About

Jin Wei-Kocsis is an Associate Professor in the Department of Computer and Information Technology at Purdue University. Her research focuses on cooperative and trustworthy physics-informed artificial intelligence aimed at advancing cyber-physical systems (CPS). She leads a multidisciplinary research team dedicated to addressing critical challenges in the design and operation of cyber-physical-social systems, such as smart grids and multi-human, multi-robot systems. Under her leadership, the team has developed novel research directions and methodologies to create cooperative and trustworthy deep learning-powered solutions. Her impactful work has been recognized through multiple prestigious awards, including the NASA Early Career Faculty Grant, NSF awards, the Homeland Security National Training Program/Continuing Training Grant, the Transportation Research Board Grant, and the DOE/SuNLaMP Award.

Selected publications

• Remote Physical Control for Upgrading Heavy Construction Equipment

  Proceedings of the ... ISARC · 2025-07-27

  articleOpen access

  The construction industry heavily uses many different categories of heavy equipment on a daily basis.Generally, such equipment is used extensively and lasts a long time with limited to no possibility of updating or enhancing.Especially, with the current advancements of technology, there is a need to modernize such mechanical equipment.With regard to equipment control, most equipment requires users to stay close to or directly control the equipment.In this paper, the authors propose an upgrading system that allows the remote operation of heavy equipment through push and pull mechanism.By utilizing a single-board computer such as Raspberry Pi with linear actuators, the system can allow the users to control the heavy equipment remotely.The proposed system is successfully demonstrated by upgrading an indoor tower crane in the D. Dorsey Moss Construction Lab at Purdue University.This type of upgrade could support other heavy equipment in the construction field through integrating various sensors and actuators for digital enhancement.

  PublisherOA PDFDOI

• Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method

  arXiv (Cornell University) · 2025-01-09

  preprintOpen accessSenior author

  Emerging Knowledge Tracing (KT) models, particularly deep learning and attention-based Knowledge Tracing, have shown great potential in realizing personalized learning analysis via prediction of students' future performance based on their past interactions. The existing methods mainly focus on immediate past interactions or individual concepts without accounting for dependencies between knowledge concept, referred as knowledge concept routes, that can be critical to advance the understanding the students' learning outcomes. To address this, in this paper, we propose an innovative attention-based method by effectively incorporating the domain knowledge of knowledge concept routes in the given curriculum. Additionally, we leverage XES3G5M dataset, a benchmark dataset with rich auxiliary information for knowledge concept routes, to evaluate and compare the performance of our proposed method to the seven State-of-the-art (SOTA) deep learning models.

  PublisherOA PDFDOI

• QoE-Aware Airborne Communication Infrastructure for Surveillance in Wildfires

  2024-02-19

  articleSenior author

  Global warming is one of the fundamental threats to all living beings today. Various side effects are triggered as a consequence of global warming. Frequent wildfires are one of the side effects causing the loss of lives, vegetation, and economies on a significant scale each year. Therefore, sophisticated mechanisms for surveilling wildfires, including communication systems that are resilient enough to transmit surveillance information to first responders even in the presence of wildfires are urgent. In this work, we leverage unmanned aerial vehicles (UAVs), cognitive radio (CR), and deep reinforcement learning (DRL) technologies to propose a Quality of Experience (QoE)–aware airborne communication infrastructure that can deliver surveillance video streams to a destination in a disaster such as wildfire. In our simulation results, we evaluate the performance of our proposed communication infrastructure by considering different scenarios.

  PublisherDOI

• Unsupervised Machine Learning for Detecting and Locating Human-Made Objects in 3D Point Cloud

  2024-12-15 · 1 citations

  article

  3D point clouds are unstructured, sparse, and irregular data collected by airborne LiDAR systems over a geological region. Laser pulses emitted from the systems reflect off objects both on and above the ground, resulting in data with the longitude, latitude, and elevation of the points, and the corresponding laser pulse strengths. Ground filtering is important. The aim is to partition the points into ground and non-ground subsets. In addition, this research introduces a novel task: detecting and identifying human-made objects amidst natural tree structures. The task is performed on the non-ground subset derived given by the ground filtering stage. Marked Point Fields (MPFs) are used to these tasks. The proposed methodology consists of three stages: ground filtering, local information extraction (LIE), and clustering. In the ground filtering stage, a statistical method called One-Sided Regression (OSR) is devised to overcome the limitations of prior ground filtering methods on uneven terrains. In the LIE stage, a kernel-based method for the Hessian matrix of the MPF is developed. In the clustering stage, the Gaussian Mixture Model (GMM) is applied to the results of the LIE for partitioning the non-ground points into trees and human-made objects. The underlying assumption is that LiDAR points from trees exhibit a three-dimensional distribution, while those from human-made objects follow a two-dimensional distribution. The Hessian matrix of the MPF effectively captures the difference. Experimental results demonstrate that the proposed ground filtering method outperforms previous techniques, and the LIE method successfully distinguishes between points representing trees and human-made objects.

  PublisherDOI

• Unsupervised Machine Learning for Detecting and Locating Human-Made Objects in 3D Point Cloud

  arXiv (Cornell University) · 2024-10-25

  preprintOpen access

  A 3D point cloud is an unstructured, sparse, and irregular dataset, typically collected by airborne LiDAR systems over a geological region. Laser pulses emitted from these systems reflect off objects both on and above the ground, resulting in a dataset containing the longitude, latitude, and elevation of each point, as well as information about the corresponding laser pulse strengths. A widely studied research problem, addressed in many previous works, is ground filtering, which involves partitioning the points into ground and non-ground subsets. This research introduces a novel task: detecting and identifying human-made objects amidst natural tree structures. This task is performed on the subset of non-ground points derived from the ground filtering stage. Marked Point Fields (MPFs) are used as models well-suited to these tasks. The proposed methodology consists of three stages: ground filtering, local information extraction (LIE), and clustering. In the ground filtering stage, a statistical method called One-Sided Regression (OSR) is introduced, addressing the limitations of prior ground filtering methods on uneven terrains. In the LIE stage, unsupervised learning methods are lacking. To mitigate this, a kernel-based method for the Hessian matrix of the MPF is developed. In the clustering stage, the Gaussian Mixture Model (GMM) is applied to the results of the LIE stage to partition the non-ground points into trees and human-made objects. The underlying assumption is that LiDAR points from trees exhibit a three-dimensional distribution, while those from human-made objects follow a two-dimensional distribution. The Hessian matrix of the MPF effectively captures this distinction. Experimental results demonstrate that the proposed ground filtering method outperforms previous techniques, and the LIE method successfully distinguishes between points representing trees and human-made objects.

  PublisherOA PDFDOI

• Stealthy Adversarial Attacks Against Automated Modulation Classification in Cognitive Radio

  2023-06-20 · 3 citations

  articleSenior author

  In cognitive radio systems, wireless spectrum sensing plays a crucial role in identifying the state of the wireless environment, which leads to the effective utilization of scarce spectral resources for various application fields. As a critical part of wireless spectrum sensing, automatic modulation classification (AMC) is used to identify the modulation types of the received signals automatically. With the advances in sensing and computing technologies, artificial intelligence (AI), especially deep learning (DL), has been widely applied to enhance the effectiveness and timely response of AMC. However, in recent years, increasing evidence shows that carefully-crafted adversarial noise can mislead learning models, which raises concerns about the trustworthiness of DL solutions in AMC. Therefore, it is crucial to sufficiently mitigate the adversarial perturbations in DL-powered AMC. To realize a successful mitigation strategy, it can be beneficial to first adopt an adversarial mindset and formulate threat models of practical perturbations in DL-powered AMC. However, there is still limited work focusing on generating practical adversarial attacks in DL-powered AMC that has complex domain knowledge. In this paper, we propose an innovative and stealthy adversarial attack method that can practically compromise the DL-based decision-making of AMCs. In the performance evaluation section, different scenarios will be considered to illustrate the effectiveness of our proposed adversarial attack method in achieving a high success rate and sufficient stealthiness in AMC.

  PublisherDOI

• Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

  IEEE Transactions on Education · 2023-12-21 · 13 citations

  article1st authorCorresponding

  <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> A novel proactive and collaborative learning paradigm was proposed to engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary artificial intelligence (AI)-cybersecurity knowledge. Specifically, the proposed learning paradigm contains: 1) an immersive learning environment to motivate the students for exploring AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects and 2) a proactive education paradigm designed with the use of collaborative learning activities based on game-based learning and social constructivism. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Increasing evidence shows that AI techniques can be manipulated, evaded, and misled, which can result in new and profound security implications. There is an education and training gap to foster a qualified cyber-workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. Efforts have been made to incorporate a comprehensive curriculum to meet the demand. There still remain essential challenges for effectively educating students on the interaction of AI and cybersecurity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intended Outcomes:</i> A novel proactive and collaborative learning paradigm is proposed to educate and train a qualified cyber-workforce in this new era where security breaches, privacy violations, and AI have become commonplace. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Application Design:</i> The development of this learning paradigm is grounded in the pedagogical approaches of technology-mediated learning and social constructivism. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> Although the research work is still ongoing, the prototype learning paradigm has shown encouraging results in promoting the learners’ engagement in applied AI learning.

  PublisherDOI

• Deep Learning in Audio Classification

  Communications in computer and information science · 2022 · 10 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science
  PublisherDOI

• A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems

  2022-04-24 · 4 citations

  preprintSenior author

  Due to the advances in computing and sensing, deep learning (DL) has widely been applied in smart energy systems (SESs). These DL-based solutions have proved their potentials in improving the effectiveness and adaptiveness of the control systems. However, in recent years, increasing evidence shows that DL techniques can be manipulated by adversarial attacks with carefully-crafted perturbations. Adversarial attacks have been studied in computer vision and natural language processing. However, there is very limited work focusing on the adversarial attack deployment and mitigation in energy systems. In this regard, to better prepare the SESs against potential adversarial attacks, we propose an innovative adversarial attack model that can practically compromise dynamical controls of energy system. We also optimize the deployment of the proposed adversarial attack model by employing deep reinforcement learning (RL) techniques. In this paper, we present our first-stage work in this direction. In simulation section, we evaluate the performance of our proposed adversarial attack model using standard IEEE 9-bus system.

  PublisherDOI

• Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

  2022 IEEE Frontiers in Education Conference (FIE) · 2022-10-08 · 8 citations

  article1st authorCorresponding

  This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposed learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks.

  PublisherDOI

Labs

• Cyber-Physical Social Systems Design LabPI

Awards & honors

• NASA Early Career Faculty Grant
• Homeland Security National Training Program/Continuing Train…
• Transportation Research Board Grant
• DOE/SuNLaMP Award
• Firestone Research Initiative Fellowship Award