Department of Computational and Data Sciences (CDS)
Recent Submissions
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Structural and functional studies on the hypothetical protein TTHA1873 from Thermus thermophilus
This thesis reports a detailed study on the structural and functional characterization of the hypothetical protein (TTHA1873) from Thermus thermophilus. In this study, both the structure and function of TTHA1873 were ... -
Integrating Coarse Semantic Information with Deep Image Representations for Object Localization, Model Generalization and Efficient Training
Coarse semantic features are abstract descriptors capturing broad semantic information in an image, including scene labels, crude contextual relationships between objects in the scene, or even objects described using ... -
Scalable Video Data Management and Visual Querying System for Autonomous Camera Networks
Video data has been historically known for its unstructured nature, rich semantic content and scalability issues in terms of storage. With advances in computer vision and Deep Neural Net works (DNNs) it is now possible ... -
Efficient and Effective Algorithms for Improving the Robustness of Deep Neural Networks
Deep Neural Networks achieve near-human performance on several benchmark datasets, yet they are not as robust as humans. Their success relies on the proximity of test samples to the distribution of training data, resulting ... -
Modeling physiological transport at scales: connecting cells to organs
The physiological system is a complex network in which each organ forms a subsystem, and the functional networks in different subsystems communicate to maintain the body’s overall homeostasis. The ability to simultaneously ... -
A scalable asynchronous discontinuous Galerkin method for massively parallel flow simulations
Accurate simulations of turbulent flows are crucial for understanding numerous complex phenomena in engineered systems and natural processes. Notably, under realistic conditions with high Reynolds numbers and complex ... -
Fast and Scalable Algorithms for Intelligent Routing of Autonomous Marine Agents
Autonomous marine agents play a pivotal role in diverse ocean applications. These agents serve as indispensable instruments for acquiring crucial environmental information. They are used to explore and monitor harsh ... -
Sequence Alignment to Cyclic Pangenome Graphs
The growing availability of genome sequences of several species, including humans, has created the opportunity to utilize multiple reference genomes for bioinformatics analyses and improve the accuracy of genome resequencing ... -
Data-Driven Approach to Estimate WCET for Real-Time Systems
Estimating Worst-Case Execution Time (WCET) is paramount for developing Real-Time and Em- bedded systems. The operating system’s scheduler uses the estimated WCET to schedule each task of these systems before the assigned ... -
Intelligent Methods for Cloud Workload Orchestration in Data Centers
Cloud workload orchestration plays a pivotal role in optimizing the performance, resource utilization, and cost effectiveness of applications in data centers. As modern businesses and IT operations are migrating their ... -
An importance sampling in N Sphere Monte Carlo and its performance analysis for high dimensional integration
Statistical methods for estimating integrals are indispensable when the number of dimensions (parameters) become greater than ~ 10, where numerical methods are unviable in general. Well-known statistical methods like ... -
A co-kurtosis tensor based featurization of chemistry for scalable combustion simulations
For turbulent reacting flow systems, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale ... -
An arbitrary lagrangian eulerian volume of fluid method for floating body dynamics simulation
The floating body dynamics is treated as a Fluid-Structure Interaction (FSI) problem. A FSI problem is where the forces from the fluid move/deform the interacting structure, and the movement of the structure, in turn, ... -
Sparsification of Reaction-Diffusion Dynamical Systems on Complex Networks
Graph sparsification is an area of interest in computer science and applied mathematics. Spar- sification of a graph, in general, aims to reduce the number of edges in the network while preserving specific properties of ... -
End-to-end Resiliency Analysis Framework for Cloud Storage Services
Cloud storage service brought the idea of a global scale storage system available on-demand and accessible from anywhere. Despite the benefits, resiliency remains one of the key issues that hinder the wide adaptation of ... -
Development of Novel Deep Learning Models with Improved Generalizability for Medical Image Analysis
Medical imaging is a process of visualization of disease/tissue in a non-invasive manner. Several imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and ... -
Abstractions and Optimizations for Data-driven Applications Across Edge and Cloud
Modern data driven applications have a novel set of requirements. Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments ... -
Constrained Stochastic Differential Equations on Smooth Manifolds.
Dynamical systems with uncertain fluctuations are usually modelled using Stochastic Differential Equations (SDEs). Due to operation and performance related conditions, these equations may also need to satisfy the constraint ... -
Data-efficient Deep Learning Algorithms for Computer Vision Applications
The performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of ... -
Data Driven Stabilization Schemes for Singularly Perturbed Differential Equations
This thesis presents a novel way of leveraging Artificial Neural Network (ANN) to aid conventional numerical techniques for solving Singularly Perturbed Differential Equation (SPDE). SPDEs are challenging to solve with ...
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