Adam Pah
Adam Robert Pah

MANAGEMENT & ORGANIZATIONS
Clinical Assistant Professor of Management & Organizations
Associate Director, Research Assistant Professor Northwestern Institute on Complex Systems (NICO)

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Adam Pah is a Clinical Assistant Professor at the Kellogg School of Management and Organizations at Northwestern University. He also serves as the Associate Director of and an Assistant Research Professor at NICO, the Northwestern Institute on Complex Systems. He received his BS from Arizona State University, PhD from Northwestern University, and has worked industrially as a Data Scientist.

His research is interdisciplinary and covers a range of topics; however, his work is mainly concerned with healthcare, networks, and social phenomena. Specific projects that he is currently involved with are innovation adoption in physician networks, improving healthcare quality and cost through data-driven analysis, assessing the effects of missing information on social dynamics, and social contagion.



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Education
Ph.D., 2013, Biological Sciences, Northwestern University
B.S., 2008, Biosciences & Biotechnology, Arizona State University, Tempe, Summa Sum Laude

Academic Positions
Postdoctoral Fellow, Chemical & Biological Engineering, Northwestern University, 2013-present
Postdoctoral Fellow, General Internal Medicine, Northwestern University, 2013-2014
Data Scientist, TTX Corporation, 2013-2013
Data Scientist, Datascope Analytics, 2012-2012
Visiting Scholar, The Donnelly Centre, University of Toronto, 2011-2011
Student Researcher, Arizona State University, The Biodesign Institute, 2004-2008

Honors and Awards
Biotechnology Training Program Trainee, Northwestern University, 2009-2011
CBC Scholar, Chicago Biomedical Consortium, 2011-2012
President's Scholarship, Arizona State University, 2004-2008

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Research Interests

Innovation adoption in professional networks; Predicting social dynamics with missing network information; Healthcare networks and decision support; Social policy



Articles
Pah, Adam Robert, Aditya Jain, Adam J Hockenberry and Luis AM Amaral. 2017. Economic insecurity and the rise in gun violence at US schools. Nature Human Behavior. 1(0040)
Burk, David R, Adam Robert Pah and J T Ruth. 2017. Analysis of Musculoskeletal Injuries Sustained in Falls From the United States-Mexico Border Fence. Orthopedics.(DOI: 10.3928/01477447-20170117-05)

 
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Full-Time / Evening & Weekend MBA
Human and Machine Intelligence (KACI-950-5)
An executive's most prized network contact for crafting new strategies and making evidence-based judgments may no longer be a person, but a machine. Machines help solve complex problems, lessen decision bias, scale human effort, and spot hidden patterns in big data. However, they lack the creativity and insight that top executives possess. Together, executives and machines have the potential to make powerful “thought partnerships.” Using hands-on cases and applications --- including IBM’s Deep Blue and Google’s AlphaGo that beat Chess and Go Grand Masters --- this course shows how to use a critical set of machine learning decision tools, such as natural language processing, sentiment analysis, and pattern recognition to discover new competitive strategies, turn raw numbers into convincing stories, and make less biased judgments. The overarching goal is to enable you to confidently lead data science and design teams, know the expansiveness and limits of machine-learning complex decision support tools, and be capable of applying human+machine thought partnerships to grow businesses or disrupt Grand Masters in any field.

Computational Social Science: Methods and Applications (KPHD-540-0)
This course is designed to prepare PhD students as research leaders in the new field of computational social science (CSS). The digital, connected, sensor rich world is generating extraordinary amounts and variety of data (“Big Data”). CSS is an exciting new scientific perspective that incorporates new methods and models for studying human behavior from the level of neurons to collective behavior. This change in approach has already made breakthroughs possible in understanding human creativity, scientific performance, the sharing economy, human conflict, and consumer behavior. This seminar will teach computational analysis skills. These skills include null model design and programming, and data mining for structured and unstructured data (topic models, bag of words, etc.). Students will leave the course with the technologies and intuitions needed for sophisticated independent research. Prerequisites: Students must choose and complete one of two options in order to prepare. The two options are roughly equivalent in terms of the number of hours of work, plan on spending 40-50 hours to complete one of the pre-requisite choices. 1. Register for and pass NICO-101 Introduction to programming for big data (P/NP or A/B/C are both allowed) in the pre-term (September 6-8, 12-15 2016). NICO-101 is a course offered by the Northwestern Institute for Complex Systems and there are no prerequisites or programming knowledge needed before attending. This option is for students that would like to have a guided experience to learn the basics. 2. Register for Datacamp (https://www.datacamp.com/) online and pass a set of courses at your own pace. This option is intended for those that learn best on their own or already know the basics of programming (in Python or another language). The following courses must be passed before the start of CSSMA: a. Intro to Python for Data Science b. Intermediate Python for Data Science c. Python Data Science Toolbox (Part 1) d. Python Data Science Toolbox (Part 2) e. pandas Foundations f. Manipulating DataFrames with pandas g. Importing Data in Python (Part 1) h. Importing Data in Python (Part 2)

Executive MBA
Human and Machine Intelligence (KACIX-950-0)