Ayshika Kapoor | Computer Science | Women Researcher Award

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Dr. Ayshika Kapoor | Computer Science | Women Researcher Award

Indian Institute of Technology Roorkee,  India

Dr. Ayshika Kapoor is a researcher in Electronics and Communication Engineering with a Ph.D. specializing in privacy-preserving and communication-efficient federated learning. Her work focuses on secure AI, urban sensing systems, and domain-adaptive learning. She has experience as a research scientist, contributing to intelligent mobility and autonomous systems. With multiple high-impact publications, conference presentations, and a granted copyright, she has earned prestigious fellowships and research recognition. Her contributions advance scalable, secure, and efficient AI solutions for real-world applications.

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Featured Publications


A resource adaptive secure aggregation protocol for federated learning based urban sensing systems


– Joint International Conference on Data Science (Cited by 6 · Year: 2023)


Optimization of user resources in federated learning for urban sensing applications


– Federated Learning for Distributed Data Mining Workshop (Cited by 3 · Year: 2023)

Sivakamasundari | Computer Science | Best Researcher Award

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Ms. Sivakamasundari | Computer Science | Best Researcher Award

SRM Institute of Science and Technology | India

Ms. P. Sivakamasundari is a dedicated academic and researcher in Computer Science and Engineering, recognized for her contributions to deep learning-based medical image analysis. With qualifications spanning Diploma, Bachelor’s, and Master’s degrees in Computer Science and Engineering, she is currently pursuing her Ph.D. at SRM Institute of Science and Technology. She has extensive teaching experience as an Assistant Professor for more than a decade, during which she has guided students in core computing subjects including algorithms, computation theory, compiler design, and image classification. Her research focuses on advanced deep learning frameworks for healthcare applications, particularly diabetic retinopathy and diabetic foot ulcer detection, resulting in book chapters, conference publications, and journal manuscripts under review. She has published and filed patents related to medical imaging and automated disease detection systems, demonstrating her innovation-driven approach. Her scholarly presence includes 1 citation, 1 h-index, and 0 i10-index, indicating emerging research visibility. She has completed multiple professional certifications and participated in workshops, FDPs, and internships in machine learning, biometrics, accelerated computing, and high-performance healthcare analytics. Her work reflects strong commitment toward applying AI for societal benefit, and she continues to advance her expertise through active research and academic contributions.

Profile: Google Scholar

Featured Publications

Sivakamasundari, P., Anandhi, S., Kumaran, A. A., Vijayakumar, K., Birnica, Y. J., & others. (2024). Early detection of glaucoma utilizing retinal nerve fiber layer (RNFL) investigation. International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems.

Sivakamasundari, P., & Niranjana, G. (2025). An automatic detection and classification of diabetic foot ulcers using Chebyshev chaotic ladybug beetle optimized extended Swin Transformer–InceptionV3 model. Biomedical Signal Processing and Control, 110, 108268.

Gomathi, G., Sumathy, V., Sivakamasundari, P., & Deepa, R. (2024). A various approaches of machine learning algorithms for kidney disease prediction. International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems.

Sivakamasundari, P., & Niranjana, G. (2024). Diabetic foot ulcer classification using deep learning approach. International Conference on Computer, Communication and Signal Processing (ICCCSP).

Sivakamasundari, P., & Niranjana, G. (2023). A critique on deep learning methodologies employed for the identification of diabetic retinopathy using fundus images. Intelligent Computing and Control for Engineering and Business Systems (ICCEBS).

Sunil Datt Sharma | Computer Science | Best Researcher Award

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Dr. Sunil Datt Sharma | Computer Science | Best Researcher Award

Central University of Jammu | India

Dr. Sunil Datt Sharma is a distinguished researcher in the fields of Digital Signal Processing, Adaptive Signal Processing, and Machine Learning Applications, recognized for his contributions across computational biology, biomedical signal analysis, and intelligent imaging systems. With 289 citations, an h-index of 9, and 9 indexed documents, his research is widely acknowledged for its technical depth and interdisciplinary impact. He has authored numerous journal articles, conference papers, and book chapters covering areas such as CpG island detection, promoter identification using deep learning, image de-noising, transfer learning for fault diagnosis, micro-Doppler signature analysis, anisotropic diffusion models, and advanced frequency-domain algorithms. His academic background encompasses strong training in electronics, computing, and signal processing, complemented by extensive experience in teaching, research, and scholarly reviewing for reputed international journals. His research interests span computational genomics, machine learning-based biomedical systems, pattern recognition, and intelligent signal analysis. He has been actively engaged in professional peer-review activities for more than twenty journals, reflecting his standing within the global research community. His work integrates innovative algorithms with real-world applications, contributing to both theoretical advancement and practical solutions. Dr. Sharma continues to advance cutting-edge research aimed at addressing complex challenges across science and engineering.

Profile: Google Scholar

Featured Publications

Sharma, S. D., Shakya, K., & Sharma, S. N. (2011). Evaluation of DNA mapping schemes for exon detection. In 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). (Cited by: 42).

Sharma, S., Sharma, S. N., & Saxena, R. (2020). Identification of short exons disunited by a short intron in eukaryotic DNA regions. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(5). (Cited by: 33).

Sharma, S. D., Saxena, R., & Sharma, S. N. (2015). Identification of microsatellites in DNA using adaptive S-transform. IEEE Journal of Biomedical and Health Informatics, 19(3), 1097–1105. (Cited by: 23).

Garg, P., & Sharma, S. (2020). Identification of CpG islands in DNA sequences using short-time Fourier transform. Interdisciplinary Sciences: Computational Life Sciences, 12(3), 355–367. (Cited by: 19).

Sharma, S. D., Saxena, R., Sharma, S. N., & Singh, A. K. (2015). Short tandem repeats detection in DNA sequences using modified S-transform. International Journal of Advances in Engineering and Technology, 8(2). (Cited by: 16).

Major Singh Goraya | Computer Science | Best Researcher Award

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Prof. Major Singh Goraya | Computer Science | Best Researcher Award

Sant Longowal Institute of Engineering and Technology | India

Dr. Major Singh Goraya is a Professor in the Department of Computer Science and Engineering at Sant Longowal Institute of Engineering and Technology (SLIET), Longowal. He holds a Ph.D. from Punjabi University, complemented by strong academic foundations in computer engineering. His primary research areas include cloud computing, resource management, green computing, fault tolerance, and load balancing. With 27 Scopus-indexed publications, 460 citations across 399 documents, and an h-index of 10, Dr. Goraya has made consistent scholarly contributions to high-performance and sustainable computing. His studies have explored dynamic resource allocation, energy-efficient scheduling frameworks, and deep learning-based optimization techniques. He has supervised several Ph.D. and M.Tech. research scholars and continues to guide emerging researchers in cloud resource efficiency and intelligent computation. His international exposure through conferences in the UK, Canada, and Malaysia reflects his active engagement in global research forums. In addition, he has successfully organized numerous academic workshops, conferences, and training programs. Dr. Goraya’s innovative contributions strengthen the integration of artificial intelligence and cloud technology, promoting scalable and eco-efficient computational solutions that advance modern computer engineering research.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Goyal, N., Singh, T., & Goraya, M. S. (2025). Deep convolutional neural networks vs. vision transformers for video-based human activity recognition. In Proceedings of Lecture Notes in Computer Science (pp. xx–xx). Springer.

Singh, J., & Goraya, M. S. (2023). An autonomous multi-agent framework using quality of service to prevent service level agreement violations in cloud environment. International Journal of Advanced Computer Science and Applications, 14(3).

Goyal, N., Goraya, M. S., & Singh, T. (2023). An axiomatic analysis for object detection and recognition using deep learning. In Smart Innovation, Systems and Technologies (pp. xx–xx). Springer.

Thakur, A., & Goraya, M. S. (2022). A workload and machine categorization-based resource allocation framework for load balancing and balanced resource utilization in the cloud. International Journal of Grid and High Performance Computing, 14(2).

Hasan, M., Goraya, M. S., & Garg, T. (2022). E-FFTF: An extended framework for flexible fault tolerance in cloud. In Lecture Notes in Networks and Systems (pp. xx–xx). Springer.

Priyanka Dhaka | Computer Science | Best Researcher Award

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Dr. Priyanka Dhaka | Computer Science | Best Researcher Award

Maharaja Surajmal Institute | India

Dr. Priyanka Dhaka is an accomplished academician and researcher in Computer Science and Engineering, currently serving as an Assistant Professor at Maharaja Surajmal Institute. With strong expertise in software development, data analytics, machine learning, and big data technologies, she has made significant contributions to healthcare, cybercrime investigation, and IoT-based predictive modeling. Her work encompasses both theoretical and applied research, resulting in publications in reputed journals and conferences. She is also a patent holder and has been actively involved in innovative projects. Dr. Dhaka is dedicated to advancing technology through impactful teaching, research, and development in emerging computing fields.

Professional Profile

Google Scholar

Education

Dr. Priyanka Dhaka holds a Ph.D. in Computer Science and Engineering, supported by prior academic qualifications in M.Tech in Information Technology and B.Tech in Computer Science and Engineering. Her education has provided a solid foundation in programming, data analysis, software development, and machine learning. She has also undertaken specialized certifications in advanced technologies, including MATLAB for machine learning. Her academic journey reflects a consistent focus on emerging computing domains, enabling her to develop advanced problem-solving capabilities. The blend of her technical education and research-oriented mindset equips her to contribute effectively to both academia and the software industry.

Professional Experience

Dr. Priyanka Dhaka has served as an Assistant Professor at reputed academic institutions, including Maharaja Surajmal Institute and Maharaja Surajmal Institute of Technology, where she has contributed to teaching, research, and curriculum development. Her professional journey includes delivering guest lectures, coordinating academic events, and mentoring students in technical projects. She has also been involved in industry-oriented training, applying her expertise in programming, big data analytics, and software engineering. In addition to academia, she has practical experience in developing software solutions, handling data-driven projects, and collaborating with multidisciplinary teams to address real-world technological challenges.

Awards and Recognition

Dr. Priyanka Dhaka has been recognized for her academic and research contributions through publications in reputed international journals and IEEE-sponsored conferences. She holds a granted patent for an innovative Braille display device, highlighting her commitment to impactful technological solutions. She has participated in and coordinated various academic and technical events, earning recognition for her contributions. Additionally, she has been a runner-up in a software development competition and invited to deliver expert lectures on big data analytics. Her involvement in workshops on advanced computing fields further reflects her continuous pursuit of professional excellence and contribution to the academic community.

Research Skills

Dr. Priyanka Dhaka possesses extensive research expertise in machine learning, deep learning, big data analytics, and IoT-based healthcare applications. Her work has addressed critical areas such as heart disease prediction, healthcare data management, and cybercrime investigation through innovative computational models. She is proficient in data handling using tools like Hadoop, MongoDB, and advanced programming languages. Her research outcomes include multiple high-quality publications, book chapters, and collaborative projects with interdisciplinary teams. She applies analytical and experimental approaches to develop intelligent systems, integrating domain knowledge with modern computational techniques to solve real-world problems effectively and efficiently.

Notable Publications

Big data application: Study and archival of mental health data, using MongoDB
Author: P Dhaka, R Johari
Journal: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)
Year: 2016
Citations: 24

WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier
Author: P Dhaka, B Nagpal
Journal: Multimedia Tools and Applications 82 (16), 25061-25082
Year: 2023
Citations: 23

CRIB: Cyber crime investigation, data archival and analysis using big data tool
Author: P Dhaka, R Johari
Journal: 2016 International Conference on Computing, Communication and Automation (ICCCA)
Year: 2016
Citations: 14

HCAB: HealthCare analysis and data archival using big data tool
Author: P Dhaka, R Johari
Journal: 2016 1st India International Conference on Information Processing (IICIP), 1-6
Year: 2016
Citations: 8

An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model
Author: P Dhaka, R Sehrawat, P Bhutani
Journal: Engineering, Technology & Applied Science Research 13 (6), 12396-12403
Year: 2023
Citations: 6

Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT
Author: P Dhaka, R Sehrawat
Journal: Fusion: Practice & Applications 14 (1)
Year: 2024
Citations: 2

Conclusion

Dr. Priyanka Dhaka stands out as a dedicated educator, skilled researcher, and technology innovator. Her academic qualifications, teaching experience, and research contributions reflect a deep commitment to advancing computer science and engineering. She bridges the gap between theory and practice through her involvement in cutting-edge projects, impactful publications, and industry-relevant training. With expertise spanning programming, big data, AI, and IoT, she continues to inspire students and contribute to technological innovation. Her career demonstrates a strong balance between academic excellence and practical application, positioning her as a valuable contributor to the advancement of modern computing solutions.

Chaitanya Kulkarni | Computer Science | Outstanding Educator Award

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Dr. Chaitanya Kulkarni | Computer Science | Outstanding Educator Award

Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati | India

Dr. Chaitanya Shrikant Kulkarni is a distinguished academician and Head of the Department of Artificial Intelligence and Data Science at Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering & Technology, Baramati. With extensive experience in undergraduate and postgraduate teaching, he has contributed significantly to curriculum development, research, and innovation in AI, machine learning, and data-driven technologies. His research portfolio spans AI-powered mental health monitoring, predictive maintenance, and financial forecasting. He has authored research papers in reputed indexed journals, published books, and filed multiple patents. His role as an editor and reviewer reflects his active engagement in the academic and research community.

Professional Profile

Scopus

Education

Dr. Chaitanya Kulkarni earned his Bachelor’s degree in Computer Engineering from Shivaji University and a Master of Technology in Computer Engineering from Bharati Vidyapeeth, Pune. He later completed his doctoral studies in Computer Engineering, focusing on advancements in artificial intelligence and machine learning applications. His academic journey has been enriched through participation in various short-term training programs, quality improvement programs, and faculty development initiatives sponsored by recognized educational and professional bodies. These academic achievements have built a strong foundation for his teaching, research, and leadership in emerging technologies, allowing him to mentor students and guide research projects effectively across diverse domains.

Professional Experience

Dr. Chaitanya Kulkarni has a rich career in academia, beginning as a lecturer and advancing to senior faculty and departmental leadership roles. He has served in reputed engineering institutions, contributing to teaching at both undergraduate and postgraduate levels. His responsibilities have included curriculum design, thesis review, and mentoring research initiatives. As a Principal Investigator, he has successfully executed projects in sonar sediment classification and automated silkworm egg counting using image processing. His consultancy work with industry stakeholders has provided practical applications for academic research. Additionally, he has been actively involved in organizing academic programs and fostering interdisciplinary collaboration.

Awards and Recognition

Dr. Chaitanya Kulkarni has received recognition for his scholarly contributions, academic leadership, and innovative research. His work has been published in prominent indexed journals, and he has authored a book with an ISBN registration. He has published and filed several patents, reflecting his focus on translating research into practical solutions. His appointment as an editor for a multidisciplinary research journal and as a reviewer for prestigious international publications, including IEEE Access, highlights his expertise and professional standing. He is also a member of professional bodies, which enables him to stay connected with the broader research and technology community.

Research Skills

Dr. Chaitanya Kulkarni possesses strong research skills in artificial intelligence, machine learning, and data science, with applications in healthcare, industrial maintenance, and financial analytics. His expertise includes developing predictive models, designing intelligent systems, and applying image processing techniques for problem-solving. He has led projects that bridge the gap between theoretical research and industry requirements, ensuring practical applicability of solutions. His publications in Scopus-indexed journals and his patents demonstrate his ability to contribute original and impactful work. He is also adept at supervising postgraduate research, reviewing scholarly articles, and maintaining high standards of academic integrity in his research endeavors.

Notable Publication

Enhanced ubiquitous system architecture for securing healthcare IoT using efficient authentication and encryption
Journal: International Journal of Data Science and Analytics
Year: 2025
Citations: 2

Conclusion

Dr. Chaitanya Kulkarni’s career reflects a blend of academic excellence, research innovation, and leadership in emerging technologies. His commitment to integrating advanced AI and machine learning techniques into practical solutions has led to meaningful contributions in diverse sectors. With his extensive teaching experience, strong research background, and industry collaborations, he continues to mentor the next generation of engineers and researchers. His published works, patents, and editorial roles demonstrate his dedication to knowledge dissemination and scholarly engagement. Dr. Kulkarni remains focused on advancing technological research, fostering academic growth, and contributing to the evolving landscape of artificial intelligence and data science.

Vaibhav C. Gandhi | Computer Science | Best Researcher Award

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Mr. Vaibhav C. Gandhi | Computer Science | Best Researcher Award

Madhuben and Bhanubhai Patel Institute of Technology (MBIT) The Charutar Vidya Mandal (CVM) University, India

Author Profile

GOOGLE SCHOLAR

🎓 EARLY ACADEMIC PURSUITS

Prof. Vaibhav C. Gandhi embarked on his academic journey with a Bachelor’s degree in Computer Engineering from Gujarat University in 2011. Fuelled by a keen interest in computing and intelligent systems, he completed his Master’s degree in Computer Engineering from Gujarat Technological University in 2013. His foundational education laid a robust platform for a future steeped in academic inquiry and technological innovation.

🏢 PROFESSIONAL ENDEAVORS

Prof. Gandhi brings over 13 years of rich academic experience and 1.5 years in the IT industry. His academic career includes tenure at esteemed institutions such as:

  • Charusat University

  • Navrachana University

  • Parul University

  • Gujarat Technological University

  • MBIT – Charutar Vidya Mandal University, where he currently serves as Assistant Professor.

In the IT domain, he worked as a Software Developer at Odysseus Solutions, Vadodara, acquiring critical skills in:

  • ASP.NET, MVC, C#

  • Scrum Master methodologies

  • SQL Server, GitHub, Postman, and Web Services

📚 CONTRIBUTIONS AND RESEARCH FOCUS IN COMPUTER SCIENCE

Prof. Gandhi’s research bridges core areas of Computer Vision, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Data Mining, with a strong interdisciplinary emphasis on healthcare applications—notably the early detection of Glaucoma Disease using CNNs and image processing.

He is currently pursuing his Ph.D. in Machine Learning and Deep Learning from Gujarat Technological University, further solidifying his specialization in medical diagnostics powered by intelligent algorithms.

🏅 ACADEMIC CITATIONS, ACCOLADES AND RECOGNITION

Prof. Gandhi has authored over 25+ publications, including research papers and book chapters in internationally reputed platforms such as:

  • Springer

  • Web of Science

  • Wiley Scrivener Publishing

  • Apple Academic Press – CRC, Taylor and Francis Group

He is a recognized member of notable global and national bodies:

  • ISTE – Indian Society for Technical Education

  • IAENG – International Association of Engineers

  • SDIWC – Society of Digital Information and Wireless Communications

🌍 IMPACT AND INFLUENCE

Prof. Gandhi’s influence extends beyond the classroom. He has delivered numerous expert sessions and FDP lectures at prestigious institutions such as:

  • IIT Roorkee (Image Processing & Pattern Recognition)

  • M.S. University, Vadodara

  • Shri Vishnu Engineering College for Women, Andhra Pradesh

  • Bonam Venkatachalamayya Engineering College, Andhra Pradesh

Through these platforms, he has mentored faculty, researchers, and students, sharing deep insights into:

  • Generative AI

  • Convolutional Neural Networks (CNNs)

  • MLOPs (Machine Learning Operations)

  • Medical Image Enhancement and Analysis

🧭 LEGACY AND FUTURE CONTRIBUTIONS

Prof. Gandhi’s legacy lies in his hybrid academic-industry background, his pedagogical innovations, and his pioneering research in AI for healthcare. His future contributions are expected to further evolve the use of ML/DL in biomedical diagnostics, enhance faculty training in AI systems, and build collaborative research ecosystems across institutions.

 ✅CONCLUSION

Prof. Vaibhav C. Gandhi exemplifies a committed academician and technologist whose work harmonizes rigorous scholarship, impactful teaching, and real-world problem solving. With a visionary outlook toward AI-led healthcare diagnostics, he continues to shape minds, mentor scholars, and drive innovation in computer science.

🔬NOTABLE PUBLICATION:

Title: A Survey: Background Subtraction Techniques
Authors: H.M. Desai, V. Gandhi
Journal: International Journal of Scientific & Engineering Research
Year: 2014

Title: Review on Comparison between Text Classification Algorithms
Authors: Vaibhav C. Gandhi, Jignesh A. Prajapati
Journal: International Journal of Emerging Trends & Technology in Computer Science
Year: 2012

Title: A Survey – Insights of ML and DL in Health Domain
Authors: V.C. Gandhi, P.P. Gandhi
Conference: 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)
Year: 2022

Title: Forecasting Maternal Women’s Health Risks using Random Forest Classifier
Authors: D. Thakkar, V.C. Gandhi, D. Trivedi
Conference: 2024 International Conference on Inventive Computation Technologies (ICICT)
Year: 2024

Title: Cloud Computing with Data Warehousing
Authors: V.C. Gandhi, J.A. Prajapati, P.A. Darji
Journal: International Journal of Emerging Trends & Technology in Computer Science
Year: 2012

Taveena | Computer Science | Best Researcher Award

Published-on by Indian Scientist

Ms. Taveena | Computer Science | Best Researcher Award

Indian Institute of Technology Roorkee, India

Author Profile

SCOPUS

ORCID

🎓 EARLY ACADEMIC PURSUITS

Ms. Taveena began her academic journey in Computer Science and Engineering at Punjabi University, Patiala, where she completed her undergraduate studies. She pursued her M.Tech. in CSE from IIT (ISM) Dhanbad, where she first ventured into deep learning for audio classification using recurrent neural networks. She is currently pursuing her Ph.D. at IIT Roorkee, focusing on decoding mental imagery through physiological signals under the guidance of Prof. Partha Pratim Roy.

🏢 PROFESSIONAL ENDEAVORS

Ms. Taveena has over 6 years of rigorous research experience, combining deep learning, physiological signal processing, and multimodal data analysis. She is adept at designing efficient neural architectures, contributing to diverse research areas such as neural architecture search (NAS), EEG-fMRI fusion, audio–EEG classification, and cross-modal generation tasks. She has been a key contributor to cutting-edge projects involving parameter-efficient tuning and low-rank adaptation techniques.

📚 CONTRIBUTIONS AND RESEARCH FOCUS IN COMPUTER SCIENCE

Her primary focus lies in developing deep learning frameworks for complex time-series and multimodal signals, particularly in:

  • EEG-based motor imagery and speech imagery classification

  • Silent speech decoding and mental imagery task adaptation

  • Cross-session classification using self-supervised contrastive learning

  • Multi-modal EEG–fMRI and image–EEG fusion

  • Lightweight neural tuning with adapters

Her contributions extend into broader domains of natural language processing (NLP) and computer vision, focusing on foundational model architectures and cross-modal learning.

🏅 ACADEMIC CITATIONS, ACCOLADES AND RECOGNITION

  • 📌 Journal Articles:

    • Biomedical Signal Processing and Control (IF: 4.9)

    • IEEE Transactions on Industrial Informatics (Under review, IF: 9.9)

    • International Journal of Activity and Behavior Computing

  • 📌 Conference Presentations:

    • ICPR 2022 (Canada) and ICPR 2025 (India)

    • ABC Conference 2025 (Winner of SSDC challenge and Best Paper Award)

  • 📌 Preprints & Community Contribution:

    • Active on arXiv with high-engagement preprints

🌍 IMPACT AND INFLUENCE

Ms. Taveena’s work addresses real-world challenges in neurotechnology, enhancing human–computer interaction, silent communication, and cognitive state monitoring. Her research on EEG signal decoding is paving the way for assistive technologies, particularly benefiting neurologically impaired individuals. She has influenced the academic and open research community through arXiv preprints and open-access contributions.

🧭 LEGACY AND FUTURE CONTRIBUTIONS

Ms. Taveena is poised to leave a lasting impact through:

  • Novel deep learning tools for brain signal decoding

  • Foundational model adaptation for low-resource and multimodal scenarios

  • Open scientific collaboration via preprints and peer reviewing

  • Future goals include bridging neuroscience and AI, making cognitive computing more accessible and inclusive

She aims to further contribute to large-scale cross-modal learning, AI for healthcare, and foundational model efficiency for real-time applications.

 ✅CONCLUSION

Ms. Taveena represents the next-generation AI researcher, combining deep theoretical knowledge with practical application in neuroscience, speech, and multimodal learning. With an excellent academic pedigree and significant contributions to EEG decoding and multimodal AI, she is a promising leader in Computer Science and Brain–AI interfaces.

🔬NOTABLE PUBLICATION:

Native Arabic EEG-based Silent Speech Decoding Using Deep Learning Techniques
Authors: Taveena Lotey, Salini Yadav, Partha Pratim Roy
Journal: International Journal of Activity and Behavior Computing
Year: 2025

EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters
Authors: Taveena Lotey, Aman Verma, Partha Pratim Roy
Journal: Lecture Notes in Computer Science
Year: 2024

Cross-Session Motor Imagery EEG Classification using Self-Supervised Contrastive Learning
Authors: Taveena Lotey, Prateek Keserwani, Gaurav Wasnik, Partha Pratim Roy
Journal: 2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022

Nisha Agrawal | Computer Science | Best Researcher Award

Published-on by Indian Scientist

Nisha Agrawal | Computer Science | Best Researcher Award

Centre for Development of Advanced Computing, India

Author Profile

SCOPUS

🎓 EARLY ACADEMIC PURSUITS

Ms. Nisha Agrawal began her academic journey with a Bachelor of Engineering (B.E.) in Information Technology from the University of Rajasthan (2001–2005). Demonstrating exceptional academic excellence, she went on to pursue a Master of Technology (M.Tech) in Computer and Information Technology from Savitribai Phule Pune University (2016–2018), graduating with an Outstanding grade. Her early interest in computational systems and performance optimization laid the foundation for a career immersed in high-performance computing (HPC) and GPGPU technologies.

🏢 PROFESSIONAL ENDEAVORS

Ms. Agrawal has been associated with the Centre for Development of Advanced Computing (C-DAC), Pune since 2005, rising through the ranks to her current role as Scientist E. Over the past two decades, she has been instrumental in architecting, optimizing, and deploying scientific applications on India’s national supercomputing infrastructures. A NVIDIA-Certified Mentor, she actively mentors teams in global OpenHackathons, nurturing the next generation of HPC professionals.

📚 CONTRIBUTIONS AND RESEARCH FOCUS IN COMPUTER SCIENCE

Her core research interests span:

  • GPGPU Computing: CUDA, OpenACC, OpenCL

  • Parallel Programming Models: MPI, OpenMP

  • Application Porting & Optimization: ANUGA (Flood modeling), WRF (Weather simulation), NAMD (Molecular dynamics), DFT (Density Functional Theory)

She has extensively worked on heterogeneous architectures (CPU+GPU), focusing on performance tuning and energy efficiency of HPC workloads. Her work contributes significantly to the computational sciences, particularly in areas demanding real-time and large-scale simulation capabilities.

🏅 ACCOLADES AND RECOGNITION

With over a dozen peer-reviewed publications, including those in IEEE, ACM, Springer, and SupercomputingAsia, Ms. Agrawal has established herself as a recognized voice in scientific computing. Some key contributions include:

  • Scalability Analysis of WRF on NVIDIA Ampere (2022)

  • Performance Evaluation of AMDKIIT for DFT (2025)

  • Memory Bandwidth Analysis: Xeon Phi vs Xeon (Women in HPC)
    Her work is increasingly cited in scientific literature addressing performance optimization, GPU utilization, and edge computing.

🌍 IMPACT AND INFLUENCE

A respected HPC specialist, Ms. Agrawal has delivered 30+ invited talks and tutorials at India’s top institutions such as IITs, IISERs, and international forums. She contributes to the HPC ecosystem not just through development, but also through education and mentorship, fostering innovation and skill-building among students and researchers.

Her participation in Women in HPC at ISC, Grace Hopper Conference (GHCI), and IEEE and ACM symposiums underscores her advocacy for diversity and excellence in computational sciences.

🧭 LEGACY AND FUTURE CONTRIBUTIONS

Ms. Agrawal’s legacy lies in shaping India’s HPC landscape, especially through:

  • Democratizing GPU-based computing

  • Promoting women in scientific computing

  • Championing energy-efficient simulations for societal applications like climate modeling and disaster prediction

Her future endeavors focus on enhancing AI-HPC convergence, cloud-native HPC architectures, and mentor-based innovation programs, ensuring a sustainable pipeline of research talent and technology integration.

 ✅CONCLUSION

Ms. Nisha Agrawal is not only a pioneer in HPC and GPGPU computing but also a dedicated mentor, educator, and researcher. Her two-decade journey from student to Scientist E at C-DAC exemplifies technical brilliance, scientific curiosity, and a vision for inclusive technological growth. Her contributions continue to empower research, education, and innovation across India and beyond.

 🔬NOTABLE PUBLICATION:

Experience with adapting to a software framework for a use-case in computational science

Authors: V.V. Shenoi, V. Venkatesh, Nisha Agrawal.
Journal: Journal of Parallel and Distributed Computing
Year: 2025