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. My research interests lie in the distinct areas of big data systems, (deep) machine learning, cloud dependability, and performance modeling. My research is supported by the Swiss National Science Foundation, the European Union, IBM Research, and TU Delft.
Hiring. I am recruiting motivated PhD students, visiting scholars and postdocs to work on private machine learning, adversarial learning, fair learning, systems for AI and their applications. Please send me your CV and research interests.
Over the years, I have worked on resource management problems of various computing systems, such as web services, cloud data centers, and big data processing systems. My recent focus on big data analytics and processing systems leads me to address exciting areas.
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?
Robust learning systems: how to make learning algorithms robust against adversaries that maliciously manipulate data input?
Large-scale machine learning systems: how to optimize energy-performance for machine learning clusters via scheduling and sprinting policies?
Associate editor at IEEE Transactions on Dependable and Secure Computing
Associate editor at IEEE Transactions on Parallel and Distributed Systems
Technical Program Committee at IJCAI-20
Accepted paper “"SlimML: Removing Non-critical Input Data in Large-scale Iterative Machine Learning, IEEE Transactions on Knowledge and Data Engineering (TKDE)
Accepted paper “Differential Approximation and Sprinting for Multi-Priority Big Data Engines” ACM Middleware
Accepted paper “Robust (Deep) Learning Against Dirty Labels (Attacks).” IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS)
Accepted paper “Automating Deep Neural Network Model Selection for Edge Inferences.” IEEE International Conference on Cognitive Machine Intelligence (CogMI)
Awards & Honors
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, 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 90 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 Service Computing and IEEE Transactions on Network and Service Management. She is a Senior Member of IEEE.