Thrust Areas
CIVIL ENGINEERING
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Traffic Systems and Management | Traffic simulation, control, and management is a key thrust area focused on improving the efficiency, safety, and sustainability of transportation systems. It involves the use of advanced simulation models, real-time data, and intelligent algorithms to analyze traffic flow, predict congestion, and evaluate control strategies. Techniques such as adaptive signal control, intelligent transportation systems (ITS), and AI-based decision support help optimize road network performance. This thrust area supports better urban planning, reduces travel time and fuel consumption, minimizes emissions, and enhances overall mobility in rapidly growing cities. |
| 2 | Sustainable and Advanced Geomaterials for Infrastructure Applications | Develops sustainable geomaterials utilizing industrial by-products such as Fly ash, GGBS, Phosphogypsum, Nano-silica and magnesia-activated blast furnace slag. Research evaluates mechanical behavior, stress–strain characteristics, stabilization mechanisms, and durability performance for concrete and soil improvement applications, supporting environmentally sustainable infrastructure development. |
| 3 | Sustainable & Advanced Construction Materials | Focuses on development and performance enhancement of sustainable construction materials using industrial by-products (red mud, fly ash, slag), recycled aggregates, fibers, and 3D printing technologies. Research emphasizes mechanical behavior, durability, microstructural evolution, topology optimization, and circular construction principles to reduce environmental footprint while improving structural performance. |
| 4 | Structural Engineering & Stability Analysis | Focuses on structural performance evaluation, including local buckling behavior in steel sections and performance enhancement through material modification. Emphasis is placed on analytical, experimental, and numerical investigations for improving structural safety and design efficiency. |
| 5 | Geospatial & Environmental Engineering Applications | Applies geospatial technologies, remote sensing, and data analytics for agricultural productivity assessment, soil nutrient mapping, and sustainable construction planning. Integrates GIS, Google Earth Engine, NDVI trend analysis, and spatial modeling for environmental monitoring and decision support. |
| 6 | Sustainable Bio-based Geosynthetics and Eco-friendly Ground Improvement Materials | This thrust area focuses on the development of sustainable, bio-based geosynthetics and ground improvement solutions using agro-industrial waste materials and natural fibers. The research emphasizes durability enhancement of natural geotextiles through bio-treatment, eco-friendly alternatives to conventional geofoam using recycled and silicon-rich agro-waste ash, and bamboo-based geocells for slope stabilization under extreme rainfall conditions. The overall objective is to reduce environmental impact, promote circular economy principles, and develop cost-effective, biodegradable geotechnical materials suitable for sustainable infrastructure and slope protection applications. |
| 7 | 3D Printing Concrete (Additive Manufacturing in Construction) | 3D Printing Concrete is an advanced construction technology that enables automated, layer-by-layer fabrication of affordable housing with minimal formwork and reduced labor dependency. It significantly lowers construction time, material wastage, and overall project cost while allowing design flexibility and efficient structural optimization. This approach is highly suitable for large-scale housing development and rapid deployment in disaster-relief and low-income regions. Key research challenges include ensuring interlayer bond strength, long-term durability, quality assurance, and compliance with structural design standards. |
COMPUTER SCIENCE & ENGINEERING
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Computational Intelligence and Data Analytics | Artificial Intelligence, Machine Learning, Deep Learning, Soft Computing, Pattern Recognition, Speech Processing, Natural Language Processing, Large Language Models, Agentic AI, Prompt Engineering, Online Machine Learning Algorithms, Data Augmentation Techniques, Study of Advanced AI Tools, Data Science, Big Data and Analytics, Bioinformatics and Geoinformatics are some popular fields of Computational Intelligence and Data analytics. This type of research focuses on discovering non-trivial information from vast amounts of data to make predictions and decisions. Intelligent computational models can be employed to make operating decisions in a more efficient, data-driven manner helping decision makers across health care, environmental and business applications. |
| 2 | Computer Networks, Cyber Security, and Forensics | Computer Networks, Cyber Security and Forensics are the set of fields which are based on communication technologies and protecting these digital resources. Subdomains fall under Computer Networks, Software-Defined Networks, Sensor Networks, Network Function Virtualization, Cybersecurity and Cyber Forensics. Cybersecurity research address key topics such as secure and reliable communication systems, cyber threat detection, and digital forensic analysis to ensure the network infrastructure operates securely and reliably. |
| 3 | Emerging Computing Technologies and Systems | This includes Quantum Computing, Blockchain Technology, Autonomous Systems, Healthcare Systems, Social Networks, Formal Modelling, GPU Programming and Robotics and Automation are among other advanced technologies that are changing modern computing systems. The focus is on the innovative computing methodologies and intelligent solutions to complex problems. Research supports promote the use of secure systems, automation and streamlined data processing. These applications range from digital transactions, healthcare control systems, smart solutions, and social data assessment to increased efficiency, reliability improvements, and future technology advancement. |
| 4 | IoT, Cloud, and Edge Computing | Connected devices and real-time data processing rely on distributed computing technologies. This domain covers Embedded Systems, Internet of Things, Cloud Computing, Fog computing, Edge Computing and Drone Technologies. Research revolve towards building scalable and efficient systems for smart device data collection and processing. Applications span from smart homes to agriculture and industrial automation as well as environmental monitoring. Low-latency processing, resource optimization, and reliable communication are emphasized to allow for efficient data handling and intelligent applications. |
| 5 | Software Engineering and System Development | Software Engineering, Agile Project Management, Mobile Application Development, Full Stack Development and User Interface & User Experience Design, and and Advanced Database Systems are systematic design and development of structured dependable and efficient software systems. Industry-wide practices to ensure software is of high quality, maintainable, and usable through a structured set of methodologies and modern development techniques. These topics involve software testing, requirement analysis, project management and the deployment of a system. The intention is to create scalable and easy-to-use software products for business, education, healthcare, and other purposes. |
| 6 | Visual Computing and Interaction | Image and Video Processing, Computer Vision, Remote Sensing and GIS, Augmented Reality, Virtual Reality and Computer Generated Imagery fall under this area. Research aims to capture useful information in the images and videos, and build interactive visual systems. Applications span several fields like surveillance, medical imaging, environmental monitoring, and virtual environments. The field focuses on effective visualization and intelligent analysis to enhance the understanding of visual information and the human-computer interaction. |
| 7 | Theoretical Computer Science and Algorithms | Theoretical and mathematical concepts of computing consists of Automata Theory, Number theory, Graph Theory, Algorithm Design and Analysis, Quantum Theory. This research area encompasses the design of algorithms and computational models to tackle difficult tasks. Topics range from optimization, through computational complexity and formal languages. The aim is to enhance the computational efficiency, and provide a solid theoretical foundation for modern computing in areas such as cryptography, artificial intelligence, data processing. |
ELECTRONICS AND COMMUNICATION ENGINEERING
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Communication System | Focuses on wireless and mobile communication (5G/6G), optical fiber communication, satellite communication, MIMO systems, cognitive radio, spectrum sharing, underwater and visible light communication. Also includes IoT architectures, industrial IoT, smart sensors, and edge/fog computing for reliable, scalable, secure, and low-latency connected systems. |
| 2 | Signal Processing, Machine Learning & Computer Vision | Covers digital signal processing, image and video processing, speech and audio processing, biomedical signal processing, multirate and adaptive signal processing, and compressive sensing. Integrates machine learning and deep learning for signal analysis, pattern recognition, object detection, segmentation, and real-time intelligent systems. |
| 3 | VLSI & Embedded Systems | Includes VLSI design and verification, low-power and high-speed IC design, SoC/NoC architectures, FPGA-based design, embedded systems with RTOS, and hardware–software co-design for optimized and reliable electronic hardware platforms. |
| 4 | Microelectronics & Nanoelectronics | Encompasses semiconductor devices, MEMS/NEMS, nanoelectronics, nanofabrication, advanced CMOS technologies, and power electronic devices focusing on miniaturization, high performance, and energy-efficient electronic components. |
| 5 | RF & Antenna Engineering | Covers RF circuit design, microwave engineering, antenna design and arrays, metamaterials, radar and remote sensing, and mmWave/THz technologies emphasizing high-frequency system design, impedance matching, radiation characteristics, and compact antenna structures. |
ELECTRICAL AND ELECTRONICS ENGINEERING
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Renewable Energy Systems | There is research on modeling, parameter extraction, optimization, and performance enhancement of solar PV systems and hybrid renewable energy systems. The researchers utilize advanced metaheuristic algorithms such as Grey Wolf Optimization and Chimp Optimization to improve PV parameter estimation accuracy. The research includes techno-economic analysis, solar thermal performance studies, and renewable integration in distribution feeders for sustainable energy development. |
| 2 | Smart Grid, EV Integration & Energy Storage Systems | These thrust areas include grid stability, distributed renewable integration, EV-based V2G/G2V systems, harmonic mitigation, market-based scheduling, and energy storage coordination. Research covers optimal active-reactive power scheduling, EV fast charging infrastructure, droop control strategies, and hybrid grid energy management to enhance economic performance and reliability in deregulated markets. |
| 3 | Artificial Intelligence & Deep Learning in Power & Energy Systems | Research in this domain embraces artificial intelligence, machine learning, deep learning, and reinforcement learning methodologies for power system operation, forecasting, converter control, and smart grid optimization. Bi-LSTM, GRU, and Deep Reinforcement Learning models are used to enhance load prediction accuracy, improve control performance, and enable intelligent energy management and predictive analytics. |
ELECTRONICS AND INSTRUMENTATION ENGINEERING
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | AI-driven Healthcare Systems | The area “AI-driven Healthcare Systems with Hardware Acceleration for Real-Time Medical Diagnosis” focuses on designing intelligent medical solutions that combine advanced deep learning models with high-performance hardware platforms such as FPGAs and embedded systems. The objective is to enable fast, accurate, and energy-efficient diagnosis directly at the point of care. By integrating CNN-based medical image analysis, real-time signal processing, model optimization, and hardware acceleration, these systems reduce latency and improve reliability in clinical environments. This thrust area supports scalable deployment, telemedicine integration, and automated decision support, ultimately enhancing early disease detection, clinical workflow efficiency, and accessible healthcare delivery in resource-constrained settings. |
| 2 | Industrial Automation | Manual control and lack of real-time monitoring in rural water distribution result in water wastage, supply delays, and inefficient operations. There is a need for an integrated automated system that ensures timely and optimized water supply with monitoring and reporting capabilities. |
| 3 | MEMS Accelerometer | MEMS accelerometers are compact sensors that measure acceleration, vibration, and orientation in real time. When integrated with AI, they provide high-resolution motion data that algorithms instantly analyze for meaningful insights. In industry, they enable predictive maintenance by detecting abnormal vibration patterns and forecasting machine faults. In healthcare wearables, they monitor activity, rehabilitation progress, and detect events such as falls or neurological disorders. In autonomous vehicles and drones, they support sensor fusion for accurate navigation, stability control, and safer operation. |
| 4 | Non-Invasive Biomedical Instrumentation | Development of smart non-invasive systems for early detection of diabetes, anemia, blood type identification, and vitamin deficiencies using optical sensing, embedded instrumentation, and intelligent signal processing for affordable healthcare solutions. |
| 5 | Crop Disease Detection using AI | This project aims to develop an AI-based system for early detection and classification of crop diseases. Using image processing and deep learning, the system will analyze leaf images to identify pests, nutrient deficiencies, and infections. The model will be trained on diverse crop datasets for reliable performance. Integrated with IoT sensors and mobile or edge devices, it will provide real-time alerts and recommendations, helping farmers reduce crop losses, optimize pesticide use, and improve productivity. |
| 6 | Nano Sensors and Electronics | Nano sensors and nanoelectronics represent a rapidly advancing research field focused on designing and integrating devices at the nanometer scale to achieve ultra-high sensitivity, low power consumption, and enhanced functionality. Nano sensors exploit unique properties of nanomaterials such as high surface-to-volume ratio, quantum confinement, and tunable electrical characteristics to detect gases, biomolecules, temperature, pressure, and chemical species with exceptional precision. Nanoelectronics enables miniaturized circuits, improved switching speeds, and novel device architectures beyond conventional CMOS limits. Applications span healthcare diagnostics, environmental monitoring, wearable systems, and smart IoT platforms. This field bridges materials science, semiconductor technology, and device physics for next-generation intelligent systems. |
| 7 | Edge AI for Smart Healthcare | The Photoacoustic technique has been implemented for the detection of blood glucose. Empirical parameters have been identified from the photoacoustic signal, and a correlation has been established between the empirical parameters and the blood glucose values. Machine Learning algorithms have been developed in the correlation process. Piezoelectric sensors are utilised for the generation of photoacoustic signals. Lead-free piezoelectric transducers have also been developed and integrated into the photoacoustic setup |
| 8 | GNSS Signal Processing and Error Mitigation | Mitigation of errors in Global Navigation Satellite System (GNSS) received signals is essential for ensuring precise and dependable positioning. GNSS measurements are affected by ionospheric and tropospheric delays, satellite clock errors, receiver noise, and multipath interference. These factors degrade pseudorange and carrier phase observations, leading to inaccurate position estimates. In applications such as aviation, surveying, and intelligent transportation, reliable positioning is critical. Therefore, effective error estimation and mitigation techniques are necessary to improve positioning accuracy, integrity, and overall navigation performance. |
INFORMATION TECHNOLOGY
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Artificial Intelligence, Machine Learning and Deep Learning | Artificial Intelligence, Machine Learning and Deep Learning is a thrust area that focuses on building intelligent and self-learning systems capable of analyzing large volumes of data and generating reliable insights for decision support. The area promotes research on data-driven models, neural networks, intelligent automation, language understanding and visual perception. It aims to develop trustworthy, scalable and responsible AI solutions for real-world domains such as education, healthcare, agriculture, smart infrastructure and cyber security, enabling improved performance, adaptability and digital innovation. |
| 2 | Big Data and Data Analytics | The Data Analytics and Big Data Research Group is a multidisciplinary research unit dedicated to extracting actionable insights from large, complex, and high-velocity data. The group focuses on advanced data mining, machine learning, deep learning, statistical modeling, and scalable big data technologies. Research areas include predictive analytics, business intelligence, natural language processing, real-time data processing, and AI-driven decision support systems. By leveraging distributed computing frameworks and gpus, the group addresses real-world challenges in healthcare, smart cities, finance, cybersecurity, and sustainability, contributing to data-driven innovation and impactful societal solutions. |
| 3 | Computer Vision, Remote Sensing and Image Processing | Computer Vision, Remote Sensing, and Image Processing is a multidisciplinary research area focused on enabling machines to interpret, analyze, and extract meaningful information from visual data. It integrates advanced algorithms, machine learning, and deep learning techniques to process images and videos for automated understanding and decision-making. Research in this domain covers object detection, pattern recognition, image enhancement, and scene analysis. In remote sensing, data from satellites, UAVs, and aerial platforms are analyzed for applications such as environmental monitoring, urban planning, agriculture, and disaster management. By combining computational models with real-world imagery, this field contributes to innovative solutions in science, engineering, and societal development. |
| 4 | Network Security, Cyber Security and Information Security | NNetwork Security, Cyber Security, and Information Security are closely related domains that work together to protect systems, networks, and data from various threats. Information Security is the broadest area, focusing on safeguarding information in all forms—digital, physical, or verbal—based on the CIA Triad principles of confidentiality, integrity, and availability. Cyber Security protects digital systems, devices, applications, and networks from threats such as ransomware, phishing, hacking, malware, and data breaches. Network Security, which is a subset of Cyber Security, specifically protects network infrastructure and data in transit using technologies like firewalls, IDS/IPS, VPNs, and secure routing and switching. Together, these domains ensure the security of modern applications such as banking, healthcare, e-commerce, cloud services, and enterprise systems. |
| 5 | Software Reliability and Software Mining | Software Reliability focuses on developing methods and models to ensure that software systems perform consistently without failure under specified conditions for a defined period. Software reliability engineering applies statistical and probabilistic methods to estimate failure rates, Mean Time Between Failures (MTBF), and system availability. It involves reliability prediction, fault detection, risk assessment, and testing strategies to improve software quality and dependability. Software Mining, also known as Mining Software Repositories, applies data mining and machine learning techniques to analyze software artifacts such as source code, bug reports, and version histories. By extracting patterns and insights from large-scale development data, it supports defect prediction, process improvement, maintenance optimization, and evidence-based decision-making in modern software engineering practices. Software mining helps organizations make data-driven decisions by identifying patterns in source code changes, issue reports, and development activities. It supports predictive modeling for defect detection, effort estimation, and risk assessment. This is a research area that combines data mining, machine learning, artificial intelligence, and software engineering techniques to analyze historical software data for improving software development processes and product quality |
MECHANICAL ENGINEERING
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Thermo-Fluids and Sustainable Energy Systems | This research area covers hypersonic aerodynamics, re-entry control, scramjet combustion, rocket propulsion, fluid–structure interaction, marine hydro-elasticity, MAVs, bio-composites, biodiesel engines and solar thermal systems, integrating CFD and experiments. applied advanced CFD to multiphase flow and heat transfer in cement plants, achieving validated 3D modeling, optimization, reduced pressure drop, and energy savings. This area also focuses on fuel cells, refrigeration, hydrogen and alternate fuels, catalytic emission reduction and solar energy systems to enhance efficiency, sustainability and clean energy conversion for industrial and aerospace applications. |
| 2 | Thermal Characterisation of Biodegradable Composites and Thermal Management of Electronic Devices | The research focuses on the thermal characterization of biodegradable composites and their application in thermal management of electronic devices. It involves developing sustainable composite materials using natural fibers and bio-based matrices, and evaluating their thermal conductivity, diffusivity, heat capacity, and thermal stability. These materials are explored as eco-friendly alternatives to conventional polymers for heat dissipation and insulation in electronic enclosures and components. The study aims to enhance device reliability, reduce overheating, and promote sustainable material solutions aligned with green manufacturing and energy-efficient technologies. |
| 3 | Advanced Manufacturing and Composite Process Optimization | This research area focuses on advanced manufacturing and composite process optimization, integrating friction stir welding, CAD-driven design, additive manufacturing, and advanced machining. Studies emphasize tool design, microstructural evolution, defect analysis, and joining of aerospace-grade alloys and composites. Additive manufacturing research includes material development, parameter optimization, topology optimization, and metal 3D printing for medical and industrial applications. Composite analysis involves finite element modeling, failure prediction, fatigue, and damage mechanics for lightweight structures. Process optimization using DoE, Taguchi, ANOVA, Grey Relational Analysis, and PCA enhances quality and efficiency. Advanced machining techniques such as abrasive water jet machining are applied to difficult-to-machine materials for sustainable, high-performance solutions. |
| 4 | Analysis of Composite Materials | Composites are used in space applications due to their high specific modulus and specific strength. Since the classical mathematical approaches are limited to simple geometrical shapes, material models and loading & constraints, Numerical methods such as Finite Element Method are to be applied to solve these cases. Analysis softwre such as ANSYS can be used to minimize the time in the development of numerical models. Since the numerical models give approximate solution, proper convergence studies are to be made for validation of numerical model before application. |
MBA
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | AI-driven decision-making and business intelligence | AI-driven decision-making integrates machine learning, predictive analytics, and real-time data processing into organizational decision systems. It enhances traditional business intelligence by transforming large volumes of structured and unstructured data into actionable insights. AI tools support forecasting, risk assessment, customer segmentation, fraud detection, and operational optimization. Unlike conventional analytics, AI systems continuously learn and improve accuracy over time. Organizations leveraging AI-driven decision systems achieve faster response times, improved strategic alignment, and competitive advantage. However, successful implementation requires data quality, ethical safeguards, transparency, and human oversight to ensure responsible, bias-free, and value-generating outcomes. |
| 2 | Digital leadership and transformation strategies | Digital leadership focuses on guiding organizations through technological change by aligning strategy, culture, and digital capabilities. It requires visionary thinking, agility, and the ability to manage innovation, disruption, and workforce transformation. Effective digital leaders foster experimentation, encourage cross-functional collaboration, and promote digital literacy across all organizational levels. Transformation strategies involve cloud adoption, automation, AI integration, and platform-based business models. Beyond technology, digital transformation reshapes organizational structure, customer engagement, and value creation mechanisms. Sustainable digital transformation depends on strong leadership commitment, change management practices, continuous learning, and alignment between technology investments and long-term strategic objectives. |
| 3 | Industry 4.0 and smart manufacturing | Industry 4.0 represents the integration of cyber-physical systems, Internet of Things (IoT), artificial intelligence, robotics, and big data analytics into manufacturing processes. Smart manufacturing enables real-time monitoring, predictive maintenance, automation, and decentralized decision-making. These technologies improve operational efficiency, product customization, supply chain integration, and resource optimization. Industry 4.0 also supports sustainable production by reducing waste and energy consumption. However, implementation challenges include cybersecurity risks, workforce skill gaps, infrastructure investment, and organizational resistance. Research in this area explores digital capability development, innovation ecosystems, human-machine collaboration, and the impact of smart technologies on productivity and competitiveness. |
| 4 | Data-driven organizational culture | A data-driven organizational culture emphasizes evidence-based decision-making, analytics adoption, and performance measurement across all functions. It encourages employees to rely on data insights rather than intuition alone. Such cultures prioritize data transparency, accessibility, and continuous learning. Leadership plays a critical role in promoting analytical thinking, investing in data infrastructure, and embedding metrics into daily operations. Organizations with strong data cultures improve forecasting accuracy, operational efficiency, and innovation outcomes. However, challenges include data silos, resistance to change, privacy concerns, and the need for analytical skill development. A mature data culture balances quantitative insights with contextual and ethical considerations. |
| 5 | AI and governance frameworks | AI governance frameworks establish policies, ethical guidelines, and regulatory mechanisms to ensure responsible AI development and deployment. These frameworks address issues such as algorithmic bias, transparency, accountability, privacy protection, and risk management. Effective governance requires interdisciplinary collaboration among technologists, managers, legal experts, and policymakers. Organizations must implement monitoring systems, audit mechanisms, and compliance structures to align AI use with legal and societal expectations. As AI adoption expands across industries, governance frameworks play a critical role in building stakeholder trust, mitigating reputational risk, and ensuring that AI systems promote fairness, inclusivity, and sustainable value creation. |
MCA
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Mathematical Modelling & Theory of Relativity | Mathematical modeling : (1) Harvesting of renewable resources (2) Road networking and Road traffic (3) Prey – predator models, Multispecies interactions (4) Corruption and Racism models (5) Water pollution models (6) Epidemics including HIV/AIDS, Pneumonia, Typhoid fever, Malaria, HPV Virus, Cholera etc. |
| 2 | Machine Learning | Machine Learning (ML) is a branch of Artificial Intelligence that enables systems to learn from data, identify patterns, and make predictions with minimal human intervention. By using algorithms and statistical models, Machine Learning systems improve performance over time through training and evaluation. One major application of Machine Learning is in recommender systems, which personalize user experiences by suggesting relevant products,books or services based on data analysis. These systems enhance user experience, increase engagement, and support data-driven personalization in digital platforms. Recommender systems mainly use collaborative and content-based techniques. Collaborative filtering recommends items by analyzing similarities between users or their behavior, such as ratings or purchase history. Content-based filtering, on the other hand, suggests items similar to those a user previously liked by analyzing item features. Modern systems often combine both approaches to enhance accuracy and address challenges like the cold-start problem. |
| 3 | Image Processing | Image processing is the field of analyzing and transforming digital images to extract meaningful information or improve their quality. It involves techniques such as filtering, enhancement, segmentation, feature extraction, and classificationThese methods help reduce noise, highlight important patterns, and support accurate interpretation of visual data. In hyperspectral image processing, segmentation plays a vital role by grouping pixels with similar spectral characteristics. Using unsupervised algorithms, images can be segmented without labeled data, enabling efficient analysis of large datasets. This approach is widely applied in remote sensing, agriculture, environmental monitoring, and medical diagnostics, making image processing essential for intelligent decision-making systems. |
| 4 | Big Data Analytics | This research focuses on improving the accuracy, consistency, reliability, and efficiency of healthcare data through advanced data mining and predictive analytics models. By applying machine learning algorithms and optimization techniques to large-scale healthcare datasets, the study aims to enhance decision-making, patient care outcomes, and operational efficiency. The work integrates Big Data analytics, health informatics, and intelligent systems to develop a structured framework for maintaining high-quality medical data standards across healthcare organizations. |
| 6 | Database Security | Database security focuses on protecting data from unauthorized access, misuse, and breaches through secure design, encryption, access control, auditing, and monitoring. The thrust area emphasizes secure database architectures, role-based access control, privacy preservation, intrusion detection, and compliance with security standards. It also explores emerging challenges in cloud databases, big data security, and secure transaction processing. The objective is to ensure confidentiality, integrity, and availability of organizational data while minimizing vulnerabilities and cyber threats. |
| 7 | Information Security | This thrust area focuses on strengthening cyber defense mechanisms through intelligent intrusion detection systems, anomaly detection, DDoS mitigation, and vulnerability classification. The research emphasizes applying machine learning, ensemble learning, and semi-supervised models to improve detection accuracy in real-time network environments. It also explores reinforcement learning techniques to enhance adaptive cyber attack detection and mitigation. The outcomes support secure communication, resilient networks, and advanced threat intelligence frameworks for modern digital infrastructures. |
| 8 | Artificial Intelligence | Artificial Intelligence improves security, resource use, and service reliability,and Accuracy across many systems. In security, AI detects cyber threats, fraud, and unusual behavior faster than traditional methods. It helps prevent attacks and automates responses. In resource management, AI optimizes energy consumption, cloud computing, and supply chains, reducing waste and operational costs. For service reliability, AI enables predictive maintenance, system monitoring, and automatic scaling to prevent downtime and improve performance. However, AI also consumes significant energy and can introduce risks like data breaches or biased decisions. Therefore, responsible development and monitoring are essential to ensure safe, efficient, and reliable AI systems. |
CHEMISTRY
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Corrosion Science and Protection | The experimental work involves selection of suitable corrosive media and studying the corrosion rates of commercially important metals and proposing new chemical compounds as corrosion inhibitors after thorough study of the molecules using different methodologies. On the other hand, the water quality parameters are determined to calculate the corrosion rates or corrosion indices which reflect the corrosiveness of the samples. The actual corrosion rates will be determined and they will be correlated with the theoretical corrosion aggressiveness. |
| 2 | Thermodynamics of Liquid Systems | Research explores the intersection of statistical mechanics and continuum thermodynamics to decode how molecular interactions dictate macroscopic behavior. By employing Molecular Dynamics (MD) simulations, the work visualizes and quantifies the spatio-temporal evolution of complex fluids, such as ionic liquids and near-critical mixtures. |
| 3 | Pharmaceutical Solid-State Chemistry | The research focuses on the design and synthesis of multicomponent solid forms of active pharmaceutical ingredients (APIs). APIs belonging to BCS Classes II and IV often suffer from poor solubility and permeability. My work applies crystal engineering principles and cocrystallization with Generally Recognized as Safe (GRAS) coformers to enhance solid-state properties, supported by comprehensive characterization and in silico studies. |
| 4 | Ultrasonic Physicochemical Studies | Ultrasonic and volumetric investigations give the data with regard to molecular interactions of solute-solvent as well as hydrogen bonding among the molecules. In chemical industry, the density and the speed of sound information of fluid mixtures has been essential in various applications such as mass transfer operations, pipeline systems, surface facilities, etc., The systematic investigation on the thermodynamic functions of binary fluid mixtures helps in understanding molecular behavior and its structural properties of fluid mixtures. It has gained much attention in both practical and theoretical points of view. This knowledge is quite useful to study the state of liquid in local structure and macroscopic properties of liquid mixtures. |
| 5 | Analytical and Environmental Nano chemistry | Environmental Chemistry: Adsorption and chemical precipitation methods are employed to remove toxic heavy metal ions from wastewater and industrial effluents. Analytical Chemistry: Voltammetry and UV-visible spectroscopic methods are developed to analyse metal ions present in water, industrial wastewater and food samples. HPLC and spectroscopic methods are employed to determine major phytochemicals present in plants. Electrochemical methods like Voltammetry are developed for qualitative and quantitative analysis of phytochemical. Material Science: Green methods are employed for synthesis of metal oxide nanoparticles and AB2O4 type spinels. Prepared materials are characterized using SEM, Spectroscopy and XRD methods. Preparation and charecterizaion of nano-fluids. Organic Chemistry: Various polymer composites are prepared and their physical and mechanical properties are studied. Indole derivatives were synthesized via catalytic cyclization and confirmed via FTIR, NMR, and MS spectroscopy. Their therapeutic potential was then evaluated through various biological activity assays. |
| 6 | Sustainable and Natural Product Chemistry | A Strong Sustainability professional with a bachelor’s degree in Farm Science and Rural Development and a Master’s degree in Environmental studies, M.Phil in Water Quality. Ph.D Worked on Ethno-Medicinal Plants (Medicinal Value of Biodiversity). worked on restoration of degraded ecosystems in my locality, to make a better place to the next generation and me. Worked on plant growth & poultry. Would like to work on sustainable agriculture and protection of food resource. |
ENGLISH
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Language | Language focuses on the systematic study of how language functions, develops, and influences communication in different contexts. It includes fields such as linguistics, sociolinguistics, psycholinguistics, applied linguistics, and language technology. Language examines grammar, phonetics, semantics, discourse, and language acquisition, along with the relationship between language and society. Language also explores multilingualism, translation studies, language preservation, and digital communication. Language promotes research that improves language teaching, policy making, and cross-cultural understanding. By addressing contemporary linguistic challenges, it ensures effective communication, supports cultural diversity, and enhances global interaction in academic and professional environments worldwide. |
| 2 | Literature | Literature addresses specific themes, issues, or research priorities within literary studies. It highlights important topics such as post colonialism, feminism, environmental literature, cultural identity, digital humanities, and marginalized voices. It encourages critical thinking, interdisciplinary approaches, and contemporary relevance. By concentrating on selected themes, scholars analyze how texts reflect social realities, historical contexts, and human experiences. Literature helps, guide academic research, curriculum development, and scholarly discussions. It also ensures that literary studies remain dynamic, socially responsive, and aligned with evolving cultural, political, and global concerns in modern society. |
| 3 | Linguistics | Linguistics covers study of language structure, use, and development. It examines core branches such as phonetics, phonology, morphology, syntax, semantics, and pragmatics to understand how languages are formed and interpreted. It also explores sociolinguistics, psycholinguistics, computational linguistics, and historical linguistics to analyze language variation, cognition, technology, and change over time. Research in this field contributes to language teaching, translation, speech therapy, and artificial intelligence. By investigating how humans acquire and process language, the thrust area of linguistics enhances communication, preserves linguistic diversity, and supports interdisciplinary studies across education, psychology, and computer science. |
MATHEMATICS
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Fluid Dynamics and Mathematical Modeling | Governed by the fundamental conservation laws of mass, momentum, and energy, this research area focuses on modeling complex flow behaviors in natural and engineered systems. The primary scope includes theoretical and numerical investigations of non-Newtonian fluid mechanics, magnetohydrodynamics (MHD), and convective heat and mass transfer within porous media. A significant portion of this work addresses the rheology of nanofluids and hybrid nanofluids to enhance thermal transport efficiency. The domain also extends to bio-fluid mechanics, specifically the mathematical modeling of blood flow dynamics in human arteries, analyzing factors such as arterial stenosis and hemodynamic stresses using advanced continuum mechanics. |
| 2 | Computational Intelligence and Scientific Computing | Rooted in the principles of numerical analysis and computational logic, this thrust area leverages advanced algorithms to solve data-driven and multiphysics problems. The research integrates Deep Learning architectures, such as CNNs and LSTMs, with evolutionary optimization techniques to address challenges in cybersecurity and healthcare diagnostics. A key focus involves Scientific Computing and High-Performance Computing (HPC), utilizing Physics-Informed Neural Networks (PINNs) and sensor-driven uncertainty quantification to model atmospheric systems and chemically reactive flows. This interdisciplinary work bridges the gap between theoretical mathematics and practical applications in weather prediction, combustion simulation, and automated disease diagnosis. |
| 3 | Algebra and Discrete Mathematical Structures | Built upon the axioms of algebraic structures and order theory, this area investigates the fundamental properties of discrete mathematical systems. Research concentrates on Lattice Theory and Semirings, exploring the algebraic and fuzzy properties of congruence kernels, sum-ordered partial Γ-semirings, and semimodules. Furthermore, the group applies the Lie Algebraic framework to physical chemistry, utilizing symmetry-adapted techniques to compute the vibrational frequencies of molecules. This work provides rigorous structural characterizations of abstract systems while demonstrating their utility in spectroscopy and molecular modeling, connecting pure mathematics with physical reality. |
| 4 | Operations Research and Statistical Modeling | Grounded in statistical inference and optimization theory, this research focuses on modeling decision-making processes under conditions of uncertainty and variability. The primary objective is to advance Inventory Management through Economic Production Quantity (EPQ) models that account for real-world constraints such as deterioration rates (Weibull and Pareto distributions) and time-dependent demand. Additionally, the research explores the evolution of classical inventory models into Fuzzy optimization models to handle ambiguity. The domain also encompasses applied statistics in reliability engineering, specifically the development of Hybrid Group Adoption Sampling Plans for life testing, ensuring robust quality control and operational efficiency. |
PHYSICS
| S. No. | Thrust Area | Description (within 100 words) |
|---|---|---|
| 1 | Binary Liquid Mixtures | Research explores the intersection of statistical mechanics and continuum thermodynamics to decode how molecular interactions dictate macroscopic behavior. By employing Molecular Dynamics (MD) simulations, the work visualizes and quantifies the spatio-temporal evolution of complex fluids, such as ionic liquids and near-critical mixtures. |
| 2 | Ferrites and Nanomaterials | This research work explore the relationship between structure, properties, and performance to create innovations in electronics, healthcare, aerospace, and sustainable energy.Acoustical studies evaluate mechanical properties and structural stability using sound wave techniques. Spectroscopic methods like UV-Vis, FTIR, Raman, and XRD help determine chemical composition, bonding, and structural changes. Nanotechnology enables unique properties at the atomic scale, while biomaterials revolutionize medical implants and drug delivery. Overall, material science underpins progress in modern engineering, shaping the future of technology and sustainability. |
| 3 | Thermo Acoustics with & without Nano Doping | This Research focuses on the the sudy of properties of materials, especially at the nanoscale. Acoustical and spectroscopic properties help reveal changes in elasticity, bonding, and molecular interactions, both with and without nano doping. Nano doping enhances electrical, optical, and mechanical performance by introducing controlled impurities. Physical and spectroscopic studies, including UV-Vis, FTIR, and Raman analysis, provide insights into structural modifications, enabling advanced applications in optoelectronics,medicine and environmental technologies. |
| 4 | Glass Science and Nanomaterials (Repeated) | This research work explore the relationship between structure, properties, and performance to create innovations in electronics, healthcare, aerospace, and sustainable energy. Nanotechnology enables unique properties at the atomic scale, while biomaterials revolutionize medical implants and drug delivery. Energy materials drive clean technologies like batteries and solar cells. Overall, material science underpins progress in modern engineering, shaping the future of technology and sustainability. |
| 5 | Glass Science and Nanomaterials (Repeated) | The research focuses on Condensed Matter Physics and Materials Science, with emphasis on advanced materials and their functional properties, which include nanomaterials, glasses, biomaterials, energy materials, phosphors, and advanced structural materials. The work involves synthesis and characterization of these materials, aiming to understand the relationship between microstructure and physical properties. This research contributes to applications in energy storage, optoelectronics, gas- sensing and biomedical fields, bridging fundamental physics with engineering innovations. By combining experimental techniques with theoretical insights, his studies advance knowledge in material design and performance optimization. |
| 6 | Ferroelectrics and Multiferroics | Emphasizes the design and development of lead-free ferroelectric and multiferroic ceramics, with a focus on perovskite and Aurivillius-type oxides. Rare-earth doping in sodium bismuth titanate–barium titanate (NBT–BT) systems and Bi-based layered ceramics is investigated to tailor structural, dielectric, and ferroelectric properties for advanced applications. Systematic studies involving Dy, Ho, Gd, Nd, and other substitutions have yielded valuable insights into phase transitions, dielectric relaxation, energy storage, and electrocaloric effects in functional oxides. This work advances sustainable, environmentally friendly materials for energy devices, and solid-state cooling technologies. |
| 7 | Nanophosphor Materials and Luminescent Studies | Double orthophosphates are inorganic salts that contain the orthophosphate ion (PO₄³⁻) combined with two different cations in the same compound. They are called “double” because two distinct positive ions, such as metal ions or ammonium ions, are present in one crystalline structure. Their general formula is often written as M₁M₂(PO₄)ₓ, where the total positive charge balances the phosphate ion’s negative charge. These compounds are usually crystalline solids and may occur in hydrated forms. They are important in fertilizers, mineral formations, analytical chemistry, and various industrial applications |