Winter Session 2020

Gain new insights that reframe your thinking, specific tools to advance current projects, and perspectives to set new directions.

Dates: January 6 - 17

Location: MIT, Cambridge, MA

The NECSI Winter School offers two intensive week-long courses on complexity science: modeling and networks, and data analytics. You may register for any of the weeks. If desired, arrangements for credit at a home institution may be made in advance.

Week 1: January 6-10 CX201: Complex Physical, Biological and Social Systems

Week 2: January 12-17 CX202: Complex Systems Modeling and Networks

Group Projects

Group projects are one of the most rewarding parts of the winter and summer courses. Participants split into project teams and put together a publication quality research project using complex systems tools learned during the week. On the final day of each week, groups present their projects. We consistently receive positive feedback about the projects.

Credit

Arrangements to receive credit for NECSI courses at a home institution should be made in advance. To do so, contact us at programs@necsi.edu.

Monday – Thursday, Jan. 6-9: 
Lecture 9 AM – 5 PM 
Group Projects 6 – 8 PM

Friday, Jan. 10: 
Group Presentation 9 AM – 12 PM 
Evaluations and Class Photo 12 – 12:15 PM 
Exam (required if taking course for credit, optional otherwise) 12:30 – 1:30 PM

Sunday, Jan. 12: 
Lab 9 AM – 5 PM

Monday – Thursday, Jan. 13-16: 
Lecture 9 AM – 5 PM 
Group Projects 6 – 8 PM

Friday, Jan. 17: 
Group Presentation 9 AM – 12 PM 
Evaluations and Class Photo 12 – 12:15 PM 
Exam (required if taking course for credit, optional otherwise) 12:30 – 1:30 PM

January 6-10

This course provides an introduction to (a) essential concepts of complex systems and related mathematical methods and simulation strategies with application to physical, biological and social systems, and (b) the fundamentals of data analytics, machine learning, and artificial intelligence.

Concepts to be discussed include: emergence, complexity, networks, self-organization, pattern formation, evolution, adaptation, fractals, chaos, cooperation, competition, attractors, interdependence, scaling, dynamic response, information and function.

Methods to be discussed include: statistical methods, cellular automata, agent-based modeling, pattern recognition, system representation and informatics.

Demonstration of the application of complex systems methods will be made through studies of:

• Social systems: education system, health care system, military system

• Psychosocial systems: patterns of social behavior, mind, creativity

• Biological systems: physiology, brain, cellular systems, genetic networks

• Physical systems: meteorology

The data analytics lessons will cover skills needed to transform raw data into visualizations and insight. The course will cover fundamental construction and analysis of models including identifying what is to be modeled, constructing a mathematical representation, analysis tools and implementing and simulating the model in a computer program.

Lab January 12, Course June 13-17

CX102 (Lab): Computer Programming for Complex Systems

This one day lab introduces computer programming in the Python language for those with little or no computer programming experience. It is designed as a precursor to CX202.

The lab will present programming concepts and hands-on exercises. Topics to be covered include: data structures, algorithms, variables and assignments, numerical and logical operations, lists and dictionaries, user-defined functions, flow control, loops, and visualization.

CX202: Complex Systems Modeling and Networks

This course provides an introduction to building models of complex systems (physical, biological, social and engineering), and network architectures dynamic processes.

It will cover the basic construction and analysis of models including identifying what is to be modeled, constructing a mathematical representation, analysis tools and implementing and simulating the model in a computer simulation. Particular attention will be paid to choosing the right level of detail for the model, testing its robustness, and discussing which questions a given model can or cannot answer. Python will be used as a primary computer programming language for modeling and simulation. Prior computer programming experience is helpful, but not required. Students are encouraged to bring to class their own laptop computers.

The study of networks will introduce the use of network topologies and the characterization of networks describing complex systems, including such concepts as small worlds, degree distribution, diameter, clustering coefficient, modules, motifs. Different types of network topologies and network behaviors that model aspects of real complex systems will be described including: modular, sparse, random, scale-free, influence, transport, transformation, and structure.

Alex 'Sandy' Pentland has helped create and direct MIT’s Media Lab, the Media Lab Asia, and the Center for Future Health. He chairs the World Economic Forum’s Data Driven Development council, is Academic Director of the Data-Pop Alliance, and is a member of the Advisory Boards for Google, Nissan, Telefonica, the United Nations Secretary General, Monument Capital, and the Minerva Schools. In 2012 Forbes named Sandy one of the “seven most powerful data scientists in the world,” and in 2013 he won the McKinsey Award from Harvard Business Review.

Hyejin Youn is an assistant professor at Kellogg School of Management at Northwestern University, and Northwestern Institute on Complex Systems (NICO). She was a research fellow at Santa Fe Institute and Harvard Kennedy School, and visiting scientist at MIT Media Lab. Her research aims to develop a mathematical and computational framework to understand complex systems including Science of Cities, Pathway of Innovation, and Linguistics (Semantic shift).

Josh Bongard is Professor at the University of Vermont and head of the Morphology, Evolution & Cognition Laboratory. His research centers on how cognition is incorporated in evolutionary robotics, evolutionary computation and physical simulation. He is the author of How the Body Shapes the Way We Think. He was named one of MIT Technology Review's top 35 young innovators under 35 and awarded a prestigious Microsoft Research New Faculty Fellowship and a Presidential Early Career Award for Scientists and Engineers (PECASE) by Barack Obama at the White House.

Elena N. Naumova is Professor and Chair of the Division of Nutrition Data Science at the Friedman School of Tufts University and NECSI co-faculty. Her research includes development and applications of a broad range of analytic tools for spatio-temporal data analysis applied to emergent disease surveillance, exposure assessment, environmental epidemiology, molecular biology, nutrition, and growth.

Blake LeBaron is the Abram L. and Thelma Sachar Chair of International Economics at the International Business School, Brandeis University. He was a Sloan Fellow, and is a recent recipient of the Market Technician’s Association Mike Epstein award. He recently spent two years as a visiting researcher with the Office of Financial Research in the U.S. Treasury Department. He currently directs the Masters of Science in Business Analytics program at Brandeis, and is part of a Brandeis interdisciplinary research and teaching group interested in modeling dynamics in a wide range of fields.

"Excellent course...useful thematic overview... applications in diverse contexts were exciting. Particularly appreciated the group project - excellent experiential pedagogy."

"The course was an eye-opening framework to analyze my work through a different lens."

"Presentations were extremely useful for me in understanding how to begin modeling complex systems and assessing them. Helped me understand a lot of things I have been doing so far without clearly understanding the principles."

"This class very much stretched my mind to apply the ideas of complexity to the world... I believe I learned more on a grander scale... will help enrich my vocabulary and the way of thinking in the world with respect to complexity."

"Excellent class. I hope to take a more active role in the community."

"This course contained more insight than any other 'complexity' themed course that I have taken."