國立臺北大學統計學系

專題演講

講題： 從方法正確到決策可行：在 LLM 與 Agentic Workflow 時代重構資料分析師角色

主講人：劉嘉凱 執行長 (智庫驅動股份有限公司)

時間：114年10月15日 (星期三，13：10~15：00)

地點：三峽校區商學院3F13教室

Abstract

當大型語言模型（LLM）與代理人工作流（Agentic Workflow）正逐步重塑企業的決策模式，資料分析師的價值也從「方法正確」轉向「決策可行」。 本演講將帶領聽眾思考：在分析自動化與智慧代理快速普及的時代，統計與資料科學的專業訓練，該如何轉化為「能被採用的洞察」？ 我們將從學術研究所追求的顯著性與模型精準度，延伸至實務場域更關注的決策採用率、投資報酬率（ROI）與責任鏈（Accountability Chain），並探討資料分析師如何進化為「智能決策設計師（Intelligent Decision Designer）」——能定義問題、設計決策流程，並監督人機協作的可信任運作。透過真實案例與互動討論，參與者將理解如何在 LLM 與 Agentic Workflow 時代，打造兼具理論嚴謹與實務影響力的專業競爭力。

 

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國立臺北大學統計學系 敬邀
114.10.08

刊登時間：2025-10-14 13:49:15

附件：演講摘要

 

國立臺北大學統計學系

專題演講

講題：AI Agents as Data Partners: Elevating the Role of the Analyst

主講人：詹欣諭 Senior Technical Trainer (Microsoft)

時間：114年10月08日 (星期三，13：10~15：00)

地點：三峽校區商學院3F13教室

Abstract

As data complexity and real-time insight demands accelerate, traditional analytics workflows face mounting bottlenecks. This talk posits a new paradigm: making AI agents partners in data analysis. Rather than passive tools, these agents interpret intent, plan workflows, invoke auxiliary tools, detect errors, and self-adjust—supporting the full pipeline from exploration and modeling to insight generation. Through this lens, we ask: how might analysts evolve? What design challenges and trade-offs emerge in agentic systems? And how might future data analysis place greater emphasis on strategy, verification, and human–agent collaboration?

 

 

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國立臺北大學統計學系  敬邀

114.10.05

刊登時間：2025-10-08 11:54:54

附件：演講摘要

 

國立臺北大學統計學系

專題演講

講題： Nonparametric Control Charts with the Exceedance Probability Criterion: Advances from Univariate to Multivariate Methods

主講人：楊宏基 教授 (東華大學應數系)

時間：114年10月01日 (星期三，13：10~15：00)

地點：三峽校區商學院3F13教室

Abstract

Statistical process monitoring plays a crucial role in ensuring product quality and process stability. However, traditional control chart methods often face limitations when sample sizes are small or when distributional assumptions are difficult to justify. To address these challenges, this talk introduces a new class of nonparametric control charts built upon the Exceedance Probability Criterion (EPC). For univariate settings, fractional order statistics (FOS) are employed to construct control limits, offering both rigorous theoretical guarantees and practical applicability under small to moderate sample sizes. The framework is further extended to multivariate processes by integrating depth functions with the EPC, thereby enhancing sensitivity to multidimensional anomalies and subtle process shifts. Simulation studies and real data applications demonstrate that the proposed approaches outperform existing methods in terms of coverage probability, detection power, and robustness. This talk highlights the theoretical foundation, methodological innovations, and practical applications of EPC-based nonparametric control charts, presenting a unified and flexible framework for modern process monitoring.

 

 

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國立臺北大學統計學系  敬邀

114.09.18

刊登時間：2025-09-19 13:03:43

附件：演講摘要

 

國立臺北大學統計學系

專題演講

講題： AI 的思維與創新

主講人：張志勇 教授 (淡江大學資工系)

時間：114年9月17日 (星期三，13：10~15：00)

地點：三峽校區商學院3F13教室

Abstract

This lecture focuses on “AI Literacy and Thinking,” beginning with a review of the development of artificial intelligence—from electrification and data-driven technologies to the rise of generative AI—explaining how it imitates and extends human cognition, perception, and reasoning. It then highlights AI applications in fields such as image processing, natural language, healthcare, finance, and transportation, showcasing examples from smart factories, financial analysis, medical diagnosis, and social media to underscore both the value and challenges of AI in industry. The lecture further emphasizes the emergence and impact of generative AI. Generative AI can learn from vast amounts of text, audio, and visual data, demonstrating remarkable capabilities in writing, code generation, and image and video creation, greatly enhancing industrial efficiency. At the same time, it warns of challenges brought by generative AI, such as hallucinations, responsibility attribution, and over-reliance on expertise, urging individuals to cultivate proper literacy and critical thinking. Finally, the lecture stresses that AI cannot replace human emotion, trust, and creativity; only by leveraging new tools and integrating them with professional expertise can we create greater value in collaboration with AI.

 

 

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國立臺北大學統計學系  敬邀

114.09.12

刊登時間：2025-09-15 15:36:50

附件：演講摘要

國立臺北大學統計學系

專題演講

講題： 企業 AI 應用以及導入實作

主講人：葉盈伶 資深經理

時間：114年9月10日 (星期三，13：10~15：00)

地點：三峽校區商學院3F13教室

Abstract

本演講以企業 2023 年內部引入 AI 實例為出發點，說明企業導入 AI 的心法；說明 2025 年 LLM 的最新發展與應用場景，並探討 AI 對未來工作的影響與 AI 工程師必備技能。內容由實務經驗延伸至技術趨勢與未來挑戰，提供了企業端的觀察視角。 

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台北大學統計學系

114.09.10

刊登時間：2025-09-15 15:35:38

附件：演講摘要