How Could Machines Reach Human-Level Intelligence?

Yann LeCun, How Could Machines Reach Human-Level Intelligence?, Columbia Engineering, 2024/10/24 (slide)

Neural Coordination and Capacity Control for Inventory Management

C Eisenach, U Ghai, D Madeka, K Torkkola, D Foster, S Kakade, Neural Coordination and Capacity Control for Inventory Management, 2024, arXiv preprint arXiv:2410.02817. (SCOT, Amazon)

Some information and papers related to the seminar

(10/23/2024) 洪一薰教授 (Prof. I-Hsuan Hong) 

• Avinash K. Dixit and Barry J. J. Nalebuff,  The Art of Strategy: A Game Theorist's Guide to Success in Business and Life, W. W. Norton & Company, 2010. (思辨賽局)

Some books and information on machine learning and AI

General introduction (without math)

• 簡禎富，工業3.5：台灣企業邁向智慧製造與數位決策的戰略，天下雜誌，2019
• Alex J. Gutman and Jordan Goldmeier, Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, Wiley, 2021.
• John Maccormick, Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today’s Computers, Princeton Univ Press, 2013. (陳正芬譯，改變世界的九大演算法：讓今日電腦無所不能的最強概念，經濟新潮社，2014)
• Cade Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, Dutton, 2021. (王曉伯譯，AI製造商沒說的祕密: 企業巨頭的搶才大戰如何改寫我們的世界?，時報文化，2022)
• Andrew Ng, How to Build Your Career in AI, 2023. (Free ebook, new)
• Annalyn Ng and Kenneth Soo  , Numsense! Data Science for the Layman: No Math Added, 2017. (沈佩誼譯，文科生也看得懂的資料科學，碁峰，2018)
• Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, 2016 (許瑞宋譯，大數據的傲慢與偏見：一個「圈內數學家」對演算法霸權的警告與揭發，大寫出版，2017)

Optimization Automates Emergency Department Nurse Scheduling at Hartford Hospital

Liangyuan Na, Jean Pauphilet, Ali Haddad-Sisakht, Louis Raison, Audrey Silver, Patricia Veronneau, Nicole Vogt, Dimitris Bertsimas (2024) Optimization Automates Emergency Department Nurse Scheduling at Hartford Hospital. INFORMS Journal on Applied Analytics.

Business Analytics Laboratory (商業分析實驗室)

If you are interested in operations research, machine learning, data science, and its real-world applications (in supply chain, business, smart manufacturing, etc.), please email me your resume and transcript to schedule a meeting or online chat. Please refer to this blog for more details about your resume. (Guides for students,  paper presentation in the group meeting)

Guides for students in Business Analytics Laboratory (商業分析實驗室學生指引)

Knowledge to master for a better foundation (and future)

Tools and general: 

• 李琳山老師，碩博士生應該要知道的事情  (YouTube)

服務和計畫 (Service and projects)

產學合作 (Industry-Academia Cooperation)

• 金屬工業研究發展中心 (Metal Industries Research & Development Centre)，最佳化決策於工站排程應用之研究 (Optimal decision-making in work station scheduling) (3)，主持人 (PI)，2024

Data-Driven Performance Guarantees for Classical and Learned Optimizers

R. Sambharya and B. Stellato, Data-Driven Performance Guarantees for Classical and Learned Optimizers, arXiv e-prints:2404.13831,2024. (Python code)

Learning production functions for supply chains with graph neural networks

Serina Chang, Zhiyin Lin, Benjamin Yan, Swapnil Bembde, Qi Xiu, Chi Heem Wong, Yu Qin, Frank Kloster, Alex Luo, Raj Palleti, Jure Leskovec, Learning production functions for supply chains with graph neural networks, arXiv:2407.18772. (Python code)

Amazon Locker Capacity Management

Samyukta Sethuraman, Ankur Bansal, Setareh Mardan, Mauricio G. C. Resende, Timothy L. Jacobs (2024) Amazon Locker Capacity Management. INFORMS Journal on Applied Analytics.

Optimization of Optimal Sparse Decision Trees

Cynthia Rudin, Optimization of Optimal Sparse Decision Trees

專題和論文的製作與報告 (tips for the final project and your thesis)

• (at the bottom) Avoid common phenomena, final written report (for your ppt content)
• (大學生) 重要任務

• 人生困境：

• 計算機程式補考後的人生 (1/2) 

• 我的方法：運動，音樂 (馬友友)，閱讀 (哲學，堅忍的挑戰，老莊)

• Tal Ben-Shahar, Happier: Learn the Secrets to Daily Joy and Lasting Fulfillment, McGraw Hill, 2007. (譚家瑜譯，更快樂：哈佛最受歡迎的一堂課，天下雜誌，2012)

Fluid Approximations for Revenue Management

When one encounters a stochastic optimization/control problem, one popular approach is to transform it into a deterministic problem by fluid approximation. The following highly-cited classic papers illustrate the applications of this approach:   

預備研究生 (4 + 1) (包含資料科學相關研究所和職涯準備)

預備研究生規定 (資訊處，新增加同學的問題)

• 培養
• 商業分析實驗室 (Business Analytics Laboratory)：論文。
• 人少，老師 (每週) 單獨討論。可於辦公室時間詢問庭煜。
• 學生的光，老師看得見。

Unforeseen Consequences of “Tabu” Choices

Fred W. Glover, Unforeseen Consequences of “Tabu” Choices—A Retrospective, INFORMS Journal on Computing, 2022, Volume 34, Issue 3, Pages 1306-1308.

This retrospective is written upon receiving The IJOC Test of Time Award for the papers “Tabu Search—Part 1 and —Part 2,” a delightful capstone to an unanticipated (and exceptionally fun) adventure.

Decarbonizing OCP

Dimitris Bertsimas, Ryan Cory-Wright, Vassilis Digalakis, Jr. (2023) Decarbonizing OCP. Manufacturing & Service Operations Management 0(0). (arXiv, talk) (Finalist, 2023 Manufacturing & Service Operations Management Practice-Based Research Competition)

End-to-End Predict-then-Optimize Library for Linear and Integer Programming

Bo Tang and Elias B. Khalil, PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming, arXiv:2206.14234v2.

Catastrophe Insurance Pricing

C. Zeng and D. Bertsimas, Catastrophe Insurance Pricing: A Robust Optimization Approach, In preparation for Management Science, 2023. 

Customer Choice Models vs. Machine Learning

Jacob Feldman, Dennis J. Zhang, Xiaofei Liu, and Nannan Zhang (2021) Customer Choice Models vs. Machine Learning: Finding Optimal Product Displays on Alibaba. Operations Research 70(1):309-328. (Best OM Paper in Operations Research Award: Finalist, pdf, implementation details)