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
Robust Class Parallelism – Error Resilient Parallel Inference with Low Communication Cost
sensAI: Fast ConvNets Serving on Live Data via Class Parallelism
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
Scaling Video Analytics to Large Camera Deployments
Helen: Maliciously Secure Coopetitive Learning for Linear Models
Serverless Computing: One Step Forward, Two Steps Back
The Case for GPU Multitenancy: The OoO VLIW JIT Compiler for GPU Inference
Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification
Dynamic Space-Time Scheduling for GPU Inference
SkipNet: Learning Dynamic Routing in Convolutional Networks
Context: The Missing Piece in the Machine Learning Lifecycle
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
Selecting the Best VM across Multiple Public Clouds: A Data-Driven Performance Modeling Approach
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
Ground: A Data Context Service