Richard Cornelius Suwandi

I am a fully-funded PhD student at School of Artificial Intelligence, CUHK-Shenzhen, advised by Prof. Feng Yin and Prof. Tsung-Hui Chang. Prior to my PhD, I obtained my BSc degree in Statistics (with first-class honors) from CUHK-Shenzhen.

I am interested in building adaptive intelligence for scientific discovery and engineering design: AI systems that learn probabilistic models of unknown environments, choose informative experiments under limited budgets, and revise their hypotheses from feedback. The long-term goal is to build closed-loop AI systems that learn what to model, what to test, and what to discover. See my Research page for details.

Recently, I has become increasingly interested in agentic and autoresearch systems. I built PlugBO, a modular framework that enables an agent to dynamically adapt the optimization configuration on the fly. I also co-developed OpenEvolve, an evolutionary coding agent for discovering and optimizing algorithms, and helped build Kai, an autonomous agent that evolves codebases by finding and patching software vulnerabilities.

I stay connected these emerging fields as a founding committee member of the Institute for AI-driven Discovery of Algorithms (AIDDA), and previously served as a community leader for the AI4Science community at alphaXiv.

News

Aug 10, 2026

🎉 Joined Qwen as a dev ambassador!

Jun 09, 2026

💻 Co-organized AIDDA 2026, a two-day virtual technical conference focusing on AI-driven algorithm discovery. Recordings of the talks are available on our YouTube channel.

May 01, 2026

🎉 Our paper titled “MIMOMamba: From Scalar Duality to Matrix-Valued Attention” has been accepted to ICML 2026!

Apr 24, 2026

🎓 Successfully passed my PhD candidate exam!

Apr 13, 2026

💻 Joined ByteDance’s Creation & Music Recommendation Team as a Research Intern to work on generative recommendation, LLM4Rec, and AIGC-powered content discovery!

Mar 25, 2026

✨ Invited to serve as a reviewer for NeurIPS 2026!

Jan 18, 2026

🎉 Our paper titled “Breaking the Curse of Dimensionality in Gaussian Process Training With Zeroth-Order Adaptive Perturbation” has been accepted to ICASSP 2026 as oral!

Jan 05, 2026

🎓 Transferred to the School of Artificial Intelligence, CUHK-Shenzhen with a fully-funded PhD scholarship!

Nov 17, 2025

🏆 Selected as the recipient of the Guangdong Government Outstanding International Student Scholarship

Oct 09, 2025

✨ Invited to serve as a reviewer for ICASSP 2026!

Sep 24, 2025

✨ Invited to serve as a reviewer for ICLR 2026!

Sep 19, 2025

🎉 Our paper titled “Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs” has been accepted to NeurIPS 2025!

Sep 15, 2025

💻 Joined Dria as a Research Intern to work on evolutionary coding agents!

Jul 20, 2025

🏆 Won the 2nd prize award at the 2025 Doctoral Research and AI Innovation Conference held by CUHK-Shenzhen!

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!

Mar 01, 2025

💻 Joined Huawei as a Research Intern to work on 5G network optimization!

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 a 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

2026

ABSTRACT PAPER View on alphaXiv

MIMOMamba: From Scalar Duality to Matrix-Valued Attention

Yanbo Li, Richard Cornelius Suwandi, Feng Yin, 3 more authors Yiyong Sun, Wei Huang, Wenqiang Pu

43rd International Conference on Machine Learning (ICML), 2026

Abstract

The state space duality (SSD) framework, central to modern state-space models (SSMs) such as Mamba, has established an efficient attention-like mechanism by leveraging the commutative property of linear recurrences. However, existing formulations are limited to single-input single-output (SISO) systems that enforce commutativity with a restrictive scalar-identity constraint, which prevents cross-dimensional interactions within the state dynamics. In this work, we generalize SSD to the multi-input multi-output (MIMO) setting by introducing a matrix polynomial parameterization. This approach not only provides a principled way to ensure commutativity for generalized duality but also induces a shared algebraic structure across state transitions, thereby significantly reducing parameter redundancy. Building on this foundation, we present MIMOMamba, a multi-head SSM architecture that captures rich cross-dimensional dynamics while retaining linear-time training. Empirical results on a sequence modeling benchmark show that MIMOMamba matches or exceeds the performance of standard Transformers with only approximately one-third the parameters of the baseline.
2026

ABSTRACT PAPER CODE View on alphaXiv

Breaking the Curse of Dimensionality in Gaussian Process Training With Zeroth-Order Adaptive Perturbation

Richard Cornelius Suwandi, Feng Yin, Tsung-Hui Chang

ORAL 51th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026

Abstract

Hyperparameter optimization remains a core challenge in training Gaussian processes (GPs), primarily due to the high computational cost and numerical instabilities associated with gradient-based optimizers. These issues are exacerbated in high-dimensional spaces, where the optimization landscapes become increasingly complex and difficult to navigate efficiently. We propose zeroth-order adaptive perturbation (ZAP), a scalable gradient-free algorithm that leverages simultaneous perturbations sampled from a Bernoulli distribution to obtain accurate gradient estimates with only two function evaluations per iteration, regardless of the hyperparameter dimensionality. Our theoretical analysis establishes that the gradient estimator is asymptotically unbiased and that ZAP converges to a stationary point under standard assumptions. Empirically, ZAP outperforms state-of-the-art gradient-based and gradient-free baselines on real-world datasets, achieving an average of 20 times reduction in mean-squared-error and a 118 times speedup in optimization time.
2025

ABSTRACT PAPER CODE POSTER View on alphaXiv

Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs

Richard Cornelius Suwandi, Feng Yin, Juntao Wang, 3 more authors

39th Conference on Neural Information Processing Systems (NeurIPS), 2025

Abstract

The efficiency of Bayesian optimization (BO) relies heavily on the choice of the Gaussian process (GP) kernel, which plays a central role in balancing exploration and exploitation under limited evaluation budgets. Traditional BO methods often rely on fixed or heuristic kernel selection strategies, which can result in slow convergence or suboptimal solutions when the chosen kernel is poorly suited to the underlying objective function. To address this limitation, we propose a freshly-baked Context-Aware Kernel Evolution (CAKE) to enhance BO with large language models (LLMs). Concretely, CAKE leverages LLMs as the crossover and mutation operators to adaptively generate and refine GP kernels based on the observed data throughout the optimization process. To maximize the power of CAKE, we further propose BIC-Acquisition Kernel Ranking (BAKER) to select the most effective kernel through balancing the model fit measured by the Bayesian information criterion (BIC) with the expected improvement at each iteration of BO. Extensive experiments demonstrate that our fresh CAKE-based BO method consistently outperforms established baselines across a range of real-world tasks, including hyperparameter optimization, controller tuning, and photonic chip design. Our code is publicly available at https://github.com/richardcsuwandi/cake.
2025

ABSTRACT PAPER CODE View on alphaXiv

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

Richard Cornelius Suwandi, Zhidi Lin, Feng Yin, 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 View on alphaXiv

Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data

Richard Cornelius Suwandi, Zhidi Lin, Yiyong Sun, 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 View on alphaXiv

Demystifying Model Averaging for Communication-Efficient Federated Matrix Factorization

Shuai Wang, Richard Cornelius Suwandi, 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.