Research

Dive into my research contributions that highlight innovative applications of AI and machine learning, delivering measurable improvements in talent management, predictive maintenance, customer experience, and real-time analytics. My work has been presented at leading conferences and has driven significant advancements in AI adoption across multiple industries.

  • Conference Presentations

    1. AI in HR: Leveraging Machine Learning for Talent Management

    Event: HR Tech Conference, 2022

    Summary: Explored the integration of AI in HR to optimize employee turnover prediction, recruitment efficiency, and performance forecasting.

    Key Outcomes:

    • 25% improvement in employee retention strategies using predictive analytics.

    • Enhanced recruitment efficiency, reducing hiring timelines by 30%.

    • Demonstrated 10% increase in workforce productivity through AI-driven performance forecasting.

    Link: View Presentation on Slideshare

    2. Deep Learning Models for Predictive Maintenance

    Event: NeurIPS 2021

    Summary: Presented a deep learning-based predictive maintenance model that reduced equipment downtime and maintenance costs.

    Key Outcomes:

    • 30% reduction in equipment failures through real-time failure prediction.

    • Achieved 40% cost savings in maintenance operations by deploying optimized models.

    • Deployed models with 95% accuracy for failure prediction in industrial settings.

    Link: Read More on Slideshare

    3. Enhancing Customer Experience with AI-Powered Chatbots

    Event: AI Summit, 2020

    Summary: Highlighted the development of NLP-driven chatbots that transformed customer support systems.

    Key Outcomes:

    • Improved customer satisfaction rates by 20% through enhanced interactions.

    • Reduced average response times by 50%, achieving faster query resolutions.

    • Successfully deployed AI chatbots for 5+ enterprises, supporting millions of user interactions monthly.

    Link: View Presentation on Slideshare

  • Conference Presentations Cont.

    4. Real-Time Data Analytics with Apache Kafka and Spark

    Event: Data Science Conference, 2021

    Summary: Showcased scalable real-time data analytics platforms using Apache Kafka and Spark to process massive datasets efficiently.

    Key Outcomes:

    • Designed data pipelines that processed 1M+ events per second in real time.

    • Reduced data latency by 60%, enabling near-instantaneous insights for critical business decisions.

    • Improved data pipeline reliability by 40% through optimized architecture.

    Link: View Presentation on Slideshare

    5. Advanced Machine Learning Techniques for Financial Forecasting

    Event: Financial Analytics Summit, 2021

    Summary: Explored the use of machine learning for financial forecasting, enhancing accuracy in predicting market trends.

    Key Outcomes:

    • Improved financial forecast accuracy by 35% using time-series analysis and deep learning.

    • Reduced data processing times by 25%, enabling faster decision-making for trading platforms.

    • Delivered insights that drove a 15% increase in portfolio returns for clients.

    Link:  View Presentation on Slideshare