2025 研究主題清單 (Research List)
Updated 2025.02.19(持續更新中)
| 主持人(PI) | 研究主題(Research Topic) | 研究介紹(Introduction) | 其他資訊(Other Information) | 參考網頁 |
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| 張明中 Ming-Chung Chang |
多階層因子設計的理論探討 A Theoretical Exploration of Multi-Stratum Factorial Designs |
多階層因子設計廣泛應用於處理實驗單位中的複雜異質性。在這個暑期實習計畫中，學生將首先學習多階層因子設計的統一理論。接下來，他們將探索此類設計的理論性質，包括擴展現有理論以開發能適應實驗單位中一般異質性結構的更全面框架。此外，學生還將深入了解這些設計的各種應用，涵蓋從傳統實驗設計到最前沿的人工智慧應用。 Multi-stratum factorial designs are widely used to address complex heterogeneity in experimental units. In this summer internship program, students will first be introduced to a unified theory of multi-stratum designs. Following this, they will explore the theoretical properties of such designs, including extending existing theories to develop more comprehensive frameworks that accommodate general heterogeneity structures in experimental units. Additionally, students will gain insight into various applications of these designs, ranging from traditional experimental setups to cutting-edge AI-driven applications. |
PI 個人首頁(PI's Information): https://sites.google.com/view/mcchang/ 實驗室網頁(LAB's Information): https://sites.google.com/view/mcchang/ Email: mcchang0131@gmail.com |
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| 梁埈豪 Junho Yang |
空間點過程的統計分析 Statistical analysis of spatial point processes |
實習計畫目標：在此實習計畫中，學生將學習空間點過程的當代統計技術。主題包括但不限於 (1) 點模式資料在測量誤差下的統計推論、(2) 點過程技術在腦造影數據中的應用、(3) 分析大型空間點模式資料的高效計算方法。 資格要求：我們尋找主修統計學、數學或相關領域的學生，無論是來自國內還是國際的大學或研究機構皆可。成功的申請者需具備扎實的大學數學基礎，並熟練使用 R 和/或 Python 。 由於所有溝通將以英文進行，因此申請者需具備良好的英語口語和書寫能力。 在實習計畫結束後，表現優異的學員可能獲邀參與進一步的研究計劃，並獲得經費支持。如有任何問題，請隨時發送電子郵件與我聯繫。 Goals: In this internship program, students will learn modern statistical techniques for analyzing spatial point processes. Topics include, but are not limited to: (1) inference of point patterns with measurement error, (2) applications of point process techniques to neuroimaging data, and (3) computationally efficient methods for large spatial point pattern datasets. Requirements: We are looking for students majoring in Statistics, Mathematics, or related fields from domestic or international universities/institutes. Successful candidates should have a strong background in undergraduate-level mathematical skills and be proficient in R and/or Python. Since all communication will be conducted in English, good oral and written English skills are also required. Upon completion of the internship program, outstanding trainees may be invited to participate in further research projects, with financial support provided. If you have any inquiries about this position, please feel free to email me. |
PI 個人首頁(PI's Information): https://sites.google.com/view/junhoyang Email: junhoyang@stat.sinica.edu.tw |
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| 潘建興 Frederick Kin Hing Phoa |
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我的研究小組旨在為有興趣應用統計和數據分析技術來解決尖端現實問題的學生提供第一手經驗的暑期實習機會。下列包括一些適合學生在兩個月內展開並獲得足夠研究成果的潛在主題。
1.實驗設計與分析： (a) 設計臨床試驗的實驗。 (b) 設計網路調查或廣告實驗。 (c) 設計人工智慧和機器人測試實驗。 (d) 分析生物醫學診斷的實驗數據。 (e) 分析功能性磁振造影 (fMRI) 的實驗數據。 2.大規模網路數據分析： (a) 研究真實網絡的網絡演化機制。 (b) 在大規模網路中有效地偵測社群。 (c) 從世界上最大的科學網絡分析引文和科研合作的行 為。 (d) 從大型語言模型中開發自動主題識別和內容摘要。 (e) 在 3D 球面中繪製網路以達到平均節點分配。 3.優化方法及應用： (a) 開發新的混合最佳化技術。 (b) 優化物流業配送路線。 (c) 優化超級電腦設施的任務調度。 (d) 開發高效率的翻譯器來解碼加密訊息。 (e) 在給定的平面圖中有效地分佈監視系統。 4.環境與交通大數據分析 (a) 透過網路時間序列聚類分析空氣污染(PM 2.5)空氣箱 大數據。 (b) 透過網絡時間序列聚類分析地下水變化大數據。 (c) 透過分散式聲學感測資料分析台灣地震。 (d) 最佳化包含可能人為延誤的火車/公車時刻表。 (e) 優化共享單車配送路線和站點分配。 5.人工智慧與智慧製造 (a) 發展半監督大數據的最佳子取樣技術。 (b) 設計智慧漁業監測系統。 (c) 開發數據分析中區塊鏈技術的新方法。 (d) 量化數位孿生的不確定性和變化。 本次實習機會開放給各層級的學生（學士、碩士、博士），工作內容依參與學生的程度進行調整。在這兩個月內，學生將與我進行文獻討論，然後在我的指導下開始研究主題。成果優良的學生會收到繼續參與研究的邀請，並會以產生足夠結果來在國際期刊上發表的為最終目標。我們鼓勵所有有興趣參加本研究小組暑期實習的學生以電子郵件方式向我獲取進一步的說明和詢問。 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 data from functional magnetic resonance imaging (fMRI). 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) drawing the networks in 3D spherical surfaces with uniform node allocations. 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 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. Artificial Intelligence and Smart Manufacturing (a) developing optimal subsampling techniques for semi-supervised big data. (b) designing surveillance systems for smart fishery. (c) developing new methods of blockchain technology in data analytics. (d) 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 for further clarifications and enquiries. |
PI 個人首頁(PI's Information): https://staff.stat.sinica.edu.tw/fredphoa/ Email: fredphoa@stat.sinica.edu.tw |
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| 謝叔蓉 Shwu-Rong Grace Shieh |
精準醫療 Precision Medicine |
我的團隊發展統計、機器學習等計算方法，分析基因組數據、基因表現和組學(omics)數據、生物資料庫等大數據，應用於預測癌症患者的對免疫治療/化療/標靶藥物反應，並發現免疫治療(immunotherapies)的生物標記 (biomarkers)。實習生透過學習上述方法與實際分析數據參與計畫。在這兩個月內，學生將在我的指導下開始研究主題。成果優良的學生會收到繼續參與研究的邀請，並會以產生足夠結果來在國際期刊上發表的為目標。本次實習歡迎 學士、碩士、博士生申請。 My team develops statistical, machine learning, and AI methods to analyze genomics (DNA) data, gene expression (mRNA), and other omics data to predict drug responses to chemo-, targeted- and immuno-therapies of cancer patients and conduct statistical analysis to find biomarkers, etc. Additionally, we may analyze data from large biobanks. Within the two months, interns will conduct research topics under my guidance. Those who perform well will be invited to continue investigating the projects, with the goal to publish it in international journals. Undergraduate, master, and PhD students are all welcome to apply. |
應徵資格 : 歡迎 (1)對統計資料科學、精準醫療或人工智慧具熱情 (2) 會程式設計(R or Python)，具統計、資工、生統、應數…等相關科系的大四及以上學生加入。畢業後計畫深造尤佳。 應備文件 : 履歷表、自傳、大學(及研究所)成績單、及推薦人2位。 Qualification : (1) passionate about statistics and data science and analysis of real data; (2) fluent programming in Ｒ, Python, or other scientific software; senior undergraduate students or above in Statistics, Computer Science, Biostatistics, Applied Mathematics, or any relevant majors are welcome to join. Those planning to pursue further studies after graduation are especially encouraged. Required documents: resume, autobiography, university (and graduate school, if applicable) transcripts, and contact information for two referees. |
PI 個人首頁(PI's Information): https://staff.stat.sinica.edu.tw/gshieh/ Email: gshieh@stat.sinica.edu.tw |
| 楊欣洲 Hsin-Chou Yang |
智慧健康 Smart Health |
參與智慧健康研究團隊（陳君厚博士和楊欣洲博士聯合指導），學習統計學習、機器學習、深度學習、資料視覺化的方法與技巧，實際分析醫學影像、基因資料、環境暴露、就醫記錄、人口統計等大數據，開發資料科學與人工智慧的自動化方法，進行特徵擷取、疾病分型和診斷、病變分割與偵測、風險評估和預測、大型語言模型應用等。 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. |
要求：(1) 對統計資料科學、人工智慧、精準醫療、智慧健康、醫學影像、資料視覺化、電腦視覺處理、生醫訊號處理、大語言模型等充滿熱情。(2) 喜歡實際資料分析與研究。 Requirements: (1) Passion for statistical data sciences, artificial intelligence, smart health, medical imaging, data visualization, computer vision, biomedical signals, and large language models; (2) Interest in real data analysis and research. |
PI 個人首頁(PI's Information): https://staff.stat.sinica.edu.tw/hsinchou/ 實驗室網頁(LAB's Information): https://sites.stat.sinica.edu.tw/SH/ Email: hsinchou@stat.sinica.edu.tw |
| 陳君厚 Chun-houh Chen |
智慧健康 Smart Health |
參與智慧健康研究團隊（陳君厚博士和楊欣洲博士聯合指導），學習統計學習、機器學習、深度學習、資料視覺化的方法與技巧，實際分析醫學影像、基因資料、環境暴露、就醫記錄、人口統計等大數據，開發資料科學與人工智慧的自動化方法，進行特徵擷取、疾病分型和診斷、病變分割與偵測、風險評估和預測、大型語言模型應用等。 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. |
要求：(1) 對統計資料科學、人工智慧、精準醫療、智慧健康、醫學影像、資料視覺化、電腦視覺處理、生醫訊號處理、大語言模型等充滿熱情。(2) 喜歡實際資料分析與研究。 Requirements: (1) Passion for statistical data sciences, artificial intelligence, smart health, medical imaging, data visualization, computer vision, biomedical signals, and large language models; (2) Interest in real data analysis and research. |
PI 個人首頁(PI's Information): https://gap.stat.sinica.edu.tw/ 實驗室網頁(Lab's Information): https://sites.stat.sinica.edu.tw/SH/ Email: cchen@stat.sinica.edu.tw |
| 顏佐榕 Tso-Jung Yen |
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1. 以超音波影像做疾病分類：我們將訓練一個白盒深度學習模型，不僅能夠做出可信的決策，還能提供可解釋的決策。我們將構建一個深度學習模型，以處理來自不同來源的外部數據。此外，我們將開發一個多模態數據編碼器，使其能夠處理包含不完整信息的多模態數據。 2. 以強化學習微調分子生成模型：我們的目標是開發一個深度生成模型，能夠在研究人員指定的條件下模擬分子。此外，我們還希望利用模型生成的數據對其進行微調。為了實現第一個目標，我們可能會參考條件生成模型的概念，設計一個模型，其輸入由隨機種子和指定的分子屬性（如幾何、能量、電子和熱力學屬性）組成。為了實現第二個目標，我們計劃在強化學習框架下開發微調程序。 1. Disease classification from ultrasound images: We will train a white-box deep learning model that is not only able to deliver faithful decisions but is also able to provide explainable decisions. We will build a deep learning model that is able to deal with external data from different sources. We will design an encoder for multimodal data so that the encoder is able to deal with multimodal data that contain incomplete information. 2. Molecule generation via reinforcement learning: We aim to develop a deep generative model that can simulate molecules under conditions specified by researchers. In addition, we also aim to fine-tune the generative model with the data it generates. To achieve the first goal, we may follow the ideas of conditional generative models and design a model in which the input consists of the random seed and the specified molecular properties such as geometric, energetic, electronic, and thermodynamic properties of the molecule. To achieve the second goal, we intend to develop the fine-tuning procedure under the reinforcement learning framework. |
歡迎有化學或是物理背景且對生成式AI用於解決科學問題有興趣的學生。 | PI 個人首頁(PI's Information): https://sites.stat.sinica.edu.tw/tjyen/ Email: tjyen@stat.sinica.edu.tw |
| 張馨文 Hsin-wen Chang |
協助發展統計及機器學習方法 Help develop statistical and machine learning methodology. |
本研究室發展分析生醫資料之統計及機器學習方法，以處理包含穿戴型裝置、臨床試驗、分佈飄移所產生的資料。 This lab develops statistical and machine learning methodology for analyzing various types of biomedical data, including functional data such as wearable device data, clinical trial data with time-to-event outcomes, and data with distribution shift. |
PI 個人首頁(PI's Information): https://www.stat.sinica.edu.tw/cht/index.php?act=researcher_manager&code=view&member=2 Email: hwchang@stat.sinica.edu.tw |
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| 楊振翔 Chen-Hsiang Yeang |
癌症基因體 單細胞與空間轉錄體學 分子演化與群體遺傳學 生物醫學多模態資料整合的機器學習應用 網路拓撲分析 cancer genomics single-cell and spatial transcriptomics molecular evolution and population genetics machine learning applications of biomedical multimodal data integration network topology analysis |
我的研究集中在大規模生物醫學數據的分析和處理。 我的團隊開發機器學習和統計演算法來分析癌症體學、單細胞和空間轉錄組、人類遺傳變異、生物醫學影像、蛋白質穩定性測定和神經元網路的數據。 My research is centered around analysis and process of large-scale biomedical data. My group develops machine learning and statistical algorithms to analyze the data of cancer omics, single-cell and spatial transcriptomes, human genetic variants, biomedical images, protein stability assays and neuronal networks. |
PI 個人首頁(PI's Information): https://staff.stat.sinica.edu.tw/chyeang/ Email: chyeang@stat.sinica.edu.tw |
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| 陳璿宇 Hsuan-Yu Chen |
數據科學與精準智慧醫療 Data Science and Precision Smart Healthcare |
我現職為中研院統計所研究員，亦為台大、中興與高醫等大學合聘教授，也是台灣精準健康暨毒理基因體學會理事長，我主持的實驗室為數據科學與精準智慧醫療研究室。我們團隊的工作聚焦於多體學與臨床大數據的分析演繹，最後提出疾病發生與治療的預測模型，以提供疾病篩查與治療的分子導引。多體學包含基因體、轉錄體以及蛋白質體，為目前在各種領域如醫學、農學與生態學等最熱門的研究領域，我們為台灣與國際上該領域的知名研究團隊且參與了美國癌症登月計劃 (https://www.thenewslens.com/article/49769)。 除了數據科學外，我們亦與電機系教授合作，開發疾病監控相關的硬體與軟體，像是開發氣喘病人的智慧吸入器等智慧醫材產品。 我們研究團隊成員包含臨床醫師、統計學家、流行病學家、資訊與電機工程學家以及生物學家，為國內與國際上少見的跨領域整合團隊，目前有博士生7名、博士後研究員3名、碩士級研究助理7名． I am currently a research fellow at the Institute of Statistical Science, Academia Sinica, and a jointly appointed professor at National Taiwan University, National Chung Hsing University, and Kaohsiung Medical University. Additionally, I serve as the President of the Taiwan Society of Precision Health and Toxicogenomics. I lead the Data Science and Precision Smart Healthcare Research Lab, where our team focuses on multi-omics and clinical big data analysis to develop predictive models for disease onset and treatment. These models aim to provide molecular guidance for disease screening and therapy selection. Multi-omics, which includes genomics, transcriptomics, and proteomics, is one of the most cutting-edge research areas across various fields such as medicine, agriculture, and ecology. Our team is well recognized in Taiwan and internationally in this domain and has participated in the U.S. Cancer Moonshot Initiative (https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative/about ). Beyond data science, we collaborate with professors in electrical engineering to develop hardware and software for disease monitoring, including smart medical devices such as intelligent inhalers for asthma patients. Our research team is a highly interdisciplinary group, consisting of clinicians, statisticians, epidemiologists, computer scientists, electrical engineers, and biologists. Such cross-disciplinary integration is rare both domestically and internationally. Currently, our team includes 7 Ph.D. students, 3 postdoctoral researchers, and 7 master’s-level research assistants. |
PI 個人首頁(PI's Information): http://staff.sinica.edu.tw/hychen Email: hychen0808@gmail.com 我的相關報導 |
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附件：OOO | PI 個人首頁(PI's Information): http://staff.sinica.edu.tw/OOOOOO Email: OOOOOO@stat.sinica.edu.tw |
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