About

Ming-Hung Kao’s primary research interest is in the design of experiments. Motivated by real-world applications, he has been developing insightful statistical theory and efficient computational methods for identifying optimal experimental designs that allow experimenters to collect the most informative data at minimum cost to make precise and valid statistical inference. His current research focus is on the development of high-quality designs for functional neuroimaging experiments where pioneering brain mapping technologies are utilized to improve knowledge about the inner workings of the human brain.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Mathematical optimization
• Statistics
• Machine Learning
• Computer Security
• Econometrics
• Algorithm

Selected publications

• Real-Time Online Training Framework with Lesson-Based Curriculum for Minecraft Battle Bots

  Communications in computer and information science · 2025-01-01

  book-chapter
  PublisherDOI

• New Pilot-Study Design in Functional Data Analysis

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Optimal Experimental Designs for Sparse Functional Data: A Review

  Wiley Interdisciplinary Reviews Computational Statistics · 2025-08-12

  reviewSenior authorCorresponding

  ABSTRACT Sparse functional data analysis (FDA) is powerful and increasingly popular for extracting insightful information from functional/longitudinal data collected sparsely over a continuous domain. Although numerous methods have been developed for analyzing such data, the effectiveness of resulting inferences depends critically on the experimental design used for data collection. Optimal design theory provides a principled framework for improving estimation accuracy and efficiency through strategically chosen sampling schemes. This review synthesizes the current literature on optimal experimental design in the context of sparse FDA, highlighting recent methodological developments, comparing key design criteria, and discussing practical applications and existing limitations.

  PublisherDOI

• Optimal Designs for Functional Principal and Empirical Component Scores

  Statistica Sinica · 2023 · 2 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Sparse functional data analysis (FDA) is powerful for making inference on the underlying random function when noisy observations are collected at sparse time points. To have a precise inference, knowledge on optimal designs that allow the experimenters to collect informative functional data is crucial. Here, we propose a framework for selecting optimal designs to precisely predict functional principal and empirical component scores. Our work gives a relevant generalization of previous results on the design for predicting individual response curves. We obtain optimal designs, and evaluate the performance of commonly used designs. We demonstrate that without a judiciously selected design, there can be a great loss in statistical efficiency.

  PublisherOA PDFDOI

• Hybrid exact-approximate design approach for sparse functional data

  Computational Statistics & Data Analysis · 2023 · 3 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Algorithm
  PublisherDOI

• Alignment of Visual Senses and Acoustical Senses based on Emotion Recognitions

  2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · 2022-11-29 · 1 citations

  article

  Recently, due to the rapid advances on multimedia technology, multimedia data such as pictures and music has heavily invaded our daily life. By the mobile camera, users capture the interested scenes as the preferred pictures, and then browse these pictures in either computers or mobile devices. This incurs an interesting issue that, how to provide the users with a better browsing/listening atmosphere and experience by music. Actually, it can be viewed as the problem for aligning photos with music and also as an exciting research topic for cross-media retrieval. However, very few studies focus their attention on this topic. To aim at this issue, in this paper, we propose a novel cross-media alignment method that bridges visual pictures to harmonic music by visual emotions and acoustical emotions. In this method, the pictures and music are projected onto the emotion space first. Next, the emotion features are fuzzed to be near the human senses. Finally, the effective alignment method for associating pictures with music is performed. The results of subjective and objective evaluations on the real datasets reveal that, our proposed method can successfully provide the users with a rich experience on audiovisual presentations. Also, the proposed alignment algorithm is shown to be effective in terms of Normalized Discounted Cumulative Gain.

  PublisherDOI

• Bagging-Enhanced Sampling Schedule for Functional Quadratic Regression

  Journal of Statistical Theory and Practice · 2021 · 5 citations

  • Computer Science
  • Artificial Intelligence
  • Mathematics
  PublisherDOI

• Maximin Designs for Ultra-Fast Functional Brain Imaging

  ICSA book series in statistics · 2019-01-01

  book-chapterSenior author
  PublisherDOI

• Locally optimal designs for mixed binary and continuous responses

  Statistics & Probability Letters · 2019-01-18 · 3 citations

  articleSenior author
  PublisherDOI

• Optimal experimental designs for fMRI when the model matrix is uncertain

  NeuroImage · 2017-05-08 · 1 citations

  article1st authorCorresponding
  PublisherDOI

Recent grants

• CAREER: New Developments on Experimental Designs for Pioneering Functional Brain Imaging Technologies

  NSF · $400k · 2014–2020

Frequent coauthors

• Abhyuday Mandal

  University of Georgia

  11 shared

• John Stufken

  11 shared

• Dibyen Majumdar

  University of Illinois Chicago

  9 shared

• Frederick Kin Hing Phoa

  Institute of Statistical Science, Academia Sinica

  6 shared

• Ching-Shui Cheng

  University of California, Berkeley

  4 shared

• Yuan-Lung Lin

  Institute of Statistical Science, Academia Sinica

  4 shared

• Kamlesh Jangid

  Agharkar Research Institute

  3 shared

• Rong Pan

  2 shared

Education

• Ph.D.

  University of Georgia

  2009

• M.S.

  National Central University, Taiwan

  1999

• B.S.

  National Central University, Taiwan

  1997

Awards & honors

• CAREER: New Developments on Experimental Designs for Pioneer…
• RTG: Data-Oriented Mathematical and Statistical Sciences. Co…
• Design of Experiments with Dynamic Responses. Co-PI, NSF-CMM…
• Pragmatic Strategies for Designing Complex Multi-Response St…