Columbia’s Center of Artificial Intelligence Technology (CAIT) supports, in collaboration with Amazon, new and established researchers by awarding the PhD. -Grants and faculty research prices. Each year, CAIT receives dozens of scholarships nominations for highly qualified PhD students whose work affects the broad area of artificial intelligence.
In February 2022, Amazon and CAIT announced to Ph.D. -Students scholarships and five faculty research prices. Now Cait is announcing four new scholarships for the study in 2023.
“Cait fellows represent the most creative minds among the young talents we aim to care for in CAIT,” said Shih-Fu Chang, Dean of Columbia Engineering. “The projects proposed by future leaders stand out as tackling urgent challenges related to AI with new approaches. We look forward to seeing the result of their innovative research.”
“We are pleased with the high quality of the fellow nominations,” said Prem Natarajan, Vice President Alexa AI. “We are happy to invest in tomorrow’s AI leaders, and I can’t wait to see the progress that 2023 Columbia-Amazon PhD fellows are doing to overcome some of the most difficult and fundamental problems in AI.”
“Progress in AI has borrowed mathematics and methods from across several disciplines,” said Ido Rosen, senior rector of Amazon Core AI. “For that reason, I am not only inspired by 2023 Columbia-Amazon Phd Fellows’ academic strength, but also of their thought diversity.”
The four fellows include two Ph.D. -Candidates in Operations Research, Haoxian Chen and Rachitesh Kumar; and two Ph.D. -Candidates in computer science, Melanie Subbiah and Kevin Xia.
Chen, advised by Henry Lam, Associate Professor of Industrial Technology and Operating Research, is “Development of Scalable Machine Learning Methods (ML) Methods with proving benefit guarantees by utilizing tools for the used probability, uncertainty quantification and stochaste analysis.” His goal is “to create a new method called Pseudo-Bayesian Optimization (PBO), which provides a principled and theoretically entitled approach to building an uncertainty quantifier leading to calculation-efficient exploration utilization strategy.”
Kumar, advised by Christian Kroer, an assistant professor in industrial engineering and operational research, seeks “to develop algorithms for data -driven revenue management that are robust to demand uncertainty and perform well in both of these settings. Our robust algorithms can be changed based on the confidence that has in demand forecast and warranty, good performance, when the prams are accurate, while we maintain a minimum level. in order to be a trusting performance. “
Subbiah, advised by Kathleen McKeown, Henry and Gertrude Rothschild professor in computer science and an Amazon-dear, investigating “Automatic summary of storytelling, a problem at the intersection of artificial intelligence and humanities and social science. Much of the previous work is limited to a single document. The article.
Xia, advised by Elias Bareinboim, Associate Professor of Computer Science, conducts research “Focused on Machine Learning, Specifically Causal Compensation. In particular, I hope to answer two research questions: (1) How can deep learning models be used to perform causal association and vice versa (2) How can cause information guidance Deep learning?”
In addition, 2022 Fellows Mathumidha Sridharan and Tuhin Chakrabarty continue their scholarships for another year.