Richard Cornelius Suwandi

I am a PhD student at CUHK-Shenzhen, advised by Prof. Feng Yin and Prof. Tsung-Hui Chang. Prior to my PhD, I obtained my BSc degree in Statistics from CUHK-Shenzhen.

I am currently interested in building robust and adaptive intelligent systems that can learn efficiently from limited data and generalize effectively across diverse tasks. Some of my research topics include:

Bayesian optimization for sample-efficient optimization and sequential decision-making
Gaussian processes for probabilistic machine learning and uncertainty quantification
Large language models for automated algorithm discovery and engineering design

If you are interested in collaborating or discussing research ideas, feel free to reach out via email.

News

May 09, 2025

📚 Our latest work on grid spectral mixture product (GSMP) kernel has been featured in the “Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models” book!

Jan 28, 2025

🎉 Our paper titled “Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel” has been accepted to IEEE TNNLS!

Sep 30, 2024

🏆 Selected as the recipient of the IEEE Signal Processing Society Scholarship!

Sep 10, 2024

📝 My blog post on “Optimize Your Signal Processing with Bayesian Optimization” has been published on IEEE SPS!

Sep 09, 2024

✨ Invited to serve as a reviewer for ICLR 2025!

Jul 17, 2024

🏆 Selected as the recipient of the Shenzhen Universiade International Scholarship Foundation Program!

Aug 14, 2023

🎓 Joined Bayesian Learning for Signal Processing Group as PhD student!

May 04, 2022

🎉 Our paper titled “Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data” has been accepted to FUSION 2022!

Jan 30, 2021

🎉 Our paper titled “Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization” has been accepted to ICASSP 2021!

Selected Works

2025

ABSTRACT PAPER CODE

Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel

Richard Cornelius Suwandi , Zhidi Lin , Feng Yin , and 2 more authors , Zhiguo Wang, Sergios Theodoridis

IEEE Transactions on Neural Networks and Learning Systems, 2025

Abstract

Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyper-parameter optimization. This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework to optimize the hyper-parameters. The newly proposed grid spectral mixture (GSM) kernel is tailored for multi-dimensional data, effectively reducing the number of hyper-parameters while maintaining good approximation capabilities. We further demonstrate that the associated hyper-parameter optimization of this kernel yields sparse solutions. To exploit the inherent sparsity property of the solutions, we introduce the Sparse LInear Multiple Kernel Learning (SLIM-KL) framework. The framework incorporates a quantized alternating direction method of multipliers (ADMM) scheme for collaborative learning among multiple agents, where the local optimization problem is solved using a distributed successive convex approximation (DSCA) algorithm. SLIM-KL effectively manages large-scale hyper-parameter optimization for the proposed kernel, simultaneously ensuring data privacy and minimizing communication costs. Theoretical analysis establishes convergence guarantees for the learning framework, while experiments on diverse datasets demonstrate the superior prediction performance and efficiency of our proposed methods.
2022

ABSTRACT PAPER CODE SLIDES

Gaussian process regression with grid spectral mixture kernel: Distributed learning for multidimensional data

Richard Cornelius Suwandi , Zhidi Lin , Yiyong Sun , and 3 more authors , Zhiguo Wang, Lei Cheng, Feng Yin

25th International Conference on Information Fusion (FUSION), 2022

Abstract

Kernel design for Gaussian processes (GPs) along with the associated hyper-parameter optimization is a challenging problem. In this paper, we propose a novel grid spectral mixture (GSM) kernel design for GPs that can automatically fit multidimensional data with affordable model complexity and superior modeling capability. To alleviate the computational complexity due to the curse of dimensionality, we leverage a multicore computing environment to optimize the kernel hyper-parameters in a distributed manner. We further propose a doubly distributed learning algorithm based on the alternating direction method of multipliers (ADMM) which enables multiple agents to learn the kernel hyper-parameters collaboratively. The doubly distributed learning algorithm is shown to be effective in reducing the overall computational complexity while preserving data privacy during the learning process. Experiments on various one-dimensional and multidimensional data sets demonstrate that the proposed kernel design yields superior training and prediction performance compared to its competitors.
2021

ABSTRACT PAPER POSTER

Demystifying model averaging for communication-efficient federated matrix factorization

Shuai Wang , Richard Cornelius Suwandi , and Tsung-Hui Chang

46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021

Abstract

Federated learning (FL) is encountered with the challenge of training a model in massive and heterogeneous networks. Model averaging (MA) has become a popular FL paradigm where parallel (stochastic) gradient descent (GD) is run on a small sampled subset of clients multiple times before uploading the local models to a server for averaging, which has been proven effective in reducing the communication cost for achieving a good model. However, MA has not been considered for the important matrix factorization (MF) model, which has vast signal processing and machine learning applications. In this paper, we investigate the federated MF problem and propose a new MA based algorithm, named FedMAvg, by judiciously combining the alternating minimization technique and MA. Through analysis, we show that gradually decreasing the number of local GD and only allowing partial clients to communicate with the server can greatly reduce the communication cost, especially in heterogeneous networks with non-i.i.d. data. Experimental results by applying FedMAvg to data clustering and item recommendation tasks demonstrate its efficacy in terms of both task performance and communication efficiency.

Honors & Awards

2024

A scholarship worth US$7,000 for up to three years of consecutive support to students who have expressed interest and commitment to pursuing signal processing education and real-world career experiences.

2024

A specially raised fund to support the eligible international students with a view to carrying forward the spirit of the Universiade Shenzhen and promoting cultural and educational exchanges among international young people.

2023

The studentship provides financial support through a tuition waiver, teaching assistant stipend, and research assistant (RA) opportunities.

2022, 2023

A monthly cash award for working on a research project under the supervision of a faculty member.

2020, 2021, 2022, 2023

The award is given by the Dean and the School to students who attained a year GPA of 3.5 or above (out of 4.0) as a recognition of hard work and outstanding academic performance.

2020, 2021

The scholarship is awarded to outstanding international students in colleges and universities across the Guangdong province.

2019

Full tuition and accommodation scholarship awarded upon admission and subject to annual renewal in line with outstanding academic achievement.