Engineering Associate
Kittelson & Associates, Inc.
Baltimore, MD, United States
Ananta Sinha, PhD - Engineering Associate at Kittelson & Associates, Inc.
I am an engineer and researcher with experience in transportation systems, infrastructure design, and deep interest in transportation data science applications. My career spans academic and professional roles, with a focus on applying advanced machine learning (ML) and statistical techniques to solve real-world transportation challenges. I earned my PhD in Engineering from the University of Georgia, where my research was focused on resilient infrastructure systems, bridge reliability, and data-driven decision-making for transportation agencies.
Throughout my academic journey, I developed innovative quality control algorithms for Weigh-In-Motion (WIM) datasets, created forecasting models for bridge capacity, and applied ML techniques to analyze bridge inventory data. My dissertation involved using deep learning, cognitive approaches, and extreme value theory to improve the reliability of highway infrastructure under uncertain conditions.
I further expanded my expertise at the Arkansas Department of Transportation (ARDOT) as a Research Engineer. There, I developed predictive algorithms for Average Daily Traffic (ADT) and Vehicle Miles Traveled (VMT) using advanced ML models, including Long Short-Term Memory (LSTM) networks.
Currently, I am an Engineering Associate at Kittelson & Associates, Inc., where I explore the intersection of transportation systems and data science. My expertise spans time-series forecasting, sensor data validation, travel behavior analysis, and transportation management strategies, contributing to several state and federal initiatives.
In addition to my PhD, I hold a Master of Technology in Structural Engineering from the Indian Institute of Technology (IIT) Guwahati and a Bachelor of Technology in Civil Engineering from the Meghnad Saha Institute of Technology in Kolkata. My technical skillset includes proficiency in Python, SQL, MATLAB, and ML frameworks such as TensorFlow, Keras, and scikit-learn.
With a passion for data-informed decision-making, I strive to enhance infrastructure resilience through advanced modeling techniques, deep learning algorithms, and innovative analytics. My work has been documented in peer-reviewed journals, technical reports, and conference publications, reflecting my commitment to advancing transportation engineering.
I continuously seek to expand my knowledge in transportation data analysis, predictive modeling, and AI applications. My long-term goal is to create impactful solutions that improve infrastructure systems and support data-driven planning for sustainable mobility.
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