About me
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 Intelligent System Lab at TU Delft and Generatrix, a startup for data synthesizing. 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.
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Privacy-preserving learning systems: how to maximize the discovery capability of (deep) machine learning algorithms while maintaining data privacy with a minimum amount of resources?
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Robust learning systems: how to make learning algorithms robust against adversaries that maliciously manipulate data input?
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Federated machine learning systems: how to incentize the participants to contribute and well-behaved on federated learning systems?
News
Associate editor at TPDS, TDSC, TSC, and TNSM
Technical Program Co-Chairs at IC2E 2021, BDCAT 2021
Technical Program Committee at ATC2021, Sigmetrics 2021, Middlewawre 21, SoCC21, SRDS21
We (Generatrix) enter the semi-final list for European Social Innovation Competition
Awarded NWO Take-off Grant, Tabular Data Synthesizer, and 4TU TTT voucher
Accepted paper “LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision” MobiCom21
Accepted paper “On Influencing the Influential: Disparity Seeding” CIKM21
Accepted paper “Online Label Aggregation: A Variational Bayesian Approach,” WWW21
Accepted paper “MASA: Responsive Multi-DNN Inference on the Edge,” IEEE PEROCOM'21
Accepted paper “SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition,”IEEE Trans. on Parallel and Distributed Systems (TPDS)
Awards & Honors
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
Biography
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, resource allocation for large-scale data processing systems and services. More specifically, her work focuses on developing stochastic and machine learning 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 ICAC 2019, Middleware Industry track 2018, track vice-chair for ICDCS 2018, and DIAS 2017. She has served 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 a Senior Member of IEEE.