Introducing and Integrating Machine Learning in an Operations Research Curriculum

Justin J. Boutilier and Timothy C. Y. Chan, Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course, INFORMS Transactions on Education, 22 Sep 2021.

Artificial intelligence (AI) and operations research (OR) have long been intertwined because of their synergistic relationship. Given the increasing popularity of AI and machine learning in particular, we face growing demand for educational offerings in this area from our students. This paper describes two courses that introduce machine learning concepts to undergraduate, predominantly industrial engineering and operations research students. Instead of taking a methods-first approach, these courses use real-world applications to motivate, introduce, and explore these machine learning techniques and highlight meaningful overlap with operations research. Significant hands-on coding experience is used to build student proficiency with the techniques. Student feedback indicates that these courses have greatly increased student interest in machine learning and appreciation of the real-world impact that analytics can have and helped students develop practical skills that they can apply. We believe that similar application-driven courses that connect machine learning and operations research would be valuable additions to undergraduate OR curricula broadly.

Efficient and targeted COVID-19 border testing via reinforcement learning

Bastani, H., Drakopoulos, K., Gupta, V. et al. Efficient and targeted COVID-19 border testing via reinforcement learning. Nature 599, 108–113 (2021). https://doi.org/10.1038/s41586-021-04014-z (EVA Public Dataset, Off-Policy and Counterfactual Analysis, Open-Source code for Project Eva)

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-essential travel while safeguarding public health, from quarantining all travellers to restricting entry from select nations on the basis of population-level epidemiological metrics such as cases, deaths or testing positivity rates. Here we report the design and performance of a reinforcement learning system, nicknamed Eva. In the summer of 2020, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols, Eva allocated Greece’s limited testing resources on the basis of incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios, we show that Eva identified 1.85 times as many asymptomatic, infected travellers as random surveillance testing, with up to 2–4 times as many during peak travel, and 1.25–1.45 times as many asymptomatic, infected travellers as testing policies that utilize only epidemiological metrics. We demonstrate that this latter benefit arises, at least partially, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies3 that are based on population-level epidemiological metrics. Instead, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.

我永遠站在「雞蛋」的那方

村上春樹主講，張翔一整理，我永遠站在「雞蛋」的那方，天下雜誌 418期 ，2009/03

今天我以一名小說家的身分來到耶路撒冷。而小說家，正是所謂的職業謊言製造者。

當然，不只小說家會說謊。眾所周知，政治人物也會說謊。外交官、將軍、二手車業務員、屠夫和建築師亦不例外。但是小說家的謊言和其他人不同。沒有人會責怪小說家說謊不道德。相反地，小說家愈努力說謊，把謊言說得愈大愈好，大眾和評論家反而愈讚賞他。為什麼？

今天，我不打算說謊

我的答案是：藉由高超的謊言，也就是創作出幾可亂真的小說情節，小說家才能將真相帶到新的地方，也才能賦予它新的光輝。

紀念鄭南榕

南榕人生

鄭南榕出生於一九四七年，也就是發生二二八事件的那一年，那個驚悚的年代深深影響鄭南榕的一生；在其第一次求職的履歷表上，他這麼寫著：「我出生在二二八事件那一年，那事件帶給我終生的困擾。因為我是個混血兒，父親是在日本時代來台的福州人，母親是基隆人，二二八事件後，我們是在鄰居的保護下，才在台灣人對外省人的報復浪潮裡，免於受害。」後來他之所以強烈主張台灣獨立，並且不惜以身殉道，都和二二八事件有關。他認為：「第一、台灣要走上民主政治的話，一定要先破除國民黨的統治神話；台灣只有獨立，才可能真正民主化，才可能真正回歸人民主權。第二、二二八事件之所以發生，是因為中國與台灣兩地經濟、文化、法治、生活水平相差太遠，強行合併，悲劇自然發生。現在，這種情況再度發生於海峽兩岸，只有台灣獨立，才可以避免另一次二二八事件。」

ACM Turing Award Honors Jack J. Dongarra

ACM, ACM Turing Award Honors Jack J. Dongarra for Pioneering Concepts and Methods Which Have Resulted in World-Changing Computations, March 30, 2022.

ACM, the Association for Computing Machinery, today named Jack J. Dongarra recipient of the 2021 ACM A.M. Turing Award for pioneering contributions to numerical algorithms and libraries that enabled high performance computational software to keep pace with exponential hardware improvements for over four decades. Dongarra is a University Distinguished Professor of Computer Science in the Electrical Engineering and Computer Science Department at the University of Tennessee. He also holds appointments with Oak Ridge National Laboratory and the University of Manchester.