About Me

  • Currently, I serve as a dedicated Data Scientist within the Industrial AI group at Shell. My expertise is centered on extracting crucial information from unstructured data, utilizing natural language processing (NLP) and large language models (LLM) to enhance decision-making processes in asset management, supply chain optimization, and tender processing. This includes a strategic emphasis on minimizing deferment, ensuring that operational efficiencies are maximized and delays are significantly reduced. In addition, I am involved in developing advanced solutions for diagnostics, prognostics, and predictive maintenance of industrial systems, employing state-of-the-art techniques in machine learning, time series analysis, and computer vision. My work is driven by a strong conviction in the transformative power of data and a commitment to leveraging this potential to revolutionize how industries manage and maintain their assets. By focusing on efficiency and innovation, I aim to contribute to the creation of more resilient and agile operational frameworks that can better withstand the challenges of modern industry.

  • Simultaneously, I’m pursuing a Master’s degree in Computer Science at the University of Texas at Austin. This academic endeavor serves to deepen my understanding and enhance my expertise in this ever-evolving field. In addition, I’ve had the privilege of serving as a Learning Facilitator in the Department of Computer Science, bolstering teaching efforts for the Advanced Linear Algebra for Computing/Numerical Analysis course, with a special emphasis on high-performance computing.

  • Before joining Shell, I was a Senior Prognostics and Health Management Engineer at the Golisano Institute for Sustainability (GIS), part of the Rochester Institute of Technology (RIT) in Rochester, New York. My role at RIT encompassed crafting advanced machine learning algorithms for condition-based maintenance, especially focusing on engine and transmission systems. I also played an instrumental role in developing computer vision systems for recycling and resource recovery, leveraging image analytics techniques. My proficiency also covered the assessment of intricate microstructures, including micro circuit boards.

  • Before RIT, I made contributions as a Structural & Analytics Engineer at the Internet of Things (IoT) Tower within Xerox’s Palo Alto Research Center (Xerox PARC). At Xerox PARC, my focus was on holistic structural assessments for transportation infrastructures, particularly bridges. I integrated advanced technologies like fiber optic sensing, machine learning, IoT devices, and structural health monitoring to optimize the resilience and safety of these vital structures.

  • On the academic front, I embarked on a Ph.D. in Structural Engineering journey at the University of Central Florida (UCF). Under the esteemed guidance of Professor F. Necati Catbas in the Laboratory for Civil Infrastructure Technologies for Resilience and Safety (CITRS), my doctoral research led to a comprehensive computer vision-based structural health monitoring and evaluation framework. This innovative approach melded the input-output information of structural systems from images/videos (loads and responses) to gauge the health and performance of civil infrastructures. Post-Ph.D., I furthered my research at UCF as a Postdoctoral Scholar, exploring cutting-edge topics in multi-level structural assessment and AI-driven asset management.

  • As a researcher, my interests are diverse, covering System Performance Diagnostics, Prognostics and Health Management (PHM), Predictive Maintenance, Structural Health Monitoring, Machine Learning, Computer Vision, Generative AI, NLP, Structural Dynamics, and much more.

In essence, my goal is to harness my vast expertise and drive innovations that equip asset owners with powerful tools for informed decision-making, optimizing management and maintenance strategies.

Near-Future Goals

  • Explore cutting-edge technologies and theories to enhance the safety, efficiency, and reliability of our infrastructures, ultimately aiming to improve quality of life.
  • Translate research discoveries into practical tools and products that can benefit researchers and practitioners in infrastructure monitoring and condition assessment.

Lifetime Aspirations

  • Become a professor and impart valuable courses to inspire the next generation.
  • Author a novel that resonates with readers.
  • Possibly pursue a Ph.D. in History, delving deep into the annals of our past.
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