Christine Allen-Blanchette

Talk Title: Learning video models with Lagrangian/Hamiltonian neural networks

Abstract: The dynamics underlying object and camera motion in a video typically evolve on a low-dimensional manifold with unknown structure and dimension. While prior work has used the Hamiltonian formalism to give a physically meaningful interpretation to this manifold, the problem of discovering the manifold structure and dimension remains unaddressed. We introduce a Hamiltonian neural network for video generation where the structure and dimension of the phase-space are learned from data. To achieve this we introduce a GAN-based video generation pipeline which embeds a learned transformation from a Gaussian distribution to the phase-space manifold, and captures the underlying dynamics of the video in a Hamiltonian neural network motion model.

Bio: Christine Allen-Blanchette is an Assistant Professor co-appointed in the Department of Mechanical and Aerospace Engineering, and Center for Statistics and Machine Learning at Princeton University where they are pursuing research at the intersection of deep learning, geometry, and dynamical systems. They completed their PhD in Computer Science and MSE in Robotics at the University of Pennsylvania, and their BS degrees in Mechanical Engineering and Computer Engineering at San Jose State University. Among their awards are the Princeton Presidential Postdoctoral Fellowship, NSF Integrative Graduate Education and Research Training award, and GEM Fellowship sponsored by the Adobe Foundation.

Zahra Aminzare

Talk Title: Characterizing Stable and Robust Synchronization Patterns in Stochastic Networks with Non-Diffusive Coupling

Abstract: Synchronization patterns play a critical role in ensuring reliable functioning across various networks, both in natural and artificial systems. Stable and robust synchronization depends on the intricate dynamics of individual elements, their interconnections, and the underlying network topology. In this presentation, we draw inspiration from chemical synapses found among neurons and focus on networks of non-linear stochastic systems coupled through non-diffusive mechanisms, where the complete synchronization manifold is not invariant. Leveraging Contraction Theory for stochastic differential equations, we provide conditions that guarantee the emergence of stable and robust synchronization patterns within such networks. To illustrate our theory, we apply it to a couple of neuronal networks, demonstrating its effectiveness in characterizing synchronization behavior in complex biological systems.  

Bio: I am an Assistant Professor in the Department of Mathematics at the University of Iowa, with affiliations in the Applied Mathematical and Computational Sciences Program and The Interdisciplinary Graduate Program in Neuroscience. I am also a member of The Iowa Neuroscience Institute. My research interests lie at the intersection of applied dynamical systems, partial differential equations, and mathematical biology, with a particular emphasis on contraction theory and synchronization patterns in biological systems. Prior to joining the University of Iowa, I worked as a Postdoctoral Research Associate at PACM, Princeton University, from 2015 to 2018, where I was mentored by Professors Phil Holmes and Naomi Leonard. In 2015, I earned my Ph.D. in Mathematics from Rutgers University under the guidance of Professor Eduardo Sontag. 

Francesco Bullo

Talk Title: Contracting Dynamics for Optimization and Neural Networks

Abstract: We survey the application of the Banach contraction principle to dynamical systems as arising in generalized gradient dynamics and recurrent neural networks.  We start with some historical highlights.  Next, we generalize the basic contraction property from discrete to continuous time, from Euclidean to non-Euclidean norms, and from single to interconnected systems.  We apply these theoretical tools to modern problems involving artificial and biological neural networks, ranging from unsupervised representation learning to implicit machine learning models.

Bio: Francesco Bullo is a Distinguished Professor of Mechanical Engineering at the University of California, Santa Barbara, CA, USA. He was previously with the University of Padova (Laurea degree, 1994), Italy, the California Institute of Technology (Ph.D. degree, 1998), Pasadena, CA, and the University of Illinois at Urbana-Champaign, IL, USA. His research interests include contraction theory, network systems, and distributed control. He is the author or coauthor of Geometric Control of Mechanical Systems (Springer, 2004), Distributed Control of Robotic Networks (Princeton, 2009), Lectures on Network Systems (KDP, 2022), and Contraction Theory for Dynamical Systems (KDP, 2023). He served as IEEE CSS President and SIAG CST Chair. He is a Fellow of ASME, IEEE, IFAC, and SIAM.

Jonathan Cohen

Talk Title: The Dynamics of Task Switching and Optimal Control

Abstract: The human brain exhibits a remarkable, and still unique position on the landscape of computational devices: its combination of flexibility and efficiency of processing is, as yet, unmatched by any other computational architecture, natural or artificial.  One of the core elements thought to contribute to this profile of performance is the capacity for self-regulation, often referred to as "cognitive control."  I will present work, much of which was carried out in collaboration with Naomi's lab, on how flexibility and optimization of performance are implemented in the brain, with a focus on task switching as an empirical setting in which this has been studied.

Bio: I am a computational cognitive neuroscientist, with an interest in the neural mechanisms responsible for cognitive control, how these contribute to the remarkable combination of flexibility and efficiency exhibited by the human brain, and its capacity for natural intelligence.

Iain Couzin

Talk Title: The Geometry of Decision Making

Abstract: A central challenge for animals when alone, or when grouping with others, is deciding where to go. Running, swimming, or flying through the world, animals are constantly making decisions while on the move—decisions that allow them to choose where to eat, where to hide, and with whom to associate. I will discuss work my group has undertaken to understand spatial decision-making in natural and robotic systems within a long-term collaboration with Naomi. Using a range of experimental technologies, including ‘holographic’ virtual reality for freely-moving animals, bio-mimetic robotics and artificial intelligence, I will present evidence that there exist fundamental geometric principles of spatiotemporal computation that transcend scales of biological organization; from neural dynamics to individual decision-making, and from individual decision-making to that at the scale of the collective. I will also show how by understanding these aspects of evolved spatiotemporal computation we may be able to improve human-engineered systems, such as effective coordination among autonomous robots.

Bio: Iain Couzin is Director of the Max Planck Institute of Animal Behavior and a Professor and Speaker of the Excellence Cluster “Centre for the Advanced Study of Collective Behaviour” at the University of Konstanz, Germany. Previously he was an Assistant and then Full Professor in the Department of Ecology and Evolutionary Biology at Princeton University, and prior to that a Royal Society University Research Fellow in the Department of Zoology, University of Oxford, and a Junior Research Fellow in the Sciences at Balliol College, Oxford. His work aims to reveal the fundamental principles that underlie evolved collective behavior, and consequently his research includes the study of a wide range of biological systems, from cellular collectives to insect swarms, fish schools and primate groups. In recognition of his research he has been recipient of the Searle Scholar Award in 2008, top 5 most cited papers of the decade in animal behavior research 1999-2010, National Geographic Emerging Explorer Award in 2012, the Scientific Medal of the Zoological Society of London in 2013, a Web of Science Global Highly Cited Researcher since 2018, the Lagrange Prize in 2019, the Falling Walls Life Sciences Award and the Gottfried Wilhelm Leibniz Prize—Germany’s highest research honor—in 2022, and the Rothschild Distinguished Fellowship at the University of Cambridge in 2023.

Alessio Franci

Talk Title: Excitable decision-making

Abstract: Understanding how the functional scale of cognition and the cellular scale of excitable neuronal activity are related is a fundamental question of neuroscience with important implications for artificial intelligence, robotics, and neuromorphic engineering applications. Answering this question could help us unravel basic principles underlying neuronal systems functioning and could provide us new methodologies for the design of intelligent machines capable of interacting with changing and uncertain environments with the same robust adaptability observed in biological beings.

Here, we approach the problem of understanding the relation between cognition and excitability by focusing on decision-making as a fundamental cognitive function and by using mathematical modeling grounded in control and bifurcation theory. From the proposed angle of attack, excitability emerges as the minimal dynamical behavior needed to provide nonlinear signal-processing systems, modeling single neurons, neuronal circuits or networks, with adaptive decision-making capabilities. More precisely, excitability can be understood as the synthesis of two defining properties of adaptive decision-making: the existence of a tunable threshold that determines the making of a decision and the existence of a reset mechanism that allows returning to a neutral decision state and the making of new decisions. The derived theoretical framework provides both new modeling and design tools to understand flexible cognition and reproducing it in artificial machines.

Bio: Alessio Franci got his Master 2 (Laurea Specialistica) Degree in Theoretical Physics from the University of Pisa in 2008 and his PhD in Physics and Control Theory from the University of Paris Sud 11 in 2012. Between 2012 and 2015 he was a postdoctoral researcher at the University of Liege and at INRIA Lille. Between 2013 and 2015 he was also a long term visiting researcher at the University of Cambridge. Between 2015 and 2022 he was professor in the Math Department of UNAM – National Autonomous University of Mexico. Since 2023 he has been professor in the Department of Electrical Engineering and Computer Science of the University of Liege.

His research interests span different fields but the central focus is on the control-theoretical and computational principles of biological and bio-inspired behaviors like excitable neuronal behavior, collective decision making, and neuromorphic computing. Thanks to local and international collaborations, his research has a strong interdisciplinary inclination.

Deborah Gordon

Talk Title: The ecology of collective behavior

Abstract: Many natural systems, from brains to ant colonies, operate without central control, using local interactions that in the aggregate allow the system to adjust to the current situation.  Examples from two ant species, harvester ants in the desert and turtle ants in the tropical forest, suggest broad analogies across natural systems in how the dynamics of collective behavior evolve to fit the dynamics of the environment.  The rates, feedback regimes and modularity of interaction networks correspond to the stability of the environment and the distribution of resources and demands.  

Bio: Deborah M Gordon received her PhD from Duke University, then did postdoctoral research in the Harvard Society of Fellows, at Oxford University, and the Centre for Population Biology at the University of London, and joined the faculty at Stanford in 1991. She is the author of three books, Ants at Work (Norton 2000); Ant Encounters: Interaction Networks and Colony Behavior (Primers in Complex Systems, Princeton University Press, 2010), and The Ecology of Collective Behavior (forthcoming 2023, Princeton University Press). Her awards include a Guggenheim Fellowship, fellowships at the Center for Advanced Study in Behavioral Sciences, and the Quest award of the Animal Behavior Society. Her lab studies collective behavior in ants and the role of chemical signaling and olfaction in the regulation of tasks in response to changing environmental conditions and cues, including the role of dopamine signaling. More broadly, she is interested in bridging insights from different disciplines that study dynamic systems and feedback control circuits, ranging across ecology, evolutionary biology, cell biology, and neuroscience.  http://www.stanford.edu/~dmgordon/

P. S. Krishnaprasad

Talk Title: The Falcon and the Flock

Abstract: Avian flocking behavior is arguably an adaptation with a purpose – mitigating the risk of predation. The geometry of configuration space of a flock suggests dynamical decompositions of the behavior into natural modes. Emerging from interactions between individuals, modes appear as flock-scale strategies for limiting the likelihood of predation. Following earlier work on the confusion effect experienced by a predator, we suggest that a quantitative measure of risk mitigation lies in the temporal variability of modes. We compute this measure as a solution to an optimal control problem associated with an evolutionary game. This is joint work with Udit Halder, Vidya Raju, Matteo Mischiati and Biswadip Dey.

Bio: P. S. Krishnaprasad received the Ph.D. degree from Harvard University in 1977. He taught in the Systems Engineering Department at Case Western Reserve University from 1977 to 1980. Since August 1980, he has been with the University of Maryland, currently a Professor of Electrical & Computer Engineering, with a joint appointment at the Institute for Systems Research. His interests lie in the areas of geometric mechanics and control theory, filtering and signal processing, robotics, acoustics, and biologically-inspired approaches to control, sensing and computation. His current work includes the study of natural and artificial collectives, control in statistical physics, and the geometry of human movement. He is a Fellow of the IEEE and was the Hendrik W. Bode Lecturer of the IEEE Control Systems Society in 2007.

Simon Levin

Talk Title: Collective intelligence and public goods

Abstract: Phil Anderson, in a landmark essay, highlighted the challenges in understanding the emergent properties of what were later termed “complex adaptive systems,” and the need to go beyond reductionistic approaches, themes later echoed in another brilliant essay, by Francois Jacob, approaching the challenge through a very different lens.  No problem facing us better exemplifies these issues than that of achieving a sustainable future for humanity.

The continual increase in the human population, magnified by increasing per capita demands on Earth's limited resources, raise the urgent mandate of understanding the degree to which these patterns are sustainable.  The scientific challenges posed by this simply stated goal are enormous, and cross disciplines.  What measures of human welfare should be at the core of definitions of sustainability, and how do we discount the future and deal with problems of intra-generational and inter-generational equity?  How do environmental and socioeconomic systems become organized as complex adaptive systems, and what are the implications for dealing with public goods at scales from the local to the global?  How does the increasing interconnectedness of coupled natural and human systems affect the robustness of aspects of importance to us, and what are the implications for management.  What is the role of social norms, and how do we achieve cooperation at the global level?  All of these issues have parallels in evolutionary biology, and this lecture will explore what lessons can be learned from ecology and evolutionary theory for developing a collective intelligence to address the problems posed by the challenge of achieving a sustainable future.

Bio: Simon A. Levin is James S. McDonnell Distinguished University Professor in Ecology and Evolutionary Biology at Princeton University.  He received his B.A. (Math) from Johns Hopkins University and Ph.D. (Math) from University of Maryland.  Levin is Fellow of the American Academy of Arts and Sciences and American Association for the Advancement of Science, and Member of U.S. National Academy of Sciences and American Philosophical Society.  He is an elected Fellow of multiple societies, and formerly President of Ecological Society of America and Society for Mathematical Biology, Chair of Council of IIASA, Chair of Board of  Beijer Institute, and Chair of Science Board of Santa Fe Institute.  He has won the Kyoto Prize in Basic Sciences, Heineken Prize for Environmental Sciences, Margalef Prize in Ecology, Tyler Prize for Environmental Achievement, U.S. National Medal of Science, and BBVA Foundation Frontiers of Knowledge Award for Ecology and Conservation Biology.  Levin studies biological diversity at all levels, from the molecular diversity of diseases to the diversity of global ecological and cultural systems; the importance for humans; and interactions with human social and economic systems.  He has mentored more than 150 Ph.D. students and Postdoctoral Fellows.

Derek Paley

Talk Title: Bioinspired sensing and control for underwater robotics

Abstract: The long-term goal of this research is to investigate close-proximity swimming in fish-inspired underwater vehicles using a principled approach to modeling and control that results in a physical demonstration of multiple free-swimming prototypes whose interactions yield hydrodynamic benefits. The specific research objective is to apply tools from fluid dynamics, continuum mechanics, and automatic control theory to solve the problem of optimally regulating the swimming behavior of a flexible robotic fish using distributed sensing of the body deformation, adjacent fluid structures, and the relative position/orientation, velocity, and shape of nearby propulsive bodies.

Bio: Derek A. Paley is Director of the Maryland Robotics Center and Willis H. Young Jr. Professor of Aerospace Engineering Education in the Department of Aerospace Engineering and the Institute for Systems Research at the University of Maryland. Paley received the B.S. degree in Applied Physics from Yale University in 1997 and the Ph.D. degree in Mechanical and Aerospace Engineering from Princeton University in 2007. Paley’s research interests are in the area of dynamics and control, including cooperative control of autonomous vehicles, adaptive sampling with mobile networks, spatial modeling of biological groups, and bioinspired robotics.

Eduardo D. Sontag

Talk Title: Balanced Graphs: from Sociology to Systems Biology

Abstract: Naomi's recent preprint "Multi-topic belief formation through bifurcations over signed social networks" (arXiv, August 2023) is motivated by social science and control applications of signed graphs, with a specific focus on balanced graphs. In dynamical systems and control theory, balanced graphs represent systems in which coherent signals are transmitted through networks. Jacobians of vector fields give rise to balanced signed graphs if and only if the corresponding flow is monotone with respect to some orthant cone order. This talk will explore how the property of monotonicity (linearizations along all trajectories have balanced signed graphs) helps analyze nonlinear dynamical systems.

Bio: Eduardo D. Sontag received his Licenciado in Mathematics at the University of Buenos Aires (1972) and a Ph.D. in Mathematics (1977) under Rudolf E. Kalman at the University of Florida. From 1977 to 2017, he was at Rutgers University, where he was a Distinguished Professor of Mathematics and a Member of the Graduate Faculty of the Departments of Computer Science of Electrical and Computer Engineering and the Cancer Institute of NJ. He directed the undergraduate Biomathematics Interdisciplinary Major, the Center for Quantitative Biology, and was Graduate Director at the Institute for Quantitative Biomedicine. In January 2018, Dr. Sontag became a University Distinguished Professor in the Departments of Electrical and Computer Engineering and of BioEngineering at Northeastern University, where he is also affiliated with the Mathematics and the Chemical Engineering departments. Since 2006, he has been a Research Affiliate at the Laboratory for Information and Decision Systems, MIT, and since 2018 he has been the Faculty Member in the Program in Therapeutic Science at Harvard Medical School. His major current research interests lie in several areas of control and dynamical systems theory, systems molecular biology, cancer and immunology, and computational biology. Sontag has authored over five hundred research papers and monographs and book chapters in the above areas with over 59,000 citations and an h-index of 105 (56 since 2018). He is a Fellow of various professional societies: IEEE, AMS, SIAM, and IFAC, and is also a member of SMB and BMES. He was awarded the Reid Prize in Mathematics in 2001, the 2002 Hendrik W. Bode Lecture Prize and the 2011 Control Systems Field Award from the IEEE, the 2022 Richard E. Bellman Control Heritage Award, the 2023 IFAC Triennial Award on Nonlinear Control, the 2002 Board of Trustees Award for Excellence in Research from Rutgers, and the 2005 Teacher/Scholar Award from Rutgers.

Dawn Tilbury