The coming wave

Mustafa Suleyman and Michael Bhaskar, The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma, Crown, September 5, 2023

洪慧芳譯，控制邊緣：未來科技與全球秩序的抉擇，感電出版，2024

Some information and papers related to the seminar

(4/9/2025) 許志華 (The original speaker needs to take care of emergency affairs.)

• Dimitris Bertsimas and Michael Lingzhi Li, Scalable holistic linear regression, Operations Research Letters, Volume 48, Issue 3, May 2020, Pages 203-208.
• We could not reproduce their results in the paper, so I asked Prof. Li for his code. He told me he finished the paper as a course project (MIT style?!😂), and the codes were stored on another obsolete hard drive. 
  

2024 Franz Edelman Award

2024 Edelman Competition (video, special issue, INFORMS Journal on Applied Analytics)

Pierre Pinson, Mikkel Bjørn, Simon Kristiansen, Claus B. Nielsen, Lasse Janerka, Jesper Skovgaard, Kristian Durhuus (2025) Data-Driven at Sea: Forecasting and Revenue Management at Molslinjen. INFORMS Journal on Applied Analytics 55(1):5-21. https://doi.org/10.1287/inte.2024.0177 (2024 Franz Edelman Award Winner) (Keywords: ferry operations, demand forecasting, revenue management, machine learning  (by XGBoost))

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.

10 個改變世界的統計觀念

Leda Liang, Ten Statistical Ideas that Changed the World, (YouTube, Stanford course, Tibshirani's GitHub)

Catastrophe Insurance Pricing

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

Machine-Guided Discovery of a Real-World Rogue Wave (瘋狗浪) Model

台北天文館，AI 找到如何預測巨浪的公式，2023 年 12 月 02 日 

Dion Häfner, Johannes Gemmrich, Markus Jochum, Machine-Guided Discovery of a Real-World Rogue Wave Model,  Proceedings of the National Academy of Sciences (2023), 120(48). (arXiv, data, code)

Artificial Intelligence and the Future of Universities

Dimitris Bertsimas, Artificial Intelligence and the Future of Universities, Texas A&M Institute of Data Science, 2023.

Champion-level drone racing using deep reinforcement learning

Kaufmann, E., Bauersfeld, L., Loquercio, A. et al. Champion-level drone racing using deep reinforcement learning. Nature 620, 982–987 (2023). https://doi.org/10.1038/s41586-023-06419-4

First-person view (FPV) drone racing is a televised sport in which professional competitors pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the perspective of their drone by means of video streamed from an onboard camera. Reaching the level of professional pilots with an autonomous drone is challenging because the robot needs to fly at its physical limits while estimating its speed and location in the circuit exclusively from onboard sensors. Here we introduce Swift, an autonomous system that can race physical vehicles at the level of the human world champions. The system combines deep reinforcement learning (RL) in simulation with data collected in the physical world. Swift competed against three human champions, including the world champions of two international leagues, in real-world head-to-head races. Swift won several races against each of the human champions and demonstrated the fastest recorded race time. This work represents a milestone for mobile robotics and machine intelligence, which may inspire the deployment of hybrid learning-based solutions in other physical systems.

National energy system optimization modelling for decarbonization pathways analysis

F.A. Plazas-Niño, N.R. Ortiz-Pimiento, E.G. Montes-Páez, National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review, Renewable and Sustainable Energy Reviews, Volume 162, July 2022, 112406.

Energy planning is fundamental to ensure a sustainable, affordable, and reliable energy mix for the future. Energy system optimization models (ESOMs) are the accurate tools to guide decision-making in national energy planning. This article presents a systematic literature review covering the main ESOMs, the input and output data involved, the trends in scenario analysis for decarbonization pathways in national economies, and the challenges associated with energy system optimization modelling. The first part introduces the characterization of ESOMs, showing a trend in modelling focused on long-term, multisector, multiperiod, bottom-up, linear programming, and perfect foresight. Secondly, the analysis shows the intensive data requirements, including future demand profiles, fuel price projections, energy potentials, and techno-economic characteristics of technologies. This review also reveals that decarbonization pathways are the principal objective in energy system optimization modelling, including key drivers such as high-share renewable energy integration, energy efficiency increase, sector coupling, and sustainable transport. The last section presents ten challenges and their corresponding opportunities in research, highlighting the improvement of spatiotemporal resolution, transparency, the inclusion of social aspects, the representation of developing country features, and quality and availability data.

A Practical End-to-End Inventory Management Model with Deep Learning

Meng Qi, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun (Max) Shen (2023) A Practical End-to-End Inventory Management Model with Deep Learning. Management Science 69(2):759-773. (Data and Python codes) 

We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD’s current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances.

2022 Franz Edelman Award

 2022 Edelman Competition (videos)

Leonardo J. Basso et al., Analytics Saves Lives During the COVID-19 Crisis in Chile, INFORMS Journal on Applied Analytics, 2023, 53(1):9-31. (2022 Franz Edelman Award) (statistical analysis, integer programming, regression)

During the COVID-19 crisis, the Chilean Ministry of Health and the Ministry of Sciences, Technology, Knowledge and Innovation partnered with the Instituto Sistemas Complejos de Ingeniería (ISCI) and the telecommunications company ENTEL, to develop innovative methodologies and tools that placed operations research (OR) and analytics at the forefront of the battle against the pandemic. These innovations have been used in key decision aspects that helped shape a comprehensive strategy against the virus, including tools that (1) provided data on the actual effects of lockdowns in different municipalities and over time; (2) helped allocate limited intensive care unit (ICU) capacity; (3) significantly increased the testing capacity and provided on-the-ground strategies for active screening of asymptomatic cases; and (4) implemented a nationwide serology surveillance program that significantly influenced Chile’s decisions regarding vaccine booster doses and that also provided information of global relevance. Significant challenges during the execution of the project included the coordination of large teams of engineers, data scientists, and healthcare professionals in the field; the effective communication of information to the population; and the handling and use of sensitive data. The initiatives generated significant press coverage and, by providing scientific evidence supporting the decision making behind the Chilean strategy to address the pandemic, they helped provide transparency and objectivity to decision makers and the general population. According to highly conservative estimates, the number of lives saved by all the initiatives combined is close to 3,000, equivalent to more than 5% of the total death toll in Chile associated with the pandemic until January 2022. The saved resources associated with testing, ICU beds, and working days amount to more than 300 million USD.

Generalized Synthetic Control for TestOps at ABI

Luis Costa, Vivek F. Farias, Patricio Foncea, Jingyuan (Donna) Gan, Ayush Garg, Ivo Rosa Montenegro, Kumarjit Pathak, Tianyi Peng, and Dusan Popovic, Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure, To appear in INFORMS Journal on Applied Analytics (Winner, Daniel H. Wagner Prize 2022)

We describe a novel optimization-based approach– Generalized Synthetic Control (GSC)– to learning from experiments conducted in the world of physical retail. GSC solves a long-standing problem of learning from physical retail experiments when treatment effects are small, the environment is highly noisy and non-stationary, and interference and adherence problems are commonplace. The use of GSC has been shown to yield an approximately 100x increase in power relative to typical inferential methods and forms the basis of a new large-scale testing platform: ‘TestOps’. TestOps was developed and has been broadly implemented as part of a collaboration between Anheuser Busch Inbev (ABI) and an MIT team of operations researchers and data engineers. TestOps currently runs physical experiments impacting approximately 135M USD in revenue every month and routinely identifies innovations that result in a 1-2% increase in sales volume. The vast majority of these innovations would have remained unidentified absent our novel approach to inference: prior to our implementation, statistically significant conclusions could be drawn on only ∼ 6% of all experiments; a fraction that has now increased by over an order of magnitude.

Bridging physics-based and data-driven modeling for COVID-19 forecasting

Rui Wang, Danielle Robinson, Christos Faloutsos, Yuyang Wang, and Rose Yu, AutoODE: Bridging physics-based and data-driven modeling for COVID-19 forecasting, NeurIPS 2020 Workshop on Machine Learning in Public Health. (best paper award at the NeurIPS Machine Learning in Public Health Workshop)

As COVID-19 continues to spread, accurately forecasting the number of newly infected, removed and death cases has become a crucial task in public health. While mechanics compartment models are widely-used in epidemic modeling, data-driven models are emerging for disease forecasting. In this work, we investigate these two types of methods for COVID-19 forecasting. Through a comprehensive study, we find that data-driven models outperform physics-based models on the number of death cases prediction. Meanwhile, physics-based models have superior performances in predicting the number of infected and removed cases. In addition, we present an hybrid approach, AutoODE, that obtains a 57.4% reduction in mean absolute errors of the 7-day ahead COVID-19 trajectories prediction compared with the best deep learning competitor.

The Impacts of Technological Invention on Economic Growth

A Reamer, The impacts of technological invention on economic growth–a review of the literature, The George Washington Institute of Public Policy, 2014. 

靠數據賣雞蛋

作者／張紹敏 圖片來源：卓杜信，麥當勞、美芝城都是他客戶！七年級面板主管回大武山「賣雞蛋」，靠數據幫爸爸重振家業，Cheers：快樂工作人，2022/08/13

通路

為什麼要自己賣蛋？魏毓恆解釋：「傳統蛋商只要缺蛋，每天都會開車在出貨碼頭等你，不讓你把蛋給別人；可是蛋很多的時候，他們的貨車就會『連續壞一個禮拜』，遲遲不來。」產銷的不平衡，成為大武山陷入困境一大原因，而魏毓恆不打算視而不見。

Analysis and prediction of future stock price

Tasha Keeney, ARK’s Expected Value For Tesla In 2026: $4,600 Per Share, ARK, April 14, 2022. (GitHub)

Nicholas Grous and Andrew Kim, ARK’s Expected Value For Roku in 2026: $605 Per Share, ARK, July 07, 2022. (GitHub)

This is one way to predict the stock price, not for purchase recommendation. 

Machine learning and statistical testing at vantagescore

Kimberly Adams, How one credit scoring company is thinking about financial inclusion, Marketplace Tech,  JULY 8, 2022. (more around July 2022)

Bloated inventories hit Walmart, Target and other retailers’ profits, trucking demand

Mark Solomon, Bloated inventories hit Walmart, Target, and other retailers’ profits, trucking demand, FreightWaves, May 20, 2022. 

However, furniture, home furnishings and appliances, building materials and garden equipment, and a category known as “other general merchandise,” which includes Walmart and Target, among others, reported higher inventory-to-sales ratios, according to government data analyzed by Michigan State.