Columbia Management Science And Engineering – Columbia’s Center for AI Technology (CAIT) has announced its first faculty research awards, as well as two PhD scholarships. Last fall, Columbia Engineering announced the creation of CAIT.
Columbia’s AI Technology Center has announced a research award for faculty and two PhD students that will provide $5 million in funding over five years to support programs in research, education and outreach.
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Last fall, Columbia Engineering announced the creation of the Columbia Center for AI Technology (CAIT). In conjunction with Columbia News, it has issued an internal request for research proposals, and its call for nominations for PhD student fellowships.
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Now after four months, CAIT announced the initial faculty research awards, as well as two PhD scholarships.
“CAIT’s mission is to establish a world-class research and training center capable of making a social impact,” said Shih-Fu Chang, CAIT director, senior executive vice president and Richard Dicker Professor, Columbia Engineering, and Masters. . “The research of our PhD colleagues, and the early research projects we funded demonstrate the kind of transformative research and multidisciplinary approaches supported by CAIT.”
“It’s exciting to see CAIT gathering momentum so quickly,” said Prem Natarajan, Alexa AI’s vice president of natural understanding and CAIT affiliate. “The research conducted by the colleagues, and the five research projects within them, support our mission to tackle some of the toughest challenges in AI, democratize access to new results from this work, and fund future AI leaders.”
PhD fellowships were awarded to Noemie Perivier, PhD candidate in operations research, and Mia Chiquier, PhD candidate in computer theory and multimodal learning.
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Perivier, whose faculty advisor is Vineet Goyal, associate professor of industrial engineering and operations research, Columbia Engineering, is studying sequential decision making under uncertainty, and online algorithm design for environments. – informative, using income management problems. Chiquier, whose faculty advisor is Carl Vondrick, assistant professor, computer science, Columbia Engineering, is developing a mathematical model that combines sound and vision, and hopes that by taking a more integrated understanding of agents, his work will lead to improved machine cognitive processes. .
Papadimitriou and Roughgarden will use machine learning, algorithms and social science techniques to explore through analysis and experimentation how the enormous power of machine learning can be used to make machine learning more equitable. Can their information be identified fairly, directly and treated unfairly? And what is the true nature of motivation and learning behavior involved in interacting with users of software tools on the Internet?
Columbia Engineering recently announced their initiative to create the Columbia Summer Undergraduate Research Experience (SURE) program aimed at increasing diversity and inclusion in the technology field. Learn more about the eight-week summer research and professional development program.
Inventory control in multi-site and multi-product systems, Awi Federgruen, professor of management, Columbia Business School; Charles Daniel Guetta, associate professor of entrepreneurship, Columbia Business School; and Garud Iyengar, Tang Family Professor of Mechanical Engineering and Operations Research, Columbia Engineering.
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Inventory management is like the age of retail – keeping too much inventory on hand leads to tied up capital, and high storage costs; maintaining low risk of sales, lost revenue, and customer satisfaction. Retailing has changed dramatically over the past two decades – orders are now filled by complex fulfillment networks, facilities are often located in developing cities with very limited storage capacity, and products are Many different people competing for a place in these centers. In this project, the researchers build on a long research on this problem, and extend it to be able to adapt to the new face of retail and fulfillment in the 21st century.
Using speech and language to identify at-risk patients in hospital and emergency department visits in home care, Zoran Kostic, associate professor, electrical engineering, Columbia Engineering; Maxim Topaz, Elizabeth Standish Gill Professor of Nursing, Columbia University School of Nursing; and Maryam Zolnoori (Nursing).
This study is a first step in exploring an emerging data stream that has been previously studied – verbal communication between healthcare providers and patients. A collaboration between Columbia Engineering, Columbia School of Nursing and the largest home health care organization in the United States, the study will examine how to use standard audio-recorded communication between patients and nurses to aid in identifying patients at risk of hospitalization. or an emergency department visit. The study will combine speech recognition, machine learning and natural language processing to achieve the goal.
The research conducted by the colleagues, and the five research projects within them, support our mission to tackle some of the toughest challenges in AI, democratize access to the innovative results of this work, and fund future AI leaders.
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One of the most common tasks encountered in information gathering settings (including clinical research, education, business analysis) is the individual decision-making problem, that is, deciding whether a particular intervention will lead to a desired outcome based on personal attitudes and experience. We note that the current generation of offline/online learning that attempts to solve this problem (1) ignores (offline) observational data, except in some appropriate situations, or (2) ) ignores fundamental contradictions. causal structure. This leads to poor decision making and lack of explanation. This project will develop new tools to advance the modern paradigm of studying personal politics through the lens of causality.
Most abstract research today focuses on summaries of news articles and in this genre, most abstract words are taken directly from the abstract article. On the other hand, in many other forms, the summary uses a very different language than the input. This type of abstraction is very difficult in today’s deep learning systems. The researchers plan to develop a method capable of creating three types of abstraction: translation, compression, and synthesis. They aim to create a unique way for each and compare it to the collective learning process. This work will be done in a controlled generation mode, where the system can determine the most appropriate abstraction technique depending on the context.
Besides providing research support and PhD Fellowships, CAIT will also launch annual seminars and research series. More information on CAIT’s activities and research focus areas can be found on the CAIT website.
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