Ms. Deepika | Machine Learning | Best Researcher Award

The NorthCap University, India

Author Profile

ORCID

🎓 EARLY ACADEMIC PURSUITS

Ms. Deepika has consistently demonstrated academic excellence throughout her education. She holds a B.Tech in Computer Science Engineering from YMCA Institute of Engineering, where she ranked among the top three students in her batch. She went on to complete her M.Tech in Computer Science from Lingaya’s University, securing the second rank. She is currently pursuing a Ph.D. in Computer Science & Engineering at The NorthCap University, specializing in medical imaging and deep learning

🏢 PROFESSIONAL ENDEAVORS

Ms. Deepika brings over 5 years of corporate R&D experience from Ericsson Global India, where she worked as a Senior Solution Integrator. Her projects focused on telecom fault management, Netcool-based automation, and alarm handling systems. Her contributions in automating trouble ticketing and fault detection workflows earned her the Power Award and performance-based cash rewards. She displayed leadership in system integration, scripting, testing, and production support, translating technical expertise into tangible organizational benefits.

📚 CONTRIBUTIONS AND RESEARCH FOCUS IN MACHINE LEARNING

Ms. Deepika’s doctoral research lies at the convergence of machine learning, deep learning, and functional brain imaging for the diagnosis of neuropsychiatric disorders, particularly ADHD. Her contributions include:

• Kolmogorov-Arnold Network (KAN) for parameter-efficient ADHD diagnosis

• Hybrid metaheuristic–fuzzy logic systems for multi-disease classification

• Multimodal neuroimaging frameworks utilizing fMRI, EEG, and structural MRI

• Explainable AI (XAI) methods promoting interpretability and trust in medical AI

She is well-versed in cutting-edge tools like TensorFlow, PyTorch, Nilearn, FSL, SPM, and leverages advanced statistical and optimization techniques for robust model development.

🏅 ACADEMIC CITATIONS, ACCOLADES AND RECOGNITION

• UGC-NET Qualified (Computer Science, 2018)

• HTET Qualified (2016)

• Awarded UGC Research Fellowship

• Best Paper Awards at national and international conferences

• Top Rank Holder in both undergraduate and postgraduate programs

• Power Award and cash incentives from Ericsson for outstanding contributions

🌍 IMPACT AND INFLUENCE

Her research contributes significantly to the field of AI-driven healthcare diagnostics, focusing on low-parameter models and cross-platform compatibility for deployment in resource-constrained environments. Her work emphasizes biomarker discovery, data fusion, and interdisciplinary collaboration between AI and clinical neuroscience. She promotes standardization and reproducibility in neuroimaging-based machine learning, ensuring her models are accessible and implementable in real-world clinical settings.

🧭 LEGACY AND FUTURE CONTRIBUTIONS

Ms. Deepika aims to develop real-time, interpretable diagnostic tools integrating multi-modal brain data with scalable AI architectures. Her future research envisions:

• Cross-disorder AI frameworks (e.g., ADHD, autism, depression)

• Deployment-ready solutions for rural healthcare centers

• Contribution to open-access neuroimaging repositories

• Ethical and explainable AI models aligned with global health guidelines

She is committed to mentorship, capacity-building in AI for healthcare, and inclusive research practices.

 ✅CONCLUSION

Ms. Deepika represents a powerful blend of academic brilliance, industrial innovation, and societal impact. Her work bridges gaps between machine learning and medicine, offering transformative solutions for mental health diagnostics. With a clear vision and deep technical foundation, she is well-positioned to become a leading figure in neuro-AI research.

🔬NOTABLE PUBLICATION: