Yahoo Research is excited to announce the 2019 Faculty and Research Engagement Program (FREP) recipients. This year, we received 100+ proposals from a variety of prestigious institutions around the world. The competition was intense, the review process was difficult, and making the final decisions wasn’t easy. The grants will support professors and students who explore a diverse set of fields, including machine learning, distributed systems, online security, content understanding and recommendation, and images and video understanding.
FREP awards grants to faculty members in support of research to enhance people's lives by improving the internet. FREP was founded in 2012 to foster cutting-edge collaborations between scientists in academic settings and those at Yahoo Research. We look forward to the insights, scientific advances, and relationships that will grow from FREP over the coming year and for many years to come!
Congratulations to these very impressive researchers!
Title of Proposal |
Academic's Name |
University |
Acceleration for Data Science and Machine Learning |
Moscow Institute of Physics and Technology (State University) & MIT |
|
Scalable Online Detection of Complex Patterns in Rapid Event Streams |
Technion |
|
Optimal-Transport Bayesian Sampling with Applications to Repulsive Attentions in NLP |
State University of New York at Buffalo |
|
Interactive learning from weak annotations |
Columbia University |
|
Deep Learning for Analyzing Ultrasound Movie Images |
Stanford University School of Medicine |
|
Detecting Intrinsic Visual Privacy Threats |
Stony Brook University (SUNY) |
|
Network Derivative Mining |
University of Illinois at Urbana-Champaign |
|
PASTE: PArallel Synthesis, Training and Enhancement via Distributionally Robust Optimization and Optimal Transport |
Stanford University |
|
DNS or Anycast? Better Mechanisms for Finding the Closest Server to a User |
University of Illinois |
|
Representation Learning for Product Graphs |
University of Southern California |
|
Large-scale multi-objective sequential decision making |
Paul Weng (co-PI) & Wojciech Kotlowski (co-PI) |
Shanghai Jiao Tong University & Poznan University of Technology |
Large-Scale Graph Embeddings |
Stony Brook University |
|
Communication-Efficient Federated Learning |
Stanford |
|
Adversarial Reformulation-Aware Query Suggestion with Graph Convolutional Networks |
UCLA |
|
Modeling Temporal Dynamics of User Behavior for Improved Advertising |
Temple University |