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Integrated Modeling, Inference, and Computing
Integrated Modeling, Inference and Computing will focus on the advancement of the integration of core areas of engineered modeling approaches, machine learning and computation to address barriers in smart modeling with applications in bioengineering for health & disease, environmental health monitoring & climate change, and engineering & design of advanced material systems. It will identify testbeds that define broad application areas that demand new developments in our three fundamental core areas to address barriers in smart modeling.
Northeastern University has significant strengths and expertise in computational modeling, machine learning, and computing. While each of these areas represent a rich technology base, the future lies in their integration to allow the development of smart modeling of complex systems and quantitative model-based learning that can leverage compute-enabled discovery to truly take advantage of the explosion of available data. The concept of smart systems suggests that models can “learn”, and leverage this knowledge to create systems that can tune their parameters, both within and across scales; add new model components; as well as profile and optimize their behavior to provide new levels of specificity and accuracy. Model-based learning expands data exploration methods beyond purely statistical approaches to use tailored computing methods to incorporate domain knowledge into data analysis. We envision bold break throughs in the areas of material design, environmental monitoring and human health.
The initiative will focus on fundamental advances to the integration of the core areas of modeling approaches, machine learning and computation. To motivate these advances, we identify testbeds that define broad application areas that demand new developments in our three fundamental core areas to address barriers in smart modeling. Initial system-level applications that will motivate our research include: designing new nano-materials that comprise interactions at multiple scales and dimensions, understanding malignant cancerous cell behavior, integrating psychological and mechanistic models of happiness and learning with real-time measurements, and characterizing environment factors that result in drastic climate change. These are representative of a compelling set of applications that remain major challenges to science and engineering.
The modeling challenges in these applications inherently involve huge volumes of data. Smart models require new machine learning techniques to adapt and expand mechanistic models to fit complex phenomena. Model-based inference requires new methods of modeling to frame and guide data analysis. Both require advances in computing and algorithm rethinking to achieve reasonable time and memory burdens and to move from the laboratory into the real world. Efficient computation is a critical enabler in all these approaches, and innovations are sorely needed to address the scale of the processing required in modeling spatial, temporal and networked dynamic systems, and seamless integrating processing from sensors, distributed embedded processors, heterogeneous complex networks and heterogeneous processing facilities. Our model of computation includes software and hardware at all these levels and involves data structures, algorithms, architectures and application-specific devices. We view each of these components as an integrated, complex, processing architecture, which can support the integrated modeling and learning we propose.
Specific applications will be used to advance modeling, machine learning and computation. The goal is unifying approaches that span these applications. Some applications which researchers at Northeastern have established expertise, but continue to face significant computational barriers include:
Engineering and design of advanced material systems: COE has expertise in scale bridging efforts, from atoms to continua, aimed at understanding structure-property relations in materials and their rationale design. There is an urgent need for novel and highly efficient algorithms that allow seamless handshaking of the scales to make contact with experimentation; an integrated approach that involves heterogeneous computing and machine learning can pave the way for transformative approaches that have immediate impact for quantifying the materials genome. Systems of interest include structural materials such as metals and ceramics, electronic and photonic materials, nanomaterial behavior and manufacturing, material mechanics, and catalysis.
Bioengineering for health and disease: Human health involves complex interacting systems at spatial scales from molecular to societal and time scales from nanoseconds to decades. Full mechanistic models are infeasible, while generic statistical learning ignores known constraints (genetic, biophysical, environmental, social). Advances from genetics and imaging to smart phones provide an avalanche of health-related data. We will integrate modeling, learning, and computing to extract information from these data for increased understanding of health and disease processes, eventually leading to fundamental health improvement. As examples, blood-borne sensors monitoring low-level physiological processes communicating with sophisticated smart models and learning algorithms could detect and treat early stage metastasis or infection, and real-time monitoring of autonomic or non-conscious mental phenomena could allow entirely new methods of behavioral and psychological therapy.
Environmental health monitoring and climate change: From fundamental advances in the science of climate change and associated weather extremes, to preventing hazards from turning into catastrophic disasters through improved resilience of critical infrastructures and sensor-based early warning systems, computational modeling and analysis have had increasingly large roles in societal priorities.., Machine learning is barely developing methods for handling extreme values or complex dependence structures, while high-performance computation is struggling to keep up with the volumes and velocity of data from models and sensor systems. COE has recognized capabilities in climate extremes, hydrology, environmental health, life cycle analysis, sensor-based monitoring in geosciences, and critical infrastructures assessment.
Associated Faculty & Staff
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- Assistant Professor, Mechanical and Industrial Engineering
Applied operations research, healthcare, supply chain, large scale optimization and big-data analytics -
- Professor & Associate Dean, Khoury College of Computer Sciences
Big Data and Geospatial Computation, Machine Learning -
- University Distinguished Professor, Physics
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- University Distinguished Professor, Psychology
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- Associate Professor, Electrical and Computer Engineering
Wireless networks; ad hoc networks; underwater and terrestrial sensor networking; protocol design and testing -
- Professor, Electrical and Computer EngineeringAffiliated Faculty, Bioengineering
Biomedical signal and image processing; medical imaging; statistical signal processing; inverse problems; electrocardiography; bio-optical imaging; magnetic resonance imaging; transcranial neuromodulation; estimation of protein conformations from Xray scattering; regularization; optimization -
- Associate Professor & Associate Dean, Chemistry and Chemical Biology
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- Professor, Electrical and Computer Engineering
Robust computer vision, image processing, and machine learning -
- Professor & Associate Chair of Research, Chemical EngineeringAffiliated Faculty, Bioengineering
intestinal tissue engineering, retinal regenerative medicine, oral drug delivery -
- Associate Professor, Electrical and Computer Engineering
Dynamic spectrum access; energy harvesting sensor networks; 5G technology; intra-body communication; protocol design for wireless -
- Professor, Khoury College of Computer SciencesAffiliated Faculty, Electrical and Computer Engineering
High-performance computing and large applications in computational algebra -
- Associate Professor, Electrical and Computer EngineeringAffiliated Faculty, Mechanical and Industrial EngineeringAffiliated Faculty, Bioengineering
Optics, microscopy, coherent detection, interaction of light and sound waves, hyperspectral imaging, diffusive optical tomography and ultrasound, landmine detection, magneto-optic sensors, and multi-model imaging. Activities include computer modeling, designing, building, and testing of hardware, and processing the resulting data -
- Professor, Electrical and Computer Engineering
Machine learning; data mining; statistical pattern recognition; computer vision and image processing -
- Professor, Electrical and Computer EngineeringAffiliated Faculty, Bioengineering
machine learning, signal and image analytics, cyber-human systems -
- Assistant Professor, Physics
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- Associate Professor, Electrical and Computer EngineeringInterdisciplinary with, Khoury College of Computer Sciences
Machine learning and computational intelligence, social media analytics, human-computer interaction, and cyber-physical systems -
- Professor, Civil and Environmental EngineeringProfessor (by courtesy), Khoury College of Computer SciencesProfessor (by courtesy), Marine and Environmental ScienceProfessor (by courtesy), Political ScienceProfessor (by courtesy), School of Public Policy and Urban Affairs
Water & Climate Science, Data & Network Science, and Infrastructure Resilience -
- Assistant Professor, Health Sciences
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- Associate Professor, College of ScienceAffiliated Faculty, Civil and Environmental Engineering
statistical and dynamical modeling; theoretical ecology -
- Professor and Associate Chair, Mechanical and Industrial EngineeringAffiliated Faculty, Chemical Engineering
Biomechanics, material science, engineering mechanics -
- Associate Professor, Khoury College of Computer SciencesAffiliated Faculty, Electrical and Computer Engineering
Applying pattern recognition and sensor-driven healthcare technologies to measure physical activity and promote behavior changes that improve health -
- Associate Dean, Clinical Education, Rehabilitation, and New Initiatives, Bouvé College of Health Sciences
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- COE Distinguished Professor, Electrical and Computer EngineeringAffiliated Faculty, Bioengineering
Computer architecture; GPUs; heterogeneous computing; performance analysis; security and information assurance; hardware reliability and recovery; Big Data analytics; workload characterization -
- Professor, PhysicsAffiliated Faculty, Bioengineering
computational modeling of cardiac arrhythmia mechanisms from cellular to organ scales including systems biology approaches -
- Professor, Electrical and Computer Engineering
Accelerators for compute intensive applications: reconfigurable hardware and graphics processing units (GPUs). Applications including biocomputing, machine learning, software-defined radio. Uses and implementations of computer arithmetic. -
- Professor, Electrical and Computer Engineering
Adaptive filtering, Statistical signal processing, spectrum analysis and estimation, networked dynamic state estimation. -
- Professor and Chair, BioengineeringInterdisciplinary with, Chemistry and Chemical BiologyAffiliated Faculty, Electrical and Computer Engineering
Image and signal processing as applied to biophysical data designed to answer fundamental questions about the molecular basis of living systems -
- Professor, Interim MIE Department Chair and Director, Industrial Engineering
Deterministic operations research and multi-criteria optimization; facility location; supply chain, transportation and logistics; wireless sensor network lifetime maximization with sink mobility; wildfire prediction and mitigation -
- Associate Chair & Associate Professor, Electrical and Computer EngineeringAffiliated Faculty, Bioengineering
Combinatorial optimization, algorithm design and analysis, scheduling, machine learning, parallel computing. -
- Associate Professor, Electrical and Computer Engineering
Capacity planning; MapReduce/Hadoop scheduling; cloud computing; resource management; performance evaluation; workload characterization; simulation; virtualization -
- Professor, Electrical and Computer Engineering
Electromagnetics and optics, quantum systems, nanoscale materials and metamaterials, nanoantennas, THz-IR Devices, multiscale computation and mathematical-numerical models -
- Professor, Mechanical and Industrial EngineeringAffiliated Faculty, Civil and Environmental Engineering
Mechanics and tribology of axially moving materials, webs; numerical simulation of tissue healing and bone remodeling; high velocity impact of micron scale particles -
- Professor, Chemical EngineeringDirector, Sherman CenterAffiliated Faculty, Mechanical and Industrial Engineering
Microfluidic isolation of stem and progenitor cells, point-of-care diagnostics, cell surface phenomena during microfluidic flow, nanoscale probes for cell stimulation, and biopassive/bioactive coatings for neurological implants -
- Associate Professor and Associate Chair For Research, Bioengineering
Biomedical optics and non-invasive imaging, rare cell detection and tracking in the body, ultrafast time-domain diffuse optical imaging, image reconstruction and biomedical signal processing. -
- Associate Professor, Electrical and Computer Engineering
Design of analog, radio frequency, and mixed-signal integrated circuits; built-in test and calibration techniques for systems-on-a-chip; on-chip temperature sensors for thermal monitoring and built-in testing. -
- Professor, Chemistry and Chemical BiologyAffiliated Faculty, Bioengineering
enzyme catalysis; functional genomics; modeling of enzyme substrate interactions; drug discovery; bioinformatics; protein design -
- Assistant Professor, Electrical and Computer Engineering
Machine learning/pattern recognition; computer vision, affective computing, human-machine interaction -
- Professor, Khoury College of Computer SciencesAffiliated Faculty, Electrical and Computer Engineering
Speech communication and human computer interfaces -
- Principal Research Scientist, Center for Applied Social Research
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- Associate Professor, Electrical and Computer Engineering
Understanding and exploiting the fundamental properties of micro/nanomechanical structures and advanced nanomaterials to engineer new classes of micro and nanoelectromechanical systems (M/NEMS) with unique and enabling features applied to the areas of chemical, physical and biological sensing and low power reconfigurable radio communication systems -
- Professor, Bioengineering
tissue engineering of load-bearing matrix (bone, cornea); bioreactor design; multi-scale mechanobiochemistry; statistical mechanics; energetics microscopy; high-resolution imaging; biopolymer self-assembly -
- Associate Professor, Electrical and Computer Engineering
Embedded computer systems; novel architectures for embedded vision; cyber-physical systems; system-level design and methodologies; hardware/software co-design -
- Professor, Mechanical and Industrial Engineering
Control systems and mechatronics; stability analysis and control synthesis of dynamical systems with delays; interplay between stability, delays, and graphs; control-systems-aided human-machine systems; engineering education research; disability research; systems biology -
- Associate Professor, PhysicsAffiliated Faculty, Bioengineering
Theoretical Neuroscience, Bioimaging & Signal Processing, Integrated Modeling, Inference, and Computing. -
- Professor, Electrical and Computer EngineeringInterdisciplinary with, BiologyAffiliated Faculty, Bioengineering
Motor control and learning, variability and stability, virtual rehabilitation, dynamic modeling, rhythmic and discrete movements as primitives for action -
- Professor, Electrical and Computer EngineeringAffiliated Faculty, Bioengineering
Wireless communications and networks, underwater acoustic transmission, statistical system characterization, adaptive signal processing -
- Assistant Professor, Civil and Environmental Engineering
Smart and resilient infrastructure; advanced sensing, big data analytics, machine learning, uncertainty quantification and inverse computational mechanics, for structural health monitoring and resilience assessment; physics-informed AI for engineering applications -
- Professor, Khoury College of Computer Sciences
Big Data and Geospatial Computation, Networks -
- Dennis Picard Trustee Professor, Electrical and Computer Engineering
Robust Control; Reduced Order Models; video-based control; applications to dynamics in Imaging and video processing; information extraction from high volume data streams -
- Professor Emeritus, Electrical and Computer Engineering
Control systems; dynamical systems; low order modeling and estimation in complex systems; medical imaging -
- Professor, Mechanical and Industrial Engineering
Computational techniques that span multiple scales, atomic- to continuum, to quantify the structure property relations in established and emerging material systems, both in technology and nature. -
- Professor, Mechanical and Industrial Engineering
Solid Mechanics, materials, computational methods, biomechanics, nanotechnology -
- Professor, Mechanical and Industrial EngineeringAffiliated Faculty, Civil & Environmental Engineering
Cellular biomechanics; water filtration; thin film adhesion and characterization; subsurface mechano-sensing; shell adhesion; fundamental intersurface forces -
- Associate Professor & Associate Chair of Graduate Studies, Chemical Engineering
development of detailed microkinetic models for complex reacting systems; automating the discovery and calculation of reaction pathways; heterogeneous catalysis -
- Affiliated Faculty, BioengineeringAssistant Professor, Physics
dynamics of large-scale molecular machines, working to identify the physical principles that guide biomolecular dynamics, using molecular simulation approaches to interpret experimental data from a wide range of techniques, including biochemical, small-angle X-ray scattering and cryogenic electron microscopy