The SPS dissertation award is judged on two categories: their Scientific impact, impact on the scientific discipline and practice as evidenced by citations, downloads, journal and conference papers published from the dissertation, other awards received, patents, or adoption into practice, and the overall quality of dissertation, creativity, quality of writing, relevance, significance and timeliness of the research questions addressed, level of novelty compared to the state of the art, quality and rigor of scientific method, quality of critical thinking in discussion and conclusions, quality and scope of the bibliography.
His PhD dissertation is entitled, "Eﬃcient Methods for Distributed Machine Learning and Resource Management in the Internet-of-Things." Tianyi’s dissertation is centered around a unified algorithmic framework for distributed machine learning and resource management that addresses the challenges emerging from implementing machine learning in the Internet-of-Things. The framework encompasses a set of new computational methods that make quantiﬁably better use of limited resources (e.g., communication, memory, and energy) and require fewer modeling assumptions than existing methods. Results of this dissertation have demonstrated that the new distributed machine learning algorithms will lead to signiﬁcant improvement in resource eﬃciency of machine learning; and the new learning-aided model-free resource management schemes achieve performance competitive to existing model-based methods.
Prior to joining RPI as an Assistant Professor in 2019, Tianyi Chen received his PhD in Electrical and Computer Engineering at the University of Minnesota under the supervision of Professor Georgios B. Giannakis. He was a recipient of the Doctoral Dissertation Fellowship at the University of Minnesota.
Congratulations again, Prof. Chen!