complexnetworks2021

About

The Complex Systems and Dynamics Group is an interdisciplinary initiative of Indian Institute of Technology Madras, focussed towards carrying out research on the broad areas involving complex networks and nonlinear dynamics. The aim of the group is to contribute to the development of new techniques and tools for mathematical modelling and analysis to investigate challenging dynamical problems in climate science, neuroscience, biological systems, multi-physics systems and active flows. The centre is envisaged as a hub for promoting interdisciplinary research drawing on expertise and synergy from science, engineering and humanities streams.

Anirban Chakraborti
Jawaharlal Nehru University

Deciphering complexity of financial networks

Abstract: The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. In this talk, I will try to present some of our studies on complex financial networks, which can significantly enhance our ability to understand market instability as well as measure the fragility of global financial systems.

Bio: Anirban Chakraborti is a Professor at the School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi. Earlier, he had worked as an Associate Professor at the Chair of Quantitative Finance, École Centrale Paris, France, and as a Lecturer in Theoretical Physics, Banaras Hindu University, Varanasi. He obtained a Ph.D. in Physics from Saha Institute of Nuclear Physics, India and later completed the Habilitation (HDR) in Physics from Université Pierre et Marie Curie (Paris VI), France. He has more than two decades of experience as a scientist, working in many reputed universities and research institutions in India, Europe, Japan, and USA. He was awarded the prestigious Young Scientist Medal of the Indian National Science Academy in 2009. His main research interests lie in the areas of Econophysics, Sociophysics, Data Science, Complex Systems, Statistical Physics, Quantum Physics and Nanomaterial Science.
Website

Video of seminar

Animesh Mukherjee
Indian Institute of Technology Kharagpur

Unfolding bias and segregation through related item networks

Abstract: In this talk, I shall cover our 3 year-long efforts in studying biases in recommendation platforms using concepts from network science. I shall cover one/two case studies; in the first case study (ACM FAccT 2021), I shall present an end-to-end audit of the biases toward private label products in e-commerce platforms (Amazon.in) and how these biases get pronounced due to sponsored ads. In the second case study (IEEE Infocom 2019), I shall discuss segregation that takes place in recommendations on otts.

Bio: Animesh Mukherjee is a faculty member in the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur. Prior to this, he was working as a post doctoral researcher in the Complex Systems Lagrange Lab, ISI Foundation, Italy. He received his PhD from the Department of Computer Science and Engineering, IIT Kharagpur with a thesis on self-organization of human speech sound inventories. His main research interests center around applying complex system approaches (mainly complex networks and agent-based simulations) to different problems in (a) human language evolution and change, (b) web social media, (c) information retrieval and (d) natural language processing.
Website

Video of seminar

Areejit Samal
Institute of Mathematical Sciences

Biologically meaningful functions in Boolean network models of living systems

Abstract:In the past two decades, there has been a dramatic increase in the reconstruction and analysis of Boolean models of biological networks. In such models, neither network topology nor Boolean functions should be expected to be random. In this talk, I describe our work which focuses on biologically meaningful types of Boolean functions, and performs a systematic study of their preponderance in gene regulatory networks. We find that most Boolean functions astonishingly have odd bias in a reference biological dataset. Subsequently, we are able to explain this observation along with the enrichment of read-once functions (RoFs) and its subset, nested canalyzing functions (NCFs), in the reference dataset in terms of two complexity measures: Boolean complexity which is yet unexplored in the biological context, and the average sensitivity. I will also provide an analytical proof that NCFs minimize not only the Boolean complexity, but also the average sensitivity in their k[P] set.

Bio: Areejit Samal is a faculty member in the computational biology group of IMSc Chennai. Before joining IMSc in 2014, he had completed his Bachelors, Masters and PhD in Physics from University of Delhi, and thereafter, postdoctoral training from MPIMIS Leipzig, CNRS/LPTMS Orsay, ISB Seattle and ICTP Trieste. His primary research interests are in the area of complex networks and systems biology.
Website

Video of seminar

Arun Tangirala
Indian Institute of Technology Madras

Discovering Causal Dynamical Network Structures from Data: An Overview

Abstract: Network representations or graphical models are emerging class of models to describe systems, especially of large size and complexity. They prove to be highly useful in determining disturbance propagation pathways, root cause analysis, process design, etc. Constructing the structure of a network model for a given process is rendered very difficult owing to the lack of sufficient process knowledge and system complexities. This talk is centred on methods and concepts for reconstructing the causal network (directed graphical) models for dynamical systems from observations (data). The objective is to provide a historical perspective and an overview of data-driven causal discovery methods, concepts and the challenges abound. We shall particularly place attention on methods that rest on the notions of Granger causality and convergent cross mapping that are suited for stochastic and deterministic dynamical systems, respectively. Case studies on simulated and real-life data sets will be presented to support and illustrate the theoretical underpinnings of these methods.

Bio: Arun K. Tangirala is a Professor at the Department of Chemical Engineering, IIT Madras since 2004 and a Faculty Affiliate at the Robert Bosch Centre for Data Science and AI, IIT Madras. His research interests span the fields of process control and monitoring, identification, applied data science and complex networks. He is also the author of a comprehensive classroom text on System Identification titled "Principles of System Identification: Theory and Practice", published by CRC Press. A recipient of a few prestigious teaching and research awards, he is also the Editor-in-Chief of the Journal of the IEI: Series E and an Associate Editor of the ASME Journal of Dynamic Systems, Measurement and Control.
Website

Video of seminar

Auroop Ganguly
Northeastern University, USA

Physics-Guided Uncertainty Quantification for Scientific Machine Learning in Complex Spatiotemporal Dynamical Systems

Abstract: This presentation will discuss how physics-guided uncertainty quantification in complex spatiotemporal dynamical systems can enhance the credibility of scientific machine learning discoveries and enable translation to risk-informed decisions and policy.
Research and translational barriers, as well as methods and solutions, will be discussed in the context of two interconnected grand challenge areas:
(a) predictive understanding of the water cycle within the earth system, and (2) preparedness to hydrological extremes for ensuring the resilience of coupled natural-human systems.

Bio: Auroop Ganguly is a Professor at Northeastern University in Boston, MA, and a joint Chief Scientist at the Pacific Northwest National Laboratory in Richland, WA.
His research interests encompass climate risks and infrastructural resilience with spatiotemporal machine learning and complex network science. Prior to Northeastern, he has worked at the US DOE's Oak Ridge National Laboratory and at Oracle Corporation.
He is a co-founder and the chief scientific adviser of risQ, a Boston-based climate analytics company. Ganguly is an ASCE Fellow and obtained a PhD from MIT.
Website

Video of seminar

Chandan Bose
University of Liége, Belgium

Characterization of dynamical systems using nonlinear time series analysis - a hands-on tutorial

Abstract: This tutorial talk aims to glimpse into nonlinear time series analysis methods used in characterizing dynamical signatures of different physical and engineering systems. Nonlinear time series analysis tools enable us to gain a proper understanding of predictability, transitions, synchronization, and characterization of the system response solely from scaler time histories, often easier to measure experimentally. These tools are also quite valuable for discerning the dynamical signatures of a system from short time histories simulated through computationally intensive approaches, such as computational fluid dynamics. The present talk will be a hands-on tutorial, where the speaker will play around with underlying code to show the parametric dependence of the response dynamics of some canonical systems. The first part of the talk will comprise a short primer on dynamical systems, attractors, bifurcations using a bifurcation diagram and time-frequency analysis in terms of time history, FFT, wavelet spectra, among others. Next, the time delay embedding, a widely used approach of state-space reconstruction, will be demonstrated, including the methods to determine the optimum time delay and minimum embedding dimension. Recurrence plots and various recurrence plot-based time series analysis methods will be discussed next, followed by calculating topological measures of the system attractors, such as the Lyapunov exponent. If time permits, the speaker will present the tools to unravel the synchrony characteristics of a coupled system using phase-based measures.

Bio: Chandan Bose is a post-doctoral research associate in the Fluid-Structure Interactions and Experimental Aerodynamics Laboratory and the Multi-physics and Turbulent Flow Computation Laboratory, Dept. of Aerospace and Mechanical Engineering, University of Liège, Belgium. He has obtained his M.S. and Ph.D. degrees from the Dept. of Applied Mechanics, IIT Madras, India, where he worked at the Uncertainty Laboratory and the Biomimetics Laboratory. He is passionately interested in fluid dynamics problems of different tastes, especially with flavours of turbulence and nonlinear dynamics. He aims to investigate a variety of complex multiphysics problems involving coupled nonlinear interactions, among which fluid-structure interaction and turbulence are his primary research focus.
Website

Video of seminar

Karthik Raman
Indian Institute of Technology Madras

Learning on, using and from networks in biology

Abstract: Every living cell comprises an intricate network of various biomolecules that ultimately contribute to exquisite functions. Graph theory, or network science, has a number of important applications in biology, and has enabled many interesting discoveries. In this talk, I will briefly introduce network science and focus on diverse applications. I will first discuss a motivational example of graph mining, where we use networks to learn and represent the extant knowledge of metabolic reactions to predict possible pathways to synthesise known and novel compounds. Following this, I will discuss the classic problem of community detection and outline various challenges in applying such algorithms to biological networks and identify modules of genes that are relevant in disease. Lastly, I will outline another interesting application of how we can learn from networks, to predict essential genes from interactomes based on various network properties.

Bio: Karthik Raman is an Associate Professor at the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras. Karthik’s research group works on the development of algorithms and computational tools to understand, predict and manipulate complex biological networks. Broadly spanning computational aspects of synthetic and systems biology, key areas of research in his group encompass microbiome analysis, in silico metabolic engineering, biological network design and biological data analysis. Karthik also co-ordinates the Initiative for Biological Systems Engineering at IIT Madras and is a core member of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI). Karthik teaches courses on computational biology and systems biology at IIT Madras, and has also authored a textbook on Computational Systems Biology.
Website

Video of seminar

Madhav Marathe
University of Virginia, USA

Foundations of coevolving multiplex networks with applications to networked epidemiology

Abstract: Reasoning about real-world social habitats often represented as multiplexed co-evolving networks is complicated and scientifically challenging due to their size, co-evolutionary nature and the need for representing multiple dynamical processes simultaneously. The 2014 Ebola epidemic, the ongoing covid pandemic, 2009 financial crisis, global migration, societal impacts of natural and human initiated disasters and the effect of climate change provide examples of the many challenges faced when developing such environments. Computational advances have fundamentally altered how such networks can be synthesized, analyzed and reasoned.

The talk will focus on foundations and advanced technologies to study multiplexed co-evolving socio-technical networks with the aim of developing scalable and practical decision support systems. I will draw on our work in urban transport planning, national security and public health epidemiology to guide the discussion.

Bio: Madhav Marathe is a Distinguished Professor in Biocomplexity, the division director of the Network Systems Science and Advanced Computing Division at the Biocomplexity Institute and Initiative, and a Professor in the Department of Computer Science at the University of Virginia (UVA). His research interests are in network science, foundations of computing, Human and engineered intelligence at scale, computational epidemiology, socially coupled system science and high performance computing.

Before joining UVA, he was the director of the Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute and a professor in the department of computer science, Virginia Tech. Before coming to Virginia Tech, he was a Team Leader in the Basic and Applied Simulation Science Group that is a part of the Computer and Computational Sciences division at the Los Alamos National Laboratory (LANL). He is a Fellow of the IEEE, ACM, SIAM and AAAS.

Over the last 20 years, his division has supported federal and state authorities in their effort to combat epidemics in real-time, including the H1N1 pandemic in 2009, the Ebola outbreak in 2014 and most recently the COVID-19 pandemic. Before joining UVA, he held positions at Virginia Tech and the Los Alamos National Laboratory. He is a Fellow of the IEEE, ACM, SIAM and AAAS.
Website

Video of seminar

Mahesh Panchagnula
Indian Institute of Technology Madras

Introduction to chaos

Abstract: Chaos are exhibited by a wide range of physical systems from financial markets to weather patterns. This talk will cover a basic graduate level introduction to chaos. We will begin our discussion with an overview of bifurcation theory. We will then discuss the signatures of a system exhibiting chaos and tools to identify the state of a system as being chaotic. Different routes to chaos will be discussed and tools to characterize the routes will be presented. Finally, examples from a wide range of complex systems will be presented to illustrate the behaviour of the system exhibiting chaos.

Bio: Mahesh Panchagnula is a professor in the Department of Applied Mechanics, IIT Madras. He obtained his B. Tech. from IIT Madras, M.S. and Ph.D. in Mechanical Engg. from Purdue University. His expertise is in spray nozzle design, drying, atomization, combustion and experimental fluid dynamics.
Website

Video of seminar

Malayaja Chutani
Indian Institute of Technology Madras

Programming algorithms for complex networks

Abstract: This tutorial will cover the basics of networks, using Python and Networkx, on Jupyter Notebooks. Participants are requested to download the Anaconda distribution of Python (which bundles together many Python libraries and packages) prior to the workshop. Alternatively, a Google Colab Notebook can be used.
https://docs.anaconda.com/anaconda/install/
https://colab.research.google.com/notebooks/intro.ipynb

Bio: Malayaja Chutani is a PhD scholar in the Department of Physics, IIT Madras. Her research interests are in simplicial characterization of networks and is working on this and related areas with Prof Neelima Gupte.
Website

Video of seminar

Miroslav Andelkovic
University of Belgrade, Serbia

To be announced

Abstract:

Bio: Miroslav Andelkovic is a researcher at the Vinca Institute of Nuclear Science, University of Belgrade, Serbia.
Website

Satyam Mukherjee
Shiv Nadar University

Complex Systems Approach to Team Dynamics in Indian Premier League

Abstract: The small but growing body of research on the team vs. team competition focuses on predicting the winner based on multilevel factors including the team’s strength and prior relations among team members within a team. A fundamental contribution of our research is demonstrating the significance and power of prior relations among members between the competing teams in predicting the outcome of a contest. As such it seeks to predict a team’s performance by taking into account the prior relations of its team members with members across the entire ecosystem of teams. In the context of the Indian Premier League, the ecosystem refers to a member’s prior relations with any player who is a member of any of the franchise-owned teams. We use data over a period of 8-seasons of the Indian Premier League, to demonstrate the effects of competing against former teammates on the outcome of a Cricket match. If two teams A and B are competing in a match, and nA players from A are former teammates of players on B and nB players from B are former teammates of players on A, then the larger between nA and nB predicts which team will have a competitive advantage over the other team. We call the magnitude of this difference the “ecosystem” factor in predicting performance. Using linear regression, Multiple Regression Quadratic Assignment Procedure, and Exponential Random Graph Models we find that the ecosystem factor significantly improves the team’s odds of winning, beyond the team members’ individual skills and within team relations. These findings provide novel empirical insights to deepen our understanding of the impacts of team assembly mechanisms on performance.

Bio: Satyam Mukherjee completed his doctoral degree from the IIT Madras. He also holds a bachelor's degree in Physics from Presidency College, University of Calcutta, and a master's degree in Physics from the University of Pune. His research interests include network science, data analytics, open-source collaboration, and sports analytics. Prior to joining Shiv Nadar University, Delhi NCR Satyam was a faculty member at IIM Udaipur. He has previously worked with Kellogg School of Management and Northwestern Institute on Complex Systems (NICO), Northwestern University Evanston, USA as a Postdoctoral Fellow.
Website

Sitabhra Sinha
Institute of Mathematical Sciences Chennai

Framing the fearful symmetry: Developmental pattern formation as interaction between global fields and local interaction among cells

Abstract: The phenomenon of morphogenesis, in which a zygote (fertilized egg) starting from a single cell develops - via a sequence of coordinated cell division and differentiation - into an organism having a characteristic form, provides some of the most fascinating examples of pattern formation in nature. Inspired by the pioneering work of Vincent Wigglesworth and Alan Turing on spontaneous emergence of spatial heterogeneity as a result of lateral inhibition and that of Lewis Wolpert on how cells proceed along different developmental trajectories using positional information provided by morphogen gradients, we are working towards understanding how these two distinct pattern formation paradigms come together in different phases of development. In this talk we will see how the process of cell fate determination - that dictates the characteristic pattern of distinct cells making up a tissue or an organ - arises through interaction between self-organized (a la Turing) and boundary-organized (a la Wolpert) mechanisms of symmetry breaking in a cellular array.

Bio: Sitabhra Sinha is a Professor in the Physics group of IMSc and adjunct faculty of the National Institute of Advanced Studies (NIAS), Bangalore. His Ph.D. work was on the nonlinear dynamics of recurrent neural network models done at the Machine Intelligence Unit, Indian Statistical Institute, Calcutta. He did postdoctoral research on nonlinear dynamics of spatially extended systems with focus on biological systems at the Department of Physics, Indian Institute of Science at Bangalore and Weill Medical College of Cornell University at New York City. He joined the faculty of IMSc in September 2002. His areas of research fall broadly under complex systems, nonlinear dynamics and theoretical & computational biophysics.
Website

Video of seminar

Srinath Srinivasa
International Institute of Information Technology Bangalore

Modeling Sustainability in Social Networks
Abstract: An abstract notion of "sustainability" of a system of being, has formed the basis for social organization in many Eastern cultures. Sustainability forms the basis on which other social constructs like ethics, justice and harmony are developed. Sustainability in social systems, are influenced by several factors including the physical environment, network properties, and quirks of human cognition and agency. This talk presents a disparate set of research efforts conducted by our group, to computationally model different elements of sustainability in social interactions. We look at how varying social emphasis on constructs like resilience, cost and efficiency, results in the emergence of different network topologies in the same population. We then address the impact of triadic closure in social networks and the resultant entrenched clusters, on the emergence of cooperation. Finally, we address computational modeling of social identity, and its role in the emergence of cooperation in a diverse population.

Bio: Srinath Srinivasa heads the Web Science lab and is the Dean (R&D) at the International Institute of Information Technology – Bangalore (IIITB), India. Srinath holds a Ph.D from the Berlin Brandenburg Graduate School for Distributed Information Systems Germany, an M.S. (by Research) from IITM and B.E. in Computer Science and Engineering from The National Institute of Engineering Mysore. His research interests are in understanding how the WWW is affecting humanity; and how the web can enable social empowerment and capability building.
Website

Video of seminar

Sunetra Sarkar
Indian Institute of Technology Madras

Dynamics of bio-mimetic flapping and the related multi-physics systems

Abstract:Natural flyers like birds, insects and robotic flapping devices flap their wings periodically to generate the aerodynamic loads required to fly. A variety of wake patterns in the trail of flapping wings holds the key to the interpretation of the aerodynamic loads. Normally, periodic flapping generates strictly periodic wakes but recent studies have reported a range of periodic flapping parameters that can lead to chaos in the flow-field. Chaos is highly undesirable from the viewpoint of flapping and presents an important practical problem. We demonstrate the use nonlinear time series methods to characterise the dynamical states. These problems must be analysed as multi-physics problems when coupled with other dynamical systems, like the presence of the flexible wings coupling with the time dependent flow-field. For very high fidelity models, it is difficult to isolate how they mutually interact and how these interactions change when key parameters are varied. We explore the use of time series network based tools to understand the transitions in such systems.

Bio: Sunetra Sarkar is a Professor at the Department of Aerospace Engineering, IIT Madras, India. She obtained her Ph.D. from Indian Institute of Science, Bangalore in 2005. She did he postdoctoral research at the Faculty of Aerospace Engineering, Technical University of Delft, Netherlands 2005-2006. She was also a NWO Rubicon fellow in the Netherlands during 2006-2007. She was a visiting scientist in the Department of Applied Mathematics, Chalmers University, Sweden in 2010. She had won the prestigious Amelia Earhardt fellowship award for women scientists in aerospace sciences & engineering. She joined IITM in 2007, where she heads the Bio-mimetics and Dynamics Laboratory at the Aerospace Engineering dept.

Her research focuses on aerodynamics of flapping wing flights, deterministic & stochastic treatment of nonlinear flow and fluid-structure-interaction problems.
Website

Video of seminar

Udit Bhatia
Indian Institute of Technology Gandhinagar

Robustness and recovery of built and natural systems subject to hydro- meteorological extremes: Integrating data, dynamics, and complexity

Abstract: In the presentation, I will discuss how the integration of non-linear dynamics, data with complex network representation help us understand the robustness and recovery characteristics of built critical infrastructure systems and natural ecosystems, which can inform the resilient design and near-optimal restoration strategies for such systems. My talk will include specific examples from our work on understanding tolerance of Indian Railways Network, US National Airspace Airport Network, unfolding of concurrent hazards on regional transportation networks during 2018 extreme precipitation events in Kerala, and generalisable restoration strategies for degraded 32 ecological networks located across the globe.

Bio: Udit Bhatia is an Assistant Professor in Civil Engineering Discipline at IIT Gandhinagar. His research interests include the recovery and resilience of networked systems, uncertainty assessment in hydroclimate extremes, and physics-guided data sciences. Udit received his PhD in Interdisciplinary Engineering from Northeastern University, Boston (USA). His research has been highlighted by national and international media, and publications including ScienceDaily, AAAS Eurekalert, R&D Magazine, Yahoo!, and Big Data Journal. He is co-author of the book" Critical Infrastructures Resilience: Policy and Engineering Perspectives," published by Taylor and Francis. His work in applied network science, infrastructure resilience, and extreme value analysis is currently being used by policymakers at various levels. His work has contributed substantially and directly towards publications in disciplinary and interdisciplinary venues, UNA-UK report, encyclopedia articles, peer-reviewed book chapters, award-winning conference papers, and US Patent in his name.
Website

Video of seminar

August 25, 2021

Welcome Session

Sayan Gupta &
Arun Tangirala

14:30-15:00 (UTC +5:30)

Video 1: Sayan Gupta
Complex Systems & Dynamics

Video 2: Arun Tangirala
Introducing the Center

Satyam Mukherjee

Complex Systems Approach to Team Dynamics in Indian Premier League

15:00-16:00 (UTC +5:30)

Video

Malayaja Chutani

Programming algorithms for complex networks

(Tutorial session)

16:00 -17:30 (UTC + 5:30)

Video

Madhav Marathe

Foundations of coevolving multiplex networks with applications to networked epidemiology

19:00 -20:00 (UTC + 5:30)

Video

Animesh Mukherjee

Unfolding bias and segregation through related item networks

20:00-21:00 (UTC+5:30)

Video

August 26, 2021

Arun Tangirala

Discovering Causal Dynamical Network Structures from Data: An Overview

(Tutorial Session)

15:00-16:30 (UTC +5:30)

Video

Anirban Chakraborti

Deciphering complexity of financial networks

17:00-18:00 (UTC +5:30)

Video

Sitabhra Sinha

Framing the fearful symmetry: Developmental pattern formation as interaction between global fields and local interaction among cells

19:00 -20:00 (UTC +5:30)

Video

Udit Bhatia

Robustness and recovery of built and natural systems subject to hydro-meteorological extremes: Integrating data, dynamics, and complexity

20:00 -21:00 (UTC +5:30)

Video

August 27, 2021

Mahesh Panchagnula

Introduction to chaos

(Tutorial Session)

15:00-16:30 (UTC +5:30)

Video

Areejit Samal

Biologically meaningful functions in Boolean network models of living systems

17:00-18:00 (UTC +5:30)

Video

Karthik Raman

Learning on, using and from networks in biology

19:00 -20:00 (UTC +5:30)

Video

Auroop Ganguly

Physics-guided uncertainty quantification for scientific machine learning in complex spatiotemporal dynamical systems

20:00 -21:00 (UTC +5:30)

Video

August 28, 2021

Chandan Bose

Characterization of dynamical systems using nonlinear time series analysis - a hands-on tutorial
(Tutorial Session)

15:00-16:30 (UTC +5:30)

Video

Srinath Srinivasa

Modelling sustainability in social networks

17:00-18:00 (UTC +5:30)

Video

Sunetra Sarkar

Dynamics of bio-mimetic flapping and the related multi-physics systems

19:00 -20:00 (UTC +5:30)

Video

Closing Remarks

20:00 -20:30 (UTC +5:30)

Networks & Dynamical Systems

An International WorkshopAugust 25-28, 2021

About

Objectives

Registration

August 25, 2021

Welcome SessionSayan Gupta &Arun Tangirala

Video 1: Sayan GuptaComplex Systems & Dynamics Video 2: Arun Tangirala Introducing the Center

Satyam Mukherjee

Malayaja Chutani

Madhav Marathe

Animesh Mukherjee

August 26, 2021

Arun Tangirala

Anirban Chakraborti

Sitabhra Sinha

Udit Bhatia

August 27, 2021

Mahesh Panchagnula

Areejit Samal

Karthik Raman

Auroop Ganguly

August 28, 2021

Chandan Bose

Srinath Srinivasa

Sunetra Sarkar

Closing Remarks

Local organizing team

Sunetra Sarkar

Aerospace Engg, IITM

Neelima Gupte

Physics, IIT M

Arun Tangirala

Chemical Engg, IITM

Sayan Gupta

Applied Mechanics, IITM

An International Workshop
August 25-28, 2021

Welcome Session

Sayan Gupta &
Arun Tangirala

Video 1: Sayan Gupta
Complex Systems & Dynamics

Video 2: Arun Tangirala
Introducing the Center