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programs

“Recovering tree models via spectral graph theory”

Speakers: Yariv Aizenbud and Ariel Jaffe (Yale Math)   

Event time: 
Friday, May 14, 2021 - 1:00pm
Event Type: 
Speakers, Conferneces & Workshops

YINS Alum Summer Seminar

Speaker: Rasmus Kyng
Assistant Professor, ETH Zurich

To participate:

Join from PC, Mac, Linux, iOS or Android: https://yale.zoom.us/j/97920219362
     Or Telephone:203-432-9666 (2-ZOOM if on-campus) or 646 568 7788
     Meeting ID: 979 2021 9362
     International numbers available: https://yale.zoom.us/u/afjMjLJlB

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Event time: 
Wednesday, June 16, 2021 - 12:00pm to 1:00pm
Event Type: 
Weekly Seminar

YINS Distinguished Lecturer

“Banach Space Representer Theorems for Neural Networks”

Speaker: Robert D. Nowak
Nosbusch Professor of Engineering, University of Wisconsin-Madison

Event time: 
Wednesday, May 26, 2021 - 12:00pm to 1:00pm
Event Type: 
Weekly Seminar

“Random Features for the Neural Tangent Kernel”

Speaker: Insu Han

Event time: 
Friday, April 30, 2021 - 12:00pm
Event Type: 
Speakers, Conferneces & Workshops
COVID-19 prevention CDC

Nicholas Christakis featured in CNN Opinion series: “Why the pandemic won’t be over until 2024”

YINS
February 3, 2021

YINS Co-Director Nicholas Christakis explains the legacy Covid-19 will leave behind and his prediction for how long the pandemic will last.

To view this video on CNN.com, click here

External link: 
Professor Drago Radev

Dragomir Radev selected as fellow of the Association for Computational Linguistics

YINS
December 18, 2018

Dragomir Radev, A. Bartlett Giamatti Professor of Computer Science, has been selected as a 2018 fellow of the Association for Computational Linguistics (ACL).

Radev was one of five new fellows chosen this year by the ACL’s nominating committee. The organization cited Radev’s “significant contributions to text summarization and question answering, as well as large scale efforts to expand and diversify the computational linguistics pipeline.”

Radev has served as secretary of the Association for Computational Linguistics, is co-founder of the North American Computational Linguistics Olympiad, and is a fellow of the Association for Computing Machinery.

Founded in 1962, the ACL is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP).

External link: 
Amin Karbasi

For Data Science that Delves into the Brain, Amin Karbasi Wins CAREER Award

YINS
May 16, 2019

For research that could lead to - among other advances – better analysis of the brain, Amin Karbasi has won a 2019 Faculty Early Career Development CAREER Award from the National Science Foundation (NSF).

Karbasi, assistant professor of electrical engineering & computer science, will use the award to focus on developing fundamentally new algorithms to assist in searching through massive amounts of data to make quick and informed decisions. Specifically, he’s focusing on discrete data, which takes on a finite set of possible values - these include phrases found in text and particular objects in an image. Nearly all aspects of data science involve methods of discrete optimization. And as science and engineering become more reliant on computational methods, it’s critical to know which discrete formulations can be solved efficiently and how to do so.  
 
Part of the project involves developing robust algorithms - that is, those that stand up to real-world conditions and not just the controlled circumstances of theoretical models. Changes in data can wreak havoc on a theoretical model, for instance, or scaling up in size can cause the model to fall apart.
 
“So far, most of the discrete optimization methods we’re using have been relying on the assumption that the world is perfect,” said Karbasi, who is also a faculty member at the Yale Institute for Network Science (YINS).  “Of course, if you abstract everything away and assume everything is perfect, it’s a mathematical problem. It’s easy to come up with methods in a sterilized world, and they work very well. But change the parameters, and it completely collapses.”  
 
One application Karbasi is aiming for with this research is a better way to read the brain. Functional magnetic resonance imaging (fMRI) is one of the technologies that have made it possible to measure neural activity in the human brain. The data sets it produces, though, are massive and extremely complex. As a result, analyzing the human brain is difficult due to the prohibitive computational cost and lack of predictive power. Karbasi and his team of researchers aim to develop the first machine learning algorithms that can process and interpret billions of neural signals in real-time. The difference could be getting results of a test in minutes, rather than days.  
 
For another potential application, Karbasi aims to design algorithms that automatically teach humans using only a small number of examples. Doing so could help in understanding and predicting how humans learn, which is important not only for areas such as education and cognitive psychology, but could also inspire the development of new machine learning models.
External link: 
Professor Drago Radev

Coached by Dragomir Radev U.S. linguistics team takes prizes at IOL

YINS
September 5, 2019

Members of the U.S. linguistics teams, coached by Yale’s Prof. Dragomir Radev, along with Lori Levin of Carnegie Mellon University and Aleka Blackwell of Middle Tennessee State University, took home seven individual medals, an honorable mention, and a best solution award at this year’s 2019 ​International Linguistics Olympiad (IOL) in Yongin, Republic of Korea.

The students were selected earlier this year at NACLO, the North American computational linguistics competition, in which high school students work on language-based puzzles that test their skills in logic, pattern recognition, analytical thinking, and problem solving. NACLO was co-founded in 2006 by Radev, the A. Bartlett Giamatti Professor of Computer Science. He also serves as the program chair and head coach. 
 
Problem solving at the IOL - this year hosted in Yongin, South Korea - stresses the ability of contestants to decipher the mechanisms of languages by using logic and reasoning to explore a wide range of hypotheses.  
 
The U.S. students were divided into two teams, USA Red and USA Blue. Wesley Zhang and Andrew Tockman, both of USA Red, earned gold medals. Silver medals went to Ziyan Lei and Russell Emerine of USA Red, and Skyelar Raiti, Jeremy Zhou, and Pranav Krishna of USA Blue. Katherine He of USA Blue received an honorable mention. Wesley Zhang was second overall in the individual contest with a score of 95.6 and one of three contestants awarded best solution to a problem. USA Red won the trophy for the highest score among the 53 teams participating. 
 
Radev said he was proud of all the competitors, especially considering how much work they put into preparing for the competitions. That involved solving problems from previous NACLO and IOL competitions, lectures on various linguistic topics, and writing NACLO-style problems from scratch. 
 
“We practiced for three months online, then in person in Seattle before leaving for South Korea,” Radev said.
 
The IOL consists of two events: the individual and the team contest. The individual contest is a six-hour exam with five problems. This year, the individual round featured the following languages and scripts: Yonggom, Yurok, Book Pahlavi script, West Tarangan, and Nooni. In the team contest, team members collaborate to solve one particularly challenging problem. This year, teams were given three hours to work out the rules of the notation system used by rhythmic gymnastics judges. Problem solving at the IOL stresses the ability of contestants to decipher the mechanisms of languages by using logic and reasoning to explore a wide range of hypotheses.
 
 
External link: 
Nicholas Christakis

Nicholas Christakis Among "Highly Cited Researchers" list

YINS
November 22, 2019
YINS member Nicholas Christakis, the Sterling Professor of Social and Natural Science, Internal Medicine & Biomedical Engineering, was among Six SEAS faculty members listed in the 2020 Web of Science Group’s compilation of “Highly Cited Researchers.”
 
The Web of Science Group, a Clarivate Analytics company, compiles the list by drawing on the data and analysis performed by its bibliometric experts. The rankings identify researchers worldwide who published the most papers over a recent 11-year period and rank in the top 1 percent of total citations in their field.
 
For the full list recipients, click here.
External link: 
PNAS Cover 117 Issue 12

Robots that admit mistakes foster better conversation in humans

YINS
March 9, 2020
Three people and a robot form a team playing a game. The robot makes a mistake, costing the team a round. Like any good teammate, it acknowledges the error.
 
“Sorry, guys, I made the mistake this round,” it says. “I know it may be hard to believe, but robots make mistakes too.”
 
This scenario occurred multiple times during a Yale-led study of robots’ effects on human-to-human interactions.
 
The study, which published on March 9 in the Proceedings of the National Academy of Sciences, showed that the humans on teams that included a robot expressing vulnerability communicated more with each other and later reported having a more positive group experience than people teamed with silent robots or with robots that made neutral statements, like reciting the game’s score.
 
“We know that robots can influence the behavior of humans they interact with directly, but how robots affect the way humans engage with each other is less well understood,” said Margaret L. Traeger, a Ph.D. candidate in sociology at the Yale Institute for Network Science (YINS) and the study’s lead author. “Our study shows that robots can affect human-to-human interactions.”
 
Because social robots are becoming increasingly prevalent in human society, she said, people are encountering them in stores, hospitals and other everyday places. This makes understanding how they shape human behavior important.
 
“In this case,” Traeger said, “we show that robots can help people communicate more effectively as a team.”
 

The researchers conducted an experiment in which 153 people were divided into 51 groups composed of three humans and a robot. Each group played a tablet-based game in which members worked together to build the most efficient railroad routes over 30 rounds. Groups were assigned to one of three conditions characterized by different types of robot behavior. At the end of each round, robots either remained silent, uttered a neutral, task-related statement (such as the score or number of rounds completed), or expressed vulnerability through a joke, personal story, or by acknowledging a mistake; all of the robots occasionally lost a round.

People teamed with robots that made vulnerable statements spent about twice as much time talking to each other during the game, and they reported enjoying the experience more compared to people in the other two kinds of groups, the study found.

Conversation among the humans increased more during the game when robots made vulnerable statements than when they made neutral statements. Conversation among the humans was more evenly distributed when the robot was vulnerable instead of silent.

The experiment also showed more equal verbal participation among team members in groups with the vulnerable and neutral robots than among members in groups with silent robots, suggesting that the presence of a speaking robot encourages people to talk to each other in a more even-handed way.

We are interested in how society will change as we add forms of artificial intelligence to our midst,” said Nicholas A. Christakis, Sterling Professor of Social and Natural Science. “As we create hybrid social systems of humans and machines, we need to evaluate how to program the robotic agents so that they do not corrode how we treat each other.”

Understanding the social influence of robots in human spaces is important even when the robots do not serve an intentionally social function, said Sarah Strohkorb Sebo, a Ph.D. candidate in the Department of Computer Science and a co-author of the study.

Imagine a robot in a factory whose task is to distribute parts to workers on an assembly line,” she said. “If it hands all the pieces to one person, it can create an awkward social environment in which the other workers question whether the robot believes they’re inferior at the task. Our findings can inform the design of robots that promote social engagement, balanced participation, and positive experiences for people working in teams.”

Other co-authors on the study are Yale’s Brian Scassellati, professor of computer science, cognitive science, and mechanical engineering; and Cornell’s Malte Jung, assistant professor in information science.

The research was supported by grants from the Robert Wood Johnson Foundation and the National Science Foundation.  

by Mike Cummings, YaleNews

 
 
External link: 

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