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 largely focused in two different areas: healthcare and conflict. His work in healthcare is focused on innovation adoption and leveraging Electronic Health Records (EHR) data to improve targeting for change management approaches. His research on conflict is split between studying gun violence at schools in the United States and modelling the organizational dynamics of terrorist organizations and the impact of those dynamics on attack lethality and frequency.

Innovation adoption in professional networks; Predicting social dynamics with missing network information; Healthcare networks and decision support; Social policy; Conflict; Contagion in time series; Racial disparities in educational access and attainment. 

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

• 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

Impact of artificial intelligence on business; designing and leading data science teams, departments, and initiatives; leveraging crowds for innovation; networks of trust in decentralized contracting systems.

Human and Machine Intelligence (KACIX-950-0)

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)

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.