Wenting Zheng was awarded the prestigious IBM PhD Fellowship for her work on security and distributed systems. Wenting is actively studying new methods for scalable secure analytics, multi-party computation for machine learning, and distributed zero knowledge proofs. The IBM Ph.D. fellowship is an “intensely competitive worldwide program that honors exceptional Ph.D. students who have an interest in solving problems that are important to IBM and fundamental to innovation in many academic disciplines and areas of study.” Only 50 fellowships are awarded worldwide annually.
Publications
Scaling Video Analytics to Large Camera Deployments
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
Helen: Maliciously Secure Coopetitive Learning for Linear Models
Serverless Computing: One Step Forward, Two Steps Back
Context: The Missing Piece in the Machine Learning Lifecycle
SkipNet: Learning Dynamic Routing in Convolutional Networks
IDK Cascades: Fast Deep Learning by Learning not to Overthink
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
RLlib: Abstractions for Distributed Reinforcement Learning
Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling
Selecting the Best VM across Multiple Public Clouds: A Data-Driven Performance Modeling Approach
Ground: A Data Context Service
Opaque: An Oblivious and Encrypted Distributed Analytics Platform.
Random Projection Design for Scalable Implicit Smoothing of Randomly Observed Stochastic Processes
Clipper: A Low-Latency Online Prediction Serving System