klogW Seminar Series

The klogW Series

The klogW series is organized by the topical group on Statistical and Nonlinear Physics (GSNP) of the American Physical Society (APS) and started in the midst of Covid in order to keep our community together.  We have since been using the virtual format as an opportunity to host speakers from around the world and make the talks available to a worldwide audience. If you are enjoying the talks, please do consider becoming a member of GSNP.

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GSNP klogW-series committee

Narayanan Menon
University of Massachusetts, Amherst
Shima Parsa
Rochester Institute of Technology

Arjendu Pattanayak
Carleton College

Doug Durian
University of Pennsylvania

Upcoming Events

April 15th 2025: Information-thermodynamics in the quantum regime


Gabriel Landi
University of Rochester

April 15, 2025

Register Here

Abstract:

The physicist Rolf Landauer, one of the fathers of modern computing, coined the phrase “Information is physical”.  Today, this idea is more alive than ever, as it is one of the primary drives in the quantum revolution we are currently experiencing. Information is physical because it can be consumed, very much like a fuel, to perform useful tasks such as making heat flow from cold to hot (like in a freezer). But to properly account for this type of effect, the laws of thermodynamics have to be modified. In this talk I will give an overview of the recent efforts in formulating the laws of information-thermodynamics. Using these rules, I will discuss how quantum phenomena, such as quantum entanglement, can be used to make nano-devices more efficient. And also how it can be exploited to make more precise sensors, that could have a profound impact in future technologies. 

Speaker Bio:

 Professor Landi is an Associate Professor in the Department of Physics and Astronomy of the University of Rochester. Previously he was an assistant professor in Brazil, first at UFABC (2013-2016) then at the University of São Paulo (2016-2022). Professor Landi heads the Quantum Thermodynamics and Quantum Transport group (QT2), which does theory research at the boundary between quantum information sciences and statistical physics. He is a specialist in the field of open quantum systems, with applications to quantum thermodynamics, quantum transport and quantum metrology. His group has contributed extensively to the reformulation of the laws of thermodynamics in the presence of quantum coherence, as well as to the understanding of continuously measured quantum systems, and the connection between classical data and the underlying quantum dynamics.

Abstract:
A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. 

In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is even independent of the network size over several orders of magnitude, is still unknown. 

I will show that the “six degrees of separation” is in fact just the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. 

Moreover, the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. 

Thus, simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of complex networks.

 

 

Bio:
Stefano Boccaletti received the PhD in Physics at the University of Florence in 1995, and a PhD honoris causa at the University Rey Juan Carlos of Madrid in 2015.

He was Scientific Attache’ of the Italian Embassy in Israel during the years 2007-2011 and 2014-2018.

He is currently Director of Research at the Institute of Complex Systems of the Italian CNR, in Florence.

His major scientific interests are i) pattern formation and competition in extended media, ii) control and synchronization of chaos, and iii) the structure and dynamics of complex networks.

He is Editor in Chief of the Journal “Chaos, Solitons and Fractals” (Elsevier) from 2013, member of the Academia Europaea since 2016, and Fellow of the American Physical Society from 2024.

He was elected member of the Florence City Council from 1995 to 1999.

Boccaletti has published 352 papers in peer-reviewed international Journals, which received more than 41,000 citations (Google Scholar). His h factor is 76 and his i-10 index is 241.

With more than 13,500 citations, the monograph ¨Complex Networks: Structure and Dynamics¨, published by Boccaletti in Physics Reports on 2006 converted into the most quoted paper ever appeared in the Annals of that Journal.

Past Events

February 17, 2025: Why Are There Six Degrees of Separation in a Social Network?


Stefano Boccaletti 
Director of Institute of Complex System, Florence, Italy

February 17, 2025
12:00pm EST

Abstract:
A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. 

In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is even independent of the network size over several orders of magnitude, is still unknown. 

I will show that the “six degrees of separation” is in fact just the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. 

Moreover, the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. 

Thus, simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of complex networks.

 

 

Bio:
Stefano Boccaletti received the PhD in Physics at the University of Florence in 1995, and a PhD honoris causa at the University Rey Juan Carlos of Madrid in 2015.

He was Scientific Attache’ of the Italian Embassy in Israel during the years 2007-2011 and 2014-2018.

He is currently Director of Research at the Institute of Complex Systems of the Italian CNR, in Florence.

His major scientific interests are i) pattern formation and competition in extended media, ii) control and synchronization of chaos, and iii) the structure and dynamics of complex networks.

He is Editor in Chief of the Journal “Chaos, Solitons and Fractals” (Elsevier) from 2013, member of the Academia Europaea since 2016, and Fellow of the American Physical Society from 2024.

He was elected member of the Florence City Council from 1995 to 1999.

Boccaletti has published 352 papers in peer-reviewed international Journals, which received more than 41,000 citations (Google Scholar). His h factor is 76 and his i-10 index is 241.

With more than 13,500 citations, the monograph ¨Complex Networks: Structure and Dynamics¨, published by Boccaletti in Physics Reports on 2006 converted into the most quoted paper ever appeared in the Annals of that Journal.

December 4, 2024: Topology shapes dynamics of higher-order networks


Ginestra Bianconi
Professor of Applied Mathematics, Queen Mary University of London

December 4, 2024
12:00pm EST

 

Abstract:

Higher-order networks capture the interctions among two or more nodes and they are raising increasing interest in the study of complex systems including most notably brain networks. Here we show  that the interplay between structure and dynamics on higher-order networks depends non trivially on their underlying network topology. We will highlight the main results of the new field of  higher-order topological dynamics which combines higher-order interactions, algebraic topology to non-linear dynamics and has the potential to transform deeply our understanding of complex systems.
 
We reveal how non-linear dynamical processes can be used to learn the topology, by defining Topological Kuramoto model and Topological global synchronization. These critical phenomena  capture the synchronization of topological signals, i.e. dynamical signal defined not only on nodes but also on links, triangles and higher-dimensional simplices in simplicial complexes. Finally will discuss how the Dirac operator can be used to couple and process topological signal of different dimensions, giving rise to new collective phenomena such as Dirac synchronization and Dirac Turing patterns, and allowing new machine learning algorithms such as Dirac signal processing.
Bio:
Ginestra Bianconi is Professor of Applied Mathematics in the School of Mathematical Sciences of Queen Mary University of London. She is member or the European Academy of Sciences. Currently she is Chief Editor of JPhys Complexity (IOP), Editor of PloSOne, and Scientific Reports. Awards: APS Fellow, Network Science Fellow and chair Franqui 2023.
Her research activity focuses on statistical mechanics, and network theory. She has formulated the Bianconi-Barabasi model that displays the Bose-Einstein condensation in complex networks. She has introduced the statistical mechanics of  canonical and microcanonical network ensembles and she has proven their non-equivalence. She has made important contributions on the study of critical phenomena on networks. In the last years, she has been focusing on multilayer networks, simplicial complexes, network geometry and topology, percolation, synchronization and network control. She is the author of the books Multilayer Networks: Structure and Function (Oxford University Press, 2018),  Higher-order Networks: An introduction to simplicial complexes (Cambridge University Press, 2021).
 

October 16, 2024: Frontiers in Physics and Data Science

2024 Nobel Prize in Physics
October 16, 2024

 

Event details:

GSNP and GDS are pleased to host a klogW virtual seminar discussing the 2024 Nobel Prize in Physicsawarded to John Hopfield and Geoffrey Hinton
"for foundational discoveries and inventions that enable machine learning with artificial neural networks".

Our panel of experts:

Nicolas Brunel, Duke; Eun-Ah Kim, Cornell; Pankaj Mehta, BU; David Schwab, CUNY; Sara Solla, Northwestern; Yuhai Tu, IBM will give introductions to the statistical physics underpinning these advances, and then discuss the frontiers and future implications of these developments for physics and data science.

Date: October 16, 2024
Time: 12pm (Eastern time zone)

 

June 26, 2024: 2024 GSNP Student and Postdoc Speaker Winners

Date: June 26, 2024
Colin Scheibner
Princeton University

Title: Spiking at the Edge: Excitability at interfaces in reaction-diffusion systems


Abstract:  Spiking is a general phenomenon, crucial in the firing of neurons, beating of hearts, and spread of diseases. In homogeneous media, spiking arises from a local competition between amplifying and suppressing forces. But most real-world systems are far from homogeneous. In this talk, I will discuss how inhomogeneities such as interfaces and boundaries (that spatially segregate these two forces) can promote spiking, even if the system does not spike when these forces are evenly mixed. The underlying mathematics reveal a counterintuitive spiking phase diagram in which increasing the system size or decreasing the diffusive coupling can give rise to spiking. These insights apply to chemical reactions, predator–prey dynamics, and recent electrophysiology experiments, in which localized action potentials were observed at the interface of distinct, nonspiking bioelectric tissues.

Speaker Bio: 

 Colin received a BA in physics and mathematics from St. Olaf College in 2017 and completed his PhD in physics at the University of Chicago in 2023. His research interests typically involve using statistical physics (e.g. hydrodynamics and coarse graining) and math (e.g. geometry and topology) to understand emergent behavior in soft matter and biology. Colin is currently a postdoc at Princeton University in the Center for the Physics of Biological Function and the Princeton Center for Theoretical Science.

Sam Dillavou
University of Pennsylvania 

Title: Emergent Machine Learning in a Nonlinear Electronic Metamaterial
Abstract: Machine learning methods typically use gradient descent – a centralized, top-down algorithm – to optimally modify every parameter. In this talk I'll discuss our recently realized electronic learning metamaterials that perform machine learning differently, in an entirely bottom-up manner (without help from a processor). Each element of our system follows local update rules, and global learning emerges from these dynamics. This is a feature shared with the brain, albeit with different rules and dynamics. I'll discuss the construction and operation of these systems, the breadth of tasks they can accomplish even within a single architecture choice, and similarities to biological systems including the brain. Further, I'll show that the system learns complex, nonlinear tasks in a predictable sequence, by lowering polynomial modes of the error. This ordering persists regardless of the relative sizes of these modes. Our system trains in seconds and performs learned tasks in microseconds, dissipating picojoules of power across each element. Future versions have enormous potential to be faster and more efficient than state-of-the-art machine learning solutions, while providing additional benefits like robustness to manufacturing defects, similar to how biological systems can endure damage but retain functionality.

Speaker Bio:
Sam Dillavou is a postdoctoral fellow at the University of Pennsylvania in the Department of Physics and Astronomy. He completed his PhD in Physics at Harvard, where he studied memory effects in frictional interfaces. He is now interested in the overlap between experimental physics and (machine) learning, and how the two fields can inform and support each other. This includes building physical systems that can perform machine learning tasks (learn) without a processor, studying complex systems like granular flows that have resisted understanding using standard statistical methods, and using machine learning to make experimental science easier and more accessible.

May 28, 2024: Nonlinear and stochastic behavior of Kerr microcombs


Yanne Chembo
University of Maryland

Abstract:
Kerr optical frequency combs (or microcombs) are expected to play a major role in photonic technology, with applications related to spectroscopy, sensing, aerospace, and communication engineering. Most of these applications are related to the metrological performance of Kerr combs, which is ultimately limited by their noise-driven fluctuations. For this reason, it is of high importance to understand pattern formation in these optical cavities, as well as the influence of random noise on these patterns. Here, we present the theoretical framework used to investigate these systems. This nonlinear and stochastic model allows us to parametrize the dynamics of the system with great accuracy. The theoretical results are found to be in excellent agreement with numerical simulations and experimental measurements.

 

Bio:
Yanne K. Chembo received a Ph.D. in nonlinear dynamics from the University of Yaounde I, Cameroon (2001-2005), and a Ph.D. degree in photonics from the University of the Balearic Islands, Palma de Mallorca, Spain (2002-2006). He has been a postdoctoral fellow at CNRS in France (2007-2009), and at the NASA/Caltech Jet Propulsion Lab in California (2009-2010). He was a Research Director for the CNRS in France before joining the University of Maryland in 2019, where is currently Professor in the Department of Electrical and Computer Engineering, and Director of the Institute for Research in Electronics and Applied Physics (IREAP).  He is a Fellow of SPIE, OPTICA and APS.

May 1, 2024: Circulation in classical and quantum turbulence


K.R. Sreenivasan
New York University

Abstract:
This talk will discuss the scaling properties of circulation in classical turbulence, how they appear simpler than those of structure functions, how the ideas carry over to quantum turbulence (what is similar and what is not), etc. No prior knowledge of turbulence will be assumed.

 

Speaker Bio:
Katepalli Sreenivasan is the Eugene Kleiner Professor for Innovation in Mechanical Engineering at the New York University (NYU), Professor in the Physics Department and at the Courant Institute of Mathematical Sciences. He was previously the president of the Polytechnic Institute of New York University, the inaugural Dean of Engineering, the executive vice provost in charge of science and technology. Previously, he has held directorate and professorship appointments at the International Centre for Theoretical Physics in Trieste, Italy, University of Maryland, and Yale.

He was elected to the NAS, NAE, American Academy of Arts and Sciences, Indian Academy of Sciences, Indian National Science Academy, Academy of Sciences for the Developing World (TWAS), African Academy of Sciences, and Accademia die Lincei in Italy, among others. His honors include: Leo P. Kadanoff Prize (APS-GSNP), Guggenheim Fellowship, Otto Laporte Memorial Award (APS-DFD), TWAS Medal Lecture in Engineering Science, Distinguished Alumnus Award and Centennial Professorship of the Indian Institute of Science, the International Prize and Gold Medal in memory of Professors Modesto Panetti and Carlo Ferrari, Academia delle Scienze di Torino, Italy, National Order of Scientific Merit (the highest scientific honor) by the Brazilian Government and the Academy of Sciences, UNESCO Medal for Promoting International Scientific Cooperation and World Peace from the World Heritage Centre, Florence, Italy, the Dwight Nicholson Medal of APS, the 2009 Nusselt-Reynolds Prize, the 2009 AAAS award for International Scientific Cooperation, the 2019 G.I. Taylor Medal of the Society of Engineering Science, the 2019 Theodore von Karman Medal of the ASCE, 2020 the Charles Russ Richards Award of the ASME and Pi Tau Sigma, the 2020 Fluid Dynamics Prize of the APS, and the 2011 Multicultural Leadership Award of the National Diversity Council.

March 27, 2024: Playing with Sand: From frictional rigidity percolation to granular learners


Jennifer Schwarz
Syracuse University

Abstract:
Sand is a playful material, both in practice and in principle, given its complex,  and so unexpected, behavior. We address several aspects of this complexity, beginning with jamming in frictional granular packings from the perspective of rigidity percolation, or the emergence of a spanning rigid cluster. We will also discuss building frictional versus frictionless packings in the context of minimal rigidity proliferation. Finally, we will present ongoing efforts to create a physical learning system with a pile of sand, hence potentially bringing new meaning to the words “playful material”.

 

Speaker Bio:
Jen Schwarz, a theoretical physicist, earned a BS/BA in physics and history from the University of Maryland at College Park.  She then went on to earn her PhD in physics from Harvard. After a first post-doc at Syracuse University and a second post-doc at UCLA/UPenn, she joined the faculty at Syracuse University, where she is now a professor of physics. Her awards include a NSF CAREER award and an Isaac Newton Award for Transformative Ideas During the COVID-19 Pandemic from the DoD. She was also recently elected as an APS Fellow by the GSNP. Her citation reads: "For influential contributions to the statistical physics of disordered systems, specifically the development of models of correlated percolation, and of models of rigidity transitions in living and nonliving matter”. Besides her work on frictional jamming, Jen is thinking about the emergent properties of learning in living and nonliving systems and about how a theoretical physicist can combat cancer and COVID. When her brain is not jammed with thoughts on jamming and other physics phenomena, Jen enjoys spending time on her farm with her family, the trees, the animals (including 19 chickens), and her farm friends.

February 23, 2023: Yielding and fatigue failure in amorphous solids


Srikanth Sastry
Jawaharlal Nehru Center for Advanced Scientific Research

Abstract: 
Plasticity and yielding behaviour of amorphous solids, of relevance to a wide range of phenomena associated with glasses and other soft materials, has been investigated extensively in recent years through computer simulations and statistical mechanical approaches. In particular, the nature of yielding under cyclic deformation, with interesting connections to other reversible-irreversible transitions, have been explored through computer simulation of model glasses. These studies reveal yielding to be a discontinuous transition, with a strong dependence on the degree of annealing of the glasses. These and several other interesting features are qualitatively captured by theoretical models and corresponding simulations, which will be described. A practically important phenomenon in solids subjected to cyclic loading is that of fatigue failure, which occurs after a number of cycles of loading, with the number of cycles depending on the amplitude of deformation, with an apparent divergence as a limiting amplitude is approached from above. Simulation results for fatigue failure, and their rationalization in terms of recent theoretical approaches to understanding yielding and fatigue failure in amorphous solids will be presented.

 

Speaker Bio:
Srikanth Sastry's research interests have been in the area of statistical mechanics, with a focus on understanding a range of unusual and interesting properties of liquids and other soft condensed matter, which he addresses with computation as a major tool. Some of the areas of his research activities are: Slow dynamics and routes to structural arrest (glass transition, jamming etc) in supercooled liquids and other soft matter systems; Mechanical properties of glasses and other amorphous solids, their yielding behaviour and memory effects; Routes to jamming in sphere packings; the liquid-liquid transition in water and silicon, etc. He obtained a Bachelor’s degree in Science from Bangalore University, a Master’s degree in Physics from the Indian Institute of Technology, Bombay, and a Master’s degree and PhD in Physics from Boston University. He has been a faculty member at the Jawaharlal Nehru Centre for Advanced Scientific Research since 1998, after postdoctoral research at the National Institutes of Health, Bethesda, USA, and Princeton University, USA, Arizona State University/National Institute of Standards and Technology and was a Professor at the TIFR Centre for Interdisciplinary Sciences, Hyderabad during 2012-14. His APS fellowship citation reads: For pioneering quantitative investigations of energy landscapes, dynamics, and thermodynamics of supercooled liquids, network forming liquids, and glasses. For novel insights into the role of geometry in shear jamming of grains and into yielding transitions and memory formation in amorphous solids.

January 24th 2023: Nanoparticle transport in complex fluids


Jacinta Conrad
Professor, University of Houston

Abstract: Transport of nanoscale particles through crowded, confined matrices is essential for drug delivery, diagnostic assays, and processing of nanocomposite materials. Because nanoparticles are comparable in size to heterogeneities within these matrices, their transport properties may be altered by the local structure and dynamics within the complex fluid. I will discuss our recent work on nanoparticle transport in polymer solutions, in which dynamic and structural length scales can be controlled through polymer concentration and molecular weight and hence serve as tunable model viscoelastic liquids. We use microscopy, scattering, and simulation methods to identify the mechanisms controlling the diffusion of a variety of particles, including nanospheres, viruses, and polymer-grafted nanoparticles. This fundamental understanding of the coupling of nanoparticles dynamics to liquid relaxations will lead to better control over the spreading of nanoparticles through complex, heterogeneous materials.

Speaker Bio: Jacinta Conrad is a soft matter physicist studying transport and dynamics of soft, complex matrices using experiments and simulations for applications in materials processing, antifouling, environmental remediation, and disease detection. She earned an SB in Mathematics from the University of Chicago in 1999 and MA and PhD degrees in Physics from Harvard University in 2002 and 2005. She worked as a postdoctoral associate in Materials Science and Engineering at the University of Illinois at Urbana-Champaign before starting her faculty position at the University of Houston in 2010, where she is currently Frank M. Tiller Professor of Chemical and Biomolecular Engineering. She is Chair for the American Physical Society Division of Soft Matter (DSOFT), a member of the Fluids Programming Committee (Area 1J) for the American Institute of Chemical Engineers, an Executive Editor for ACS Applied Nano Materials, a member of the Soft Matter (RSC) Advisory Board, and a Fellow of the American Physical Society (2022) and the Society of Rheology (2021). She co-founded (2013) and continues to help organize the Texas Soft Matter Meeting, which regularly attracts 100+ attendees from Texas universities and industries. Her APS fellowship citation reads "For experimental contributions to understanding nanoparticle dynamics, bacterial adhesion, and colloid-polymer mixtures, using advanced microscopy and light scattering techniques".

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December 16, 2022: Community detection in networks


Santo Fortunato
Professor, Luddy School of Informatics, Computing, and Engineering, Indian University and Director of the Indiana University Network Science Institute

Detecting network communities, i.e. subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network, is a fundamental problem in network science. I will introduce the problem and address the limits of the most popular class of clustering algorithms, those based on the optimization of a global quality function, like modularity maximization. Validation is probably the single most important issue of network community detection, as it implicitly involves the concept of community, which is ill-defined. I will discuss the importance of using realistic benchmark graphs with built-in community structure as well as the role of metadata. I will also show that neural embeddings can be used to efficiently detect communities.

Santo Fortunato is a Professor at the Luddy School of Informatics, Computing, and Engineering at Indian University and Director of the Indiana University Network Science Institute since 2018. He received his PhD in theoretical high energy physics at the University of Bielefeld in Germany, then moved to the field of complex networks as a postdoc in the Complex Systems group of Alessandro Vespignani at Indiana University. He spent some years as a research scientist at the School for Scientific Interchange in Turin, Italy, following which he became a professor in Complex Systems at the Department of Biomedical Engineering and Computational Science (BECS) of the School of Science of Aalto University in Espoo, Finland. He has been at Indiana University since 2016. His APS fellowship citation reads "For foundational contributions to the statistical physics of complex networks, and particularly to the study of community detection in networks and applications to social and scientific networks.".

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February 22, 2022: Contagion dynamics on single, multilayer, and higher-order networks.


Prof. Yamir Moreno
Director of the Institute for Bio-computation and Physics of Complex Systems (BIFI),
Professor of Physics, Department of Theoretical Physics University of Zaragoza

Modern network science has greatly contributed to our understanding of many processes in diverse fields of science. Arguably, contagion dynamics -including network epidemiology- is the area in which network concepts have had a bigger practical impact. Nowadays, we are able to model how diseases unfold and spread with unprecedented precision, which also makes it possible to analyze other spreading-like processes, such as social contagion. In this talk, we revise this area of research by discussing how the modeling of spreading processes has evolved in the last two decades. We start by analyzing contagion dynamics in single populations that are described by different network topologies. Next, we discuss cases in which a multilayer approach is needed. Finally, we introduce contagion dynamics in higher-order networks, which shows a much rich phase space for the dynamics of the system. We round off the talk by discussing what are the challenges that remain for the future.

Prof. Yamir Moreno (Havana City, 1970) got his PhD in Physics (Summa Cum Laude, 2000) from University of Zaragoza. Shortly afterward, he joined the Condensed Matter Section of the International Centre for Theoretical Physics (ICTP) in Trieste, Italy as a research fellow. He is the Director of the Institute for Bio-computation and Physics of Complex Systems (BIFI), the head of the Complex Systems and Networks Lab (COSNET) and Professor of Physics at the Department of Theoretical Physics of the Faculty of Sciences, University of Zaragoza. Prof. Moreno is also a Deputy Director of the ISI Foundation in Italy and External Professor of the Complexity Science Hub Vienna, Austria. During the last years, he has been working on several problems such as: the study of nonlinear dynamical systems coupled to complex structures, transport processes and diffusion with applications in communication and technological networks, dynamics of virus and rumors propagation, game theory, systems biology (the TB case), the study of more complex and realistic scenarios for the modeling of infectious diseases, synchronization phenomena, the emergence of collective behaviors in biological and social environments, the development of new optimization data algorithms and the structure and dynamics of socio-technical and biological systems. At present, he is a Divisional Associate Editor of Physical Review Letters, Editor of the New Journal of Physics, Chaos, Solitons and Fractals and Journal of Complex Networks; and Academic Editor of PLoS ONE, and a member of the Editorial Boards of Scientific Reports, Applied Network Science, and Frontiers in Physics. Prof. Moreno is the elected President of the Network Science Society (NSS) and served as President of the Complex Systems Society from 2015 to 2018. He was a member of the Future and Emerging Technology Advisory Group of the European Union’s Research Program: H2020 and of the Advisory Board of the WHO Collaborative Center “Complexity Sciences for Health Systems” (CS4HS), whose headquarters is at the University of British Columbia Centre for Disease Control, in Vancouver, Canada. He was an ISI Fellow from 2013 to 2017.

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January 19, 2022: Flocking Fluids


Denis Bartolo
Professor of Physics,
Ecole Normale Superieure (ENS) de Lyon

We are active matter. We can walk jump and swim without relying on external drives to actuate our body. Beyond the specific case the human body, the term active matter now refers to any self-organised structure assembled from living or synthetic units independently driven far from equilibrium. In particular, inspired by the dazzling dynamics of animal groups,  physicists and chemists have successfully engineered active fluids driven from within by flocks of self-propelled particles.  In my talk, I will show how nearly pristine laminar flows emerge in fluids made of flocks of colloidal particles. Combining experiments simulations and theory, I will explain the generic mechanism that allow flocking matter to heal their topological singularities  and discuss the robustness of their spontaneous flows in heterogeneous environments.

Bartolo is a Professor of Physics at ENS de Lyon, France. After studying experimental physics and chemistry, he completed his PhD in theoretical physics at ESPCI-Paris. After postdoctoral research et Ecole Normale Supérieure,  he joined Université de Paris as an assistant professor in 2006 and joined the ENS de Lyon faculty as a full professor in 2012. He likes combining  experiments and theory to investigate a variety of collective phenomena in driven and active soft condensed matter. His current research interests include active-matter hydrodynamics, topological phases and crowd dynamics. 

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November 30, 2021: Wildfires, Disasters, and Open Questions for Statistical and Nonlinear Physics


Jean Carlson
Professor of Physics,
University of California, Santa Barbara

Wildfires are complex physical phenomena, which occur both naturally and as the result of human-caused ignitions. In the Western United States and globally, increases in wildfire severity and impacts on populations have drawn widespread attention in recent years as a hallmark of climate change. Until recently wildfire mitigation has received less attention compared to other forms of disasters. I will discuss emerging opportunities in this area, at both the fundamental level and practical applications. Basic patterns involving ignition, fire spread, and termination have served as a paradigm of complexity research for many years, and progress in this area contributed to advances in modern wildfire modeling. The emerging directions emphasize development of resilient communities, and focus on fire at the wildland-urban interface, cascading disasters, economic impacts on human populations and infrastructure, pollution, health, and climate change. Progress mandates a systems-based approach, and many of the fundamental questions present new opportunities for statistical and nonlinear physics. Problems of interest include pointwise and interfacial propagation in heterogeneous, dynamic environments and multi-system integration, communication, decision-making, and/or risk-management. Fundamental advances in these areas have potential applications not only in wildfire mitigation, but also more broadly in development of resilient communities.

Prof. Carlson studied electrical engineering and computer science at Princeton University and then Cornell University for her graduate studies, earning a master's in applied physics. She switched to theoretical condensed matter physics for her doctoral studies and completed her PhD under the supervision of James Sethna on the spin glass model in the Bethe Lattice. Carlson worked in the Kavli Institute for Theoretical Physics as a postdoctoral scholar with James S. Langer. Carlson was appointed to the faculty at the University of California, Santa Barbara in 1990. She works on the fundamental theory and applications of complex systems. She was elected a Fellow of the American Physical Society (APS) in 2021 after a nomination from the APS Topical Group on Statistical and Nonlinear Physics, "for the development of mathematically rigorous, physics-based models of nonlinear and complex systems that have significantly impacted a broad range of fields including neuroscience, environmental science, and geophysics".

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October 12, 2021: Soft matter concepts in quantum non-equilibrium


Juan P. Garrahan
Professor of Physics, University of Nottingham

Classical many-body systems that display interesting correlated dynamics often do so due to effective dynamical constraints, for example as a consequence of excluded volume interactions. A typical example is that of fluids near glass transitions where constraints lead to slow relaxation, dynamic heterogeneity and eventual arrest. I will discuss how these classical concepts can have relevance also for quantum many-body systems. I will describe three problems time permitting: (i) glassiness and correlated dynamics in ensembles of driven Rydberg atoms, (ii) slow dynamics in kinetically constrained quantum systems, and (iii) the extension to quantum trajectories of the method of dynamical large deviations. My aim is not to be too detailed but to highlight areas that I think are fertile ground for the exchange of ideas between classical and quantum non-equilibrium.

Professor Juan P. Garrahan has held a Chair in Physics at the University of Nottingham since 2007. His research covers a broad area of theoretical statistical physics and its applications, with particular interests in the dynamics of complex and slow relaxing materials such as supercooled liquids and glasses, quantum non-equilibrium systems, and the theory of large deviations. He obtained his PhD from the University of Buenos Aires, was a Glasstone Fellow at the University of Oxford, an EPSRC Advanced Fellow, a visiting professor at UC Berkeley, and a Visiting Fellow at All Souls College, Oxford. At Nottingham he currently leads the Centre for Quantum Non-Equilibrium Systems (CQNE) and directs the Machine Learning in Science (MLiS) Initiative.

September 14, 2021: Crises & Collective Phenomena in Socio-Economic Systems


Jean-Philippe Bouchaud
Capital Fund Management (CFM),
Professor of Physics, École normale supérieure

As P. W. Anderson wrote in 1972 in his article "More is different", the behavior of large assemblies of individuals (or molecules) cannot be of individuals (or molecules) cannot be understood by extrapolating the behavior of isolated individuals (or molecules). On the contrary, completely new behaviors, sometimes spectacular and difficult to anticipate, can appear and require new ideas and methods. The purpose of statistical physics is precisely to try to understand these collective phenomena, which do not belong to any of the underlying elementary constituents. In particular, small changes at the individual level can lead to dramatic effects at the collective level. Several simple examples will be discussed, which demonstrate the need to go beyond the models of of classical economics, based on the idea of a "representative agent" (moreover rational), and for which only exogenous events can lead to crises - whereas many socio-economic or financial phenomena seem to be endogenous in nature.

J-P Bouchaud is founder and Chairman of Capital Fund Management (CFM), professor of physics at École polytechnique and co-director of the CFM-Imperial Institute of Quantitative Finance at Imperial College London. He is a member of the French Academy of Sciences. Graduated from École Normale Supérieure in 1985, he then worked on his PhD at the Laboratory of Hertzian Spectroscopy, studying spin-polarized quantum gases with Claire Lhuillier. He then worked for the French National Center for Scientific Research, in particular on liquid Helium 3 and diffusion in random media. He spent a year at the Cavendish Laboratory, University of Cambridge in 1992 before joining the Laboratory of Condensed Matter Physics (SPEC) of the French Atomic Energy and Alternative Energies Commission (Commissariat à l'énergie atomique or CEA) at Saclay. Pioneer in econophysics, he co-founded the company Science et Finance in 1994, which later merged with Capital Fund Management (CFM) in 2000. He is now the Chairman of CFM. After teaching statistical mechanics for ten years at ESPCI, he was appointed in 2009 as an adjunct Professor at École Polytechnique. He now teaches a course From Statistical Mechanics to Social Sciences at École Normale Supérieure. His work covers the physics of disordered and glassy systems, granular materials, the statistics of price formation, stock market fluctuations and the modelling of financial risks. He has repeatedly criticized the dogma of the efficient-market hypothesis and the methodology of economics and mathematical finance, in particular the use of the Black–Scholes model which leads to a systematic underestimation of risk in options trading. Bouchaud is the recipient of the IBM Young Researcher Prize (1989), the CNRS Silver medal (1995), and was elected Risk Quant of the Year (2017) and member of the French Academy of Sciences (2017).

August 12, 2021: APS-GSNP Young Scientists Day


Cacey Bester
Department of Physics, Swarthmore

Jie Ren
Merck Cooperation

Examples of granular materials exist in abundance, from rice and cereal to sand and rocks. These particulate systems seem simple; they typically consist of rigid grains that interact by contact forces. However, granular materials present complexities that are not well-understood, such as disordered force networks that transmit forces among grains and flow behavior that can readily change between solid-like rigidity and fluid-like flow. Granular experiments can illustrate both of these aspects. My research group uses experimental imaging techniques and analysis to explore these properties of granular materials. In this talk, I will provide an overview of my research interests in this field, with focus on granular impact, the jamming transition of granular systems, and granular aggregation, while discussing my career trajectory as a professor at a liberal arts college.

Cacey Stevens Bester is an assistant professor of physics at Swarthmore College in Swarthmore, PA. She received her PhD in physics from the University of Chicago in 2015 under professor Sidney Nagel. After graduation, she held postdoctoral positions at Duke University (2015-2018) under the late professor Bob Behringer and the University of Pennsylvania (2018-2019) under professor Douglas Jerolmack. Her research interests are in experimental soft matter and granular physics, particularly studies of the granular jamming transition, granular impact, and the aggregation of granular particles due to complex fluid flows. Additionally, Cacey has been a member of APS GSNP for several years and currently serves as a member-at-large for the topical group. Cacey also serves as the chair of the committee on diversity, equity, and inclusion for the physics and astronomy department at Swarthmore.

Digital health technologies are growing tremendously over the past decade. They are becoming center-stage in public spotlight thanks in-part to the global pandemic. There is news in this space almost every day: startup companies, high-profile acquisitions, huge investments, new Apple/Fitbit features, and innovative clinical research approaches enabled by remote monitoring and virtual interactions. Multi-modal streams of information about our health, both mental and physical, become just finger taps away. Digital health is a fascinating field of research and development; at the same time, it can be dizzying to follow, and also comes with inevitable hypes and pitfalls along with its growth. I will provide a perspective of this field, including the cross-industry landscape, progresses, trends, as well as career prospects. Through interactions with colleagues in a wide variety of fields and functions, I can see that physicists have very unique strengths when it comes to data science and their applicability in scientific research. I believe my career path is a very accessible one; I would love to share my advice with students interested in entering the corporate world.

Jie Ren is an Associate Principal Scientist at Merck & Co., Inc. She did her PhD work in granular physics at Duke University under the late professor Bob Behringer. After graduation, she joined Merck and started her pharmaceutical career developing drug formulations and new manufacturing technologies. She has then moved from the physical sciences side to the clinical sciences side to build data science capabilities and digital health technologies. Her current work focuses on developing novel digital biomarkers for disease characterization and progression tracking using mobile apps and wearable devices, as well as integrating such tools into clinical trial operations. Jie is active in the APS community and has been a proud DSOFT member since grad school. In addition, she co-founded the Topical Group on Data Science (GDS) and served as the inaugural chair-elect, chair, and past-chair from 2019 to 2021. She is currently a member of the APS Committee on Career and Profession Development and works alongside APS Career Services to help physics students prepare for industry careers. She has constantly provided mentorship through the APS IMPact and other platforms for students interested in industry career opportunities.

July 14, 2021: Memory in a glassy landscape


Sid Nagel
Department of Physics,
James Franck Institute, Enrico Fermi Institute,
University of Chicago

Out-of-equilibrium systems preserve memories of their formation and training history in a variety of ways allowing for an innovative classification of material and dynamics.  I will discuss one case where a cyclically sheared suspension of particles or a charge-density-wave solid (or even a walk in the park!) remembers multiple values from a series of training inputs yet forgets all but two of them at long times despite their continued repetition; however, if noise is added all the memories can be encoded indefinitely!  When the packing density is increased, so that the particles become jammed, the evolution takes place in a very rugged energy landscape where scores of local energy minima are visited during each applied oscillation.  Nevertheless the jammed solid can readily find the periodic orbits.  Memory formation in such a system not only sheds light on how glassy ground states are selected and communicate with one another but also shows a form of memory that allows a new probe of the interactions within a material. 

Sid Nagel has been at the University of Chicago since 1976.  He likes disordered things that are out of equilibrium.  His motto is:  Experiment —  where theory comes to die. Here's a link to his research. 

June 16, 2021: Excitations of Earth's Carbon Cycle


Daniel Rothman
Earth, Atmospheric and Planetary Sciences,
Massachusetts Institute of Technology (MIT)

Mysterious, abrupt changes in the ocean's store of carbon occur intermittently throughout Earth's history.  Each of these disruptions coincides with climate change; moreover, mass extinctions are always accompanied by such events.  What causes these disruptions?  I suggest that influxes of carbon dioxide that exceed a critical rate excite characteristic nonlinear responses in Earth's carbon cycle.  Analysis of the geologic record supports this hypothesis and a model of an excitable carbon cycle suggests how it works.  Both show how to rescale the slow critical rates of the geologic past to inform our understanding of modern environmental change at much faster timescales.


Daniel H. Rothman is a Professor of Geophysics in the Department of Earth, Atmospheric, and Planetary Sciences at MIT.  He is co-founder and co-director of MIT's Lorenz Center, which is devoted to learning how climate works.  Rothman joined the MIT faculty in 1986, after receiving his AB in applied mathematics from Brown University and his PhD in geophysics from Stanford University. He has held visiting appointments at the University of Chicago, Ecole Normale Superieure, and Harvard's Radcliffe Institute for Advanced Study.  He is a Fellow of the APS and the American Geophysical Union, and the recipient of the 2016 Levi L. Conant Prize from the American Mathematical Society

May 20, 2021: Understanding machine learning via exactly solvable statistical physics models


Lenka Zdeborova
École polytechnique fédérale de Lausanne (EPFL)
Switzerland

The affinity between statistical physics and machine learning has long history, this is reflected even in the machine learning terminology that is in part adopted from physics. I will describe the main lines of this long-lasting friendship in the context of current theoretical challenges and open questions about deep learning. Theoretical physics often proceeds in terms of solvable synthetic models, I will describe the related line of work on solvable models of simple feed-forward neural networks. I will highlight a path forward to capture the subtle interplay between the structure of the data, the architecture of the network, and the learning algorithm.

Lenka Zdeborova is a professor of physics and of computer science and communication systems at EPFL (École Polytechnique Fédérale de Lausanne). She earned a master's degree in physics at Charles University in 2004 and, in 2008, completed an international dual doctorate ("en cotutelle") at both Charles University and University of Paris-Sud. Her doctoral advisors were Václav Janiš at Charles University, and Marc Mézard at Paris-Sud. After postdoctoral research at the Center for Nonlinear Studies of Los Alamos National Laboratory, she became a researcher for the French Centre national de la recherche scientifique (CNRS) in 2010, posted at the Institut de Physique Theorique of the French Alternative Energies and Atomic Energy Commission (CEA) in Paris-Saclay. She also earned a habilitation in 2015 at the École normale supérieure (Paris). Since 2020 she has been working at EPFL (École Polytechnique Fédérale de Lausanne) an Associate Professor of physics, and of computer science and communication systems in the Schools of Basic Sciences and of Computer and Communication Sciences (IC), and is the head of Laboratory of Statistical Physics of Computation. Zdeborová won the CNRS Bronze medal in 2014. In 2016 the École normale supérieure (Paris) gave her the Philippe Meyer prize in theoretical physics. She is also the 2018 winner of the Irène Joliot-Curie Prize for young female scientists.

April 6, 2021: The landscape-dependent annealing strategy in machine learning:
How Stochastic-Gradient-Descent finds flat minima


Yuhai Tu
IBM T. J. Watson Research Center,
Yorktown Heights, NY 10598

Despite tremendous success of the Stochastic Gradient Descent (SGD) algorithm in deep learning, little is known about how SGD finds ``good" solutions (low generalization error) in the high-dimensional weight space. In this talk, we discuss our recent work on establishing a theoretical framework based on nonequilibrium statistical physics to understand the SGD learning dynamics, the loss function landscape, and their relation. Our study shows that SGD dynamics follows a low-dimensional drift-diffusion motion in the weight space and the loss function is flat with large values of flatness (inverse of curvature) in most directions. Furthermore, our study reveals a robust inverse relation between the weight variance in SGD and the landscape flatness opposite to the fluctuation-response relation in equilibrium systems. We develop a statistical theory of SGD based on properties of the ensemble of minibatch loss functions and show that the noise strength in SGD depends inversely on the landscape flatness, which explains the inverse variance-flatness relation. Our study suggests that SGD serves as an ``intelligent" annealing strategy where the effective temperature self-adjusts according to the loss landscape in order to find the flat minimum regions that contain generalizable solutions. Finally, we discuss an application of these insights for reducing catastrophic forgetting efficiently for sequential  multiple tasks learning

Yuhai Tu graduated from University of Science and Technology of China in 1987. He came to the US under the CUSPEA program and received his PhD in physics from UCSD in 1991. He was a Division Prize Fellow at Caltech from 1991-1994. He joined IBM Watson Research Center as a Research Staff Member in 1994 and served as head of the theory group during 2003-2015. He has been an APS Fellow since 2004 and served as the APS Division of Biophysics (DBIO) Chair in 2017. He is also a Fellow of AAAS. For his work in theoretical statistical physics, he was awarded (together with John Toner and Tamas Vicsek) the 2020 Lars Onsager Prize from APS: "For seminal work on the theory of flocking that marked the birth and contributed greatly to the development of the field of active matter."

 

Yuhai Tu has broad research interests, which include pattern formation and nonlinear dynamics in nonequilibrium systems, surface physics, bioinformatics, biological physics, and recently physics of machine learning. He has made seminal contributions in many areas including the Toner-Tu equation in flocking theory, the growth dynamics of the Si-aSiO2 interface, pattern discovery algorithm for RNA microarray analysis, quantitative models of bacterial chemosensory system and motility, and the energy-speed-accuracy tradeoff in sensory adaptation, biochemical oscillation and synchronization.

February 17, 2021: Non-equilibrium steady states in active motile matter


Tanniemola Liverpool
Professor of Theoretical Physics,
School of Mathematics,
University of Bristol, UK

Colonies of swimming bacteria, algae or spermatozoa are examples of active systems composed of interacting units that consume energy and collectively generate motion and mechanical stresses.  Due to the anisotropy of their interactions, these active particles can exhibit orientational order at high concentrations and have been called “living liquid crystals". Biology at the cellular and multicellular scale provides numerous examples of these active systems. We will describe some recent theoretical work developing a framework for characterising the behaviour of active particle systems.

 

We show that the concept of a steady state, well known for systems at equilbrium,  must be generalised to describe driven, fluctuating physical systems such as active matter systems. In these,

the steady state is associated with a stationary probability density of micro-states and a deterministic dynamical system whose trajectories the system follows on average. These trajectories are a manifestation of  non-stationary macroscopic currents observed in these systems.  We determine precise conditions for the steady state to exist as well as the requirements  for it to be stable. Finally we illustrate this with the example of recent  experimental and theoretical work studying collections of electrically-driven colloidal rollers moving in two dimensions.

Tanniemola Liverpool is a Professor of Theoretical Physics at the School of Mathematics, University of Bristol, UK. Liverpool studied physics at Trinity Hall, Cambridge, and subsequently completed his doctoral studies also at the University of Cambridge under the supervision of Sam Edwards. After his PhD, Liverpool joined the University of Cologne as a postdoctoral researcher, then the Max Planck Institute in Mainz, and was also a Marie Curie fellow at ESPCI in Paris. Liverpool was awarded a Royal Society Research Fellowship in 2000, which he held at Imperial College in London. In 2002, he joined the University of Leeds, as a lecturer in Applied Mathematics. Since 2007, Liverpool has been at the School of Mathematics, University of Bristol first as a Reader and then Professor. He is also on the editorial board of the Journal of Theoretical Biology and the chair of the Institute of Physics Liquid and Complex Physics group.

December 3, 2020: The life and death of turbulence


Nigel Goldenfeld
Swanlund Endowed Chair
& Center for Advanced Study Professor in Physics; 
Director of NASA Astrobiology Institute for Universal Biology,
University of Illinois at Urbana-Champaign (UIUC)

Turbulence is the last great unsolved problem of classical physics. But there is no consensus on what it would mean to actually solve this problem. In this colloquium, I propose that turbulence is most fruitfully regarded as a problem in non-equilibrium statistical mechanics, and will show that this perspective explains turbulent drag behavior measured over 80 years, and makes predictions that have been experimentally tested in 2D turbulent soap films. I will also explain how this perspective is useful in understanding the laminar-turbulence transition, establishing it as a non-equilibrium phase transition whose critical behavior has been predicted and tested experimentally.  This work connects transitional turbulence with statistical mechanics and renormalization group theory, high energy hadron scattering, the statistics of extreme events, and even population biology.

Nigel Goldenfeld holds a Swanlund Endowed Chair and is a Center for Advanced Study Professor in Physics at the University of Illinois at Urbana-Champaign (UIUC). He is the Director of the NASA Astrobiology Institute for Universal Biology at UIUC, and leads the Biocomplexity Group at the University's Institute for Genomic Biology. Nigel received his Ph.D. from the University of Cambridge (U.K.) in 1982, and for the years 1982-1985 was a postdoctoral fellow at the Institute for Theoretical Physics, University of California at Santa Barbara. In 1996, Nigel co-founded NumeriX, a company that specializes in high-performance software for the derivatives marketplace.   He has served on the editorial boards of several journals, including The Philosophical Transactions of the Royal Society and Physical Biology. Selected honours include: Alfred P. Sloan Foundation Fellow, University Scholar of the University of Illinois, the Xerox Award for research, the A. Nordsieck award for excellence in graduate teaching and the American Physical Society's Leo P. Kadanoff Prize. Nigel is a Fellow of the American Physical Society, a Fellow of the American Academy of Arts and Sciences and a Member of the US National Academy of Sciences.

October 5, 2020: Realizing information engines and resetting processes to study system out of equilibrium


Yael Roichman
Professor of Physics and Chemistry
Tel Aviv University

Holographic optical tweezers afford the means to realize experimentally driving protocols that require feedback and control loops. Among these protocols, information engines and resetting processes are of great interest, since they have been extensively studied theoretically, and since they put forth different aspects of non-equilibrium. In this talk, I will describe our implementation of both protocols in a colloidal suspension. I will discuss the effects of temporal correlations in the driving of an information engine and the temporal and energetic cost of resetting.

Yael Roichman is a Professor of Physics and Chemistry in Tel Aviv University and a member of the center for physics and chemistry of living systems and the Center for Light Matter Interaction . She received her PhD form the Technion in Israel and performed her post-doctoral research at New York University before joining Tel Aviv University. Yael’s scientific interest is studying experimentally soft matter systems far from thermal equilibrium. The tools she employs for her studies are holographic optical tweezers (HOTs) for controlled optical manipulation of microscopic objects, and various optical microscopy techniques for imaging. Image analysis is used to characterize the dynamics and statistics of the studied systems.

October 5, 2020: Optofluidic-driven crystallization of colloids tethered at interfaces


Erika Eiser
Professor in Soft Matter Physics
Fellow of Sidney Sussex College
University of Cambridge

Optical tweezers have been established as indispensable tool for the manipulation of micro- and nano-sized objects. We show that colloids anchored to a water-oil interface via DNA-tethers will crystallize when only one of the particles is trapped with optical tweezers [1,2]. These DNA-anchored colloids are fully immersed in the water phase, thus they do not disturbe the oil-water interface, but allow the tethered colloids to diffuse freely along the oil-droplet surface in the absence of a trapping laser [2]. Our combined experimental and theoretical analyses show that local temperature gradients induced by trapping a single colloid cause a thermophoretic force pushing the trapped particle towards the colder oil phase, causing an attractive long-ranged hydrodynamic flow towards the laser focus thus causing out-of equilibrium crystallization of the DNA tethered colloids around the trapped particle. The crystallization is further enhanced by scattering forces known as optical binding.

 

[1] A. Caciagli, R. Singh, D. Joshi, R. Adhikari, E. Eiser, Physical Review Letters, 125 (6), 068001 (2020)
[2] D. Joshi, D. Bargteil, A. Caciagli, J. Burelbach, J. Xing, A. Nunes, D. Pinto, N. Araujo, J. Bruijc and E. Eiser, Science Advances, 2 : e1600881 (2016)

Erika Eiser: After studying physics at the Univ. of Konstanz (Germany) Erika received her PhD degree from the Weizmann Institute, Israel, in Soft Matter Physics. Following postdoctoral research at the Univ. of Montpellier and the European Synchrotron Radiation Facility in Grenoble (France), she joined the University of Amsterdam as Assistant Professor, where she started research on DNA-driven self-assembly of colloids, DNA hydrogels and the self-assembly of various other systems using various microscopy and rheology methods. Her group continues this work in Cambridge. She is also co-founder and co-director of the Edwards Centre for Soft Matter. 

September 3, 2020: Surprises in Slow Spheroid Sedimentation


Sriram Ramaswamy
Centre for Condensed Matter Theory
Department of Physics,
Indian Institute of Science Bengaluru

Two hundred years after the birth of George Gabriel Stokes, the gravitational settling of objects in a viscous fluid continues to fascinate us. My talk will present results from experiments and theory on the sedimentation of pairs and collections of anisotropic particles, in the limit of negligible inertia. We study the role of particle shape in suppressing a classic sedimentation instability, and discover a hidden Hamiltonian dynamics which opens a window to the physics of transient growth and nonlinear instability of a linearly stable system.  This work was done in collaboration with Rahul Chajwa and Rama Govindarajan (ICTS-TIFR Bangalore) and Narayanan Menon (UMass Amherst). 

Sriram Ramaswamy is currently Homi Bhabha Chair Professor in the Centre for Condensed Matter Theory, Department of Physics at the Indian Institute of Science, Bangalore. He was a postdoc with Tom Lubensky at UPenn, did his PhD with Gene Mazenko at University of Chicago, and his BS at the University of Maryland College Park. He is a theoretician who works on nonequilibrium, soft-matter, and biological physics. His research helped found the field of active matter, which studies the collective behaviour of objects that convert local energy input into autonomous motion. He is widely known for formulating the hydrodynamic equations governing the alignment, flow, mechanics and statistical properties of suspensions of self-propelled creatures, on scales from a cell to the ocean. He is a Fellow of the American Physical Society. He was awarded the Shanti Swarup Bhatnagar Prize in 2000 and the Infosys Prize in 2011, and elected a Fellow of the Royal Society in 2016. 

August 3, 2020: Doing “Statistical Mechanics” with Big Data


Andrea Liu
Hepburn Professor of Physics
University of Pennsylvania


Statistical mechanics has been the workhorse that condensed matter physicists have used to make the connection between microscopic properties and macroscopic, collective phenomena. Establishing this connection requires reducing masses of microscopic information (dimensional reduction) to a few relevant microscopic variables and their distributions. Data science methods are designed for dimensional reduction, so they are a natural tool to turn to when statistical mechanics fails. But it requires physics to identify the relevant microscopic quantities as well as the most appropriate data science methods to use to access them. I will discuss two problems where we have made progress with this approach: we have applied machine learning to glassy dynamics and persistent homology to the phenomenon of allostery. 

Andrea Liu is a theoretical soft and living matter physicist who received her A. B. and Ph.D. degrees in physics at the University of California, Berkeley, and Cornell University, respectively. She was a faculty member in the Department of Chemistry and Biochemistry at UCLA for ten years before joining the Department of Physics and Astronomy at the University of Pennsylvania in 2004. Liu is currently Speaker of the Council of the American Physical Society (APS) and Chair of the Physics Section of the American Association for the Advancement of Science (AAAS). She is a fellow of the APS, AAAS and the American Academy of Arts and Sciences, and a member of the National Academy of Sciences.