Subhodeep Moitra | Artificial Intelligence | Young Researcher Award

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Mr. Subhodeep Moitra | Artificial Intelligence | Young Researcher Award

Techno College Hooghly | India

Subhodeep Moitra is a computer science researcher focused on advancing artificial intelligence through the fusion of human-like visual perception and cognition. His academic foundation spans computer applications at both undergraduate and postgraduate levels, where he built strong expertise in machine learning, deep learning, computer vision, neural networks, adversarial robustness, and cognitive modeling. His research explores self-supervised reconstruction, adversarial recovery, AGI-oriented theoretical computing, medical prediction systems, and environmental forecasting, with publications in journals, conferences, preprint platforms, and book chapters. He has contributed to projects ranging from temperature forecasting and brain-stroke detection to adversarially robust autoencoders and AGI theory. His professional experience includes serving as a visiting faculty member, teaching programming, mentoring research projects, and engaging in active collaborative work. His technical skills extend across Python, deep learning frameworks, MERN stack development, and cloud-based AI tools, supported by multiple certifications from NASA, NVIDIA, CERN, IBM, Oracle, and Coursera. He has presented papers at international conferences and earned best paper presentation awards for his contributions in machine learning–driven forecasting and adversarial perception. His long-term research interest lies in building unified AI systems capable of perceiving, reasoning, and adapting with human-inspired intelligence, aiming to push the boundaries of next-generation cognitive AI.

Profile: Google Scholar

Featured Publications

Moitra, S., & Banerjee, D. (n.d.). Robustness as Latent Symmetry: A Theoretical Framework for Semantic Recovery in Deep Learning. OSF.

Moitra, S., & Banerjee, D. (n.d.). Are We Even on the Right Track? A Theoretical Framework for AGI Beyond Classical Computation. Authorea Preprints.

Moitra, S., & Banerjee, D. (n.d.). Skip the Chaos: A Self-Supervised Learning-Powered Autoencoder for Adversarial Recovery. OSF.

Pintu, P., Subhodeep, M., & Deblina, B. (n.d.). The mystery of Neural Network: Linked with quantum mechanics and universe. ResearchGate.

Pal, P., Moitra, S., & Banerjee, D. (n.d.). The mystery of Neural Network: Linked with quantum mechanics and universe.

Banushri S | Deep Learning | Best Researcher Award

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Mrs. Banushri S | Deep Learning | Best Researcher Award

Impact College of Engineering and Applied Sciences| India

Dr. Banushri S is an accomplished academician and researcher in Computer Science and Engineering, recognized for her contributions to machine learning, deep learning, image processing, and human activity recognition. She has published impactful research across reputed journals and conferences, contributing to the scientific community with multiple documents, citations, and an evolving h-index that reflects her growing influence in the field. Her academic journey includes strong foundational degrees in engineering and digital electronics, shaping her expertise in logic design, embedded systems, computer architecture, networking, operating systems, programming, and advanced AI-driven technologies. With extensive teaching experience as an Assistant Professor in leading engineering institutions, she has guided numerous undergraduate and postgraduate learners, supervised research projects, and supported curriculum development and departmental academic activities. Her research works span areas such as fall detection, wearable sensor data analysis, transfer learning, and long-range context modeling for human activity recognition, including contributions indexed in Scopus and other reputed platforms. She has actively participated in faculty development programs, workshops, and conferences focusing on artificial intelligence, computer vision, and full-stack technologies, while also being a member of professional bodies like ISTE and CSI. Her work reflects a commitment to advancing intelligent systems research and contributing to academic excellence.

Profile: Scopus

Featured Publications

Banushri, S. (2026). Attention-guided residual shrinkage with gated recurrent unit for human activity recognition. Information Processing and Management.

Taveena | Computer Science | Best Researcher Award

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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