Elisa Omodei, Predictive Analytics Lead, mobile Vulnerability Analysis and Monitoring at the UN World Food Programme, Vice-President Secretary, The Complex Systems Society


Talk Title:
Who, where, why: non-traditional data and predictive analytics to map socio-economic vulnerabilities


Talk Abstract:
In a rapidly changing world, severely affected by extreme weather events, epidemic outbreaks, economic shocks and conflicts, it is of fundamental importance to understand where the most vulnerable people are, how many they are, and to identify what it is that makes them more vulnerable than others to these threats. During the last decade, research has shown that data such as digital traces, phone metadata and satellite imagery carry relevant information beyond their original purpose and can be used as a proxy to measure socio-economic characteristics and detect vulnerabilities when traditional data is not available. Following an overview of these studies, the talk will deep dive into the UN World Food Programme’s original work on predicting food security. We will then conclude by discussing challenges and limitations, but also opportunities, that come with these approaches.

Speaker Bio:
Elisa Omodei is the Predictive Analytics Lead of the Hunger Monitoring Unit at the World Food Programme’s Research, Assessment and Monitoring division. She also serves as Vice-President Secretary of the Complex Systems Society. She holds a BSc and a MSc in Physics and a PhD in Applied Mathematics for the Social Sciences. After her PhD, she spent a few years in academia before joining the United Nations in 2017, first at UNICEF's Office of Innovation and now at the World Food Programme.

Casey Fiesler, Assistant Professor, Founding Faculty, Information Science, University of Colorado Boulder

Title: Data Is People: Ethics and Education for Data Science

Abstract: Big data has opened up new possibilities and transformed the ways we conduct research in nearly every discipline. However, ethical considerations and education for research has long focused on human subjects, governed in the U.S. by institutional review boards. Data science often falls through the cracks of these regulations, so it is even more imperative that we have strong ethical norms and guidelines. This starts with the reminder that though data scientists may not interact directly with people, the data collected and analyzed very often comes from people, which opens up important considerations around privacy, consent, and harm. Moreover, applications of data science research, particularly with respect to prediction, have the potential for large-scale societal impacts. In considering the broad landscape of technology ethics when it comes to uses and applications of big data, I will argue for a fundamental shift in how we teach ethics to future data scientists and researchers.

Bio: Dr. Casey Fiesler is an assistant professor and founding faculty in the Department of Information Science as well as Computer Science, by courtesy, at University of Colorado Boulder, where she researches and teaches in the areas of technology ethics, internet law and policy, and online communities. Also a public scholar, she is a frequent commentator and speaker on topics of technology ethics and policy, as well as women in STEM (including consulting with Mattel on their computing-related Barbies). Her work on research ethics for data science, ethics education in computing, and broadening participation in computing is supported by the National Science Foundation, as well as Mozilla and Omidyar Network as part of the Responsible Computer Science Challenge.

Guillermo García-Pérez - Senior Researcher, Theoretical Physics, University of Turku - Finland

Talk title: Emergent spatial entanglement in many-body quantum systems

Abstract: Are the global properties of complex quantum systems simply a consequence of the properties of their individual components? Or are there features that emerge, as a result of self-organisation, uniquely characterising the complex object as a whole? The answer to these questions might unveil up-to-now unknown properties of molecular complexes, proteins, and living organisms, as well as of engineered complex quantum communication networks.

In this talk, I will show how our holistic approach combining quantum physics and complex network theory brings to light the existence of emergent spatial structures of quantum entanglement in a paradigmatic many-particle quantum system [1]. I will also introduce an efficient algorithm for quantum network tomography, showing exponential improvement with respect to existing approaches [2], and hence allowing for the experimental observation of the new entanglement structures that we have discovered.

References:
[1] “Emergent entanglement structures and self-similarity in quantum spin chains”, Boris Sokolov, Matteo A. C. Rossi, Guillermo García-Pérez, Sabrina Maniscalco, arXiv:2007.06989.
[2] “Pairwise tomography networks for many-body quantum systems”, Guillermo García-Pérez, Matteo A. C. Rossi, Boris Sokolov, Elsi-Mari Borrelli, Sabrina Maniscalco, Phys. Rev. Research 2, 023393 (2020).

Bio: Guillermo is a physicist working at the intersection between complex systems and quantum physics. He earned his BSc in Physics and his MSc in Advanced Physics at the University of Barcelona. He obtained his PhD from the same university in 2018 for his thesis on the geometric structure of complex networks. His research in complex networks has covered very disparate topics, ranging from pure mathematics to international trade. As a postdoc at University of Turku, he has worked on quantum computing and simulation, as well as on complex quantum networks.

Dr. Karissa Y. Sanbonmatsu is a structural biologist at Los Alamos National Laboratory, where she leads the Sanbonmatsu Laboratory established in 2001. Her team uses computational and experimental approaches to understand the mechanism of a diverse array of epigenetic and non-coding RNA systems, including chromatin, ribosomes, riboswitches and long non-coding RNAs.

She has been a leading figure in structural studies of long non-coding RNAs in epigenetics and is currently studying the mechanism of epigenetic effects involving chromatin architecture. Her body of work led her to be elected as a Fellow of the American Physical Society in 2012. Most recently, her group set the record for the world’s largest published biomolecular simulation at one billion atoms, the first simulation of an entire gene.

She described her work with epigenetics and came out as transgender in a 2014 TEDxTalk. She also delivered a TED talk at TEDWomen on The biology of gender, from DNA to the brain, in November 2018. The talk, with over 2 million views, covers epigenetics and how hormones control DNA decisions during embryo development.

Talk Title: Science for the next pandemic: from large-scale simulations of genes to 3-D reconstructions of chromosomes

Talk Abstract: While most cells in the body have identical 1-D genomes, cells in different tissues have remarkably different cell shapes, internal structures and functionality. Recent studies suggest that the 3-D structure of the genome differs from cell to cell, and that these differences have profound effects on the function of the underlying DNA, impacting cell structure and functionality. Interestingly, viral infections also affect the 3-D organization of the genome. While the common paradigm suggests that chromosome compaction causes gene silencing, few mechanistic studies on full chromosomes have been performed to support this. Recently, the development of high throughput sequencing technology has produced precise information regarding gene-gene interactions. The Hi-C technique cross links chromosomes with themselves, revealing DNA-DNA interactions. These samples are then fragmented and ligated to produce small loops containing the DNA-to-DNA interaction sequences. Finally, a 2-D contact map is produced. While many suspect that patterns in the 2-D contact map reflect 3-D proximity of genes, this is difficult to discern. We use constrained molecular simulations to produce 3-D reconstructions of full chromosomes highly consistent with Hi-C experimental data for the process of X chromosome silencing, shedding light on the chromosome compaction. Changes in 3-D structure, could, in principle, be used to detect more subtle signatures of viral infections. In addition to full chromosome simulations, results from the first explicit solvent MD simulations of an entire gene locus (using the LANL Trinity supercomputer) will be presented. This pilot study is the first billion-atom simulation published in the biosciences. Simulations such as these have the ability to push HPC resources much more than conventional simulations in the physical sciences.

Talk title:
Computer Vision: Who benefits and who is harmed?

Talk abstract:
Computer vision has ceased to be a purely academic endeavor. From law enforcement, to border control, to employment, healthcare diagnostics, and assigning trust scores, computer vision systems are being rapidly integrated into all aspects of society. In research, there are works that purport to determine a person’s sexuality from their social network profile images, others that claim to classify “violent individuals” from drone footage. These works were published in high impact journals, and some were presented at workshops in top tier computer vision conferences such as CVPR.
A critical public discourse surrounding the use of computer-vision based technologies has also been mounting. For example, the use of facial recognition technologies by policing agencies has been heavily critiqued and, in response, companies such as Microsoft, Amazon, and IBM have pulled or paused their facial recognition software services. Gender Shades showed that commercial gender classification systems have high disparities in error rates by skin-type and gender, and other works discuss the harms caused by the mere existence of automatic gender recognition systems. Recent papers have also exposed shockingly racist and sexist labels in popular computer vision datasets--resulting in the removal of some. In this talk, I will highlight some of these issues and proposed solutions to mitigate bias, as well as how some of the proposed fixes could exacerbate the problem rather than mitigate it.


Bio: Timnit Gebru, Expert Research Scientist and AI Ethicist

Timnit Gebru is an expert research scientist and AI ethicist , working to reduce the potential negative impacts of AI. Timnit earned her doctorate under the supervision of Fei-Fei Li at Stanford University in 2017 and did a postdoc at Microsoft Research NYC in the FATE team. She is also the cofounder of Black in AI, a place for sharing ideas, fostering collaborations and discussing initiatives to increase the presence of Black people in the field of Artificial Intelligence.

Assistant Professor in Socio-Environmental Modeling in the Department of Environment and Society of the Quinney College of Natural Resources at Utah State University and the ASU-SFI Center for Biosocial Complex Systems Fellow at The Santa Fe Institute

Stefani A. Crabtree is Assistant Professor in Socio-Environmental Modeling in the Department of Environment and Society of the Quinney College of Natural Resources at Utah State University and the ASU-SFI Center for Biosocial Complex Systems Fellow at The Santa Fe Institute. She additionally holds external affiliation at three institutions: Research Associate at Crow Canyon Archaeological Center, Fellow at the Centre de Recherches Interdisciplinaires Paris, and Research Associate at the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage. Her research applies complex systems science modeling methodologies (such as agent-based modeling and network science) to problems in social science and ecology. Current research topics include the human place in ecosystems worldwide, the ability to use the archaeological past to calibrate our understanding of human resilience, and the feedbacks between ecosystem health and human health. Crabtree has published in general science and disciplinary journals across social science and ecology, including PNAS, Ecological Modelling, American Antiquity, Physics Today, Human Ecology, Journal of Archaeological Science, and Journal of Archaeological Method and Theory. From 2016-2018 she was a post-doctoral researcher in the Human Environmental Dynamics laboratory at the Pennsylvania State University. Dr. Crabtree holds two Ph.D.s, one from Washington State University (Anthropology, 2016) and one from the Université de Franche-Comté (Maison des Sciences de l’Homme et l’Environnement, 2017).

Stefani grew up in the small town of Bend, Oregon, and before college had never set foot out of the continental United States. As an undergraduate she studied abroad in Paris, taking courses in Art History, Egyptology and the Archaeology of Islam at Paris Sorbonne Michelet. She was a Watson Fellow in 2004-2005, living in New Zealand, Samoa, India and Vietnam doing a multi-site ethnography on how women of indigenous cultures use traditional medicine in pregnancy and childbirth. In graduate school Stefani has been an NSF graduate research fellow and a Chateaubriand fellow.


Stefani is interested in how complexity science can help us understand the archaeological past. Follow her on twitter @StefaniCrabtree

Marie-Josée Fortin, University of Toronto, Professor, FRSC, Canada Research Chair in Spatial Ecology

Talk Title:
Ecological Networks in Dynamics Landscapes

Talk Abstract:
Network ecology is an emerging field allowing researchers to conceptualize and model complex and dynamic ecological systems. Here, I highlight the parallel developments in ecological network studies and spatial network applications and their convergence in the use of network theory. Then, I summarize how ecological, spatial, temporal, and spatio-temporal networks can be formalized into the framework of multilayer networks. Specifically, I present how the spatio-temporal network can be analysed focusing either on the dynamics on the network or of the network. Such distinctions provide the foundations for the formulation of an “ecological network dynamics hypothesis” (ENDH) stating key network topologies that constrain the dynamic of ecological systems. Such an ENDH could in turn permit assessing trade-offs in network topologies and functions that affect the persistence of ecological systems. I then propose the integration of ecological networks with spatial networks to improve our understanding of complex ecological systems. This integration combines filtering the ecological interaction network based on motifs with the delineation of patches in the spatial network using an edge detection algorithm scalable to species dispersal abilities. I conclude that network ecology with its analytical methods will improve our understanding on how ecological systems will respond in spatially dynamic landscapes.

Bio:
Marie-Josée Fortin is a University Professor in the Department of Ecology and Evolutionary Biology at the University of Toronto. She is a Fellow of the Royal Society of Canada and holds a Tier 1 Canada Research Chair in Spatial Ecology. Professor Fortin is recognized internationally as a leader in spatial ecology with expertise in network theory to address both fundamental and applied research to conserve biodiversity in fragmented landscapes. She pioneered the field of spatial analysis of ecological data and contributed to the development of numerous analytical methods and models. Her research endeavors focus on conservation biology issues. She investigates how ecological processes and environmental factors affect species persistence, species dispersal, and species range dynamics. Her research is at the interface of several disciplines (spatial ecology, conservation, forest ecology, disturbance ecology, community ecology, landscape genetics, spatial epidemiology, spatial statistics, spatially-explicit modeling, and network theory) where the most important challenging problems lie.

Fernanda S. Valdovinos (PI)
Assistant Professor
Ecology & Evolutionary Biology and
Complex Systems, University of Michigan

Questions Valdovinos asks in her lab today explore important ecological issues such as pollinator declines and fish overexploitation, as well as basic theoretical questions about mutualistic networks, as described later in this article. She joined the University of Michigan’s Department of Ecology and Evolutionary Biology and the Center for the Study of Complex Systems (50/50) in January 2018 as assistant professor.

Talk Title:
Guiding the Self-Organization of Cyber-Physical Systems

Talk abstract:
Self-organization offers a promising approach for designing adaptive systems. Given the inherent complexity of most cyber-physical systems, adaptivity is desired, as predictability is limited. Here I summarize different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design. Then I mention real-world examples of systems where self-organization has managed to provide solutions that outperform classical approaches, in particular related to urban mobility. Finally, I identify when a centralized, distributed, or self-organizing control is more appropriate.

Bio: Carlos Gershenson, Research Professor, UNAM

Carlos Gershenson is a tenured, full time research professor at the Universidad Nacional Autónoma de México (UNAM), where he leads the Self-organizing Systems Lab. He was a Visiting Professor at MIT and at Northeastern University (2015-2016). He was a postdoctoral fellow at the New England Complex Systems Institute (2007-2008). He holds a PhD summa cum laude from the Vrije Universiteit Brussel, Belgium (2002-2007). He holds an MSc degree in Evolutionary and Adaptive Systems, from the University of Sussex (2001-2002), and a BEng degree in Computer Engineering from the Fundación Arturo Rosenblueth, México. (1996-2001).

He is Editor-in-Chief of Complexity Digest and member of the Board of Advisors for Scientific American. He has received numerous awards, including a Google Research Award in Latin America and the Audi Urban Future Award.

Advisors: Randall Harp, Peter Dodds
Lab: Data Ethics Lab

Juniper Lovato is the Director of partnerships and external programs at the Vermont Complex Systems Center at UVM where she organizes Complex Systems programs and thinks about multi-scale data ethics. Previously, she was the Director of Education for the Santa Fe Institute in Santa Fe, New Mexico where she was born and raised (on a rural family compound populated by hippies and Spanish/Swedish folk musicians, yup they had a dance pavilion). In her free time she makes/supports makerspaces.

Current Research Interests: Multi-Scale Data Ethics, Distributed Consent, Ethics of Science and Technology, Complex Systems, Data Science, Online Counter-publics, mobility data ethics

Advisor: Laurent Hébert-Dufresne
Lab: Laboratory for Structure and Dynamics

Center Coordinator
Contact: melissa.rubinchuk@uvm.edu

Vermont Complex Systems Center, Center Coordinator
Melissa has an educational background in marine science and real life experience in off-grid sustainable living. After moving to Vermont almost 9 years ago from the Bahamas, she began a residential and commercial property management business here in Burlington. She brings business, customer service, education, and entrepreneurial experience to her position coordinating the day-to-day logistics and operations of the MassMutual Center of Excellence and the Vermont Complex Systems Center.