Four Students Recognized at 2022 Outstanding Graduate Student Awards
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Four graduate students at the Manning College of Information and Computer Sciences (CICS) recieved 2022 Outstanding Graduate Student Awards in February 2023 for their exceptional contributions to research and instruction.
The Outstanding Teaching Assistant awards were granted to doctoral students Ashish Singh and Ojaswi Acharya. Singh was cited for his proactive approach to managing classroom issues in COMPSCI 682 by the instructor, Professor and Chair of the Faculty Erik Learned-Miller. “Ashish would predict problems before they happened and keep catastrophes at bay,” says Learned-Miller. “He went above and beyond for the students and even provided helpful strategies for modifying homework and organizing grading that streamlined the course.”
Acharya was honored for her outstanding commitment and motivation, which included coordinating grading work for COMPSCI 311 and COMPSCI 611. “What distinguishes Ojaswi is her eagerness to help and her natural ability to be a leader,” says Associate Dean for Educational Programs and Teaching & Distinguished Professor Ramesh Sitaraman, who instructed both classes. “She shows a level of dedication to her teaching and her students that I truly appreciate.”
The Outstanding Synthesis Project awards for interdisciplinary research projects were awarded to doctoral students Vignesh Viswanathan and Cen Wang. Synthesis projects are significant research projects that combine at least two different research areas and involve an intellectual stretch to bring them together. The projects are part of the unique CICS culture of collaboration, where each doctoral student is required to complete a synthesis project before they can be considered for candidacy.
Viswanathan was recognized for his work with Assistant Professor Yair Zick on learning market equilibria from data, the results of which were published at the Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). “Ultimately, this was a very successful collaboration that resulted in a synthesis of deep ideas from learning theory and algorithmic market design,” says Zick. “It did not feel like we were discussing the problem with a first-year student, but rather with a mature researcher.”
Wang was celebrated for his work with Professor Peter Haas and Associate Professor Justin Domke on developing generative neural networks to model simulation inputs. “Cen developed the first generative neural network (GNN) framework that exploits modern data-rich environments to automatically capture complex simulation input distributions and then efficiently generate samples from them,” says Professor Haas. “Overall, this work is a beautiful synthesis of simulation and machine learning and has the potential to help overcome one of the key barriers to simulation for non-experts.”