DDDAS2022 Conference, October 6-10, 2022

InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS2022)

Registration information ($100 for in-person or remote participants; student registration is free):

Conference location: MIT Media Lab. The Media Lab Complex [map] comprises two buildings:

Building E14, 75 Amherst Street
Wiesner Building (E15), 20 Ames Street (DDDAS conference venue)

Additional details may be found here.

For suggestions pertaining where to stay, there is a Marriott and a Kendall hotel close to the Media Lab. More information is available here: https://www.mit.edu/visitmit/where-stay/

** Conference Agenda **

** Building entrance **

Additional information:

COVID procedures: [Print Version]


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On behalf of the Organizing Committee of the 4th International Conference on InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS2022”[1]), it is our pleasure to invite your participation in the conference to be held on October 6-10, 2022, in Cambridge, Massachusetts. The conference will be in hybrid mode (as in-person and remotely), and proceedings will be published by Springer (paper submissions and other details are posted at www.1dddas.org).

DDDAS2022 continues and expands the path set of prior DDDAS forums on research advances and Science and Technology capabilities, stemming from the Dynamic Data Driven Applications Systems (DDDAS) paradigm, whereby instrumentation data are dynamically integrated into an executing application model while in reverse the executing model controls the instrumentation. DDDAS plays a key role in creating capabilities advances in many application areas and is also driving advances in foundational methods, through system-level (as well as subsystems-level) representation, that includes comprehensive, principle- and physics-based models and instrumentation, uncertainty quantification, estimation, observation, sampling, planning and control.  The DDDAS paradigm has shown the ability to engender new capabilities in (but not limited to) aerospace, bio-, cyber-, geo-, space-, and medical sciences, as well as critical infrastructure security, resiliency, and performance; the scope of application areas ranges “from the nano-scale to the extra-terra-scale”.

DDDAS focuses on “Systems” analysis, prediction of behaviors and operational control, all of which entail multidisciplinary collaborative research and advances in fundamental areas such as stochastic systems, modeling, simulation, sensing, information fusion, inference, planning, control, decision support, learning, optimization and awareness; and permeates a great many topics of contemporary interest, such as Informative approaches for Estimation, Control and (Machine) Learning, Planning and Decision support; Network design, and to quantify utility of modeling to deal with Big Data. In Computation, it structures resources optimally and, most recently, in Quantum Computation (QC), DDDAS provides mechanisms for Sample and Query operators, as well as utilizing the prospect of QC for more efficient closed-loop symbiosis.  In test and evaluation (T&E), DDDAS creates capabilities for lifetime assessment and optimization of the performance of components and systems incorporating them. This year’s expanded conference scope will showcase additional topics, including: biofluidic imaging and control, natural hazards and climate grand challenges, advanced dynamic data driven modeling for threat-awareness effects, and the role of DDDAS in enabling and supporting the optimized design and operation of 5G and Beyond5G networking infrastructures.

The DDDAS community has made significant progress in closing the loop among Data, Information, and Knowledge, through improved modeling processes, understanding and mitigating model error with the aid of instrumentation measurements, and controlling the instrumentation to turn the Big Data deluge into informative regimes.  Identified new opportunities in QC and T&E are expanding the impact of DDDAS.

The conference is a forum to present and discuss advances, and opportunities for advances, in a wide set of application areas and their underlying foundational methods. Participants from academia, industry, government, and international counterparts will report original work where DDDAS research is advancing scientific frontiers, engendering new engineering capabilities, and adaptively optimizing operational processes.  The conference spans a broad set of topics and interests as delineated above.

We invite you to submit a paper (of up to 8 pages in length, including title and abstract, in the Springer format, as per detailed instructions provided in the www.1dddas.org webpage). The submission process (through https://easychair.org/conferences/?conf=dddas2022.) includes the following steps:

  1. Title and ½ page abstract due by July 10, 2022; [please include title, authors and institution]
  2.  Full paper submission by July 15, 2022; 8 pages; [ submission format information ]
  3. The papers will be reviewed, and submitters will be notified on August 12, 2022, on their paper’s acceptance and possible revisions for consideration as plenary or poster presentation;
  4. Final paper – will be due by August 25, 2022.

Papers presented (for either presentation type) will be published as Conference Proceedings by Springer.

Erik Blasch and Sai Ravela, DDDAS2022 Conference co-Chairs

Frederica Darema, Nurcin Celik, Carlos Varela, Alex Aved, DDDAS2022 Program Committee co-Chairs

Program Committee

  • Newton Campbell
  • Lan Zhiling
  • Salim Hariri
  • Thomas C. Henderson
  • Artem Korobenko
  • Fotis Kopsaftopoulos
  • Richard Linares
  • Dimitris Metaxas
  • Sonia Sachs
  • Themistoklis Sapsis
  • Luda Werbos
  • Ankit Goel
  • Asok Ray
  • Craig Lee
  • Yu Chen


  • Dr. Sreeja Nag, Senior Researcher, NASA Ames Research Center
    • Title: Improving Predictive Models for Environmental Monitoring using Distributed Spacecraft Autonomy
    • Abstract: Inter-connected, heterogeneous constellations of spacecraft can be exceptionally good at environmental monitoring applications when equipped with decision autonomy and platform agility. Autonomous spacecraft operations can improve our predictive models of natural phenomena by optimizing future observations in quick response to past observations and forecasts. Better predictive models and smartly targeted data improve situational awareness of evolving phenomena and help actionate timely response. The value of a smart sense-predict-plan-act will be demonstrated via the D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) project. D-SHIELD’s scheduler is optimized such that the collection of observational data and their downlink, constrained by the constellation constraints (orbital mechanics), resources (power, data) and subsystems (instruments, attitude control), results in maximum science value for a selected scenario. Constellation topology, spacecraft and ground network characteristics serve as inputs to operations design. The scheduler can run autonomously onboard the spacecraft, or at the ground station with resultant schedules uplinked to the spacecraft for execution. D-SHIELD includes a science simulator to inform the scheduler of the predictive value of observations or operational decisions, which is custom built as a function of the use case, using novel data assimilation methods and predictive algorithms. We have applied the D-SHIELD simulation framework to several use cases spanning urban floods, cyclones, and most recently, a global soil moisture monitoring scenario using a 6 satellite constellation carrying P and L band radars, and L band radiometers. Platform agility allows the satellites to slew in any direction by changing their roll and look angle. Decision autonomy schedules the satellites for what to look at, at what time, and with what instruments and parameters. Results show potential for reduction in predictive uncertainty of global soil moisture compared to flagship missions. The framework is currently being extended to monitor wildfire spread for responsive control.
    • Bio: Dr. Sreeja Nag is a Senior Research Scientist at NASA Ames Research Center, contracted by BAER Institute, where she serves as the PI on the D-SHIELD project. She also leads Software Systems Engineering at Nuro, a Silicon Valley start-up that is building and deploying safe, self-driving robotic fleets for public roads. Sreeja completed her PhD from the Department of Aeronautics and Astronautics at Massachusetts Institute of Technology, Cambridge, USA. Her research interests include distributed space systems, space robotics for Earth observation, space traffic management, and vehicular robotics validation
  • Nathaniel D. Bastian, PhD, US Army/Lieutenant Colonel, Academy Professor, United States Military Academy at West Point
    • Title: Data Augmentation to Improve Adversarial Robustness of AI-Based Network Security Monitoring
    • Cyber security is an international challenge that is increasingly important as the inter-connectedness of the world grows. The reliance of systems on computational assets makes them vulnerable to attack. Traditionally, networks and systems are monitored by cyber security operators who rely on intrusion detection systems to provide indicators of compromise via alerts. With the growing number and frequency of alerts and the increasing sophistication of attacks, human operators are incapable of keeping pace. Data-driven and statistical tools, such as algorithms from artificial intelligence (AI), have the potential to assist in this area. These AI technologies enable the collection of synchronized, real-time capabilities to discover, define, analyze, and mitigate cyber threats and vulnerabilities with limited human intervention. Network intrusion detection systems (NIDS) are a primary component of the broader practices in network security monitoring for cyber security operations at enterprise, operational and tactical environments. Today, integrating AI components into NIDS can help identify patterns associated with known threats or detect abnormal behavior. These AI-based NIDS, however, are susceptible to adversarial AI evasion attacks. In this talk, I will discuss how data augmentation techniques can be used to improve the adversarial robustness of AI-based NIDS for network security monitoring. Particularly, I will discuss how meta-heuristics can be used to generate adversarial examples to then be combined as part of a meta-learning adversarial training framework.
  • Prof. Yuri Bazilevs, PhD, Professor, Brown University
    • Title: DDDAS for Systems Analytics in Applied Mechanics
    • We will begin the presentation by providing an overview of the DDDAS concept, with a particular emphasis on the analytics of systems coming from the field of Applied Mechanics and focusing on the applications in Aerospace Structures. The main goal of DDDAS in this context is to provide a framework where the dynamic measurement data for a given system forms a symbiotic relationship with the advanced, geometrically complex, multi-physics model of that system to reliably predict its future behavior, shield it from undesired loading scenarios that accelerate failure, and estimate its remaining useful life. It is well known that aerospace composite materials and structures exhibit a strong multiscale behavior, which necessitates the development of a multiscale DDDAS framework where measurements and models interact at all the relevant spatial and temporal scales of the system of interest to maximize the resulting predictive power. We will present a set of examples, both academic and practical, that clearly illustrate that it is precisely the combination of dynamic data and advanced models, and not exclusively one or the other, that is needed to be truly predictive.

      We will then shift gears and critically examine the modern data-driven approaches for systems analytics in applied mechanics. This topic, which has great relevance with DDDAS, has received significant attention in recent years. The applied mechanics community is trying to bring data science methods, such as Neural Networks (NNs), to bear on some of the key challenges, including the design of better materials and architected structures. NN-based approaches were also deployed as part of the so-called Physics Informed Neural Networks (PINNs) framework recently developed to bring more physics into predictions. PINNs accomplish this by defining an objective function that simultaneously minimizes the errors in the observed data, boundary conditions, and some form of the energy or PDE residual governing the problem at hand. A distinguishing feature of PINNs is that the discretization of a PDE does not make use of traditional methods like FEM, but rather NNs themselves. As a result, by construction, and in spirit, PINNs are yet another instantiation of the DDDAS concept that effectively blends data and physics-based models to achieve superior predictability. We will focus on the ability of approaches, incorporating NNs (as a tool) into DDDAS, to model large-deformation elastoplastic behavior of solids and structures and provide guidance for making such approaches more competitive than traditional modeling methods, so that they can be seamlessly integrated into structural systems analytics and beyond.

  • Sertac Karaman, PhD, Professor MIT
    • Title: Computing for Emerging Aerospace Autonomous Vehicles
    • Aerospace autonomous vehicles are going through a renaissance. Consumer drones, enabled by autonomous visual navigation capabilities, are serving a rapidly-growing number of applications, ranging from entertainment to inspection. The fast-decreasing cost of access to space is enabling miniature autonomous satellites towards better communication, Earth observation, or even launching in-orbit manufacturing and maintenance. In this talk, we outline a set of bleeding-edge technologies enabled by new advances in algorithms, computer architecture, integrated circuits and sensor design. These technologies will enable a spectrum of new vehicles, ranging from the most miniature and long endurance to the fastest and the most agile, with exciting new applications in aerospace autonomous vehicles. We outline some of the applications and recent progress towards enabling them.
  • Manolis Kellis, PhD, Professor MIT
    • Title: From genomics to therapeutics: Single-cell dissection and manipulation of disease circuitry
    • Disease-associated variants lie primarily in non-coding regions, increasing the urgency of understanding how gene-regulatory circuitry impacts human disease. To address this challenge, we generate comparative genomics, epigenomic, and transcriptional maps, spanning 823 human tissues, 1500 individuals, and 20 million single cells. We link variants to target genes, upstream regulators, cell types of action, and perturbed pathways, and predict causal genes and regions to provide unbiased views of disease mechanisms, sometimes re-shaping our understanding. We find that Alzheimer’s variants act primarily through immune processes, rather than neuronal processes, and the strongest genetic association with obesity acts via energy storage/dissipation rather than appetite/exercise decisions. We combine single-cell profiles, tissue-level variation, and genetic variation across healthy and diseased individuals to map genetic effects into epigenomic, transcriptional, and function changes at single-cell resolution, to recognize cell-type-specific disease-associated somatic mutations indicative of mosaicism, and to recognize multi-tissue single-cell effects of exercise and obesity. We expand these methods to electronic health records to recognize multi-phenotype effects of genetics, environment, and disease, combining clinical notes, lab tests, and diverse data modalities despite missing data. We integrate large cohorts to factorize phenotype-genotype correlations to reveal distinct biological contributors of complex diseases and traits, to partition disease complexity, and to stratify patients for pathway-matched treatments. Lastly, we develop massively-parallel, programmable and modular technologies for manipulating these pathways by high-throughput reporter assays, genome editing, and gene targeting in human cells and mice, to propose new therapeutic hypotheses in Alzheimer’s, obesity, and cancer. These results provide a roadmap for translating genetic findings into mechanistic insights and ultimately new therapeutic avenues for complex disease and cancer.

[1] The DDDAS/InfoSymbiotics2022, is following a number of past DDDAS forums, starting in 2003 with the DDDAS Workshops in conjunction with ICCS, and over these years additional forums (Conferences, Workshops, and Principal Investigator meetings) have been convened, including the standalone DDDAS/InfoSymbiotics forums,  in 2014, 2016, 2017, 2020 – …, all of  which can be accessed through www.1dddas.org, and Springer Conference Publications.  Notably the DDDAS2020 Conference Proceedings by Springer have had over 30,000 accesses.