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陽明交通大學衍生新創事業 AI 大數據研究發展服務
數冠科技(股)公司是由陽明交通大學生物資訊及系統生物研究所何信瑩教授所創立,和一群數據科學工程師團隊組成的陽明交大師生衍生新創事業;從陽明交通大學技術授權「數學建模與預測」專有技術,並開發「智慧型演化學習平台」,從事大數據AI建模分析、智慧商業與精準醫療系統聯合開發的研究服務公司。
「演化學習」是利用演化式演算法來解決機器學習中的最佳化問題,結合演化計算與機器學習可視為計算智慧及機率統計的巧妙結合。何信瑩教授主持的「智慧型計算實驗室」研發智慧型計算的前瞻技術,其中核心技術「智慧型演化式演算法」為高引用論文,用以解決大型參數最佳化問題,並發表60篇以上演化學習相關的國際期刊論文。在數據建模分析和產業應用有近三十年經驗,尤其是研發「演化學習」技術,其應用範疇十分廣泛,包含工程、管理、金融、農業及生物醫學等領域,研發團隊擁有豐富的產學合作經驗。
人工智慧AI技術在智慧醫療的創新研究,不論是在學術、臨床和產業領域都是熱門的議題。配合陽明交通大學BioICT和「智慧醫療推動中心」的協助,生醫研究團隊合作醫院則包括中國醫附設醫院、榮民總醫院、彰化基督教醫院、馬偕醫院、雙和醫院、長庚醫院、高醫附設醫院、臺大醫院新竹分院、三軍總醫院、桃園醫院……。以「智慧型病理組織切片影像分析系統」獲得陽明交通大學2016創業競賽獎勵,以「醫療影像人工智慧電腦輔助分析平台」獲得科技部106年度第一梯次創新創業激勵計畫的創業潛力獎。經濟成長源自於科技與商業模式的不斷創新,新數位時代的物聯網和雲端大數據加值需求日益明顯,不論是學術界、醫界和產業界都急需導入AI技術。加上政府開放鬆綁衍生新創事業法規,鼓勵5+2產業創新,扶植研究服務公司的創立,配合師生創新創業的風潮,數冠科技(股)公司乃應運而生。
數冠科技研究服務公司以嚴謹學術倫理的態度從事大數據的加值服務,從擷取知識、預測模型到輔助決策系統的建立,讓您的資料發揮創造最大價值。數冠科技提供快速、精準、最佳化的數據分析服務,讓研究人員縮短論文研究時程,以及系統聯合開發的客製化服務,幫助產業降低培養AI技術和人才的成本與風險。

Doctor How Inc. was founded by Dr. Shinn-Ying Ho, a professor and chairman of the Institute of Bioinformatics and Systems Biology of National Yang Ming Chiao Tung University (NYCU), and was composed of data scientist and engineers of NYCU, which was a derivative start-up of NYCU. Doctor How, a research services company, develops a novel Intelligent evolutionary learning platform using the technical authorization of the know-how “mathematical modelling and prediction” licensed from NYCU and engages in artificial intelligence (AI) modelling with data analysis, smart commerce and jointly developed precision medical systems.
"Evolutionary learning" uses evolutionary algorithms to solve optimization problems in machine learning. Combining evolutionary computation with machine learning can be seen as a clever combination of computational intelligence and statistics. The "Intelligent Computing Lab" hosted by Prof. Ho develops advanced technologies of intelligent computing. The core technology " Intelligent evolutionary algorithm" is a highly cited paper for solving large parameter optimization problems. Prof. Ho has published more than 60 evolutionary learning related international journal articles. He has near 30 years of experience in data modelling and industrial applications, especially in the development of evolutionary learning technology, which covers a wide range of applications, including engineering, management, finance, agriculture, and biomedical fields. His R&D team has rich experience in industry-university cooperation.
The innovative research of AI technology in smart healthcare is a hot topic in academic, clinical and industrial fields. In cooperation with the NYCU BioICT and the Smart Medical Promotion Center, the cooperative hospitals for biomedical research project includes more than 10 big hospitals in Taiwan. The evolutionary learning platform-based computer aided diagnosis system for biomedical images won an Entrepreneurial Potential Award in the FITI (From Invention to Innovation) Program Competition of the Ministry of Science and Technology in 2017. Economic growth stems from the continuous innovation of technology and business models. The demand for value-added internet of things and cloud big data in the new digital era is becoming more and more obvious. Whether it is academic, medical or industrial fields, there is an urgent need to import the AI technology. In addition, the government has opened up a new business regulation, encouraged the innovation of 5+2 industries of Taiwan, fostered the research service companies, and encouraged teachers and students to innovate and start a business. Therefore, Doctor How Inc. emerges at a historic moment.
Doctor How Inc. engages in the value-added service of big data with strict academic ethics, from the acquisition of knowledge and prediction models to the establishment of auxiliary decision-making systems, so that your data can be used to create maximum value. Doctor How Inc. provides fast, accurate and optimized data services, enabling researchers to shorten the time of research and the customized services of jointly developed systems for helping the industry reduce the cost and risk in educating technicians of AI technology.
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