The Data Science Innovation Lecture Series features the following two guest speaker events:

The Sinha Endowed Professorship Inaugural Lecture in Data Science


Virtual Reality and Augmented Reality Applications for Situational Awareness and Emergency Response using AI and Machine Learning
October 24, 2025 at 2pm

Presented by Dr. Sharad Sharma, Associate Dean of Research, Sinha Endowed Professor, and Directory of Data Visualization and Extreme Reality Lab (DVXR). In the rapidly evolving landscape of emergency response and decision making, the integration of Virtual Reality (VR) and Augmented Reality (AR) technologies with Artificial Intelligence (AI) and Machine Learning (ML) has opened new frontiers in enhancing situational awareness and response strategies. VR offers a fully immersive environment that can simulate emergency scenarios for training purposes such as active shooter events and fire evacuation drills. VR-based emergency training modules allow first responders, security personnel, and even civilians to engage in lifelike crisis situations without facing actual danger. On the other hand, AR overlays digital information onto the real-world environment through mobile devices or AR glasses. AR does not replace the real world but enhances it by making it particularly useful during actual emergencies. Utilizing the visualization and interaction capability that AR offers and the need for emergency evacuation training, this talk explores mobile AR application (MARA) constructed to help users evacuate a building in the event of emergencies such as a building fire, active shooter, earthquakes, and similar circumstances. This talk also describes the AR application to leverage the Microsoft HoloLens for emergency response during a building evacuation. This talk also presents an immersive VR environment for performing virtual building evacuation drills for active shooter and fire evacuation training scenarios using Meta Quest head-mounted displays. It incorporates the use of avatars (user-controlled agents) or agents (AI agents) may influence the engagements of the user experience for emergency response, and training in emergency scenarios. The talk will also present the digital twin research projects and Brain Computer Interface (BCI) projects in the DVXR lab. These projects leverage state-of-the-art neurotechnology to better understand and mitigate the impact of stress, anxiety, and depression. The integration of EEG data from Galea and EMOTIV HMDs facilitate richer insights and potentially transform mental health care through personalized, real-time interventions.

For more information on research projects: https://ci.unt.edu/dvxr/research/ 

The Reinburg Endowed Professorship Lecture in Data Sciences and Data Analytics

Unifying Model, Data, and Innovation Evaluation for Trustworthy AI Testing
November 7, 2025 at 2pm
Discovery Park, Room B185

Presented by Dr. Junhua Ding, Reinburg Endowed Professor in Data Science and Chair of the Anuradha & Vikas Sinha Department of Data Science at the University of North Texas. A scholar, innovator, and leader, Dr. Ding’s work bridges the gap between academic research and real-world impact. His research focuses on data-centric artificial intelligence, biomedical computing, software security, and automated software engineering, and he has authored more than 130 peer-reviewed publications in these areas and secured more than $6 million in research grants from federal agencies and industry as a PI and coPI.

This lecture introduces a systematic framework for testing and validating AI systems by integrating model behavior analysis, data quality assessment, and innovation evaluation. The presentation identifies critical gaps between traditional software testing and AI model validation, emphasizing the need for metrics that ensure robustness, fairness, and reliability.
 
The talk will present methods such as metamorphic testing, transfer learning evaluation, and data-centric analysis to address dataset redundancy, bias, and domain shift. Through a case study on medical concept normalization, it demonstrates how dataset overlap can inflate performance metrics and proposes data quality evaluation and LLM-based data augmentation to improve reliability.
 
Additionally, the talk explores formal verification of AI-generated artifacts, focusing on prompt validation using model checking, few-shot learning, and temporal logic reasoning to ensure specification correctness and sufficiency. Finally, it introduces a unified perspective that connects model testing, data validation, and innovation measurement, paving the way for more trustworthy and transparent AI systems.