Mike Schaekermann, Hong Zhou and Fiodar Kazhamiaka win Cheriton Distinguished Dissertation Awards

Tuesday, May 4, 2021

Recent PhD graduates Mike Schaekermann, Hong Zhou and Fiodar Kazhamiaka have each received a Cheriton Distinguished Dissertation Award. Established in 2019, the dissertation award was created to recognize excellence in computer science doctoral research. In addition to the prestigious recognition, each awardee receives a cash prize of $1000.

To be considered for the dissertation award, two nomination letters, typically from the student’s doctoral advisor and external examiner, along with a nomination statement are submitted to a selection committee chaired by the School of Computer Science’s Director of Graduate Studies.

“I was very impressed to see the many high quality works done by our grad students, which had made the selection process a pleasant challenge,” said Justin Wan, Director of Graduate Studies at the Cheriton School of Computer Science. “I wish the awardees all the best, and they continue to make us proud through their future endeavours.”

Because of the pandemic, review of submissions was delayed for the 2020 award competition.

2021 winners

photo of Mike SchaekermannMike Schaekermann, cosupervised by Professors Edith Law and Kate Larson, was tied for first place for his PhD thesis titled “Human-AI Interaction in the Presence of Ambiguity: From Deliberation-based Labeling to Ambiguity-aware AI.” 

Dr. Schaekermann is currently an applied scientist at Amazon AI.

About Dr. Schaekermann’s research
Ambiguity — the quality of being open to more than one interpretation — permeates our lives. It comes in many forms, arises for various reasons, and leads to disagreements that can be difficult or impossible to resolve. As artificial intelligence (AI) is increasingly infused into complex domains of human decision-making it is crucial that AI mechanisms support a notion of ambiguity. Yet, existing AI approaches typically assume a single correct answer for any given input.

Dr. Schaekermann’s dissertation shed light on how humans and AI can be effective partners on ambiguous problems. To address this question, he studied group deliberation as a tool to detect and analyze ambiguous cases in data labelling. He presented three case studies that investigate group deliberation in the context of different labelling tasks, data modalities and types of human labelling expertise.


photo of Hong ZhouHong Zhou, supervised by Professor Lap Chi Lau, was tied for first place for his PhD thesis titled “A Spectral Approach to Network Design and Experimental Design.”

Dr. Zhou is currently a postdoctoral fellow at the Cheriton School of Computer Science, working with Professor Lap Chi Lau.

About Dr. Zhou’s research
Over the past decade, the linear algebraic perspective to solving graph problems has become a powerful tool in designing fast graph algorithms, where graph spectrum (i.e., eigenvalues and eigenvectors of some matrices associated with the given graph) plays a crucial role. In this thesis, Dr. Zhou extends this spectral approach and brings new insights and interesting results to well-studied network design and experimental design problems.


2020 winner

photo of Fiodar KazhamiakaFiodar Kazhamiaka, cosupervised by Professors Srinivasan Keshav and Catherine Rosenberg, received first place for his thesis titled “Modelling, Design, and Control of Energy Systems: A Data-Driven Approach.” 

Dr. Kazhamiaka is currently a postdoctoral fellow at the Future Data Systems lab at Stanford University, working with Matei Zaharia and Peter Bailis.

About Dr. Kazhamiaka’s research
Dr. Kazhamiaka’s research explored ways to meet our growing energy needs with clean renewable energy sources. He tackled this problem by maximizing the economic value of residential photovoltaic (PV) energy storage systems for homeowners. He developed algorithms to optimally size PV-storage systems while providing guarantees on system reliability, and control solutions for PV storage systems that learn and adjust to changes in the operating environment to adaptively optimize control decisions.

“For widespread adoption, a PV-storage system has to make sense economically,” Dr. Kazhamiaka said. “Everyone understands money and that’s the Holy Grail of sustainable energy production — finding a way to provide clean energy that competes with or is cheaper than what’s available now.”
 

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