Bin Mai’s research while affiliated with Texas A&M University and other places

Teaching relatively complex quantitative topics in statistics, operations management, and management science to undergraduate as well as graduate students can pose numerous pedagogical challenges. However, several topics in these disciplines are amenable to exposition by means of dynamic interactive visualizations. We present a sample of such visualizations and discuss their implications for pedagogy. We also discuss how optimization modeling and a single data set can be leveraged to expose students to multiple variants of covering models and, more importantly, how visualizations can be used to quickly demonstrate the differences in these models. We present empirical evidence that by using dynamic interactive visualizations we are able to enhance the student learning experience.

In this paper, we demonstrate how our eye moments differ when we are watching non-interactive videos (sports clips) versus interactive videos (video games clips). We obtained the eye tracking data from Collaborative Research in Computational Neuroscience’s (CRCNS) data sharing set; we analyzed the subsets of eye movement data which were tracked while the test subjects were watching sports clips (videos whose contents are non-interactive) and video game clips (videos whose contents are interactive). We then compare the variations of both x- and y-coordinate eye movements between watching real videos and watching animated videos to identify the difference in eye movement patterns between the cases. Moreover, we also conducted tests on to see if there exists any difference in the distribution of the eye status of fixation or saccade between the cases. Our results provide insights into the cognitive processes when people are watching videos. We also discuss the implications of our results to the various applications in IS field.

In this paper, we demonstrate how electroencephalograph (EEG) signals can be used to analyze people’s mental states while engaging in cognitive processes during IS decision-making. We design an experiment in which participants are required to complete several cognitive tasks with various cognitive demands and under various stress levels. We collect their EEG signals as they perform the tasks and analyze those signals to infer their mental state (e.g., relaxation level and stress level) based on their EEG signal power.

False positive rates and their impacts have been a focal point for information security research. However, most of this research investigates false positives exclusively from the system defender’s perspective, while in reality an attacker also faces the classification decision in identifying feasible targets and the consequences of false positive rates. In this paper, we present the first comprehensive analytical model that incorporates the false positives both from the perspective of the attacker as well as of the system defender. Our results show that such false positives from the attacker’s perspective have a significant impact on the attacker’s decision making for an attack, as well as the optimal protection strategy for the defender. Our results help to shed new light on a wide range of diverse information security phenomena such as spam emails, the Nigerian scams, and the design of Honeypot as a security mechanism. In addition, we show how an attacker’s mis-estimation of certain parameters would affect the defender’s strategy, and how the heterogeneity of the systems impacts the defender’s strategy to manipulate the attacker’s possible mis-estimation.

In this paper, we propose a conceptual model for information security (InfoSec) decision making that is based on cognitive science and neuroscience, and present a comprehensive framework of InfoSec decision making process. In addition, we illustrate a specific experiment design that could be used to investigate how specific automatic affective processes would impact specific components of the InfoSec decision making process.

In this paper, we proposed a neuroscience-based general framework for IS visual systems design that uses neuroscience as the bridge that systematically integrates the principles of design aesthetics and principles of positivistic functionalities into a comprehensive model. Based on our general conceptual model, we also provide a detailed typological framework for the practical IS visual systems design.

In this paper, we study information security investment for target asset protection. Most existing studies focus on the investment in information security that impacts the target asset's vulnerability while assuming that the loss resulting from a successful security breach is irrelevant to the security investment. We recognize the practice of information security investment in mitigating the resultant loss of security breaches and incorporate this important aspect of information security investment decision making in a mathematical modeling framework. We address the simultaneous impacts of security investment on not only the vulnerability of the target system, but also to the potential loss if the attack succeeds. Our results shed significant new light on optimal information security investment, including the conditions under which the information security investment should be focused more on vulnerability enhancement than loss mitigation, or vice versa; how the target asset's inherent vulnerability and potential loss after successful breach would impact the investment on either aspect, and how total a budget constraint would affect the optimal investment decision.

In this paper, we proposed a neuroscience-based general framework for IS visual systems design that uses neuroscience as the bridge that systematically integrates the principles of design aesthetics and principles of positivistic functionalities into a comprehensive model. Based on our general conceptual model, we also provide a detailed typological framework for the practical IS visual systems design.

In this paper, we propose a conceptual model for information security (InfoSec) decision making that is based on cognitive science and neuroscience, and present a comprehensive framework of InfoSec decision making process. In addition, we illustrate a specific experiment design that could be used to investigate how specific automatic affective processes would impact specific components of the InfoSec decision making process.