Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.
His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.
His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.
As machine learning increasingly relies on public, untrusted data sources, data poisoning attacks, which inject malicious examples into training data to induce misclassification of …
We present a control barrier function (CBF) approach for headway management in fixed-line transit systems (FTLSs) through vehicular speed control. We frame headway management as a …
Vehicle bunching is a major problem for transit operators. When vehicles bunch together, the lead vehicle will service the majority of passenger demand, leaving the following …
Along with the rapid development of new urban mobility options like ride-sharing over the past decade, on-demand micro-transit services stand out as a middle ground, bridging the …
This project will develop artificial intelligence techniques to improve the operation of large-scale infrastructure systems, such as smart transportation networks and electric power grids, which are essential to modern life. These complex systems, known as societal-scale cyber-physical systems, integrate physical infrastructure with thousands of sensors, computing devices, and actuators. Due to their scale and distributed nature, managing these systems in real time poses a significant challenge. To address this challenge, the project will leverage deep reinforcement learning, a form of artificial intelligence that learns optimal decision-making strategies directly from data. By improving the efficiency and reliability of critical infrastructure systems, the research will further the national interest through reducing traffic congestion, improving emergency response times, and increasing the stability of the power grid.
High-resolution origin–destination (OD) tables are essential for a wide spectrum of intelligent transportation systems applications, from modeling traffic and signal timing …
Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing …
In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running …
A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks …
Public transportation systems worldwide face persistent challenges of efficiency, reliability, and sustainability. In this chapter, we explore how AI-powered approaches can …