New England Complex Systems Institute
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Winter Session 2019

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

Dates: January 6 - 18

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.

Lab 1: January 6 CX102: Computer Programming for Complex Systems

Week 1: January 7-11 CX201B: Concepts and Modeling

Lab 2: January 13 CX103: Setting up for Data Analytics

Week 2: January 14-18 CX202B: Networks and Data Analytics

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.

Schedule

Sunday, Jan. 6: 
Lab 9 AM – 5 PM

Monday – Thursday, Jan. 7-10: 
Lecture 9 AM – 5 PM 
Group Projects 6 – 8 PM

Friday, Jan. 11: 
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. 13: 
Lab 9 AM – 5 PM

Monday – Thursday, Jan. 14-17: 
Lecture 9 AM – 5 PM 
Group Projects 6 – 8 PM

Friday, Jan. 18: 
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


Overview | Lab 1: CX102 | Week 1: CX201B | Lab 2: CX103 | Week 2: CX202B | Guest Lecturers | Student Reviews | Register

CX102: Computer Programming for Complex Systems (Lab)

January 6

This course introduces computer programming in the Python language for those with little or no computer programming experience. It is designed as a precursor to CX201B.

The course 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.

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January 7-11

This course offers an introduction to the essential concepts and models of complex systems and related mathematical methods and simulation strategies with application to physical, biological and social systems.

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 data analytics. The course will use of multiscale representations as a unifying approach to complex systems concepts, methods and applications.

The course 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 program. 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.

There will be supervised group projects as an integral part of the course.


Overview | Lab 1: CX102 | Week 1: CX201B | Lab 2: CX103 | Week 2: CX202B | Guest Lecturers | Student Reviews | Register

CX103: Setting up for Data Analytics (Lab)

January 13

This course introduces computer programming in the Python language focused on the management of data for analysis. It is designed as a precursor to CX202B.

This lab will cover essential data handling methodologies using industry-standard tools in the Python language. The lab will cover obtaining, loading, cleaning, initial exploration, saving, and preparing data for in-depth analysis. Time permitting, other topics to be covered in the lab include database construction and management, basic plotting and visualization, and fundamental concepts for developing web-based interactive visualizations.


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January 14-18

This course provides an introduction to (a) the study of networks, including topologies and dynamics of real world networks and (b) the fundamentals of data analytics, machine learning, and artificial intelligence.

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, and 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.

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.

Students will learn to obtain and prepare data for analysis. An overview of academy- and industry-standard toolboxes for handling large datasets will be given, including the collection of data using APIs, construction of databases, visualization, and analysis. A variety of visualization techniques will be covered, including interactive representations.

Analytic methods to be covered include: distribution fitting, data mining, machine learning (regression, classification and clustering), network analysis and time series analysis. 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.

Guest Lecturers

Elena N. Naumova is Professor and Chair of the Division of Nutrition Data Science at the Friedman School of Tufts University. 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.

 

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 awarded a prestigious Microsoft Research New Faculty Fellowship and named one of MIT Technology Review's top 35 young innovators under 35 and a Presidential Early Career Award for Scientists and Engineers (PECASE) by Barack Obama at the White House. 

 

Stuart Kauffman, originally a medical doctor, is an emeritus professor of biochemistry at the University of Pennsylvania and a seminal member and an external professor of the Santa Fe Institute. Also a MacArthur Fellow and a Trotter Prize winner, Kauffman has published three major books; among them is At Home in the Universe: The Search for the Laws of Self-Organization and Complexity (1995). Kauffman was the director of the Institute for Biocomplexity and Informatics (IBI). He is the pioneer and the founding father of biocomplexity research.

 

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. 

 

Overview | Lab 1: CX102 | Week 1: CX201B | Lab 2: CX103 | Week 2: CX202B | Guest Lecturers | Student Reviews | Register

Reviews from Previous NECSI Course Students

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"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."