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Complexity and the Computational Paradigm

Stephen Wolfram published his first scientific paper at the age of 15, and received his PhD in theoretical physics from Caltech by the age of 20. Having started to use computers in 1973, Wolfram rapidly became a leader in the emerging field of scientific computing.

In 1981 Wolfram became the youngest recipient of a MacArthur Prize Fellowship. He then set out on an ambitious new direction in science aimed at understanding the origins of complexity in nature. Wolfram’s first key idea was to use computer experiments to study the behavior of simple computer programs known as cellular automata. This allowed him to make a series of startling discoveries about the origins of complexity.

Wolfram founded the first research center and the first journal in the field, Complex Systems, and began the development of Mathematica. Wolfram Research soon became a world leader in the software industry — widely recognized for excellence in both technology and business.

Following the release of Mathematica Version 2 in 1991, Wolfram began to divide his time between Mathematica development and scientific research. Building on his work from the mid-1980s, and now with Mathematica as a tool, Wolfram made a rapid succession of major new discoveries, which he described in his book, A New Kind of Science.

Building on Mathematica, A New Kind of Science, and the success of Wolfram Research, Wolfram recently launched Wolfram|Alpha — an ambitious, long-term project to make as much of the world’s knowledge as possible computable, and accessible to everyone.

Stephen Wolfram

    Founder & CEO of Wolfram Research, USA

Hiroki Sayama

    Director, Binghamton Center of Complex Systems (CoCo), Binghamton University, SUNY, USA

Effects of Functional Diversity on Social Dynamics: Simulation and Experimental Studies

Abstract: Diversity of individuals is usually discussed and advocated from a demographic point of view. However, *functional* diversity, such as diversity in behavior, background or expertise, would also play an essential role in collective social dynamics. In this talk, we present an overview of our two recent studies on the effects of functional diversity on opinion dynamics and social evolution. The first study investigated how behavioral diversity of individuals might affect informational diversity and connectivity within society, using numerical simulations of adaptive social network models. Results showed that heterogeneity in individual behavioral traits significantly increased both opinion diversity and social network connectivity. This observation was robustly obtained in several distinct models. The second study examined the effects of diversity in individual backgrounds on collaborative design and innovation, using human-subject experiments and machine-learning-based text analysis. We found that spatially clustered collectives with clustered background distributions tended to explore more diverse ideas than in other conditions, whereas collectives with random background distributions consistently generated ideas with the highest utility. Through these interrelated studies, we illustrate how different forms of functional diversity of social constituents can have different, nontrivial implications for collective social dynamics.

Biography:

Hiroki Sayama is a Professor in the Department of Systems Science and Industrial Engineering, and the Director of the Binghamton Center of Complex Systems (CoCo), at Binghamton University, State University of New York, USA. He also serves as a non-tenure-track Professor in the School of Commerce at Waseda University, Japan, an External Faculty member of the Vermont Complex Systems Center at the University of Vermont, USA, and a Visiting Professor in the School of Sciences at Christ University, Pune/Lavasa, India. He received his B.Sc., M.Sc. and D.Sc. in Information Science, all from the University of Tokyo, Japan. He did his postdoctoral work at the New England Complex Systems Institute in Cambridge, Massachusetts, USA. His research interests include complex dynamical networks, human and social dynamics, collective behaviors, artificial life/chemistry, interactive systems, and complex systems education, among others. He is an expert on mathematical/computational modeling and analysis of various complex systems. He has published more than 230 peer-reviewed journal articles and conference proceedings papers and has written or edited 15 books and conference proceedings about complex systems related topics. His open-access textbook on complex systems modeling and analysis has been downloaded more than 170,000 times globally and has become one of the standard textbooks on this subject. He currently serves as the Vice President of the International Society for Artificial Life (ISAL), an Elected Council / Executive Committee / CCS Steering Committee Member of the Complex Systems Society, a Board member of the Network Science Society (NetSci), the Chief Editor of Complexity (Wiley), the Founding Co-Editor-in-Chief of Northeast Journal of Complex Systems (NEJCS), an Associate Editor of Artificial Life (MIT Press), and as an editorial board member for several other journals.

Characterizing Disruptive Events by Modeling Dynamics in Multiplex Networks

Abstract: This presentation will delve into effective machine learning-based approaches for identifying, categorizing, and forecasting disruptive weather events, even when data is limited and labels are imprecise. We’ll showcase some of our latest techniques, which address this challenge by leveraging multiplex evolving networks to jointly analyze structured and unstructured data sources. Our findings demonstrate that by utilizing deep learning and transfer learning techniques, the accuracy and efficacy of diagnostics and risk monitoring for weather events can be greatly enhanced. Specifically, integrating information from weather, geophysical, and social media sources of varying quality and resolutions can yield significant improvements in predicting and managing weather-related disruptive events.

Biography:

Zoran Obradovic is a Distinguished Professor and a Center director at Temple University, an Academician at the Academia Europaea (the Academy of Europe), and a Foreign Academician at the Serbian Academy of Sciences and Arts. He mentored about 50 postdoctoral fellows and Ph.D. students, many of whom have independent research careers at academic institutions (e.g. Northeastern Univ., Ohio State Univ,) and industrial research labs (e.g. Amazon, eBay, Facebook, Hitachi Big Data, IBM T.J.Watson, Microsoft, Yahoo Labs, Uber, Verizon Big Data, Spotify). Zoran is the editor-in-chief of the Big Data journal and the steering committee chair for the SIAM Data Mining conference. He is also an editorial board member of 13 journals and was the general chair, program chair, or track chair for 11 international conferences. His research interests include data science and complex networks in decision support systems addressing challenges related to big, heterogeneous, spatial, and temporal data analytics motivated by applications in healthcare management, power systems, earth, and social sciences. His studies were funded by AFRL, DARPA, DOE, KAUST, NIH, NSF, ONR, PA Department of Health, US Army ERDC, US Army Research Labs, and industry. He published about 450 articles and is cited more than 33,000 times (H-index 68). For more details see http://www.dabi.temple.edu/zoran-obradovic

Zoran Obradovic

    Director, Center for Data Analytics and Biomedical Informatics, Temple University USA

Halima Bensmail

    Qatar Computing Research Institute, Qatar

Leveraging AI for Precision Health: From Machine Learning to Foundation Models

Abstract: We present an integrated model consisting of multiple components, providing a comprehensive approach to analyzing biological data. This model has the potential to enhance our understanding of genomics, including DNA sequences and enhancer sequences, proteomics (specifically, protein crystallization and solubility), and transcriptomics (improving aberrant gene detection in RNA-seq data). Our models leverage deep learning techniques to predict enhancer sequences from DNA sequences and predict protein crystallization from protein sequences using modified CNN models. Additionally, they incorporate a Variational deep autoencoder with a mixture model framework for detecting aberrant genes in RNA-seq data. This approach enables the detection of subtle deviations indicative of abnormal gene expression, thereby improving the accuracy of aberrant gene detection, which is crucial in Mendelian disease diagnosis. In conclusion, we demonstrate how the language model was applied in bioinformatics, enabling it to learn the underlying structure and semantics of biological sequences. This allows the model to generate biologically plausible sequences and enhance the overall performance of the model.

Biography:

Halima Bensmail, received her bachelor’s in mathematics from the University Mohamed V in Rabat, Morocco. She received her doctorate from Pierre & Marie Curie University in Paris, France. She has held positions at the University of Washington, Fred Hutchinson Cancer Research Center, the University of Tennessee, and the Virginia Medical School in the US. Currently, she is a principal scientist at the Qatar Computing Research Institute and a joint full professor at the Computer Science & Engineering, Hamad Bin Khalifa University. Her expertise lies in machine learning, AI, bayesian statistics and computational biology. Halima has authored over 140 papers in prestigious journals such as Nature, Nucleic Acids Research, Genome Research, Bioinformatics and others. She has received multiple research awards, taught courses internationally, and mentored numerous Master’s and PhD students. She is actively involved in research grants, including a $2 million project funded by the USDA for the obesity center at Texas Tech and others sponsored from QNRF. She has served as a reviewer for the NIH and NSF, and is currently a board member of the Artificial Intelligence and Quantum Technology Foundation in Davos. Halima also represents IEEE EMBs in MENA as an AdCom representative.

Ensemble Learning for Medical Decision Making

Abstract: Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the performance of classification and regression tasks. Other applications of ensemble learning include assigning a confidence to the decision made by the model, selecting optimal features, data fusion, incremental learning, nonstationary learning and error-correcting. This talk focuses on the use of ensemble learning in medical decision making. Different architectures of ensemble learning such as Boosting, Bagging and Stacking will be presented and discussed to screen and diagnose several diseases such as breast cancer and retinopathy diabetic.

Biography:

Ali Idri is a Full Professor at the Computer Science and Systems Analysis School (ENSIAS), Mohammed V University in Rabat since September 1994. He is also an Affiliated Professor at Mohammed VI Polytechnic University since March 2019. He holds a Ph.D. in Computer and Cognitive Sciences from the University of Québec at Montréal (2003), and a Doctorate 3rd cycle in Computer Science from the University of Mohammed V, Rabat (1997). Throughout his career, Ali has held administrative and research roles, including as the Head of the department of Web and Mobile Engineering at ENSIAS (2014-2019), and the head of the Software Project Management research team since 2010. Ali was an Associate Professor in the Software Engineering and Information Technology Department at Ecole de Technologie Supérieure (ETS) de Montréal, Quebec, Canada for the period 2013 – 2017. Ali is an Expert evaluator at the National Center of Scientific Research (CNRST) in Morocco since 2017. He was the principal investigator of several leading national and international projects on the use of artificial intelligence and information technologies in health. Ali has published more than 300 papers in well-known journals and conferences and received recognition for his contributions to the field: (1) Among the top 2% researchers around the world in all scientific fields according to the ranking of Stanford University since 2021. (2) Among the Top-Ten researchers around the world in doing Systematic Mapping Studies in Software Engineering. (3) Among the Top-Ten authors in terms of frequency of publications in Software Engineering for the period 2014-2016. (4) 3rd position of the top-ten well-recognized researchers in Software Effort Estimation, 2017. (5) the Best Moroccan Researcher Award in “Computer Science” for the period 2016-2020 during the second edition of “Research Excellence Awards” ceremony organized by Web of Science and the National Centre for Scientific and Technical Research (CNRST) on June 29, 2021. And (6) the best Moroccan scientists in the category Engineering of the AD Scientific ranking since 2021. He is an Associate Editor of two international journals: BMC Medical Informatics and Decision Making, and Scientific African. He published more than 300 articles in well-recognized journals and conferences.

Ali Idri

    Mohammed V University in Rabat and Mohammed VI polytechnic University, Morocco

Invited Speakers

On the Complexity of Living Systems and their Biosignatures

Abstract: In this talk I will cover the many efforts my research groups have made to try to characterise living systems from the perspective of methods of information theory and algorithmic complexity providing a framework to ask questions about causality in the context of algorithmic probability and reprogramming living systems. I will explain the field of Algorithmic Information Dynamics as a theory and method to explore model space to search for generative mechanistic models able to explain and control processes of living systems at the molecular and cellular level and beyond based on first principles of computability theory and complexity science. Finally, I will address how all these tools can be used in computational predictive medicine and precision healthcare.

Biography:

Dr. Hector Zenil is an Associate Professor at the School of Biomedical Engineering and Imaging Sciences at the Faculty of Life Sciences and Medicine, King’s College London. In the last ten years, he has been associated with so-called Golden Triangle institutions in the UK, affiliated with the Universities of Oxford and Cambridge, as a faculty member and senior researcher. Before that, he was an Assistant Professor and Lab leader at the Algorithmic Dynamics Lab, Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute (the institution that awards the Nobel Prize in Physiology or Medicine).

He was a senior researcher and policy advisor at The Alan Turing Institute, the U.K. National Institute on Data Science and AI, financially supported by the Office of Naval Research (U.S. Department of Defense). He remains affiliated with the Turing in an official capacity as one of only nine independent AI scientific advisors funded by Innovate UK.

He introduced the field of Algorithmic Information Dynamics (AID), a new field devoted to the study of causality in dynamical systems (in particular living systems) in what we call software space (the space of all possible computable explainable models) using a fundamental form of Artificial General Intelligence (algorithmic probability).

He holds two PhDs, one from Lille 1 France in Computer Science (highest honours) and one from the Sorbonne Paris 1 in Logic and Epistemology (highest honours), a Master’s in Logic and Philosophy from the ENS/Paris 1 (IHPST) and a first-year Master’s (PGCert) degree in Nanotechnology from Oxford University.

Hector Zenil

    King’s College, University of London, United Kingdom


Sergei Petrovskii

    University of Leicester, United Kingdom

Towards Understanding Phanerozoic Mass Extinctions: Insights from Mathematical Modelling

Abstract: BSpecies get extinct all the time with a certain background extinction rate; this is a normal part of macroevolution. However, several times through the 550 Ma (Phanerozoic era) of the recorded history of life on Earth, the extinction rates exceeded the background rate by more than an order of magnitude, resulting in 50-90% loss in the global biodiversity over a relatively short time. Apart from the well-known “Big Five”, there were many smaller mass extinctions with the global biodiversity loss ranging between 10-50%. Remarkably, the extinction rate over the last few centuries is estimated to be much higher than the background rate, suggesting that we may be witnessing the beginning of the “6th mass extinction”.
Great progress in understanding mass extinctions causes and pathways has been done over the last two decades. However, many open problems remain. In particular, how a factor or process that may result in extinction of some particular species at a particular location may be upscaled to lead to extinction on a global, massive scale through a broad variety of taxa and environments remains largely unclear. Given the inherent deficiency of the fossil data, such as spatial discontinuity and poor temporal resolution, statistical analysis alone does not normally allow to distinguish between the effect of different processes. Mechanistic process-based modelling approaches are needed.
Climate change is believed to be one event that may trigger mass extinction. Importantly, species do not only adapt to a climate change, going extinct if the change is too large or too fast. Some taxa, in particular vegetation, can attenuate the change and/or modify the environment according to their needs. This species’s active feedback has long been known in the climate science as the Gaia hypothesis but, surprisingly, has been largely overlooked by earlier modelling studies on mass extinctions.
In order to bridge this gap, in our work a novel modelling approach has been introduced to couple a conceptual climate model to a generic model of ecological dynamics. This approach has then been further developed to include the active feedback of species to a climate change along with species evolutionary response. It also takes into account the dependence of population growth rate on the ambient temperature. Our model shows that species extinction or survival following a climate change depends on the subtle interplay between the magnitude of the climate change and the rate of species’s adaptive evolution. The model predicts a distribution of extinction frequencies which is consistent with the fossil record. Our approach provides a generic framework for better understanding mass extinction and lays the foundation for future research.

Biography:

Prof Sergei Petrovskii is a Chair in Applied Mathematics at the School of Computing and Mathematical Sciences, University of Leicester (UK). He is an applied mathematician with over 30 years of research experience in mathematical ecology and ecological modelling, with a special focus on modelling complex multiscale environmental and ecological systems. His recent research on the effect of global warming on ocean deoxygenation where he discovered a new type of ecological catastrophe has been published in high-ranked scientific journals and highlighted by media around the world. Other topics include (but not limited to) biological invasions, mass extinctions, and anomalous long transient dynamics in ecologically-inspired dynamical systems. He is the “Mathematical Biology” Section Editor of Mathematics (MDPI), an Associate Editor of the Journal of Mathematical Biology (Springer) and an editorial board member of Physics of Life Reviews (Elsevier). He is the founder and the scientific coordinator of the MPDEE (Models in Population Dynamics, Ecology and Evolution) conference series. He published five books and more than 150 papers in peer-reviewed journals.

Toward Smart and Autonomous Large-scale Blockchain-based IoT Systems

Abstract: Big data, WSN, and IoT technologies have been proposed for timely gathering and analysing information (i.e., data, events) streams. Similarly, Blockchain technologies can help to build trust, reduce costs, and accelerate transactions. Furthermore, decentralization together with peer-to-peer and smart contracts principles, the main core of Blockchain technology, can eliminate single points of failure while increasing the autonomy in handling large-scale IoT applications. In this talk, we shed more light on the potential of these technologies for continuous and real-time data monitoring and processing in different real-case applications (e.g, Healthcare, energy efficient building, smart grid).

Biography:

Mohamed BAKHOUYA is a professor of computer science at the International University of Rabat. He obtained his HDR from UHA-France in 2013 and his PhD from UTBM-France in 2005. He has more than ten years experiences in participating, coordinating and working in sponsored ICT projects. He was a reviewer of research projects for Agence Nationale de la Recherche, (France, 2011), Ministero dell’ Istruzione, dell’ Università e della Ricerca (Italy, 2012, 2013, 2016, 2017), Qatar National Research Fund (2019, 2020), European Commission-FP7 (2013-2015), CHIST-ERA (2021-2022) and for CNRST (2022-2024). He was EiC of IJARAS journal and also serves as a guest editor of a number of international journals, e.g., ACM Trans. on Autonomous and Adaptive Systems, Product Development Journal, Concurrency and Computation: Practice and Experience, FGCS, and MICRO. He has published several papers in international journals, books, and conferences. His research interests include various aspects related to the design, validation, and implementation of distributed and adaptive systems, architectures, and protocols.

Mohamed Bakhouya

    International University of Rabat, Morocco


Abdul Sattar

    Griffith University, Australia


Artificial Intelligence 4 Life

Abstract: Artificial Intelligence (AI) has emerged as one of the most influential fields of study of the 21st centaury. The AI guided technologies are now being used to revolutionize various aspects of our lives. This talk will give an overview of major achievements of the field. We will then highlight on how AI can help us to address the complexity challenges in biological systems, health care and environmental management. We will conclude the talk with a summary of our recent achievements and discuss some of the open issues in the field.

Biography:

Prof Sattar is founding Director of the Institute for Integrated and Intelligent Systems (IIIS), a research centre of excellence at Griffith University established in 2003. He was involved as a Research Leader at NICTA Queensland Laboratory 2005 -2015. He has been an academic staff member at Griffith University since February 1992 as a lecturer (1992-95), senior lecturer (1996-99), and professor (2000-present) within the School of Information and Communication Technology. Prior to his career at Griffith University, he was a lecturer in Physics in Rajasthan, India (1980-82), research scholar at Jawaharlal Nehru University, India (1982-85), the University of Waterloo, Canada (1985-87), and the University of Alberta, Canada (1987-1991).

He holds a BSc (Physics, Chemistry and Mathematics) and an MSc (Physics) from the University of Rajasthan, India, an MPhil in Computer and Systems Sciences from the Jawaharlal Nehru University, India, and an MMath in Computer Science from the University of Waterloo, Canada, and a PhD in Computer Science (with specialization in Artificial Intelligence) from the University of Alberta, Canada. His current research interests include knowledge representation and reasoning, constraint satisfaction, rational agents, propositional satisfiability, temporal reasoning, temporal databases, bioinformatics and IoT for environment. He has supervised over 50 successful completions of PhD graduates and published over 300 technical papers in refereed conferences and journals in the field. His research team has won three major international awards in recent years (the gold medals for the SAT 2005 and SAT 2007 competitions in the random satisfiable category), an IJCAI 2007 distinguished paper award and PRICAI Distinguished Contribution Award 2019.

Societal Metamorphosis: On Governance and Transformation in Social Ecosystems

Biography:

Dedicated to systemic change and the governance of complexity, in theory and practice, in research and consulting, Dr Louis Klein is an internationally renowned thought and practice leader for systems governance and change navigation.

Dr Louis Klein serves as dean at the European School of Governance, Berlin, Germany and
as secretary-general of the International Federation for Systems Research, Vienna, Austria.

Educated as an economist and social scientist, Dr Louis Klein became a distinguished systems scientist and cybernetician. He served as director at the International Centre for Complex Project Management (ICCPM), as director at the World Organisation of Systems and Cybernetics (WOSC), and as VP of the International Society for the Systems Sciences (ISSS).

Dr Louis Klein is a member of the editorial board of the Project Management Journal (PMJ) and Systems Research and Behavioural Sciences (SRBS) as well as co-publisher of the German philosophical business magazine agora42.

Louis Klein

    Secretary General at International Federation for Systems Research (IFSR), Germany