Senior Applied Scientist
RecSys | GenAI & Responsible AI
Building impactful AI solutions at Microsoft Viva | Kaggle Competitions Master
RecSys | GenAI & Responsible AI
Building impactful AI solutions at Microsoft Viva | Kaggle Competitions Master
I'm a Senior Applied Scientist with deep expertise in machine learning, recommendation systems, and generative AI. At Microsoft Viva, I lead the development and large-scale deployment of GenAI features, driving cross-organizational collaborations to uphold Responsible AI practices.
I also spearhead the Ranked Comments platform, contributing to a 4% improvement in posts and comments creation. My prior work at Azure and Dell includes delivering impactful ML solutions—achieving $200M in cost savings, developing in-house NLP applications as search engine, QA and sentiment analysis. I'm passionate about leveraging AI to solve real-world problems and create meaningful social impact.
Custom T5 model with span-MLM pretraining, adversarial and multi-task learning for classifying essay effectiveness. Trained diverse set of transformer based models.
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Official certificate for achieving 3rd place in the Feedback Prize Effectiveness Challenge.
Built multi-class swipe prediction model using permutation/null feature importance to optimize feature set. Leveraged lightgbm algorithm for model training.
Built multilingual retriever-reranker pipeline with Inverse Cloze pretraining and ArcFace embedding ranking.
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Official certificate for achieving 9th place in the Learning Equality Curriculum Recommendations Challenge.
Built Transformer based multi-output regressor. Leveraged techniques like Adversarial weight perturbation, auxillary loss and multi sample dropout.
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Official certificate for achieving 9th place in the English Language Learning Assessment Challenge.
Constructed patent phrase matching models using transformer-based encoders.
📘 View SolutionOfficial certificate for achieving 10th place in the US Patent Phrase Matching Challenge.
This comprehensive survey investigates the transformative potential of AI and machine learning in next-generation computing systems. The research explores how autonomic computing principles, inspired by the human nervous system, can enable self-managing systems that adapt to environmental changes without human intervention. The paper examines emerging computing paradigms including cloud, fog, edge, serverless, and quantum computing, identifying key challenges and opportunities for integrating AI/ML technologies to achieve autonomous resource management and performance optimization at scale.
This groundbreaking systematic review explores the convergence of three transformative technologies—Internet of Things (IoT), Blockchain, and Artificial Intelligence—and their collective impact on cloud computing evolution. The research presents a comprehensive analysis of how this technological triumvirate addresses critical challenges in modern cloud systems, including energy consumption, security, reliability, and scalability. The study proposes a conceptual model for cloud futurology, examining how these emerging paradigms will reshape distributed computing architectures and enable next-generation applications across diverse domains.
This innovative patent introduces a sophisticated machine learning framework for analyzing telemetry data from computing devices to predict usage profiles and generate intelligent recommendations. The system employs advanced ML algorithms to identify usage patterns, predict component failures, and provide cost-benefit analyses for warranty extensions, hardware upgrades, and device replacements. The technology enables proactive device management by analyzing resource utilization, performance metrics, and failure patterns to optimize user experience and reduce operational costs.
View my resume to learn more about my experience and qualifications.