Scholar

Richard Cornelius Suwandi

Google Scholar ID: 28o0CkgAAAAJ

The Chinese University of Hong Kong, Shenzhen

Probabilistic machine learningGaussian processesBayesian optimizationGenerative models

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Publications

2 items

2025

Cited

2023

Cited

Resume (English only)

Academic Achievements

Paper 'Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs' accepted to NeurIPS 2025.
Paper 'Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel' accepted to IEEE TNNLS.
Paper 'Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data' accepted to FUSION 2022.
Paper 'Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization' accepted to ICASSP 2021.
Awarded 2nd prize at the 2025 Doctoral Research and AI Innovation Conference, CUHK-Shenzhen.
Recipient of the IEEE Signal Processing Society Scholarship (2024).
Selected for the Shenzhen Universiade International Scholarship Foundation Program (2024).
Work on 'grid spectral mixture product (GSMP) kernel' featured in the book 'Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models'.
Invited reviewer for ICLR (2025, 2026) and ICASSP 2026.
Published blog post 'Optimize Your Signal Processing with Bayesian Optimization' on IEEE SPS (2024).

Co-authors

8 total