learning, signal processing, and system identification. Kakade, who is an adjunct faculty member in Electrical Engineering, focuses on theoretical and applied questions in machine learning and artificial intelligence. They are joined on the project by three co-principal investigators: UW Department of Mathematics Professor Dmitriy Drusvyatskiy, UW Department of Statistics Professor Zaid Harchaoui, and Allen School Professor Yin Tat Lee.

Read the NSF announcement here.
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                            [post_content] => [caption id="attachment_2293" align="alignleft" width="173"] Maryam Fazel[/caption]

[caption id="attachment_11240" align="alignleft" width="152"] Sham Kakade[/caption]

UW Electrical Engineering (UW EE) Professor Maryam Fazel and Paul G. Allen School of Computer Science & Engineering Professor Sham Kakade are co-directors on a three-year $1.5 million award from the National Science Foundation (NSF) Transdisciplinary Research in Principles of Data Science (TRIPODS) program to develop new algorithmic tools that will advance the state of the art in data science. The funding supports the researchers’ project, entitled “Algorithms for Data Science: Complexity, Scalability, and Robustness.”

TRIPODS was designed to engage members of the theoretical computer science, mathematics and statistics communities in developing the theoretical foundations of data science to promote data-driven discovery. Kakade and Fazel’s proposal aims to produce a common language and set of design principles to guide the development of new algorithmic tools that will automate the process of extracting robust insights from vast troves of data.

“Modern data science challenges transcend the ideas of any single discipline, which is what makes this work so exciting,” Kakade said. “With the growing availability of large datasets and increasing computational resources, we need more robust algorithmic tools to address contemporary data science challenges — and we believe a unifying approach is needed to overcome those challenges, accelerate the pace of scientific discovery and generate solutions to real-world problems.”

The UW proposal is one of 12 projects that will receive TRIPODS grants. These awards represent the NSF’s first major investment in “Harnessing the Data Revolution.” This research focus is considered one of the “10 big ideas” critical for future investment.

“These new TRIPODS projects will help build the theoretical foundations of data science that will enable continued data-driven discovery and breakthroughs across all fields of science and engineering,” said Jim Kurose, assistant director for Computer and Information Science and Engineering (CISE) at NSF, in a press release.

Not only will the researchers develop the language for data-driven discoveries, they also present a strong educational impact of the work, aiming to train students and scholars to be well-versed in data science and incorporating appropriate theoretical ideas into a data science curriculum.

“Our project is unique in that it places mathematical optimization theory at the heart of this endeavor, bridging across computer science, mathematics, and statistics,” said Fazel. “The project covers both high-impact interdisciplinary research and institutional activities such as workshops and boot camps to train students with novel techniques from all three disciplines. Ultimately, the project could serve as a springboard for building a full-fledged NSF institute in Phase II of the program.”

Fazel’s research spans optimization, machine learning, signal processing, and system identification. Kakade, who is an adjunct faculty member in Electrical Engineering, focuses on theoretical and applied questions in machine learning and artificial intelligence. They are joined on the project by three co-principal investigators: UW Department of Mathematics Professor Dmitriy Drusvyatskiy, UW Department of Statistics Professor Zaid Harchaoui, and Allen School Professor Yin Tat Lee.

Read the NSF announcement here.
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                    [post_content] => [caption id="attachment_2293" align="alignleft" width="173"] Maryam Fazel[/caption]

[caption id="attachment_11240" align="alignleft" width="152"] Sham Kakade[/caption]

UW Electrical Engineering (UW EE) Professor Maryam Fazel and Paul G. Allen School of Computer Science & Engineering Professor Sham Kakade are co-directors on a three-year $1.5 million award from the National Science Foundation (NSF) Transdisciplinary Research in Principles of Data Science (TRIPODS) program to develop new algorithmic tools that will advance the state of the art in data science. The funding supports the researchers’ project, entitled “Algorithms for Data Science: Complexity, Scalability, and Robustness.”

TRIPODS was designed to engage members of the theoretical computer science, mathematics and statistics communities in developing the theoretical foundations of data science to promote data-driven discovery. Kakade and Fazel’s proposal aims to produce a common language and set of design principles to guide the development of new algorithmic tools that will automate the process of extracting robust insights from vast troves of data.

“Modern data science challenges transcend the ideas of any single discipline, which is what makes this work so exciting,” Kakade said. “With the growing availability of large datasets and increasing computational resources, we need more robust algorithmic tools to address contemporary data science challenges — and we believe a unifying approach is needed to overcome those challenges, accelerate the pace of scientific discovery and generate solutions to real-world problems.”

The UW proposal is one of 12 projects that will receive TRIPODS grants. These awards represent the NSF’s first major investment in “Harnessing the Data Revolution.” This research focus is considered one of the “10 big ideas” critical for future investment.

“These new TRIPODS projects will help build the theoretical foundations of data science that will enable continued data-driven discovery and breakthroughs across all fields of science and engineering,” said Jim Kurose, assistant director for Computer and Information Science and Engineering (CISE) at NSF, in a press release.

Not only will the researchers develop the language for data-driven discoveries, they also present a strong educational impact of the work, aiming to train students and scholars to be well-versed in data science and incorporating appropriate theoretical ideas into a data science curriculum.

“Our project is unique in that it places mathematical optimization theory at the heart of this endeavor, bridging across computer science, mathematics, and statistics,” said Fazel. “The project covers both high-impact interdisciplinary research and institutional activities such as workshops and boot camps to train students with novel techniques from all three disciplines. Ultimately, the project could serve as a springboard for building a full-fledged NSF institute in Phase II of the program.”

Fazel’s research spans optimization, machine learning, signal processing, and system identification. Kakade, who is an adjunct faculty member in Electrical Engineering, focuses on theoretical and applied questions in machine learning and artificial intelligence. They are joined on the project by three co-principal investigators: UW Department of Mathematics Professor Dmitriy Drusvyatskiy, UW Department of Statistics Professor Zaid Harchaoui, and Allen School Professor Yin Tat Lee.

Read the NSF announcement here.
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