Postdoctoral Fellow
University of Missouri
columbia, MO, United States
Dr. Mark Amo-Boateng: A Pioneering Expert in Transportation Intelligence and Advanced Computing
Dr. Mark Amo-Boateng is a distinguished post-doctoral fellow within the Civil and Environmental Engineering Department at the University of Missouri - Columbia. His groundbreaking research spans across transportation intelligence, deep learning, and advanced computing technologies, including high-performance and quantum computing. With a Doctorate degree in Hydrology and Water Resources from Hohai University, Nanjing, China, and both a Master's and Bachelor's degree in Civil Engineering from Kwame Nkrumah University of Science and Technology in Ghana, Dr. Amo-Boateng brings a unique perspective to transportation engineering.
Before his tenure at the University of Missouri, Dr. Amo-Boateng significantly contributed to academia as a lecturer and head of the Earth Observation Research and Innovation Centre at the University of Energy and Natural Resources in Sunyani, Ghana. His expertise in deep learning and quantum computing has propelled the transportation sector towards smarter, more efficient systems.
As a semi-finalist in the $7m Shell Ocean Discovery XPRIZE competition and a certified NVIDIA Deep Learning Institute instructor, Dr. Amo-Boateng has demonstrated his commitment to leveraging technology for solving complex challenges. His work trains countless researchers, engineers, and data scientists in applying cutting-edge computing solutions across various sectors.
At the upcoming ASCE Transportation Conference, Dr. Amo-Boateng will lead sessions that underscore the transformative potential of technology in transportation engineering and management. His presentations will include:
ID: 1666438: "A chat-based interface to real-time intelligent transportation system infrastructure information"
ID: 1667261: "A GENERATIVE ADVERSARIAL NETWORK FOR SPATIO-TEMPORAL TRAVEL TIME ESTIMATION"
ID: 1666656: "A Hybrid Wavelet-Vision Transformer Model for Smartphone Sensor-Based Pavement Distress Detection"
ID: 1667099: "Deciphering and Forecasting Highway Accidents via Realistic Synthetic Trajectories Using LLMs"
ID: 1666943: "Finding Efficient Transportation Pathways on a Quantum Machine during an Evacuation or Disaster"
ID: 1667141: "Modelling pedestrian behavior at road intersections using reinforcement learning frameworks"
ID: 1667264: "Open State-wide High-Resolution Connected Vehicle Synthetic Trip Dataset"
These sessions highlight Dr. Amo-Boateng's dedication to advancing the field of transportation through innovative research and technology. Attendees will gain invaluable insights into the future of intelligent transportation systems, informed by his extensive expertise in deep learning, quantum computing, and engineering solutions.
Disclosure information not submitted.
A Monocular Vision Deep Learning System for Enhanced Highway Work Zone Safety Enforcement
Sunday, June 16, 2024
1:15 PM - 2:30 PM ET