Congratulations 2019 Faculty and Research Engagement Program (FREP) Recipients!

Sep 9, 2019

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


Acceleration for Data Science and Machine Learning

Alexander Gasnikov (co-PI) & Cesar Uribe (co-PI)

Moscow Institute of Physics and Technology (State University) & MIT

Scalable Online Detection of Complex Patterns in Rapid Event Streams

Assaf Schuster


Optimal-Transport Bayesian Sampling with Applications to Repulsive Attentions in NLP

Changyou Chen

State University of New York at Buffalo

Interactive learning from weak annotations

Daniel Hsu

Columbia University

Deep Learning for Analyzing Ultrasound Movie Images

Daniel Rubin

Stanford University School of Medicine

Detecting Intrinsic Visual Privacy Threats

Haibin Ling

Stony Brook University (SUNY)

Network Derivative Mining

Hanghang Tong

University of Illinois at Urbana-Champaign

PASTE: PArallel Synthesis, Training and Enhancement via Distributionally Robust Optimization and Optimal Transport

Jose Blanchet

Stanford University

DNS or Anycast? Better Mechanisms for Finding the Closest Server to a User

Kirill Levchenko

University of Illinois

Representation Learning for Product Graphs

Mayank Kejriwal

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

Steven Skiena

Stony Brook University

Communication-Efficient Federated Learning

Tsachy Weissman


Adversarial Reformulation-Aware Query Suggestion with Graph Convolutional Networks

Wei Wang


Modeling Temporal Dynamics of User Behavior for Improved Advertising

Zoran Obradovic

Temple University