Columbia Engineering Faculty Tenured in 2022
Elias Bareinboim is a computer scientist and director of the Causal Artificial Intelligence (CausalAI) Laboratory at Columbia University. His research focuses on how to build the next generation of artificial intelligence systems and endow these systems with causal and counterfactual reasoning capabilities. His work proposes the first general, causal-based solution to the problem of “data-fusion,” and provides practical methods for combining datasets generated under different experimental conditions and with various biases (see Bareinboim and Pearl, PNAS 2016). Professor Bareinboim also studies how causal knowledge helps improve the decision-making and explainability capabilities of automated systems. With this research, Bareinboim has been a driving force in two new sub-fields -- "causal reinforcement learning" and "causal fairness analysis,” which is further elaborated at https://causalai.net.
Professor Bareinboim is currently on the editorial board of the Journal of Causal Inference, and he was named one of “AI's 10 to Watch” by the Institute of Electrical and Electronics Engineers. He is also a recipient of the National Science Foundation CAREER Award, the Office of Naval Research Young Investigator Award, the Dan David Prize Scholarship, and a number of best paper awards. His research has been generously funded by the NSF, ONR, AFOSR, DoE, Amazon, JP Morgan, and The Alfred P. Sloan Foundation. He obtained his BSc & MSc from the Federal University of Rio de Janeiro (2007) and completed his PhD in Computer Science from the University of California, Los Angeles (2014) under Professor Judea Pearl. Before joining Columbia, he was a postdoctoral fellow at UCLA and an Assistant Professor at Purdue University. He joined Columbia University as an Associate Professor (without tenure) in 2019.
Javad Ghaderi is an electrical engineer whose research focuses on Performance Analysis, Algorithms, and Stochastic Modeling, with application to communication networks and data centers. He has made significant contributions to the design of resource allocation algorithms for emerging large-scale wireless networks and data centers. His research has received “best paper” awards from several major conferences, such as IEEE INFOCOM, IFIP Performance, and ACM CoNEXT, and he is also the recipient of an NSF CAREER Award. Professor Ghaderi has taught the Core curriculum course Computer Networks, and has developed new courses in cutting-edge areas, including Network Algorithms and Reinforcement Learning in Information Systems. He is currently serving as an Associate Editor for IEEE/ACM Transactions on Networking.
Professor Ghaderi received his BSc from the University of Tehran, Iran in 2006; his MSc from the University of Waterloo, Canada in 2008; and his PhD from the University of Illinois at Urbana-Champaign in 2013, all in Electrical and Computer Engineering. He spent a one-year Simons Postdoctoral Fellowship at the University of Texas at Austin before joining Columbia.
Addis Kidane is an engineer in the field of experimental solid mechanics, with a particular focus on materials mechanics at high strain rate loading and temperature. Professor Kidane's group developed an experimental technique at the mesoscale to measure local deformation and strain in heterogeneous materials at high temporal and spatial resolutions. He has made significant advances in deformation mechanics in heterogeneous materials subjected to dynamic loading, helping elucidate how failure was initiated and propagated in energetic materials under impact loading. Professor Kidane is the recipient of the J. W. Dally Young Investigator Award from the Society for Experimental Mechanics for his commitment to education at all levels and is also the recipient of the AFOSR young investigator award. Kidane is an active member of ASME and SEM and currently serves as an associate editor for the journal Mechanics of Materials.
Professor Kidane obtained his doctoral degree in Mechanical Engineering and Applied Mechanics from the University of Rhode Island and spent two years at the California Institute of Technology as a postdoctoral scholar. Professor Kidane joined the Civil Engineering and Engineering Mechanics Department at Columbia University in January 2022, after serving for 10 years in the Department of Mechanical Engineering at the University of South Carolina.
Eugene Wu’s research is in the area of data management, where he has worked on aspects of data integrity, user interaction, large-scale data processing, and data visualization. His work seeks to improve the interface between users and data, and draws on techniques across data management, systems, machine learning, visualization, and human-computer interaction. Professor Wu has received multiple awards for his research, including the National Science Foundation Career Award, a Google Faculty Award, Amazon Research Awards, and the Very Large Data Bases Test of Time Award. Professor Wu is also co-chair of the Data Science Institute’s Center for Data, Media, and Society.
Professor Wu obtained his BS in Electrical Engineering and Computer Science from University of California, Berkeley in 2007. He earned his MS in Electrical Engineering and Computer Science from MIT in 2010, followed by his PhD in 2014. Professor Wu started his career as Assistant Professor at Columbia University in 2015 and was promoted to Associate Professor in 2020.
Yuan Yang is a materials scientist whose research focus is the design of materials and devices for energy applications, particularly in electrochemical energy storage and thermal management. In particular, his research group has recently worked on solid state batteries with high energy density and excellent thermal stability for electric vehicles and grid-level energy storage. His group has also worked on radiative cooling material which can cool itself without electricity. He has published more than 100 papers and has been cited over 30,000 times. Among other honors, he has received a 3M Non-Tenured Faculty Award, and Materials Today Rising Star Award. He was been named a Clarivate Highly Cited Scholar in 2021 and 2022.
Professor Yang received his BA in Physics from Peking University in 2003 and his PhD in Materials Science from Stanford University in 2012. From 2012-2015, he was a postdoctoral researcher at MIT.
Richard Zemel is a recognized leader in machine learning, especially in the sub-field of deep learning, where he addresses some of the field’s more fundamental problems, such as structured prediction, semi-supervised learning, and unsupervised learning. He has applied his machine learning-based research to a range of problems, such as understanding fairness in artificial intelligence, an issue with profound societal implications; and solving problems in the field of neuroscience. His research contributions include foundational work on systems that learn useful representations of data with little or no supervision; robust and fair learning algorithms; graph-based machine learning; and learning systems for automatic captioning and answering questions about images. He developed the Toronto Paper Matching System, which matches paper submissions to reviewers, and is now being used by many conference organizers. His research is supported by grants from the Natural Sciences and Engineering Research Council of Canada, CIFAR, Microsoft, Google, Samsung, the Defense Advanced Research Projects Agency and the Intelligence Advanced Research Projects Activity. His awards include an NVIDIA Pioneers of AI Award, an ONR Young Investigator Award, and a CIFAR AI Chair. He was the Co-Founder and Research Director of the Vector Institute for Artificial Intelligence, is a Fellow of the Canadian Institute for Advanced Research, and is on the Advisory Board of the Neural Information Processing Society.
Professor Zemel obtained his BA in History and Science from Harvard University in 1984. He earned his MSc in Computer Science from the University of Toronto, followed by his PhD in 1994. Prior to joining Columbia University, he was a Professor at the University of Toronto.