OR/MS Tomorrow
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Spring/Summer 2018 Issue: Machine Learning (ML) and Operations Research (OR)
The online student membership magazine for INFORMS. The bi-annual publication provides a look at operations research and management science from the perspective of young people in those fields. Edited by a team of students and junior faculty, the magazine is written for students and aims to introduce topics relevant to them, highlight their accomplishments, and promote awareness of current events and issues in OR and MS.
Mixed Integer Programming and Machine Learning
In today’s world, advancements in computing power augmented by the availability of data has ushered in an unprecedented ability to transform data into useful insights. Machine learning (ML) has earned its place as a quintessential tool in any data scientist’s toolbox. Mixed Integer Programming (MIP) has provided a long-standing framework for solving large NP-Hard problems to theoretically-proven optimality and has revolutionized many industries such as logistics and transportation. Even though both ML and MIP share a common trait - using data to influence decision making - they have [for the large part] been studied by different research communities. Squarely in the interface between computer science and operations research, interesting problems have emerged when studying ML and MIP together.
Machine Learning: Is it really the hero that the Operations Research community needs?
Machine Learning is more popular than Operations Research because of two reasons. The first is that the biggest part of the analytic decision-making process has now been partially shifted to the machine. The investment in machine learning is a natural evolution in technology and humanity’s demand to create technologies that extend our own capabilities. And while the same can be said of advancements in Operations Research, the second reason for ML’s popularity is what really seals the deal – the fact that it works!
The Use of Quantitative Methods with Two Different Perspectives: Data-Centric versus Problem-Centric
Machine Learning and Ethnography: A Marriage Made in Heaven
Simply stated, the essence of operations research is the creation of models to support better decision-making. Although ‘modelling’ is regarded as being the key term here, it is essential that we do not prioritize modelling at the expense of the ‘better decision-making’ element. At the end of the day, modelling that does not unlock value to improve decision-making is, in practical terms, useless.
Machine Learning in Material Science
Dr. Yuanyue Liu who is an Assistant Professor in the Department of Mechanical Engineering at The University of Texas at Austin. His research focuses on fundamental and technological problems in material science related to electronics, optoelectronics, energy conversion and energy storage as well as emerging materials like 2D materials and topological materials.
Machine Learning – An Opportunity for New Directions and to Engage with New Areas
Machine learning (ML) is part-buzzword, part-powerful algorithmic toolbox. It’s hyped to change the world, and people in many fields are beginning to use its techniques to understand their systems and make better decisions. As operations researchers, we spend our careers developing models to provide insights. There is a range of overlap between ML and operations research (OR), and I think the popularity of machine learning gives us the opportunity to work in new areas and solve broader, more complex problems.
Machine Learning Applications in the Energy Sector
The energy industry, and particularly the power sector, is undeniably an essential component of any modern society. In fact, electrification has been determined as the “greatest engineering achievement of the 20th century” by the National Academy of Engineering (NAE, 2018). However, this mighty engineering achievement of the previous century faces just as great of challenges in the 21st century that can no longer be addressed using analytical tools of the past.
OR/MS Tomorrow Spotlights
The Minority Issues Forum (MIF) has been an active and vibrant forum for INFORMS members interested in minority issues in OR/MS since 2004. The objectives of MIF are to 1) foster minority representation in OR/MS; 2) develop ties between those interested in increasing the number of minority participants in OR/MS; and 3) disseminate information about the issues that minority researchers and practitioners face.
The chapter holds informative academic events, workshops, and social activities, which create opportunities for professional and personal development.
INFORMS Student Chapters: University at Buffalo, Koç University Industrial Experience Society, University of Michigan, Mississippi State University, and Texas A&M University
Undergraduate Research defined - "An inquiry or investigation conducted by an undergraduate student that makes an original, intellectual, or creative contribution to the discipline...." -The Council on Undergraduate Research