I joined TU Delft as an Associate Professor in the Fall of 2018, after a decade of industry experience at the IBM Research Zurich Lab. I am currently leading Distributed Systems Section, Distributed Learning Systems Lab at TU Delft. My research interests lie in the distinct areas of big data systems, (deep) machine learning, performance modeling and privacy enhancing technology. My research is supported by the Swiss National Science Foundation, Dutch National Science Foundation the European Union, IBM Research, ABB, and TU Delft.
Over the years, I have worked on performance modeling and resource management problems of various computing systems, such as web services, cloud data centers, and big data processing systems. My recent focus on distributed machine learning algorithms and systems leads me to address exciting areas.
Privacy-preserving learning systems by synthetic data: how to maximize the knowledge of while maintaining data privacy ? I am exploring the deep generative models to synthesize data.
Robust learning systems: how to make learning algorithms robust against adversaries that maliciously manipulate data input?
Federated machine learning systems: how to decentralizedly learn deep learning models on heterogeneous clients who contentiously encounter new learning tasks and data domain shift?
Associate editor at TPDS, TDSC, and TNSM
Technical Program Committee at ATC23, and Sigmetrics 23, Middleware23, SIGCOM 23
Awarded Grant from NWO Perspectief, a large scale multi-disciplinary collaborative grant.
Accepted paper “Robust Learning via Golden Symmetric Loss of (un)Trusted Labels''SIAM SDM 23
Accepted paper “FedKNow:Federated Continual Learning with Signature Task Knowledge Integration at Edge” IEEE ICDE 23
Accepted paper “Permutation-Invariant Tabular Data Synthesis” BigData 22
Accepted paper “Trusted Loss Correction for Noisy Multi-Label Learning” ACML 22
Accepted paper “Multi Label Loss Correction against Missing and Corrupted Labels” ACML 22
Accepted paper “FreezOff: A Middleware for Heterogeneous Federated Learning Systems” ACM Middleware22
Accepted paper “Community-based Approach to Gender-Constrained Influence Maximization” ACM CIKM22
Awards & Honors
ACM MobiCom Runner-up Community Award, 2022
IEEE INFOCOM distinguished TPC Member, 2019
Delft Technology Fellowship, 2018
ACM ICAC Best Paper Award nomination 2017
ACM ICAC Best Paper Award nomination 2016
ACM eEnergy Runner-up Best Paper Award 2015
IEEE/ACM CCGrid Runner-up Best Paper Award 2015
IBM Outstanding Scientific Achievement Award 2014
IEEE/IFIP DSN Best Paper Award nomination 2014
IBM All-level Scientific Achievement Award 2012
IEEE HPDC Best Presentation Award nomination 2012
5 IBM invention plateaus
Lydia Y. Chen is an Associate Professor in the Department of Computer Science at the Delft University of Technology in the Netherlands. Prior to joining TU Delft, she was a research staff member at the IBM Research Zurich Lab from 2007 to 2018. She holds a PhD from Pennsylvania State University and a BA from National Taiwan University. Her research interests are distributed machine learning, dependability management, large-scale data processing systems and services. More specifically, her work focuses on developing machine learning and stochastic models, and applying these techniques to application domains, such as data centers and AI systems.
She has published more than 100 papers in peer-reviewed journals, including IEEE Transactions on Distributed Systems and IEEE Transactions on Service Computing, and in conference proceedings, including INFOCOM, SIGMETRICS, DSN, and EUROSYS. She was a co-recipient of the best paper awards at CCGrid’15 and eEnergy’15. She received TU Delft technology fellowship in 2018. She was program co-chair for IEEE IC2E 21, IEEE ICAC 2019, Middleware Industry track 2018, track vice-chair for ICDCS 2018, and DIAS 2017. She serves on the editorial boards of IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Service Computing and IEEE Transactions on Network and Service Management. She is an IEEE senior member.