Introduction:
The summer internship in my research group aims at providing first-hand experience to students who are interested in applying statistical and data analytics techniques to solve cutting-edge real-world problems. The following list includes some potential topics suitable for students to work on and obtain adequate research results within two months.
1. Design and Analysis of Experiments:
(a) designing experiments for clinical trials. (b) designing experiments for network surveys or advertisement. (c) designing experiments for AI and robotics testing.(d) analyzing experimental data from biomedical diagnosis.(e) analyzing experimental test data from sports.
2. Analysis of Large-Scale Network Data:
(a) studying the network evolution mechanism for real networks. (b) detecting communities efficiently in large-scale networks. (c) analyzing citation and collaboration behaviors from the world''s largest scientific database. (d) developing automatic topic identification and content summarization from large language model.(e) detecting essential nodes in large-scale networks.
3. Optimization: Methods and Application:
(a) developing new hybrid optimization techniques. (b) optimizing delivery routes for logistic industry. (c) optimizing task schedule in supercomputer facility. (d) developing an efficient translator for decoding encrypted messages.(e) developing an efficient surveillance system in a given floor plan.
4. Big Data Analytics in Seismicity, Environments and Transportation
(a) analyzing air pollution (PM 2.5) big airbox data by network time series clustering. (b) analyzing underground water variation by network time series clustering. (c) analyzing earthquakes in Taiwan by Distributed Acoustic Sensing (DAS) data. (d) optimizing train/bus schedule with potential human delay.(e) optimizing share-bike delivery route and stop allocations.
5. Applications of Digital Twins in Artificial Intelligence and Smart Manufacturing
(a) developing optimal subsampling techniques for semi-supervised big data. (b) designing surveillance systems for smart factory. (c) quantifying the uncertainty and variation in digital twins.
This internship is open to students at all levels (bachelor, master, doctoral), and the work content is adjustable according to the level of the participating students. Within these two months, the students are expected to have literature discussions with me, then start to work on the research topic under my supervision. Students with excellent results will receive an invitation to continue participating in research, with the ultimate goal of producing sufficient results to be published in an international journal.
All students who are interested in participating in my research group''s summer internship are encouraged to email me (fredphoa@stat.sinica.edu.tw) for further clarifications and enquiries.

During this summer internship, under the joint supervision of Drs. Chun-Houh Chen and Hsin-Chou Yang, you will join our research team, "Smart Health," to learn methods and techniques in statistical learning, machine learning, deep learning, and data visualization. You will apply these methods to the practical analysis of big health data, including medical images, genetic data, environmental exposures, medical records, and demographic statistics. Additionally, you will gain knowledge in developing automated approaches in data science and artificial intelligence, with a focus on feature extraction, disease classification and diagnosis, lesion segmentation and detection, risk assessment and prediction, as well as applications of large language models.

Currently we are developing 1) an automatic approach to explainable AI with applications to medical image classification. We are also applying generative modelling to solve 2.1) structured matrix generation problems and 2.2) function-on-function prediction problems. Besides the above topics, we are also exploring the AI agents for 3.1) scientific research and 3.2) social interaction simulation.