Subject to: Dimitris Bertsimas

Subject to: Dimitris Bertsimas

捐款給大學

美國大學 (和其研究、人才培育、衍生公司) 持續進步的重要原因

Michael T. Nietzel, University Of Wisconsin Receives $100 Million For Its New AI College, Forbes, Apr 28, 2026 (一億美金)

What It Takes

Stephen A. Schwarzman, What It Takes: Lessons in the Pursuit of Excellence, Simon & Schuster, 2019.

蘇世民、趙燦譯，蘇世民：我的經驗與教訓，香港中和出版，2020

我們平常人希望能從這些傳記中，學到思考、與做事情 (決策) 的方法 (151 頁)，以提升自己的能力。164 頁設定兩個組織的目標 (excellence and integrity)，使用專注 (focus) 和尋求幫忙 (ask for help when needed) 達成 100% 的卓越目標；考慮長期的名聲 （reputation)，例如誠實、努力工作、尊重別人、言出必行。張忠謀自傳中描述，德儀的基本價值是 integrity (誠信正直) ；張創辦人於 1994 年寫下10條的經營理念，最重要的第一條：台積電堅持高度職業道德，例如說真話、不誇張、不作秀、用人的首要條件是品格與才能等。

347 頁建立公司文化 (Lifetime learning, excellence, relentless innovation in action)。

Think Like an Amateur, Do As an Expert

作者：金出武雄，譯者：鄭舜瓏，像外行一樣思考，像專家一樣實踐：成功解決問題的高階技術，遠流

Jitendra S. Mudhol, Think Like an Amateur, Do As an Expert: Lessons from a Career in Computer Vision, CollaMeta, May 17, 2019. (video)

台積電建廠的選擇

 Yale Engineering Dean's Invited Speaker Series featuring C.C. Wei (魏哲家), CEO, TSMC, 2023/11/15

美國商學院強大的原因

Jacky Hsueh，美國商學院強大的原因：把商學當作科學，把數學當作工具，Inside，2019/10/07 

張忠謀在清華大學的演講提到了「台灣理工科與美國相差不大，但商學院卻比美國差很多」。就某方面來說，台灣的商學院其實不算太差，但是跟台灣的理工學院在國際間的等級相比，商學院真的很差。或許我們應該要先問的是，商學院是什麼？... 

李琳山教授 信號與人生 (2019)

李琳山教授 信號與人生 (2019) (數位典藏，歷年的信號與人生 (在信號與系統下))

密涅瓦的思考習慣訓練

李佳達、劉劭穎、黃禮宏，全球人才搶著學！密涅瓦的思考習慣訓練，究竟出版社，2022

密涅瓦大學決策思考力四大重點

Garrett van Ryzin talks about optimization

以前教營收管理的時候，讀了不少哥倫比亞商學院 van Ryzin 教授的文章，其中一篇說，他們的研究是在解決10年後的問題。各位注意喔，是商學院！

最近在讀一本書，某教授寫的序，開頭四個字，就是學用落差。

外送的經驗

之前看到一個新聞報導，因為薪水不錯，而且自由，台灣的年輕人喜歡從事外送工作。

二三年前，聽到一個國外新聞，實驗外送機器人，時速30公里的機車，可以在1公尺內完全煞車停止。今天上課的時候，跟同學分享這一個報告，忍不住地提醒同學，如果畢業後，都是從事這個行業，也少了人際間的互動；等到幾年後，公司大規模使用外送機器人而裁員，你大學學的東西全忘了，如何轉職？

To make money, study maths or economics at a top university

 Economist, To make money, study maths or economics at a top university, Nov 26th, 2019.

在台一度「學到怕」的程式語言，在史丹佛只求「會開機」的課堂上學會了！

 賴冠穎，在台一度「學到怕」的程式語言，在史丹佛只求「會開機」的課堂上學會了！現在他把方法帶回台灣，換日線，2021/11/12

在畢業門檻的 15 門課裡，扣除 10 門材料系上的課程之外，學生還必須要再跨系選修 5 門課。在學長的強烈建議下，他修了「CS106A 程式設計方法論」（Programming methodology），當時這門課的修課條件僅標註著「會開機就行」，讓原本對程式語言信心全失的 Jerry 決定再給自己一次機會。想不到這門課，也讓他對自己的職涯靈感「正式開機」！

書 Matching Supply with Demand

G. Cachon and C. Terwiesch, Matching Supply with Demand: An Introduction to Operations Management, 1st Edition, McGraw-Hill, 2006. (有第四版)

本書是作者針對 Wharton 企管碩士 (MBA) 核心課程 Operations Management (作業管理) 所發展出來的課本。

根據實際的公司和數據，透過清楚和基礎的數學分析，並融入最新的研究結果，呈現出一本讓人眼睛一亮的好書。

兩位作者的論文被高度引用，說明名校的教授能夠創造知識，也努力地將之轉成教材，教學與研究並進。

In Defense of a Liberal Education

Fareed Zakaria, In Defense of a Liberal Education, W. W. Norton & Company; 1 edition, March 28, 2016.

The liberal arts are under attack. The governors of Florida, Texas, and North Carolina have all pledged that they will not spend taxpayer money subsidizing the liberal arts, and they seem to have an unlikely ally in President Obama. While at a General Electric plant in early 2014, Obama remarked, "I promise you, folks can make a lot more, potentially, with skilled manufacturing or the trades than they might with an art history degree." These messages are hitting home: majors like English and history, once very popular and highly respected, are in steep decline. 

慈善捐款

 Nicholas Kulish, Giving Billions Fast, MacKenzie Scott Upends Philanthropy, NYT, Dec. 20, 2020.

By disbursing her money quickly and without much hoopla, Ms. Scott has pushed the focus away from the giver and onto the nonprofits she is trying to help. They are the types of organizations — historically Black colleges and universities, community colleges and groups that hand out food and pay off medical debts — that often fly beneath the radar of major foundations.

“If you look at the motivations for the way women engage in philanthropy versus the ways that men engage in philanthropy, there’s much more ego involved in the man, it’s much more transactional, it’s much more status driven,” said Debra Mesch, a professor at the Women’s Philanthropy Institute at Indiana University. “Women don’t like to splash their names on buildings, in general.”

The BAIR Blog

The Berkeley Artificial Intelligence Research (BAIR) blog

The BAIR Blog provides an accessible, general-audience medium for BAIR researchers to communicate research findings, perspectives on the field, and various updates. Posts are written by students, post-docs, and faculty in BAIR, and are intended to provide relevant and timely discussion of research findings and results, both to experts and the general audience. Posts on a variety of topics studied at BAIR will appear approximately once every two weeks.

They could explain the technical details in a nice and amazingly clear way, so I really enjoy their writing. Taking this blog as an example, they use two-way consistency and its corresponding picture to explain sparse graphical memory for robust planning. 

A Nonparametric Approach to Modeling Choice with Limited Data

Vivek F. Farias, Srikanth Jagabathula, and Devavrat Shah, A Nonparametric Approach to Modeling Choice with Limited Data, Management Science, February 2013, Vol. 59, No. 2, pp. 305-322. 

Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the “right” model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. Thus motivated, we visit the following problem: For a “generic” model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward “automating” the crucial task of choice model selection.

The authors formulated the minimum revenue problem under consumer choices as a linear programming with exponential growing of decision variables in terms of product number. Based on duality, they developed polynomial-time algorithms by using constraint sampling and efficient representation of purchase permutations. Profs. Farias and Shah then founded the company Celect and was later acquired by Nike. Once again, it demonstrates the positive cycle of advanced research and academic-industrial collaboration.

創業之國以色列

徐立妍譯，創業之國以色列：教育思維X兵役制度X移民政策X創投計畫，打造建國七十年成長50倍的經濟奇蹟，木馬文化，2017

Dan Senor and Saul Singer, Start-up Nation: the story of Israel’s economic miracle, Twelve, 2011.

以色列是全世界創投最興盛的國家之一，即使鄰國猛烈轟炸、網路經濟泡沫破滅等危機，以色列的創投資金依然不受影響，且持續成長。新加坡和杜拜亦試圖複製以色列模式，為什麼不成功？到底，以色列有什麼祕訣，在這麼多不利的條件下，仍然表現亮眼？

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

Gilbert Strang. 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning. Spring 2018. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA. (book)

Strang 教授教這門課的時候 83 歲，真的是終身學習的好典範。在美國，這種對專業的執著 (Tapley 教授) 令人欽佩。另外一個例子是 Breiman 教授，71 歲投稿隨機森林 (Random forests)，成為經典論文；其他幾篇重要論文，大都在 65 歲以後以單一作者發表！ 

COS116: The Computational Universe

Sanjeev Arora, Computer Science 116 The Computational Universe, Princeton University, 2006.

幾年前，我教資管系一下的網際網路應用。心想計算機概論已經教通訊軟體的操作，所以網際網路應用改教重要的概念，取材來自幾本書，並簡化內容，例如資訊經營法則、Networked Life、Mining of Massive Datasets。當時有許多學生棄選，不知道是不是英文字 (部分有中譯) 太多？還是概念比較抽象？方程式太多？ 

Prof. Arora 這門課的授課對象是任何學系的學生，可以視為台灣的通識教育。最近使用 Amazon Kindle (電子書閱讀器) 閱讀其講義，發現講得更生活化，例如以圖書館員取書的例子，說明計算機結構中快取 (cache) 的概念。再一次呼應了我之前的說法，世界級的研究型大學，教學也是令人驚艷。

另外一點，美國的學費驚人，而且有廣大的社會捐獻和好的投資績效，所以經費充足、課程的支援較好。這門課有實驗，大都是助教負責寫講義和上課。但是，台灣的大學學費凍漲，自然無法和其相比。