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We are excited to welcome 4 new tenure-track faculty members to our department this Winter:
Currently a postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory at MIT, SouYoung Jin works at the intersection of computer vision, machine learning and cognitive science. Her area of expertise is video understanding. As a computer vision researcher, one of her ultimate aspirations is to build an AI system that can understand and respond to the richness of human experience and emotion, much like Jarvis in the Iron Man movie.
Jin earned her PhD at the College of Information and Computer Sciences (CICS), UMass Amherst, where she researched on improving face clustering in videos. She looks forward to meeting with talented people at Dartmouth and collaborate with them to make something interesting.
Adithya Pediredla's research interests are centered around computational imaging. Computational imaging systems co-design the imaging and image processing pipelines to either acquire novel features of the scene, decrease the cost of the imaging system, or increase the robustness of the imaging system. He looks forward to setting up the Dartmouth Rendering and Imaging Science (RISC) lab, which will exploit the synergy between physics-based rendering and computational imaging.
Pediredla is currently a postdoctoral scholar at the Robotics Institute, Carnegie Mellon University. He received his PhD from the Department of Electrical and Computer Engineering at Rice University. His PhD thesis received the Ralph Budd best engineering thesis award. He received a masters' degree from the Indian Institute of Science, where he won the Prof. K. R. Kambati memorial gold medal and an innovative student project award from the Indian National Academy of Engineering. He describes himself as a chess enthusiast with an interest in hiking and travel.
Yujun Yan comes to Dartmouth CS from the University of Michigan, where she is pursuing her Ph.D. in the Graph Exploration and Mining at Scale lab. Her research lies at the intersection of machine learning (ML) and network science. Her work provides theoretical understanding and empirical practices to ML models, with application to complex real-world networks. Her research centers around two important questions: (1) what are fundamental principles in designing more expressive and generalizable graph-based ML models; (2) what are useful practices when applying graph-based ML models to various domains, such as neuroscience, and program understanding.
During her Ph.D., Yan has published multiple papers at top machine learning and data mining conferences like NeurIPS, KDD, and WebConf, and two of her works have been taught at top universities like Stanford University and Northeastern University. She interned at Microsoft Research in 2018 and 2021 and interned at Google Research in 2019 and 2020. She holds a pending patent with Google and was rewarded a Patent Filing Award while interning at Google.
Yaoqing Yang is a postdoctoral researcher at the RISE Lab at UC Berkeley. He received his PhD from Carnegie Mellon University and B.S. from Tsinghua University, China. He studies the fundamental aspects of machine learning, and his main contributions are towards improving reliability and generalization in the face of uncertainty, both in the data and the computing platform. His recent works focus on generalization and robustness of deep neural networks, and he also applies these studies to practical data analytics, such as 3D point clouds and graph neural networks.
Yang's works have won the best paper finalist at ICDCS and have been published multiple times in NeurIPS, CVPR and IEEE Transactions on Information Theory. He has worked as a research intern at Microsoft, MERL and Bell Labs, and two of his joint CVPR papers with MERL have both received more than 400 citations. He is also the recipient of the 2015 John and Claire Bertucci Fellowship. Apart from work, Yang likes road trips and loves fall colors. He looks forward to enjoying the beautiful fall foliage in the Upper Valley. He is also a fan of tennis, whether on the lawn or on the table.