Projects
XGSleeve: Real-time monitoring of fracture operations from surface
XGSleeve," a groundbreaking machine-learning solution aimed at revolutionizing the detection of sleeve incidents crucial for regulating fluid flow in hydraulic fracturing for shale oil extraction. Addressing concerns about the reliability of the sliding sleeve and the inefficiency of costly downhole cameras, the project introduces an innovative approach that integrates hidden Markov model-based clustering and the XGBoost model. This combined system accurately identifies sleeve anomalies with an impressive 86% precision, significantly reducing futile manipulation attempts. By enhancing operational efficiency and safety, XGSleeve not only advances sleeve incident management but also paves the way for data-driven decision-making in the oil and gas industry. The project's success underscores its potential to contribute to industry growth, resilience, and responsible resource management while embracing the power of technology for sustainability and optimization. For more information please check the paper.
Analyzing UN Leader Debates with NLP
📈 Understanding Global Trends and Topics:
With the help of Natural Language Processing (NLP) techniques, I've built a solution that analyzes the speeches delivered by world leaders during UN debates. This allows users to gain valuable insights into the prevailing trends and topics discussed on the international stage. 🌍📊
💡 Exploring Word Usage Over Time:
One of the most fascinating features of this app is the ability to explore word usage trends over time. By adding annotations to specific events, such as 9/11, we can observe how certain words' frequency fluctuates in response to major global events. For instance, in 2001, we witnessed a dramatic increase in the usage of the word "terrorism" in speeches following the tragic event. 📅📈
🔍 Unlocking Hidden Patterns:
Through this platform, users can uncover hidden patterns and correlations between events and the language used by world leaders. This opens up new avenues for studying the impact of significant historical occurrences on diplomatic language and discourse. 🧐🗺️
Reinforcement Learning for Schedule Optimization
In the intricate landscape of construction planning and scheduling, where challenges stem from budget constraints, resource dynamics, and uncertain environments, this portfolio entry shines a light on a pioneering effort. At its core, the project introduces a groundbreaking hybrid model that seamlessly merges reinforcement learning and graph embedding networks. This innovative approach addresses the critical gap in construction literature by providing a comprehensive decision-making framework adaptable to uncertainty-driven scenarios. By effectively simulating complex construction planning situations through agent-based modeling while optimizing computational efficiency, the model offers a solution to intricate scheduling issues, including activity sequencing and work breakdown structures. Validated through real-world case studies, the model not only optimizes project timelines while navigating resource constraints but also empowers construction professionals with invaluable decision-making support. Through this endeavor, the portfolio showcases the transformative power of interdisciplinary thinking, presenting a concrete contribution to the construction industry's evolution and its adeptness in tackling modern challenges head-on. For more information please check the paper.
Warehouse Sales Forecasting
Developing ML models for forecasting sales of 800 products in a warehouse presents a dynamic and complex challenge. This project involves harnessing the power of machine learning to analyze historical sales data, customer trends, seasonal patterns, and various influencing factors to create accurate and predictive models. By employing advanced algorithms and techniques, the goal is to provide actionable insights that optimize inventory management, streamline supply chain operations, and ultimately enhance decision-making processes. The project's successful execution not only demonstrates proficiency in data science and predictive analytics but also holds the potential to significantly improve resource allocation, reduce costs, and ensure product availability, thereby bolstering operational efficiency and customer satisfaction
