KEYNOTES.json

[
  {
    "category": "Keynote Speakers",
    "people": [
      {
        "name": "Iain Couzin",
        "affiliation": "Max Planck Institute, University of Konstanz, Germany",
        "pic": "people-pics/couzin.jpg",
        "website": "https://collectivebehaviour.com/people/couzin-iain/",
        "abstract":"<h3>The Geometry of Decision Making</h3>",
        "twitter": "",
        "BIO":"Iain Couzin is Director of the Max Planck Institute of Animal Behavior in Konstanz, Germany. <br><br>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. <br><br>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 neural collectives to insect swarms, fish schools and primate groups. <br><br>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, the Mohammed Dahleh Award in 2009, Popular Science's Brilliant 10 Award in 2010, National Geographic Emerging Explorer Award in 2012, the Scientific Medal of the Zoological Society of London in 2013, Web of Science Global Highly Cited Researcher in 2018, 2019 and 2020, and the Lagrange Prize for his contributions to complexity science in 2019.",
        "abstract":"<h3>The Geometry of Decision Making</h3>"
      },
      {
        "name": "Henk Dijkstra",
        "affiliation": "IMAU, Utrecht University",
        "pic": "people-pics/dijkstra.jpg",
        "website": "https://webspace.science.uu.nl/~dijks101/",
        "twitter": "",
        "abstract":"<h3>Climate as a Complex System</h3> In the climate system, multiple interactions between different nonlinear processes  produce the behavior of observables such as atmospheric surface temperature. These  quantities display coherent variability at a range of spatial and temporal scales which is often  difficult to understand from the external (e.g.  solar) forcing of the system and the individual processes involved. A typical example is the El Niño- Southern Oscillation variability on interannual time scales in the equatorial Tropical Pacific which arises through coupling of processes in the equatiorial ocean and global atmosphere. <br><br> In this talk, I will give an overview on how complex  systems science concepts and methods, for example network theory and stochastic dynamical systems theory,   have so far contributed to the understanding  of this variability. Focus will be on three topics: (i) diagnostics of the  climate system through analyses of observations, (ii) identification of  feedbacks involved in transition phenomena and (iii) prediction of climate variability.",
        "BIO":"Henk A. Dijkstra is professor of Dynamical Oceanography at the Institute for Marine and Atmospheric research Utrecht and director of the Centre for Complex Systems Studies within the Department of Physics of Utrecht University. <br><br>He was trained as an applied mathematician and held positions at the University of Groningen,  Cornell University, and Colorado State University. <br><br>His main research interests are on climate variability, in particular climate transitions and climate change with a focus on the role of the oceans.  <br><br> Since 2002, he is a full member of the  Royal  Netherlands Academy of Arts and Sciences and since 2009 he is a Fellow of the Society for Industrial and Applied Mathematics. <br><br>In 2005, he  received the Lewis Fry  Richardson medal from the European Geosciences Union."
      },
      {
        "name": "Sonia Kéfi",
        "affiliation": "CNRS, ISEM, Montpellier, France",
        "pic": "people-pics/kefi.jpg",
        "website": "http://sonia.kefi.fr",
        "twitter": "https://twitter.com/sonia_kefi",
        "abstract":"<h3>Spatial signatures of ecosystem resilience</h3>",
        "BIO":"I am a researcher at the CNRS based in the BioDICée team at the Institut des Sciences de l'Evolution de Montpellier (ISEM), France.  <br><br>In an era of global change, my research aims at understanding how ecosystems persist and change under pressures from changing climate and land use. What makes ecosystems resilient to changes and what makes them fragile?<br><br>I combine mathematical modeling and data analysis to investigate the role of ecological interactions (in particular facilitation) in stabilizing and destabilizing ecosystems, but also to develop indicators of resilience that could warn us of approaching ecosystem shifts."
      },
      {
        "name": "László Lovász",
        "affiliation": "Eötvös Loránd University, Hungary ",
        "pic": "people-pics/lovasz.jpg",
        "website": "https://web.cs.elte.hu/~lovasz/",
        "twitter": "",
        "abstract":"<h3>Disease propagation on networks and a switchover phenomenon</h3>",

        "BIO":"László Lovász is a Hungarian-American mathematician and professor emeritus at Eötvös Loránd University. He received the Abel Prize in 2021 jointly with Avi Wigderson for his work in combinatorics. He was the president of the International Mathematical Union from 2007 to 2010 and the president of the Hungarian Academy of Sciences from 2014 to 2020. <br><br>In graph theory, Lovász's notable contributions include the proofs of Kneser's conjecture and the Lovász local lemma, as well as the formulation of the Erdős–Faber–Lovász conjecture. <br><br>He is also one of the eponymous authors of the LLL lattice reduction algorithm.<br><br> Lovász was awarded the Brouwer Medal (1993), the Knuth Prize (1999), the Bolyai Prize (2007), and Hungary's Széchenyi Grand Prize (2008). He received the Advanced Grant of the European Research Council (2008). He was president of the International Mathematical Union between 2007 and 2010. He is member of the National Academy of Sciences (US), foreign member of the Royal Netherlands Academy of Arts and Sciences (2006) and Royal Swedish Academy of Sciences (2007), honorary member of the London Mathematical Society (2009), fellow of the American Mathematical Society (2012)."
      },
      {
        "name": "Susanna Manrubia",
        "affiliation": "Spanish National Centre for Biotechnology (CSIC), Spain",
        "pic": "people-pics/manrubia.jpg",
        "website": "https://auditore.cab.inta-csic.es/manrubia/home/",
        "abstract":"<h3>How the architecture of genotype spaces shapes evolutionary dynamics  </h3> Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, displaying nonlinear responses analogous to critical transitions, sudden state changes or hysteresis, among others. Understanding how genotype-phenotype maps partition the space of sequences into complex genotype networks is essential to update evolutionary theory. Such studies, however, have been mainly computational and, as such, are and will always be limited by the unfathomable size of sequence spaces. Theoretical advances are contributing to grasp the quantitative nature of adaptive molecular processes, whose dynamics is deeply dependent on the intrinsic network-of-networks multilayered structure of sequence spaces that we begin to unveil.",
        "twitter": "",
              "BIO":"Susanna Manrubia studied physics at the Universitat de Barcelona, and received her doctoral degree in 1996. <br><br>She was a Humboldt fellow of the Max Planck Society at the Fritz-Haber-Institut in Berlin and a postdoctoral researcher at the MPI of Colloids and Interfaces in Golm. <br>After several years at the Center for Astrobiology in Madrid, she is since 2014 associate professor of Molecular Biology and Biotechnology at the National Centre for Biotechnology (CSIC, Madrid). <br><br>She focuses on developing theoretical and computational descriptions of biological phenomena, from the genome to large-scale evolution, and maintains close collaborations with experimentalists. <br><br> Her interests include as well the emergence of cultural patterns and collective social behaviour. <br><br>She has published over 130 peer reviewed articles and three books, was Section Editor for BMC Evolutionary Biology and is current member of the Editorial Board of Virus Evolution."  
      }
    ]
  },
  {"category":"Invited Speakers",
  "people": [
      {
        "name": "Meeyoung Cha",
        "affiliation": "KAIST & IBS, Korea",
        "pic": "people-pics/cha.jpg",
        "website": "https://cs.kaist.ac.kr/people/view?idx=418&kind=faculty&menu=160",
        "twitter": "",
        "BIO":"Meeyoung Cha is an associate professor at the Korea Advanced Institute of Science and Technology (KAIST) in South Korea. Her research is on data science with an emphasis on modeling socially relevant information propagation processes. Her work on misinformation, poverty mapping, fraud detection, and long-tail content has gained more than 16,000 citations. Meeyoung has worked at Facebook's Data Science Team as a Visiting Professor and is a recipient of the Korean Young Information Scientist Award and AAAI ICWSM Test of Time Award. She is currently jointly affiliated as a Chief Investigator at the Institute for Basic Science (IBS) in Korea.",
         "abstract":"<h3>Data Science for Social Impact</h3>Artificial intelligence (AI) and big data are bringing innovations to many research fields that have a direct social impact. In this talk, I’d like to share recent efforts on research related to Sustainable Development Goals (SDGs). One of them is the inference of economic activities based on satellite imagery. Recent advances in Computer Vision algorithms and the availability of high-resolution satellite images for remote sensing help us tackle this problem from a new perspective (i.e., AI + Geography + Economy). In particular, this talk will feature our group’s efforts in one of the most remote and closed areas in the world, North Korea. The talk will show how a human-machine collaborative algorithm can compute, for the first time, local-level and district-level estimates of economic activity from publicly available satellite imagery. The multi-faceted evaluation based on partial statistics confirms that leveraging no label information, our method can explain up to 80% of the regional variation. Efforts like this on reliable and timely measurements of economic activities are fundamental for understanding economic development and designing government policies."
      },
      {
        "name": "Sundeep Chepuri",
        "affiliation": "Indian Institute of Science",
        "pic": "people-pics/chepuri.jpg",
        "website": "https://ece.iisc.ac.in/~spchepuri",
        "twitter": "",
        "sponsor":"Talk sponsored by Capital Fund Management ",
        "abstract":"<h3>Powerful Graph Neural Networks with Parallel Local Aggregations</h3>",
        "BIO":"I received the M.Sc. degree (cum laude) in Electrical Engineering and the Ph.D. degree (cum laude) from TU Delft, in July 2011 and January 2016, respectively. <br><br>Currently, I am an Assistant Professor at the Electrical Communication Engineering department at IISc. I was an Associate Editor of the EURASIP Journal on Advances in Signal Processing (2016 - 2020). <br><br>I am an elected member of the IEEE SPS Society's Sensor Array and Multichannel Technical Committee (2021 - ) and EURASIP Signal Processing for Multisensor Systems’ Special Area Team (2019 - ).<br><br>My research interests include mathematical signal processing, statistical inference and learning, applied to communication systems, network sciences, and computational imaging. The main themes of my research are computational sensing, sparse sampling, signal processing and machine learning for communications, graph signal processing, and machine learning over graphs."
      },
      {
        "name": "Vittoria Colizza",
        "affiliation": "INSERM, Sorbonne Université, France",
        "pic": "people-pics/colizza.jpg",
        "website": "https://www.epicx-lab.com/vittoria-colizza.html",
        "twitter": "",
        "abstract":"<h3>Infectious disease modeling informing policies against COVID-19 pandemic</h3>",
        "BIO":"Vittoria Colizza completed her undergraduate studies in Physics at the University of Rome Sapienza, Italy, in 2001 and received her PhD in Statistical and Biological Physics at the International School for Advanced Studies in Trieste, Italy, in 2004. She then spent 3 years at the Indiana University School of Informatics in Bloomington, IN, USA, first as a post-doc and then as a Visiting Assistant Professor. <br><br>In 2007 she joined the ISI Foundation in Turin, Italy, where she started a new lab after being awarded a Starting Independent Career Grant in Life Sciences by the European Research Council Ideas Program (more info on the EpiFor project webpage).<br><br> In 2011 Vittoria joined the INSERM (French National Institute for Health and Medical Research) in Paris where she leads the EPIcx lab within the Equipe 1 Surveillance and modeling of communicable diseases of the Pierre Louis Institute of Epidemiology and Public Health (IPLESP). <br><br>She works on the characterization and modeling of the spread of emerging infectious diseases, by integrating methods of complex systems with statistical physics approaches, computational sciences, geographic information systems, and mathematical epidemiology. <br><br>In 2017 she was promoted Research Director at INSERM. <br><br>Since 2020, she is also Visiting Professor at Tokyo Institute of Technology."
      },
      {
        "name": "Manuel Garcia-Herranz",
        "affiliation": "UNICEF Office of Innovation",
        "pic": "people-pics/garcia.jpg",
        "website": "https://manuelherranz.com",
        "twitter": "",
        "BIO":"I am currently the chief scientist at UNICEF Office of Innovation (NY). Previously I’ve worked as assistant professor with the Department of Computer Science of the Universidad Autónoma de Madrid (Spain) from which I received a Ph.D. in CS in 2009.<br><br>I am deeply interested in human behavior and dynamics, particularly in the study of computational social networks, complex systems and behavioral dynamics and in how new types of data and analysis can be used for human development, to reach the hardest to reach and provide humanitarian awareness of places in which traditionally there is little or none.",
                        "abstract":"<h3>Towards Complexity Science for Humanitarian and Development work</h3>"   
      },
      {
        "name": "Irene Giardina",
        "affiliation": "Sapienza University of Rome, Institute for Complex Systems, CNR, Italy",
        "pic": "people-pics/giardina.jpg",
        "website": "http://chimera.roma1.infn.it/IRENE/",
        "twitter": "",
        "sponsor":"Talk sponsored by Springer Complexity Lecture",
        "BIO":"Irene Giardina is Associate Professor of theoretical physics at the Department of Physics, Sapienza University of Rome. <br><br>She received her M.S. in 1994 from the University of Pavia and her Ph.D. in 1998 from the Sapienza University of Rome. After postdoctoral stays at the Department of Theoretical Physics, Oxford and at Institut de Physique Théorique, CEA Saclay, she moved to the Institute for Complex Systems, CNR in Rome. In 2013 she joined the faculty of the Department of Physics, Sapienza, where she currently coordinates the Master program in Biophysics.  <br><br>After working for several years on the statistical physics of glassy behavior, Dr Giardina's research focused on the physics of biological systems. In 2005 she founded with Andrea Cavagna the COBBS lab (Collective Behavior in Biological Systems), the first lab to collect 3D large-scale experimental data in the field on flocking and swarming behavior, and to build theory starting directly from the data. <br><br>In her research, she applies a statistical physics approach to understand how collective behavior emerges in animal groups and – more broadly – in biological systems. <br><br>In 2021 she was awarded, together with A. Cavagna, the Delbruck prize in Biological Physics of the American Physical Society.",
                                "abstract":"<h3>Collective behavior in animal groups: a statistical physics perspective </h3> Many animal aggregations display collective patterns on the large scale, ultimately due to the interactions between the individuals in the group. Recent findings on flocks of birds and swarms of insects show that these groups exhibit strong mutual correlations and quick mechanisms of information propagation, key features to maintain coherence during motion and to grant an efficient collective response to external perturbations. Besides, they obey static and dynamic scaling laws, suggesting that we can use a statistical physics approach to describe the large scale, and define novel `classes' of behavior. I will review our current understanding of collective animal behavior and discuss how a physics based perspective, from experiments to modelling, can help to define a unified description for these systems."   

      },
      {
        "name": "Eric D. Kolaczyk",
        "affiliation": "Boston University, USA ",
        "pic": "people-pics/kolaczyk.jpg",
        "website": "http://sites.bu.edu/kolaczyk/",
        "twitter": "",
        "sponsor": "Talk sponsored by Mathematics Journal",
        "BIO":"Eric Kolaczyk is a Professor of Statistics, in the Department of Mathematics and Statistics, a founding member of the Faculty of Computing and Data Sciences, and Director of the Hariri Institute for Computing at Boston University. <br><br>He is also affiliated with the Division of Systems Engineering, the Programs in Bioinformatics and in Computational Neuroscience, and the BU URBAN program.  <br><br> His research is focused at the point where statistical theory and methods support human endeavors enabled by computing and engineered systems, frequently from a network-based perspective of systems science. <br><br> He develops novel methodologies for design, representation, modeling, inference, prediction, and uncertainty quantification foundational to new paradigms for data measurement and analysis. <br><br> He has published nearly 100 articles, including several books on the topic of network analysis. As an associate editor, he has served on the boards of JASA and JRSS-B in statistics, IEEE IP and TNSE in engineering, and SIMODS in mathematics. <br><br> He formerly served as co-chair of the NAS Roundtable on Data Science Education. He is an elected fellow of the AAAS, ASA, and IMS, an elected senior member of the IEEE, and an elected member of the ISI.",
     "abstract":"<h3>Accounting for Network Noise: Counting, Epidemic Control, and Experiments </h3> While the use of network analysis has now permeated most domains, an overwhelming proportion of network analysis methods still work as if the networks we observe are noise free. In many settings, such an assumption could not be further from the truth. Examples include most biological networks, connectomes in neuroscience, contact networks in epidemiology, and, in fact, many networks used in social media studies. In this talk, I will survey a handful of projects from our group and collaborators in recent years that provide simple methods-of-moments approaches to network analysis tasks in a number of settings, which allow users to obtain unbiased inferences of network-related parameters under 'noisy networks'. These estimators are accompanied by confidence intervals deriving from a novel bootstrap algorithm. I will illustrate with application to counting subgraphs, controlling epidemic spread, and quantifying treatment effects in network experiments, as time allows."   
      },
      {
        "name": "Alessia Melegaro",
        "affiliation": "Bocconi University, Italy",
        "pic": "people-pics/melegaro.jpg",
        "website": "http://didattica.unibocconi.eu/docenti/cv.php?rif=92665",
        "twitter": "",
              "BIO":"Alessia Melegaro is Associate Professor in Demography and Social Statistics at Bocconi University, Department of Social and Political Sciences. She is Research Associate at Dondena Centre for Research on Social Dynamics and Public Policy. She is an economist by background and a Ph.D. graduate of the Department of Biological Science at Warwick University, UK. She works at the intersection of demography, epidemiology and public health. She is Director of the Bocconi Covid Crisis Lab, a multidisciplinary laboratory for research on the epidemiology, economic and social impact of Covid-19 on our societies. She is currently the PI of an ERC Consolidator Grant on the analysis of the impact of human behaviour on infection spread.",
             "abstract":"<h3>The role of demographic structure and social contact patterns in an uncontrolled SARS-CoV-2 pandemic</h3>"   

        
      },
      {
        "name": "Daniela Paolotti ",
        "affiliation": "ISI Foundation, Italy",
        "pic": "people-pics/paolotti.jpg",
        "website": "https://danielapaolotti.squarespace.com",
        "twitter": "",
        "sponsor": "Talk Sponsored by Springer Complexity Lecture",
        "BIO":"Dr. Daniela Paolotti (F) has a background in Physics (Bsc, MSc, Ph.D.). <br><br>She is a Senior Research Scientist at ISI Foundation in Turin, Italy. Her work has a strong interdisciplinary approach. For more than ten years, she has been working on applying tools from complex systems and networks science, applied mathematics, computer science, data science, behavioral sciences to study infectious disease spreading. <br><br>Since 2008, she has been developing and coordinating a Europe-wide network of Web-based platforms for participatory surveillance of Influenza-like Illness. <br><br>More recently, at ISI she has co-founded a research area devoted to themes related to Data Science and Social Impact.",
                     "abstract":"<h3>Having an impact: the challenge of using complex systems science to make a difference in the humanitarian sector</h3>"   

      },
      {
        "name": "Marcel Salathé",
        "affiliation": "EPFL, Switzerland",
        "pic": "people-pics/salathe.jpg",
        "website": "https://www.epfl.ch/labs/salathelab/",
        "twitter": "",
        "BIO":"Marcel Salathé is a digital epidemiologist working at the interface of health and computer science. <br>He obtained his PhD at ETH Zurich and spent two years as a postdoc in Stanford before joining the faculty at Penn State University in 2010 at the Center for Infectious Disease Dynamics.<br> In 2014, he spent half a year at Stanford as visiting assistant professor. <br><br>In the summer of 2015, he became an Associate Professor at EPFL where he heads the Digital Epidemiology Lab at the Campus Biotech in Geneva. <br><br>In 2016, he founded the EPFL Extension School, whose mission is to provide high quality online education in digital technology, and where he is the Academic Director.",
             "abstract":"<h3>Digital proximity tracing</h3>"   

      },
      {
        "name": "Petra Vertes",
        "affiliation": "University of Cambridge",
        "pic": "people-pics/vertes.jpg",
        "website": "https://www.turing.ac.uk/people/researchers/petra-vertes",
        "twitter": "",
        "BIO":"Petra Vertes is an MQ fellow at the University of Cambridge. <br><br>She gained her MSci in theoretical physics and a PhD in artificial neural networks from the University of Cambridge. She then moved to the Brain Mapping Unit in the Department of Psychiatry for her postdoctoral work. <br><br>In 2014 she was awarded an MRC fellowship in bioinformatics, followed by an MQ fellowship in 2018. <br><br>Petra is also one of the co-founders and organisers of the Cambridge Networks Network (CNN) - a forum of over 450 academics across different disciplines who share an interest in network science (http://www.cnn.group.cam.ac.uk/).",
                "abstract":"<h3>Layers of complexity in network neuroscience</h3>"
      },
      {
        "name": "Thierry Walzer",
        "affiliation": "Inserm, CIRI, Lyon",
        "pic": "people-pics/walzer.jpg",
        "website": "http://joriss.ens-lyon.fr/thierry-walzer-365408.kjsp?RH=JORISS-PROJECTS",
        "twitter": "",
        "BIO":"Dr. Walzer’ lab is interested in the molecular events that regulate the differentiation of cytotoxic lymphocytes and they use NK cells as cellular models. Our current research axis are i) the study of the role of several transcription factors that we selected on the basis of their expression pattern in the immune system. ii) the study of the role of several novel tyrosine kinases in NK cell differentiation. iii) the study of the coordination between S1P and chemokine receptors during NK cell trafficking in vivo.",
        "abstract":"<h3>Immune functions and dysfunctions in the defense against COVID-19</h3>"   

      },
      {
        "name": "Hyejin Youn",
        "affiliation": "Kellogg School of Management, Northwestern University",
        "pic": "people-pics/youn.jpg",
        "website": "http://hyoun.me",
        "twitter": "",
        "BIO":"I am an assistant professor at Kellogg School of Management at Northwestern University, and Northwestern Institute on Complex Systems (NICO). <br><br>I was a research fellow at Santa Fe Institute and Harvard Kennedy School, and visiting scientist at MIT Media Lab. Before that, I was a senior research fellow at Mathematical Institute at University of Oxford , and Institute for New Economic Thinking at the Oxford Martin School; and ran a National Science Foundation grant (USA) to study Technological Change from the Map of Capabilities with Aaron Cluaset, the University of Colorado at Boulder. <br><br>My PhD is in Statistical Physics at KAIST. <br><br>I serve on the editorial board of PLOS One. <br><br>My research aims to develop a mathematical and computational framework to understand complex systems. These include: Science of Cities, Pathway of Innovation, Linguistics (Semantic shift)",
                "abstract":"<h3>Pathway of innovation</h3> Technological progress plays a key role in economic growth and development. Solutions for many of humanity’s most pressing challenges—sustainable growth, poverty reduction, and climate change—demand significant additions to society's technological toolkit. The process of technological change is derived from, and governed by, accumulation of knowledge. It is therefore essential to understand how knowledge is created, shared, utilized and accumulated. Some of these processes—inventive activities—leave a footprint whose dynamics we can study in detail. Here, we propose to develop a formal methodology based on a systematic, comparative analysis of empirical data (large-scale U.S. Patent data spanning 220 years) for constructing a detailed space of technological change in the form of multilayer networks. Our aim is to illuminate potential innovation pathways both visually and mathematically. The project consists of data mining, statistical analysis, mathematical modeling, and theory development, and draws on, and further develops, techniques and concepts from distinct academic disciplines. The outcome of the project will enable us to trace dynamics of knowledge accumulation."   
      }
    ]
  }
]