Centre

Our research group actively works at the interface of chemistry, biology, and medicine to develop innovative therapeutic solutions for major human diseases. We focus on the rational design and synthesis of diverse bioactive molecules by combining modern organic synthesis, chemical biology, and computational approaches.

The group is engaged in the synthesis and development of small molecules, heterocycles, and hybrid scaffolds with promising biological activity against metabolic disorders, cancer, infectious diseases, and inflammatory conditions. We also work on the discovery of enzyme inhibitors, receptor modulators, and multifunctional therapeutics aimed at improving potency, selectivity, and safety.

Our research group is committed to translating fundamental chemical research into clinically relevant therapeutics. We aim to develop efficient synthetic methodologies, discover novel bioactive compounds, and contribute to personalized medicine. We welcome motivated students and researchers who are interested in interdisciplinary drug discovery and translational research that can make a meaningful impact on healthcare.

The research investigates the complex interactions between the Indian Summer Monsoon system, ongoing climate change, and ocean–atmosphere dynamics using advanced mathematical and dynamical oceanographic modeling frameworks.

The Indian monsoon is one of the most powerful seasonal climate systems on Earth, strongly influenced by Sea Surface Temperature (SST) variability in the Indian Ocean; air-sea heat and moisture flux exchanges, Ocean circulation patterns, Large-scale climate modes such as El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole. On the other side Climate change is altering Ocean stratification, Upper-ocean heat content, Monsoon rainfall variability, Frequency of extreme rainfall events Understanding these linkages requires a dynamical systems approach rather than purely statistical analysis. This research approach and applies partial differential equations governing ocean circulation (Navier–Stokes framework), Coupled ocean–atmosphere dynamical models, Numerical simulations of heat transport and salinity distribution that helps to Quantify feedback mechanisms, identify tipping thresholds, Improve seasonal-to-decadal monsoon predictability. The Dynamical Oceanography Component provides insights about Monsoon-driven currents (e.g., Somali Current system), Upwelling processes in the Arabian Sea Thermocline variability, Heat redistribution in the upper Indian Ocean and about ocean dynamics act as both driver and memory system for monsoon variability. In the overall frame of dimension this research explores how climate change alters Indian monsoon variability through ocean dynamical processes, using mathematical modeling to improve prediction and understand nonlinear climate feedbacks that are directly linked to the human and society daily life and are the key component to make policies by Government and Private organization.

Cosmology, the study of the universe on a grand scale, has recently ventured into the intriguing realm of dark energy. To understand its origin, nature, and impact on the cosmic fabric, researchers employ space telescopes, particle detectors, and advanced computational simulations. Through meticulous observations and sophisticated theoretical frameworks, scientists aim to unlock the secrets governing the universe’s expansion and its ultimate fate.

This field also involves analysing vast datasets, strengthening expertise in data science, AI, and machine learning. Researchers work across academia, government agencies, and private industries, pushing the boundaries of fundamental physics and cosmological modelling.

Exploring Tsallis dark energy models within general relativity offers novel insights into cosmic acceleration. Rooted in non-extensive statistical mechanics, these models provide alternative explanations for dark energy dynamics without fine-tuning. Validating predictions against observational data may help resolve the cosmological constant problem and refine existing cosmological models.

Advancements in this area could lead to:

• Enhanced dark energy constraints through high-precision observations.
• Improved cosmological simulations integrating non-extensive mechanics.
• New insights into the interplay between gravity, thermodynamics, and statistical mechanics.

By leveraging machine learning and advanced computational techniques, research in Tsallis dark energy models continues to reshape our understanding of the universe’s evolution and its fundamental forces.

Current research in theoretical astrophysics and cosmology explores a wide range of critical questions. Major topics include numerical simulations of the formation of structure from small scales (first stars) to large scales (dark matter structure), galaxy formation, black holes (evolution, jets, accretion disks and orbiting objects), neutron stars (pulsars, magnetars), particle acceleration (relativistic shocks, origin of cosmic rays), gravitational lensing, and the very early universe (inflation).

Intricate dynamics of interface evolution, a phenomenon crucial to various fields—from cell biology and computer vision to material science—encompassing applications like image segmentation, crystal growth, cell evolution, and nanowire manufacturing, is another important area being worked on. The focus is particularly on understanding this dynamic process within the context of anisotropic energy landscapes. With the help of convolution kernels and comprehensive numerical analyses, the aim is to provide a thorough understanding of these complex phenomena. Through these efforts, the research contributes to advancing the theoretical understanding of dynamic interface behaviour while also offering practical solutions applicable across multiple scientific and technological domains.

A large number of our faculty are involved in Image Processing, Pattern Recognition, and Computer Vision. Image processing involves the manipulation and analysis of images using algorithms and techniques to enhance, restore, or extract information. This includes tasks such as noise reduction, image sharpening, and feature extraction, widely applied in medical imaging (MRI, CT scans), satellite image analysis, facial recognition, and everyday applications like smartphone photo editing.

Computer Vision, an interdisciplinary field, enables computers to interpret and make decisions based on visual data using artificial intelligence, machine learning, image processing, and pattern recognition. Research areas include image analysis, retrieval, preservation, image enhancement, computer vision, auto-caption generation, and medical image analysis.

Future-ready IoT networks require multiple gateways to avoid congestion. However, placing a large number of gateways leads to underutilization and increases the overall network cost. This expansion introduces challenges such as (1) optimal gateway placement and (2) efficient link scheduling.

The research focuses on fairness-driven resource allocation—optimizing the number of gateways along with link selection and scheduling in heterogeneous networks. This approach enhances network performance by minimizing energy consumption and maximizing throughput. A distance-based “Two-phase Gateway Placement Algorithm” is implemented, which effectively reduces energy usage while improving the overall performance of the IoT network.

There is a great deal of work underway in the field of Brain–Computer Interface (BCI). The research focuses on developing technologies that enable direct communication between the brain and external devices, which can be particularly beneficial for individuals with special needs. Faculty members are exploring the potential of BCI in assessing multiple intelligences, ADHD, improving learning, lifestyle, and overall health.

The work in this area examines opportunities to assist individuals with special needs or unique learning behaviours by training their brain (EEG) waves using auditory, visual, or other signal-based stimuli. At DYPIU, researchers have identified the specific frequencies of sound and light stimuli required for effective brainwave entrainment. By influencing the balance between different brainwave types, these auditory/light stimuli may enhance cognitive function, promote relaxation, and improve mental states. Ongoing efforts are also dedicated to identifying special faculties in minimally conscious patients and supporting their ability to communicate.

DYPIU faculty is involved in developing technologies that support advancements in space exploration. The university is actively engaged in three major aspects of Space Technology:
1. In situ exploration
2. Rover development
3. Nanosatellites

Exploring the intersection of manufacturing and sustainability, our faculty is actively engaged in interdisciplinary research collaborations aimed at driving innovation in sustainable manufacturing processes. Their work involves applying operations research techniques, modelling, and simulations to machining processes.

A Sustainability Assessment Model is being developed, focusing on the potential applications of simulation and optimization techniques, along with mathematical modelling in the domains of production and industrial engineering.

Neuromorphic engineering and memristor-based circuits are among the prime areas of semiconductor research. This work holds significant potential for the development of next-generation computing systems inspired by the architecture of the human brain. By utilizing the unique properties of memristors—such as non-volatile memory and their ability to mimic synaptic functions—researchers aim to create novel computing architectures that are energy-efficient, fault-tolerant, and capable of learning and adaptation.

There is a dedicated cohort engaged in Tissue Engineering with a focus on developing organoids and assembloids for physiological and drug-screening models, supported by in silico identification of key molecules. Work is in progress to create organoids and assemble them into integrated systems for physiological modelling, disease modelling of genetic conditions, drug discovery, and therapeutic research. Organoids are known to closely replicate the cellular diversity, anatomical structure, and functional characteristics of real organs.

Recent advancements in microfluidics have opened new possibilities for designing in vitro models that mimic in vivo environments. Once standardized organoids are developed, they will be interconnected through microfluidic channels in an organs-on-chip system. The goal is to create multiple compartments, each representing a specific tissue type, capable of being exposed to varied conditions—while remaining physiologically connected through channel-based networks, simulating the real human body.

Our research explores how specific gene polymorphisms shape an individual’s susceptibility to cancer and influence their response to platinum-based chemotherapy. We focus on polymorphisms in critical genes associated with DNA repair and tumor suppression pathways—such as BRCA1, BRCA2, ATM, and TP53—to understand their role in cancer onset, progression, and therapeutic outcomes. These genetic variations can profoundly impact the cellular response to chemotherapy, thereby affecting treatment efficacy and patient prognosis.
Alongside clinical data, we employ advanced computational tools including molecular docking and Molecular Dynamics (MD) simulations to uncover the underlying molecular mechanisms driving these variations. This challenging yet highly rewarding research aims to contribute toward personalized cancer treatment strategies tailored to each patient’s unique genetic profile.
Additionally, our team is working on AI- and ML-based prediction models for assessing cardiovascular disease risk, integrating machine learning with clinical parameters to enhance early detection and treatment planning.

Food Technology is a rapidly growing field that encompasses food production, processing, preservation, and safety. Our faculty is actively engaged in Food Safety and Quality enhancement, developing innovative processing techniques with added nutritional and health benefits, and advancing food packaging systems. The work also involves applying engineering principles to design and optimize food processing equipment and systems, ensuring efficiency, safety, and sustainability in modern food manufacturing.

Developing biomaterials for therapeutics and diagnostics is a key focus area within interdisciplinary sciences, bringing together plant biotechnologists and biomaterial researchers to create innovative solutions. One such initiative involves the development of collagen–chitosan films infused with bioactive plant extracts for diabetic wound healing. Diabetic wounds pose a major clinical challenge, often resulting in prolonged recovery, higher amputation risk, and reduced quality of life.

This research investigates the therapeutic potential of combining bioactive plant extracts with chitosan–collagen films, capitalizing on their synergistic antimicrobial, anti-inflammatory, and tissue-regenerative properties. The overarching goal is to create a more effective, personalized, and long-lasting treatment solution for chronic wounds, ultimately improving healing outcomes and enhancing patient well-being.

The research group in Bioinformatics focuses on protein–protein interactions, mutation analysis, drug designing, drug repurposing, peptide therapeutics, and vaccine design for various diseases including HIV, Cancer, Malaria, and SARS-CoV-2. The overarching aim is to develop and employ advanced computational methods and artificial intelligence to address key problems in Biochemistry and Biomedical Sciences.

Nanoparticles, due to their high surface-area-to-volume ratio, significantly enhance catalytic activity, improving the efficiency of chemical reactions and increasing product selectivity. These advancements also contribute to novel separation technologies, where nanomaterial-based membranes and adsorbents demonstrate superior performance in purifying chemical products and removing impurities.

Additionally, the precise control achievable over nanomaterials in terms of size, shape, and composition enables tailored optimization of reactor designs, resulting in more efficient and compact systems. Nanomaterial-based membranes and adsorbents further enhance separation processes by offering high selectivity and efficiency, particularly beneficial in chemical purification workflows. Through interdisciplinary efforts, nanomaterials provide promising pathways for optimizing chemical processes while addressing challenges such as scalability, cost-effectiveness, and environmental sustainability—especially in applications like wastewater treatment.

Another research group focuses on the energy-storage capabilities of various nanomaterials, investigating their suitability for supercapacitor applications. These materials may play a critical role in green energy solutions due to their fast-charging ability and long lifecycle, though improving energy density remains a key challenge.

This field focuses on developing and applying mathematical models, numerical algorithms, and computational techniques to solve real-world problems across science, engineering, and industry. It integrates theoretical analysis with numerical simulations and data-driven methods to study and resolve complex systems. It includes:

The research focuses on the development of variational classical–quantum algorithms aimed at solving optimization problems. Current work involves exploring the properties and applications of Tridiagonal Toeplitz matrices within the realm of quantum computation. Additionally, efforts are underway in Quantum Imaginary Time Evolution (QITE) to address complex algorithmic challenges.

Our aim is to enhance precision and computational speed by developing advanced quantum algorithms in a hybrid classical–quantum environment. The broader scope includes quantum machine learning, quantum circuit design, reduction of algorithmic runtime, along with rigorous testing, validation, and deployment of quantum solutions.

Team -
Dr. Anju Chaurasia
Dr. Vanita Daddi
Dr. Vandana Patil
Prof. (Dr.) Rahul Sharma

Research in this domain investigates Quantum Walks (QWs) as a powerful framework for quantum computation, focusing on efficient quantum circuit design, algorithmic development, and real-world implementation. Current studies also explore the relationship between quantum information scrambling and entanglement, offering deeper insights into the dynamics and propagation of quantum information.

Another important research direction involves examining quantum correlations in mixed states under different Hamiltonians, analyzing how these correlations evolve across various quantum systems. This contributes to understanding quantum state complexity, coherence behavior, and the foundational aspects of quantum information theory.

Research in Metaheuristics and Evolutionary Computations focuses on nature-inspired optimization techniques, Multiple Criteria Decision Making (MCDM), reliability analysis, and artificial intelligence & data science. These approaches allow the development of advanced mathematical models for optimizing complex systems and processes across diverse domains.

With a strong emphasis on MCDM, the work involves creating innovative decision-making frameworks that account for multiple conflicting criteria, enabling more informed and robust choices in complex environments. In addition, reliability analysis forms a critical component of Dr. Kumar’s research, where he develops statistical and computational methods to evaluate and improve system reliability under varying operational conditions. His contributions aim to enhance performance, mitigate risks, and provide optimized solutions for real-world engineering and industrial challenges.

Research in Management explores various dimensions of consumer behaviour, digital marketing, talent management, and organizational performance optimization. The work includes examining contemporary management issues such as employee engagement, knowledge management, and talent acquisition practices in the IT industry. Additionally, studies on the role of spiritual intelligence in organizational well-being provide meaningful insights into enhancing workplace culture and employee productivity. These investigations contribute to a deeper understanding of modern organizational challenges and offer valuable perspectives for both practitioners and scholars.

Team -
Dr. Madhavi Deshpande
Dr. Ajit Dalvi
Dr. Priyanka Dhoke

Research in Economics and Finance plays a pivotal role in understanding market dynamics, financial systems, and economic policies that influence both global and national economies. It covers qualitative and quantitative analyses across areas such as economic growth, monetary policy, financial markets, risk management, and behavioural finance. With the rise of big data and advanced analytical technologies, researchers now employ econometric models, machine learning, and real-time financial data to derive insights into investment strategies, banking regulations, and emerging economic trends. Additionally, areas such as FinTech, digital currencies, and sustainable finance reflect the rapidly evolving financial landscape. Such research informs policymakers, businesses, and investors while contributing to economic stability, financial innovation, and inclusive growth.

Team -
Dr. Siddharth Gavhale

Research in Humanities encompasses diverse areas such as psychology, media studies, and societal dynamics. Ongoing work explores mental health challenges faced by sexual and gender minorities, with a special focus on the impact of societal rejection and hate crimes on young transgender individuals in India. Studies also investigate various aspects of human behaviour—including adolescent attachment patterns, emotional intelligence, and the psychological consequences of substance abuse. Significant contributions include research on emotional regulation among individuals with alcohol dependence, psychological well-being within LGBTQ+ communities, and how technology affects students’ self-esteem.

In addition, faculty members are actively studying themes at the intersection of globalization and Hindi media, including the language of journalism, parallel cinema movements, the rise of regional cinema in India, and the influence of generative AI on newsrooms, media convergence, OTT platforms, and contemporary filmmaking trends.

Environment and sustainable practices constitute a broad and essential area of research, focusing on advancements in green technology, energy conservation, and environmental management through sustainable approaches. The field encourages interdisciplinary collaboration across domains such as renewable energy, water resource management, sustainable agriculture, waste reduction, and ecological conservation. These efforts aim to address global environmental challenges while supporting long-term ecological balance and responsible resource utilization.

Team -
Dr. Kranti Shingate      Dr. Meena Pandey      Dr. Sangeeta Benni
Dr. Shailesh Ghodke      Dr. Utkarsh Maheshwari      Dr. Sunita Patil
Dr. Vikas Dive      Dr. Babuskin Srinivasan      Dr. Sonal Mahajan
Dr. Keval Nikam

Research in Smart Grids and Electric Vehicles (EVs) at DYPIU spans renewable energy systems, intelligent control mechanisms, and advanced optimization algorithms aimed at improving efficiency, sustainability, and grid resilience. The work includes innovative demand response strategies such as co-simulation-based renewable-integrated home energy management systems (HEMS) and the Water Filling Energy Distributive Algorithm for HEMS with plug-in EV coordination, enhancing energy distribution and grid–consumer interactions.

Contributions to smart grid development include frameworks for price elasticity–based peak time rebate demand response programs, IoT-enabled smart meters for real-time energy monitoring, Zigbee-based substation monitoring, and energy-efficient smart street lighting systems. Additional work includes developing eye-controlled rovers for environmental mapping and studying renewable energy integration in India’s mining sector to promote sustainable industrial practices. Collectively, these multidisciplinary efforts support the creation of a robust, intelligent, and future-ready energy ecosystem.

Bayesian estimation is a robust statistical approach for analyzing lifetime data, widely applied in engineering, healthcare, actuarial science, and risk management. By incorporating prior knowledge with probabilistic models, it provides improved predictions for system reliability, survival outcomes, and failure-time behavior, especially when traditional methods struggle with limited or censored data. Our research emphasizes Markov Chain Monte Carlo (MCMC) techniques, hierarchical Bayesian models, and shrinkage priors to enhance parameter estimation under uncertainty. These methods support predictive maintenance, medical prognosis, and environmental risk assessment, enabling better-informed decisions across domains. Ongoing work also focuses on computational advancements to make Bayesian estimation faster and more efficient, strengthening applications across biostatistics, artificial intelligence, and financial risk modeling.

High-Performance Computing (HPC) focuses on utilizing powerful computing systems to solve highly complex, data-intensive problems across scientific, industrial, and technological domains. Modern HPC research explores cutting-edge areas such as exascale computing—systems capable of performing a quintillion operations per second—along with advancements in parallel processing, interconnection networks, and scalable architectures. The integration of artificial intelligence and machine learning into HPC workflows has opened new avenues for accelerating simulations, enhancing predictive analytics, and optimizing large-scale computations. With increasing availability of cloud-based HPC resources, access to high-performance infrastructure is expanding, enabling broader research and innovation.

Core Concepts:
• Supercomputers and Clusters     • Parallel Processing     • Interconnection Networks     • Complex Calculations and Data Analysis

Key Applications:
• Scientific Research     • Multicore and Manycore Architectures     • Data Analysis     • Artificial Intelligence (AI)     • Weather Forecasting

Team
Prof. (Dr.) Rahul Sharma      Dr. Maheshwari Biradar      Prof. (Dr.) Anuj Kumar

Software Quality and Testing is a rapidly evolving research domain driven by the increasing complexity of modern software systems and the growing demand for reliability, security, and automation. Current research focuses on enhancing testing efficiency through automation, optimizing test strategies, and strengthening software security evaluation. The incorporation of artificial intelligence and machine learning has significantly transformed traditional testing paradigms by enabling intelligent defect prediction, autonomous test generation, and adaptive testing frameworks. Performance, usability, accessibility, and reliability testing remain essential components in ensuring high-quality software across diverse platforms. With the rise of Agile and DevOps methodologies, continuous testing, cloud-native testing, and the validation of AI-enabled and cyber-physical systems have emerged as critical areas of innovation.

Core Research Areas:
• Test Automation and Optimization     • Software Security Testing     • Performance and Reliability Testing
• Software Quality Measurement and Metrics     • Testing in Agile and DevOps Environments
• AI and Machine Learning for Software Testing     • Usability and Accessibility Testing

Emerging Research Trends:
• Testing of AI-Enabled Systems     • Cloud-Native Testing     • Cyber-Physical Systems Testing

The Corrosion and Tribocorrosion research area focuses on understanding how materials degrade when exposed to combined chemical, mechanical, and environmental stresses. Corrosion refers to the chemical deterioration of materials, while tribology involves friction, wear, and lubrication. Tribocorrosion integrates both phenomena, examining how mechanical wear accelerates corrosion—or vice versa—leading to faster material degradation. This field is vital in aerospace, automotive, marine, energy, and biomedical engineering, where components must withstand extreme and highly reactive conditions. Key research areas include electrochemical corrosion behavior, pitting corrosion in metals such as stainless steel, and stress corrosion cracking (SCC) caused by simultaneous tensile stress and corrosive exposure. Tribology studies investigate wear mechanisms (abrasive, adhesive, and surface fatigue), frictional behavior, and lubrication strategies to minimize damage. Tribocorrosion research further explores interactive degradation mechanisms in harsh environments such as seawater, industrial fluids, or biological systems, along with the development of protective coatings, surface treatments, high-performance alloys, and composites capable of resisting both corrosion and wear. Applications span turbine blades, automotive components, marine structures, biomedical implants, and energy systems, where enhancing material durability, performance, and service life is critical. Research in this field contributes to the advancement of next-generation materials designed for reliability and long-term sustainability in demanding operational environments.

Research in tunable terahertz (THz) devices is central to advancing next-generation technologies in wireless communication, sensing, electromagnetic shielding, and sustainable energy harvesting. The work focuses on designing and optimizing THz antennas and absorbers by exploiting the interaction between metallic resonators and graphene-based surface plasmon polaritons (SPPs), enabling superior tunability and reconfigurability. Key research directions include THz antennas with dynamic band-notch characteristics, multiband and tunable THz absorbers for selective frequency operations, electromagnetic shielding materials for secure systems, and solar-thermal absorbers aimed at efficient energy harvesting applications.

Complementing this, research on battery health prediction using machine learning addresses the growing demand for intelligent diagnostics in electric vehicles (EVs) and renewable-integrated energy systems. Machine learning techniques provide high-accuracy estimation of battery State of Charge (SOC), State of Health (SOH), degradation patterns, and Remaining Useful Life (RUL), overcoming the limitations of conventional physics-based models. These approaches enhance battery safety, performance forecasting, and energy system reliability.

Research Areas:
• THz Antennas with Reconfigurable Band-Notch Characteristics
• Multiband and Tunable THz Absorbers
• Electromagnetic Shielding
• Solar-Thermal Absorbers for Energy Harvesting
• Battery Health Prediction Using Machine Learning (SOC, SOH, RUL, Degradation Modeling)