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Suvranu De

Bionanoporous materials such as protein crystals are being increasingly used for a variety of applications involving bioseparation, catalysis and synthesis. This project involves characterizing and modeling such bionanoporous molecules and elucidating their properties as a function of temperature and hydration.

The emphasis of this project is to develop accurate  in vivo soft tissue measurements for a variety of simulation and modeling projects. Such measurements require portable devices and novel experimental paradigms involving indentation, ultrasound and other noninvasive techniques.


This project aims at developing efficient crystal plasticity solvers including single yield function approaches for modeling crystalline materials. The CPFEM techniques have been coupled to both DFT and Jacobian free meltiscale methods.

Multigrid computational strategies are being developed to accelerate solution speed. Of special interest are schemes that perform robustly for changing Dirichlet boundary conditions without reforming the sparse matrix structures at multiple levels. Techniques are being developed to represent real time changes in mesh topology at the finest scale with both structured and unstructured hierarchies.

This is a technique to simulated the response of continua undergoing large nonlinear deformations using radial basis function neural networks which have been exhaustively trained on “response surfaces” in pre-computational steps performed on full finite element models. Computations are highly scalable as the number of neurons in the network may be dynamically chosen to control computational speed,  without the need  to remesh. Higher order polynomial reproducing neural networks have been developed to improve prediction accuracy.


This is a meshfree computational environment based on the moving least squares approximation functions, compactly supported on spherical subdomains, used in a point collocation residual minimization technique. Advantages over traditional finite elements include not having to perform numerical integration, constant Jacobians which preclude distortions and smooth solutions.This is a meshfree computational environment based on the moving least squares approximation functions, compactly supported on spherical subdomains, used in a point collocation residual minimization technique. Advantages over traditional finite elements include not having to perform numerical integration, constant Jacobians which preclude distortions and smooth solutions.

A software framework is being developed for interactive simulations. This is highly modular and extensible and is intended to reduce the development time for interactive simulations by providing necessary functionalities for visualization, collision detection and response, physical modeling and networking.

Jacobian-free global-local computational approaches are being developed for coupling disparate length scales. Explicit Jacobian-free multiscale methods are suitable for solving high strain rate problems. Implicit methods are more challenging where we have proposed the use of Newton-Krylov processes across scales. However, efficient block precoditioners must be developed without prior knowledge of the current Jacobian.

This project aims at developing a computational model of the lung for the entire breathing cycle based on patient-specific 4D CT scans and physiological pressure-volume curves. The model may be used in simulating radiation therapy of lung cancer.

In robotic surgery, the surgeon controls the surgical robot through teleoperation of a master manipulator.  In telesurgery applications, in the presence of large time delays, it will be impossible for the surgeon to control movement of the robot in real-time and some sort of automation has to be provided for the robot to continue perform the surgical tasks.  In this project, the basic surgical tasks such has suturing, retraction, cutting, cauterization will be automated with the help of host of sensor inputs such as stereo camera, force and position sensors, ultrasound to guide the surgical end effector  using real-time control algorithms.

Working closely with the Fundamentals in Laparoscopic Surgery (FLS) committee, Harvard Medical School and Tufts University, this project aims at developing and validating a virtual reality simulator for the FLS tasks (peg transfer, pattern cutting, ligating loop, suturing with intracorporeal and extracorporeal knot tying).

This project aims at developing a computer aided design environment for emerging single incision procedures which minimize patient trauma by using a single port instead of the traditional five ports to perform surgery.

This project aims at developing a computer aided design environment for the natural orifice transluminal endoscopic procedure (NOTES) which is a revolutionary surgical paradigm for performing surgical operations without any external scarring.

While most commercially available surgical simulators (Gen1) are intended for psychomotor skill training, adult learning theory and literature in cognitive science indicate that immersive training is most effectively imparted in its natural context. Following this paradigm, the next generation (Gen2) simulators are being developed which also impart cognitive fidelity and feedback during training.

This project aims at analyzing electrosurgical procedures and, based upon task decomposition, develop a basic set of procedures that may be used for competency attainment and credentialing.

This project aims at developing technology for training on tele-robotic surgical procedures.

This project aims at developing models of how functional brain states emerge from their underlying structural substrate. Such understanding will greatly enhance our ability to analyze traumatic brain injuries (TBI) suffered by war veterans.

Biominerals are essential ingredients of hard tissues including bones and teeth. Contrary to traditional assumptions on their brittleness, recent nanoindentation experiments have revealed that slip related mechanisms result in plastic deformation which may have significant implications in their response to applied loading.

Detecting the skill level of the surgeons and residents are important because they indicate the level of proficiency and can be used to control the amount and length of training that one takes during residency and fellowship years.  Currently a combination of subjective and objective scoring methodology is used by trained proctors for evaluating the surgical performance. In this project, combination of instrumented tools, biomechanical markers, computer vision algorithms, machine learning algorithms , neural and muscle signals are being used to build  a automated skill evaluation system.

The traditional haptic interaction paradigm is using a point representation of the haptic cursor. This is unrealistic for interactions of extended objects which are better represented as line segments. However, such ray-based interaction paradigms are computationally expensive. The DynamicPoint algorithm is being developed to allow ray-based interactions with the computational complexity which is comparable to that of point-based interactions, independent of the number of polygons in the model.

Both client-server and peer-to-peer networking paradigms are used here in this project to allow multimodal interactive computing for geographically separated users.

The goal of reduced order modeling is to replace a computational model with a much lower order model with reasonably high accuracy over an operating range, with a significantly lower computational cost. Linear model order reduction methods have been developed for linear viscoelastic materials based on truncated  balanced realization, Hankel optimal norm method  and modal truncation for the PAFF technique. Current research is to develop nonlinear model order reduction methods.