Clara (Chen) Shao
PhD Candidate in Computer Science, Karlsruhe Institute of Technology (KIT)
cshao676 [AT] gmail.com
Bio
I am Clara (Chen) Shao, a Ph.D. Candidate in Computer Science at the Karlsruhe Institute of Technology (KIT), supervised by Prof. York Sure-Vetter and Prof. Danai Koutra. My research focuses on graph machine learning for scientific and industrial applications. I also study the theoretical foundations and applications of foundation models in genomics and materials science. My research style has been most influenced by Danai Koutra, and I am also honored to be mentored by Tobias Käfer, Kai Yuan, Michael Färber, and Pascal Friederich. Outside of research, I enjoy the violin, sport, and literature.
Publications
For the most up-to-date list, see my Google Scholar.
‡ indicates equal contribution; † corresponding author.
When Forecasting Fails: Overcoming Temporal Correlation Shift in Time Series
Chen Shao, Joella Wang, Zhanbo Huang, Danai Koutra
ECML PKDD'26: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2026.
BigElectricity: A Benchmark for Scalable Multivariate Time Series Forecasting
Chen Shao, Joella Wang, Zhanbo Huang, Tobias Käfer, Michael Färber, Benjamin Schäfer
CIKM'25: ACM International Conference on Information and Knowledge Management. 2025.
Bridging Semantics and Structure: A Theoretically Grounded Framework for Link Prediction on TAGs
Chen Shao, Ruirui Wang, Tobias Käfer, Danai Koutra
TKDD'26: ACM Transactions on Knowledge Discovery from Data. 2026.
Review of Graph Neural Networks for Materials
Patrick Reiser, Chen Shao, Chen Zhou, Andre Eberhard, Houssam Metni, Marlen Neubert, Timo Sommer, Clint van Hoesel, Luca Torresi, Pascal Friederich
Communications Materials, 3, 93. 2022.
Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs for Link Prediction
Chen Shao, Donald Loveland, Danai Koutra
Preprint.
Deep Switching Koopman Networks: Discovery of Switching Dynamic Regimes in Industrial Systems
Zhenyi Zhu, Chen Shao, et al.
CASE'26: IEEE International Conference on Automation Science and Engineering. Shenyang, China, 2026.
When Forecasting Fails: Overcoming Temporal Correlation Shift in Time Series
Chen Shao, Zhenyi Zhu, Danai Koutra
ICML'26 Workshop on Foundation Models for Structured Data (FMSD). 2026.
When Forecasting Fails: Overcoming Temporal Correlation Shift in Time Series
Chen Shao, Joella Wang, Zhanbo Huang, Danai Koutra
ECML PKDD'26: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2026.
BigElectricity: A Benchmark for Scalable Multivariate Time Series Forecasting
Chen Shao, Joella Wang, Zhanbo Huang, Tobias Käfer, Michael Färber, Benjamin Schäfer
CIKM'25: ACM International Conference on Information and Knowledge Management. 2025.
Bridging Semantics and Structure: A Theoretically Grounded Framework for Link Prediction on TAGs
Chen Shao, Ruirui Wang, Tobias Käfer, Danai Koutra
TKDD'26: ACM Transactions on Knowledge Discovery from Data. 2026.
A Novel Machine Learning-Based Equalizer for a Downstream 100G PAM-4 PON
Chen Shao, Elias Giacoumidis, Shi Li, Jialei Li, Michael Färber, Tobias Käfer, Andre Richter
OFC'24: Optical Fiber Communication Conference and Exhibition. 2024. (Oral, top 5%)
Time-Series Forecasting Methods in Fiber Nonlinearity Mitigation
Chen Shao, Elias Giacoumidis, Tobias Käfer, Michael Färber
ECOC'24: European Conference on Optical Communication. 2024. (Oral, top 5%)
Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey
Chen Shao, Elias Giacoumidis, Shi Li, Jialei Li, Michael Färber, Tobias Käfer, Andre Richter, et al.
Photonics, 11(7), 613, Special Issue: Machine Learning Applied to Optical Communication Systems. 2024. (Invited Paper)
Review of Graph Neural Networks for Materials
Patrick Reiser, Chen Shao, Chen Zhou, Andre Eberhard, Houssam Metni, Marlen Neubert, Timo Sommer, Clint van Hoesel, Luca Torresi, Pascal Friederich
Communications Materials, 3, 93. 2022.
SwitchPFN: Shared Switching Dynamics for Frozen In-Context Time Series Classification
Zhenyi Zhu‡, Jacqueline Pang‡, Peilin Shen‡, Tianyi Song†, Tingwei Zhang, Keyi Hu, Kangjun Yin, Shiwei Pu, Yingbo Zhou, Chen Shao†
Preprint.
Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs for Link Prediction
Chen Shao, Donald Loveland, Danai Koutra
Preprint.
Graph Neural Networks to Learn Joint Representations of Disjoint Molecular Graphs
Chen Shao, Chen Zhou, Pascal Friederich
arXiv:2210.09517. 2022.
Projects
LLMs & GNNs for Text-Attributed Graphs
Scalable Multivariate Time-Series Forecasting
Foundation Models for Molecular Property Prediction
Research in the AiMat Group, KIT
LLMs & GNNs for Text-Attributed Graphs
Scalable Multivariate Time-Series Forecasting
Foundation Models for Molecular Property Prediction
Equalization & Nonlinearity Mitigation (KIGLIS)
Research in the AiMat Group, KIT
Vitæ
Full CV available in PDF.
-
Max Planck Institute of Biochemistry 2026 - nowResearch Intern
Foundation Models for Protein Design
with Dr. Dexiong Chen, Dr. Till Schulz & Prof. Karsten Borgwardt -
KIT 2022 - nowPh.D. Candidate & Research Associate
Institute AIFB (Web Science) -
University of Michigan 2024 - nowPh.D. Co-supervision
Graph Learning, with Prof. Danai Koutra -
Precitec GmbH & Co. KG 2021Industry Research Intern
Deep Learning for Laser Material Processing -
KIT 2018 - 2022Research Assistant (Hiwi)
Student research assistant in signal processing and machine learning, including graph neural networks for finite element analysis (ALR Lab) and algorithm development for hyperspectral imaging (IIIT). -
KIT 2018 - 2022M.Sc. Student
Electronic and Information Engineering -
Xi'an Jiaotong University 2013 - 2017B.Eng. Student
Electronic and Information Engineering
Siyuan Scholarship (Top 5%)