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Hi there 👋

  • 🔭 I’m currently a postgraduate student at the University of Cambridge.
  • 🌱 I’m currently working on Federated Learning and AI Alignment.
  • 👯 I’m looking to collaboration or internship on privacy for LLMs or related topics.
  • 📫 How to reach me: wz341[AT]cam.ac.uk or zhaowrenee[AT]gmail.com
  • 😄 Pronouns: She/Her

Wanru Zhao's Projects

ar_piano icon ar_piano

IEEE VR2021 Poster: Hand-by-Hand Mentor: An AR-based Training System for Piano Performance

awesome-cv icon awesome-cv

:page_facing_up: Awesome CV is LaTeX template for your outstanding job application

beamerstyleslides icon beamerstyleslides

🌈Beamer风格的幻灯片模板集。包含了PowerPoint和Keynote两套格式。

brats21 icon brats21

Solution of the RSNA/ASNR/MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021

breaching icon breaching

Breaching privacy in federated learning scenarios for vision and text

buaa_bigdatacourse icon buaa_bigdatacourse

北航大数据科学导论作业-中文垃圾邮件过滤/交通出行数据分析(Spark/Hadoop)

c0-compiler icon c0-compiler

Final Project of Design of Compiler Principle Course in SCSE,BUAA

ccf-deadlines icon ccf-deadlines

⏰ Collaboratively track deadlines of conferences recommended by CCF (Website, Python Cli)/ Please star this project, thanks~

datashapley icon datashapley

Data Shapley: Equitable Valuation of Data for Machine Learning

dig icon dig

A library for graph deep learning research

doremi icon doremi

Pytorch implementation of DoReMi, a method for optimizing the data mixture weights in language modeling datasets

dsir icon dsir

Pre-filtered datasets and code for selecting relevant language model training data from The Pile.

eth-light-crawler icon eth-light-crawler

This tool uses Ethereum's peer-discovery protocol to measure the size of the Ethereum network (testnets included)

feddst icon feddst

Federated Dynamic Sparse Training

fedml icon fedml

FedML - The federated and distributed machine learning library enabling machine learning anywhere at any scale. It's backed by FedML, Inc (https://FedML.ai). Supporting large-scale geo-distributed training, cross-device federated learning on smartphones/IoTs, cross-silo federated learning on data silos, and research simulation. Best Paper Award at NeurIPS 2020 Federated Learning workshop. FedML’s core technology is backed by years of cutting-edge research represented in 50+ publications in ML/FL Algorithms, Security/Privacy, Systems, and Applications, as well as 10 years of industrial experience in Distributed Systems, Cloud Computing, and Mobile/IoT Systems.

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