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Tassiulas receives 2016 IEEE Koji Kobayashi Computers and Communications AwardProfessor Leandros Tassiulas has received the 2016 IEEE Koji Kobayashi Computers and Communications Award For contributions to the scheduling and stability analysis of networks.
Leandros Tassiulas has revolutionized how scheduling and stability analysis are performed in communications networks, providing dynamic resource allocation tools to improve performance of wireless networks and Internet switches. Developing control algorithms based on a sound mathematical foundation as an alternative to heuristic approaches, Tassiulas introduced the Max-Weight scheduling algorithm for achieving maximum throughput in systems with conflicting resources as well as the Backpressure routing algorithm for end-to-end traffic forwarding. His Maximum Connected Queue algorithm introduced the concept of opportunistic scheduling that became part of most wireless standards since 3G. Together, these three algorithms provide the basis for cross-layer network design in today’s wireless networks.
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“Malaria and Mankind: A New Hypothesis, ” Sunny Kishore, MD, PhD
Summary: In this talk, Dr. Kishore proposes a new approach to cracking the age-old problem of how human malaria parasites escape immune responses. Using approaches borrowed from molecular biology, evolutionary medicine and cooperation theory, he examines a method to understand how early animals (malaria parasites) cooperate in primate and rodent hosts.
“Gift Exchange Networks in China,” Xi Chen, PhD Event time: Wednesday, April 13, 2016 - 12:00pm to 1:30pm Location:
YINS
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17 Hillhouse Avenue
New Haven, CT
06511
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YINS Professor Amin Karbasi Receives Google Research AwardA tremendous amount of data is generated every second, every day by billions of users powering social media. Recent statistics indicate that every minute Instagram users post nearly 220,000 new photos, YouTube users upload 72 hours of video, and Facebook users share nearly 2.5 million pieces of content. Organizing and making sense of big data has become one of today’s major challenges in machine learning and data mining. Karbasi’s research, recognized by Google, aims at extracting a small subset of representative elements in order to obtain a faithful description of the whole massive data.
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