Meet new collaborators and begin researching together.
Over the course of one Saturday, you will learn about Complexity Science from a variety of perspectives. You will also brainstorm and work on research projects with future collaborators that may go on for submission to academic journals. You’ll meet mentors, coauthors, and conference partners who are ready to help you with your research career.
Here's what you can expect to get from this conference.
TBD in San Francisco Bay Area
Friday, May 15th, 2020 -
Sunday, May 17th, 2020
Gain exposure to an inter-disciplinary perspective on complex system behavior.
Brainstorm research proposals in small groups with guidance from experienced mentors.
After the event ends, we'll be heading out to eat, drink, and get to know each other.
With over 30,000 citations since 1981, Professor Fisher has had a long career of writing high impact publications describing complex behavior across various biological and physical systems, including the evolution of microbes, vaccine immunology, quantum spin chains, ultralow temperature phases of helium, fracture mechanics, and earthquakes.
Much of the intuition developed from modeling evolution and ecology is based on low-dimensional caricatures such as fitness landscapes and niches in trait space. But biology is certainly not low-dimensional: even a minimal description of the “phenotype” of a bacterial cell and its chemical environment is very high-dimensional. This talk will ask whether the high-dimensional Complexity of organisms and environments could generally lead to some seemingly surprising features of microbial evolution and ecology: continual evolution, and extensive fine-scale diversity.
"Complexity" refers to a large set of interrelated phenomena that have been observed across many academic disciplines, such as physics, biology, sociology, economics, and chemistry.
Aristotle described complex system behavior as when “the whole is more than the sum of the parts.” Such systems contain patterns of nonlinear relationships between their component parts under certain external conditions, causing an unpredictable new phase of system behavior as a whole. The main goal of Complex Systems Theory is to understand the relationship between these patterns of interaction and the properties of the system at higher scales.
All backgrounds welcome. No direct experience in Complexity Science required.
Social and Economic Networks Facilitator
Zargham holds a PhD in systems engineering from the University of Pennsylvania where he studied optimization and control of decentralized networks. His earliest work on peer-to-peer effects in business decision making was developing algorithms to reverse engineer the word of mouth effect in enterprise software licensing decisions in 2005. In the intervening years, Zargham designed data driven decision systems and built a data science team for a media technology firm. He has also worked on the mathematical specifications of blockchain enabled software systems with a focus on observability and controllability of the information state of the networks. Most recently, he founded BlockScience, an engineering, research, and analytics firm focused on design and analysis of complex networks.
Deep Learning Facilitator
Jared is a “full-stack data scientist” interested in control theory and optimization, information theory, and intelligent (AI) systems of all sorts. His background is in computational and theoretical biophysics. He is currently working at Atomwise to use deep learning to predict biological activity of drug molecules from their X-ray crystal structure.
Complex Thermostatistics Facilitator
Jason’s background is in multi-scale modeling, in particular the combination of quantum-scale Density Functional Theory with Molecular Dynamics simulations. He seeks to understand general principles behind emergent behavior in complex systems, and has recently focused his attention on understanding the immediate impact of quantum computing systems.
Computational Biology Facilitator
Chenling has a background in evolutionary genetics and is now pursuing a PhD in applying both simulation and machine learning techniques to single-cell RNA sequencing data. Her current project is focused on the immunological changes in Multiple Sclerosis patients. Through her wandering across different fields of biology, she finds Bayesian statistical thinking and a free imagination to be the common language of discoveries in complex biological systems.
Machine learning has risen to the forefront of popular consciousness as a black box that consumes "data" and outputs “accurate” predictions. While enthusiasm surrounding deep learning has enabled us to realize some its potential, the field has been hindered by an attitude of theorization and hazy speculation. We present Complexity Science as a means to remove the lid from the deep learning black box and learn to manipulate its contents with intention. After a review of the field, we will provide an overview of recent approaches to understanding and building learning systems, including emergent computation, network, and field theory approaches. Finally, we will discuss recent efforts to overcome the traditional struggle of practicality and applicability within deep learning, including tensor field networks and neural ODE (continuous fluid model) approaches.
As opposed to the common idea that rigorous, mathematical frameworks are ‘applied to’ biological systems, biology has always been at the forefront of quantitative science: the inspiration from heredity that motivated the invention of regression by Fisher, the link between information entropy and the physical world through Schrödinger's "what is life” and the development of flux balance analysis in order to understand the behavior of large metabolic networks. We will start with an overview of the breadth and complexity of the field of biology, followed by an examination into difficulties in reconciling the experimental practice of biology with its non-linear, probabilistic, and network-driven nature. Most importantly, we will look at some examples of research in biology that uses the principles of Complexity Science to better understand system behavior. We will cover topics in single-cell biology (deep Bayesian networks, metabolic modeling), evolutionary biology (speciation, density-dependent selection) and immunology (control, maximum entropy), some of which I contributed to in my own research.
Examining organizations with the lens of Complexity Theory exposes an underlying network of interactions between participants, internal and external resources, and the organization itself. Centralized organizations use hierarchical accountability to enforce coordination among participants, measure value and manage resources. Together, we will examine the network structure of decentralized organizations and related emerging technologies that have sparked a growing movement to develop more open social and economic communities.
Write, manage, and publish your research documents alongside 100,000+ other researchers. As a leading platform for writing research together, over 5,000 papers written on Authorea have been published in the world’s leading research journals.
On-demand personalized mentorship for graduate students. Mentors are published PhD graduates working in academia or industry from world-class institutions like Carnegie Mellon, Harvard, and UC Berkeley.
Jumpstart research. Meet future collaborators. Start writing.
A weekend experience designed to connect you to future collaborators and jumpstart research projects you'll want to continue after you leave.
Catered coffee and breakfast alongside a brief orientation.
Setting the vision for the day.
Get your mind blown for inspiration.
Hear from each facilitator and decide which research track to join.
Eat catered lunch with your chosen research track.
Within your chosen track, facilitators will help you choose collaborators and identify interesting lines of inquiry as you draft private research proposals.
Reflecting on the day and looking to the future.
We'll walk to a local watering hole together to chat about neat insights gained, new research proposals, and all things complex systems theory.
What will I get out of attending?
ComplexityCon allows you to jumpstart your complex systems research by sparking new ideas, connecting you to new collaborators, and helping you produce research proposals you'll want to continue working on after the conference. You will learn how research is done, what tooling is used, how published researchers think, with mentors to help you during the event.
Who is this conference for?
Attendees should want to publish research in peer-reviewed science journals. The goal of our conference is to connect future collaborators to start research proposals together that will one day become a published journal article.
What are the prerequisites for attending?
We recommend that attendees have some research experience (the equivalent to an undergraduate program's worth at a research university), but a doctorate or other degree is not required to attend.
Do I need to be enrolled in graduate school to participate?
You do not need to be in a graduate program to attend. However, we recommend that you have some research experience or are open to learning.
Should I do anything to prepare for the conference?
Don't worry about being unprepared, as no one is an expert in all the various fields intersecting with Complex System Theory. If you want to consume anything beforehand, perhaps the "Intro to Complexity" course materials from SFI's Complexity Explorer would be best. We'll have mentors on hand to help with whatever blocks your way on the research proposal you are most interested in.
Should I come with a research proposal prepared already?
Our research track facilitators have prepared a number of public "paper stubs" that may serve as the start of a research project. You are welcome to create a private document based on these stubs. Otherwise, you can bring a research proposal from home to share and meet new collaborators around (we would encourage that you add anyone you work with as a co-author), or you can simply come with a learning mindset and co-create a new research proposal with others.
Who will own the research created at the conference?
Attendees own their research. Research proposals generated during the event have explicit co-authorship defined and are private by default. Researchers will be given the opportunity to opt-in to having their proposals summarized into a one-pager emailed to all attendees and posted on the ComplexityCon website. Any degree of open-sourcing is celebrated, but not required.
I'm interested in a topic that does not have a facilitator. Can I still participate?
You are more than welcome to draft research proposals outside of the topics supported by facilitators during the event, though you will have less support than other attendees. Feel free to email [email protected] to share a topic you feel deserves a facilitation track.
Who organized this?
Shaun R Swanson
Data Scientist at CloudKnox Security
Cofounder at PhD Mentors
Jason Larkin, PhD
Research Scientist at Emerging Technology Center, CMU
Cofounder at PhD Mentors
Daniel Ari Friedman
PhD Student at Stanford University
Organizer of the Stanford Complexity Group
Jared Thompson, PhD
Senior Machine Learning Engineer at Atomwise
May 15-17, 2029 - San Francisco Bay Area
Jumpstart research. Meet future collaborators. Start writing.