About

Eric T. Matson is a Professor at Purdue University in the Department of Computer Science and Engineering, with a focus on software development and engineering, robotic systems, multiagent systems, and STEM outreach. He holds a PhD in Computer Science from the University of Cincinnati, an MSE in Software Engineering from Kansas State University, an MBA in Operations Management from Ohio State University, and a BS in Computer Science from Kansas State University. Dr. Matson serves as the Director of the RICE Research Center and is a Co-Founder of the M2M Lab. He has been recognized with awards such as the Ho Award for Outstanding Undergraduate Teaching in 2010 and 2011. His research includes contributions to agent organizations, coordination, and norms in multi-agent systems, as well as applications in sensor networks and robotics. He is actively involved in international collaborations and has received multiple research grants and awards, reflecting his engagement in advancing technology and education in his fields of expertise.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Algorithm
• Political Science
• Computer Security
• Data Mining
• Psychology
• Computer vision
• Medical education
• Distributed computing
• Mathematics
• World Wide Web
• Medicine
• Mathematics education
• Real-time computing
• Engineering
• Library science

Selected publications

• SEQUENCE RECOGNITION USING FINITE AUTOMATA WITH MACHINE LEARNING

  Bulletin of Shakarim University Technical Sciences · 2025-03-29

  articleOpen access

  Sequence recognition is a critical task across numerous disciplines. While traditional methods utilizing Finite State Machines (FSMs) offer a structured data representation and high interpretability, their flexibility is limited. Contemporary Machine Learning (ML) algorithms exhibit high accuracy but demand substantial computational resources. Combining these paradigms can enhance the effectiveness of complex sequence recognition. This study explores the integration of FSMs with ML techniques to address sequence analysis problems. Three distinct applications are examined: text classification (spam detection), recognition of genetic sequences related to Alzheimer's disease, and image-based gesture identification. For each, hybrid models were developed and tested, combining Deterministic Finite Automata (DFA), Non-deterministic Finite Automata (NFA), and ML algorithms such as Random Forest, Gradient Boosting, and Multilayer Perceptrons (MLP). Experimental results indicate that these hybrid models achieve performance comparable to traditional ML methods, and in some instances, yield more accurate predictions. In spam classification, neural network models demonstrated the best results, with FSM-neural network combinations providing similar effectiveness. For genetic sequence analysis, gradient boosting-based models exhibited the highest accuracy, with the inclusion of FSMs maintaining performance while enhancing interpretability. In gesture recognition, neural network approaches proved most effective, but integrating FSMs with ensemble methods achieved a high level of predictive capability, surpassing conventional ML models. In conclusion, the integration of FSMs and ML presents a promising avenue in sequence analysis. Future research could focus on optimizing model architectures and applying them to other domains requiring high-precision recognition of intricate structures.

  PublisherDOI

• LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques

  Sensors · 2025-04-27 · 29 citations

  reviewOpen access

  As unmanned aerial vehicles (UAVs) are increasingly employed across various industries, the demand for robust and accurate detection has become crucial. Light detection and ranging (LiDAR) has developed as a vital sensor technology due to its ability to provide rich 3D spatial information, particularly in applications such as security and airspace monitoring. This review systematically explores recent innovations in LiDAR-based drone detection, deeply focusing on the principles and components of LiDAR sensors, their classifications based on different parameters and scanning mechanisms, and the approaches for processing LiDAR data. The review briefly compares recent research works in LiDAR-based only and its fusion with other sensor modalities, the real-world applications of LiDAR with deep learning, as well as the major challenges in sensor fusion-based UAV detection.

  PublisherOA PDFDOI

• Scalable Fuzzy Neural Networks (SFNN) for Multi-Output Human Activity Recognition

  2025-07-06

  articleSenior author

  Human Activity Recognition (HAR) involves complex, multi-output datasets that demand models balancing efficiency, scalability, and interpretability. This paper proposes a Scalable Fuzzy Neural Network (SFNN), an adaptive hierarchical deep architecture with a multi-section learning mechanism. SFNN processes smaller input windows with effective dimensionality reduction, achieving competitive performance without the heavy computational cost of traditional deep models. Leveraging the transparency of fuzzy systems, it offers a more interpretable alternative to black-box approaches. The model’s theoretical convergence guarantees and strong results on the Opportunity dataset confirm its effectiveness for diverse HAR tasks.

  PublisherDOI

• Applying Computer Vision for the Detection and Analysis of the Condition and Operation of Street Lighting

  Symmetry · 2025-08-11 · 1 citations

  articleOpen access

  Urban safety critically depends on effective street lighting systems; however, rapidly expanding cities, such as Astana, face considerable challenges in maintaining these systems due to the inefficiency, high labor intensity, and error-prone nature of conventional manual inspection methods. This necessitates an urgent shift toward automated, accurate, and scalable monitoring systems capable of quickly identifying malfunctioning streetlights. In response, this study introduces an advanced computer vision-based approach for automated detection and analysis of street lighting conditions. Leveraging high-resolution dashcam footage collected under diverse nighttime weather conditions, we constructed a robust dataset of 4260 carefully annotated frames highlighting streetlight poles and lamps. To significantly enhance detection accuracy, we propose the novel YOLO-CSE model, which integrates a Channel Squeeze-and-Excitation (CSE) module into the YOLO (You Only Look Once) detection architecture. The CSE module leverages the inherent symmetry of streetlight structures, such as the bilateral symmetry of poles and the radial symmetry of lamps, to dynamically recalibrate feature channels, emphasizing spatially repetitive and geometrically uniform patterns. By modifying the bottleneck layer through the addition of an extra convolutional layer and the SE block, the model learns richer, more discriminative feature representations, particularly for small or distant lamps under partial occlusion or low illumination. A comprehensive comparative analysis demonstrates that YOLO-CSE outperforms conventional YOLO variants and state-of-the-art models, achieving a mean average precision (mAP) of 0.798, recall of 0.794, precision of 0.824, and an F1 score of 0.808. The model’s symmetry-aware design enhances robustness to urban clutter (e.g., asymmetric noise from headlights or signage) while maintaining real-time efficiency. These results validate YOLO-CSE as a scalable solution for smart cities, where symmetry principles bridge geometric priors with computational efficiency in infrastructure monitoring.

  PublisherOA PDFDOI

• Anomaly Detection in Imbalanced Datasets Using FraudX AI

  2025-09-04

  articleSenior author

  Anomaly detection in real-world domains often involves highly imbalanced datasets, where rare but critical events are challenging to identify without distorting the natural data distribution. Many existing approaches rely on resampling techniques or prioritize accuracy-based metrics, which may not accurately reflect the model’s true effectiveness in such scenarios. This paper examines the application of the FraudX AI framework, initially designed for financial fraud detection, to both network intrusion and credit card fraud detection under natural class imbalance. The framework integrates Random Forest and XGBoost classifiers using a weighted ensemble, combined with calibrated threshold tuning and SHAP-based explainability. Experiments conducted on the CIC-IDS2017 and European credit-card datasets demonstrate that the model achieves high recall and AUC-PR without applying data balancing techniques. These results highlight the framework’s robustness and generalizability for detecting anomalies in complex, imbalanced environments.

  PublisherDOI

• Real-Time Crowd Density Estimation and Stampede Risk Assessment System Using Thermal Camera

  2024-12-11 · 4 citations

  articleSenior author

  Stampede accidents frequently occur in situations where large crowds gather. Although education on how to protect oneself and prevent accidents in dense crowds has been provided, these measures mainly focus on post-accident responses. In contrast, this study proposes a proactive approach to prevent stampede accidents by utilizing thermal cameras to detect the number of people in a space in real-time and calculate the risk of a stampede. The system collects object detection and density estimation results using thermal cameras, considering population density in a specific area, and transmits the estimated results to other devices via serial communication. Thermal imaging technology excels at detecting people with high accuracy even in challenging daytime conditions or low-light environments at night. The data collected from the thermal cameras is continuously updated through machine learning and pattern analysis to assess stampede risk, and the results are provided in real-time via a web interface. This allows safety personnel and managers to effectively monitor high-density areas and take immediate action if necessary. Additionally, the system's web interface provides users with real-time information related to stampede risk. The validity of the proposed system has been demonstrated through field applicability and performance evaluation in real-world environments, contributing to enhanced public safety by preventing stampede accidents in advance.

  PublisherDOI

• Hybrid Algorithm Selection and Hyperparameter Tuning on Distribute Machine Learning Resources: Hierarchical Agent-based Approach

  ACM Transactions on Internet Technology · 2024-09-26 · 3 citations

  articleSenior author

  Algorithm selection and hyperparameter tuning are critical steps in both academic and applied machine learning (ML). These steps are becoming increasingly delicate due to the extensive rise in the number, diversity, and distributed nature of ML resources. Multi-agent systems, when applied to the design of ML platforms, bring about several distinctive characteristics, such as scalability, flexibility, and robustness, just to name a few. This article proposes a fully automatic and collaborative agent-based mechanism for selecting distributed ML algorithms and simultaneously tuning their hyperparameters. Our method builds upon an existing agent-based hierarchical ML platform and augments its query structure to support the aforementioned functionalities without being limited to specific learning, selection, and tuning mechanisms. We have conducted theoretical assessments, formal verification, and analytical study to demonstrate the correctness, resource utilization, and computational efficiency of our technique. According to the results, our solution is algorithmically correct and exhibits linear time and space complexity in relation to the size of available resources. To further verify its correctness and demonstrate its effectiveness and flexibility across a range of algorithmic options and datasets, the article also presents a series of empirical results on a system composed of 24 algorithms and 9 datasets. The findings not only highlight the efficiency and scalability of the proposed approach, but also show its flexibility and openness to responding to the dynamic and distributed ML ecosystem.

  PublisherDOI

• Self-Adapting, Integrated Apparel Robot for Responsive Wearable Comfort Utility

  AHFE international · 2024-01-01

  articleSenior author

  This research focuses on developing a prototype apparel system that integrates intelligent autonomous agents, human-based sensors, wireless networks, a mobile app, and a small-scale zipper robot. The goal is to create a practical assistive device that dynamically adjusts to user needs, particularly for the elderly and those with self-care challenges. Unlike existing wearable technologies that are overly technical, this system prioritizes usability and adaptability. It enables autonomous control of zipper speed and direction based on user profiles, enhancing comfort and functionality. Initial testing demonstrated effective communication between the zipper robot and mobile app, with adaptive adjustments and manual override features. The prototype serves as a proof-of-concept for future intelligent wearable devices, aiming to improve independence and quality of life.

  PublisherDOI

• Fuzzy Q-Table Reinforcement Learning for continues State Spaces: A Case Study on Bitcoin Futures Trading

  2024-07-01 · 1 citations

  articleSenior author

  One of the simplest approach in Reinforcement Learning (RL) is updating Q-table using Bellman operator. While theoretical expectations hint at the potential convergence achieved by modeling the discrete Q-table with the Bellman operator, practical limitations surface in real-world scenarios. The main challenges associated with it include the exponential growth of the Q-table size with an increasing number of state dimensions and the inability to use the Q-table in continuous state spaces. Alternative approaches, such as employing neural networks to approximate the parameterized Q-function, may not necessarily result in convergence.In response to these challenges, this paper introduces an simple innovative methodology inspired by the Bellman method updating. The proposed method utilizes fuzzy rules to discretize the state space, leading to the direct use of the Bellman operator for updating the fuzzy neural network weights, effectively acting as the Fuzzy Q-table. Instead of approximating the Q-function utilizing neural network/deep neural network based on gradient approaches, the proposed method establishes a Fuzzy Q-table and updates it using the Bellman equation. This strategic decision helps to solve the convergence problem in addition to prevent entrapment in local minima problems, a common challenge faced by conventional gradient methods. The efficacy of the proposed approach is demonstrated through its application to trading in the Bitcoin Futures Market, showcasing its ability to navigate complexities and uncertainties. Beyond financial markets, this methodology presents a versatile solution applicable to a diverse range of reinforcement learning problems, addressing limitations faced by traditional Q-tables or DQN.

  PublisherDOI

• Cost-Efficient and Effective Counter Unmanned Aerial System via Visual-Acoustic Sensing

  2024-12-11

  articleSenior author

  As Unmanned Aerial Vehicles (UAVs) become more accessible to the public, they become a common tool for malicious purposes. As a result, there is an increasing demand for Counter Unmanned Aerial Systems (CUAS) that can detect UAVs. Existing CUAS solutions often rely on high-priced radar systems or advanced technologies, primarily designed for military purposes. In this paper, a low-cost, effective, non-military CUAS that uses inexpensive smartphones' microphones and camera, along with machine learning models, is proposed to detect and track a malicious UAV (MUAV) in real-time. Our proposed CUAS is designed to be affordable and accessible to the general public, operating automatically to detect and track MUAVs in real-time.

  PublisherDOI

Recent grants

• CPS: Synergy: Collaborative Research: Methodologies for Engineering with Plug-and-Learn Components: Formal Synthesis and Analysis Across Abstraction Layers

  NSF · $350k · 2012–2016

Frequent coauthors

• Byung‐Cheol Min

  Purdue University West Lafayette

  24 shared

• Taskin Padir

  University of Bergamo

  18 shared

• Peter Kazanzides

  18 shared

• Pagh Schultz

  University of Bergamo

  18 shared

• Daniela D’Auria

  Vrije Universiteit Amsterdam

  18 shared

• Giovanni Pilato

  18 shared

• Adina M. Panchea

  Université de Sherbrooke

  18 shared

• Alwin Hoffmann

  18 shared

Labs

• M2M LabPI

Awards & honors

• Ho Award for Outstanding Undergraduate Teaching (2011)
• Outstanding Leadership in Globalization award (2024)