Elias Bareinboim, Brian Smith, and Shuran Song join the department.
Associate Professor, Computer Science
Director, Causal Artificial Intelligence Lab
Member, Data Science Institute
PhD, Computer Science, University of California, Los Angeles (UCLA), 2014
BS & MS, Computer Science, Federal University of Rio de Janeiro (UFRJ), 2007
Elias Bareinboim’s research focuses on causal and counterfactual inference and its application to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. His work was the first to propose a general solution to the problem of “causal data fusion,” providing practical methods for combining datasets generated under heterogeneous experimental conditions and plagued with various biases. This theory and methods constitute an integral part of the discipline called “causal data science,” which is a principled and systematic way of performing data analysis with the goal of inferring cause and effect relationships.
More recently, Bareinboim has been investigating how causal inference can help to improve decision-making in complex systems (including classic reinforcement learning settings), and also how to construct human-friendly explanations for large-scale societal problems, including fairness analysis in automated systems.
Bareinboim is the recipient of the NSF Faculty Early Career Development (CAREER) Award, IEEE AI’s 10 to Watch, and a number of best paper awards. Later this year, he will be teaching a causal inference class intended to train the next generation of causal inference researchers and data scientists. Bareinboim directs the Causal Artificial Intelligence Lab, which currently has open positions for Ph.D. students and Postdoctoral scholars.
PhD, Computer Science, Columbia University, 2018
MPhil, Computer Science, Columbia University, 2015
MS, Computer Science, Columbia University, 2011
BS, Computer Science, Columbia University, 2009
Brian Smith’s interests lie in human-computer interaction (HCI) and creating computers that can help people better experience the world. His past research on video games for the visually impaired was featured in Quartz, TechCrunch, the Huffington Post, among others.
Smith has spent the last year at Snap Research (Snap is best known for Snapchat) developing new concepts in human–computer interaction (HCI), games, social computing, and augmented reality. He will continue to work on projects with Snap while at Columbia.
He comes back to the department as an assistant professor and is set to teach a class on user interface design this fall. That class had a waitlist of 235 students hoping to be part of the class. Smith shared that back when he was a student, there were only 35 students in the class he was enrolled in. “There is definitely more interest in computer science now compared to even five years ago,” he said.
Smith hopes to start a HCI group and is looking for PhD students. He encourages students from underrepresented groups to apply.
PhD, Computer Science, Princeton University, 2018
MS, Computer Science, Princeton University, 2015
BEng, Computer Engineering, Hong Kong University of Science and Technology, 2013
Shuran Song is interested in artificial intelligence with an emphasis on computer vision and robotics. The goal of her research is to enable machines to perceive and understand their environment in a way that allows them to intelligently operate and assist people in the physical world.
Previously, Song worked at Google Brain Robotics as a researcher and developed TossingBot, a robot that learns to how to accurately throw arbitrary objects through self-supervised learning.
This fall, she is teaching a seminar class on robot learning. Song currently has one PhD student who is working on active perception — enabling robots to learn from their interactions with the physical world, and autonomously acquire the perception and manipulation skills necessary to execute complex tasks. She is looking for more students who are interested in machine learning for vision and robotics.
The department welcomes Baishakhi Ray, Ronghui Gu, Carl Vondrick and Tony Dear.
Assistant Professor, Computer Science
PhD, University of Texas, Austin, 2013; MS, University of Colorado, Boulder, 2009; BTech, Calcutta University, India, 2004; BSc, Presidency College, India, 2001
Baishakhi Ray works on end to end software solutions and treats the entire software system – anything from debugging, patching, security, performance, developing methodology, to even the user experience of developers and users.
At the moment her research is focused on machine learning bias. For example, some models see a picture of a baby and a man and identify it as a woman and child. Her team is developing ways on how to train a system and to solve practical problems.
Ray previously taught at the University of Virginia and was a postdoctoral fellow in computer science at University of California, Davis. In 2017, she received Best Paper Awards at the SIGSOFT Symposium on the Foundations of Software Engineering and the International Conference on Mining Software Repositories.
Assistant Professor, Computer Science
PhD, Yale University, 2017; Tsinghua University, China, 2011
Ronghui Gu focuses on programming languages and operating systems, specifically language-based support for safety and security, certified system software, certified programming and compilation, formal methods, and concurrency reasoning. He seeks to build certified concurrent operating systems that can resist cyberattacks.
Gu previously worked at Google and co-founded Certik, a formal verification platform for smart contracts and blockchain ecosystems. The startup grew out of his thesis, which proposed CertiKOS, a comprehensive verification framework. CertiKOS is used in high-profile DARPA programs CRASH and HACMS, is a core component of an NSF Expeditions in Computing project DeepSpec, and has been widely considered “a real breakthrough” toward hacker-resistant systems.
Assistant Professor, Computer Science
PhD, Massachusetts Institute of Technology, 2017; BS, University of California, Irvine, 2011
Carl Vondrick’s research focuses on computer vision and machine learning. His work often uses large amounts of unlabeled data to teach perception to machines. Other interests include interpretable models, high-level reasoning, and perception for robotics.
His past research developed computer systems that watch video in order to anticipate human actions, recognize ambient sounds, and visually track objects. Computer vision is enabling applications across health, security, and robotics, but they currently require large labeled datasets to work well, which is expensive to collect. Instead, Vondrick’s research develops systems that learn from unlabeled data, which will enable computer vision systems to efficiently scale up and tackle versatile tasks. His research has been featured on CNN and Wired and in a skit on the Late Show with Stephen Colbert, for training computer vision models through binge watching TV shows.
Recently, three research papers he worked on was presented at the European Conference for Computer Vision (EECV). Vondrick comes to Columbia from Google Research, where he was a research scientist.
Lecturer in Discipline, Computer Science
PhD, Carnegie Mellon University, 2018; MS, Carnegie Mellon University, 2015; BS, University of California, Berkeley, 2012
Tony Dear’s research and pedagogical interests lie in bringing theory into practice. In his PhD research, this idea motivated the application of analytical tools to motion planning for “real” or physical locomoting robotic systems that violate certain ideal assumptions but still exhibit some structure – how to get unconventional robots to more around with stealth of animals and biological organisms. Also, how to simplify tools and expand that to other systems, as well as how to generalize mathematical models to be used in multiple robots.
In his teaching, Dear strives to engage students with relatable examples and projects, alternative ways of learning, such as an online curriculum with lecture videos. He completed the Future Faculty Program at the Eberly Center for Teaching Excellence at Carnegie Mellon and has been the recipient of a National Defense Science and Engineering Graduate Fellowship.
At Columbia, Dear is looking forward to teaching computer science, robotics and AI. He hopes to continue small scale research projects in robotic locomotion and conduct outreach to teach teens STEM and robotics courses.