KSEA is very pleased to announce the winners of the 2022 KSEA Young Investigator Grants (YIG). Evaluation and selection was conducted by the KSEA Honors and Awards Committee and Technical Group Councilors based on review of the submitted application form, technology development plan, reference letters and resume.
After a strict evaluation process by the Honors and Awards Committee and approval vote by the 50th KSEA Council, Dr. Jihye Park (University of Colorado at Boulder) and Dr. Kangwook lee (University of Wisconsin-Madison) have been selected as the 2022 YIG recipients. Dr. Jihye Park and Dr. Kangwook Lee will be awarded $10,000 research grants at the award ceremony during UKC 2022.
Dr. JIHYE PARK
Dr. Jihye Park is an assistant professor of Chemistry at the University of Colorado at Boulder. Prior to joining the University of Colorado Boulder, she was a postdoctoral fellow under the mentorship of Professor Zhenan Bao in the department of Chemical Engineering at Stanford University from 2016 through 2019. She received her B.S. in Chemistry from Dankook University in 2011 and earned her Ph.D. in Chemistry from Texas A&M University in 2016, under the supervision of Professor Hong-Cai (Joe) Zhou as a Welch Foundation International Fellow from 2012-2013.
Dr. Park’s research interests lie at the interface of organic/inorganic synthesis, nanoscience, and materials chemistry. Her research group addresses challenges related to sustainable energy and human health. Her Ph.D. work focused on developing methodologies that allow for functionalization of highly stable MOFs by leveraging understanding of thermodynamics and kinetics of Metal-Organic Framework (MOF) crystal growth. During her postdoc training, Dr. Park used her synthetic experience to extend MOF synthesis to new classes of conductive MOFs for electronic applications.
Dr. Park received the ACS Division of Inorganic Chemistry Young Investigator Award in 2017 and was the recipient of the Camille and Henry Dreyfus Postdoctoral Fellowship from 2016 through 2018. She has published 29 journal papers and served on various review panels and committees. Her publications have received over 5318 citations with a Google Scholar h-index of 21.
With the KSEA Young Investigator Grant, Dr. Park will investigate “Modular Design of Metal-Organic Frameworks for Electrochemical Desalination Batteries” and the successful execution of this proposal will allow for developing new Cl– storage materials and a fundamental understanding of their storage mechanism, thus opening future opportunities to approach innovative water-energy nexus.
Dr. KANGWOOK LEE
Dr. Kangwook Lee is an Assistant Professor of Electrical and Computer Engineering at the University of Wisconsin-Madison. He received his B.S. in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST) in 2010 and continued his education at University of California, Berkeley, where he earned his M.S. and Ph.D. in Electrical Engineering and Computer Sciences in 2012 and 2016, respectively.
Dr. Lee’s research interests lie at the intersection of machine learning and information & coding theory, with a particular focus on designing trustworthy and scalable machine learning algorithms and systems. His seminal paper on coded computation is the most cited paper published in IEEE Transactions on Information Theory between 2016 and 2020 and is awarded the IEEE joint Communication Society/Information Theory Society Paper in 2020. Dr. Lee served as a program committee member for machine learning conferences, including Area Chair for NeurIPS, Program Committee for Machine Learning and Systems, and Action Editor for Transactions on Machine Learning Research. Dr. Lee has published 9 journal publications alongside 51 conference publications; these publications have received 2065 citations along with a Google scholar h-index of 19.
With the KSEA Young Investigator Grant, Dr. Lee will investigate “Information-theoretic Approaches to Federated Fair Learning.” The goal of his research is to develop a novel, interdisciplinary mix of intellectual tools from information theory, machine learning, and optimization to design next generation Federated Fair Learning (FFL) algorithms with provable guarantees.