Web Science and Digital Libraries Research Group

  I am pleased to share that I have been awarded an Internal Research and Development Grant from the Office of Enterprise Research and Innovation at Old Dominion University in the amount of $50,000, beginning July 1, 2026. The project, titled “Trust-Centered AI Avatars for Accessible Telehealth Support Among Unhoused Populations,” is co-led with Drs. Ginger Watson and Tina Gustin. This project proposes to improve healthcare access for vulnerable and underserved populations by leveraging, enhancing, and evaluating an NSF-funded telehealth kiosk, TeleHSupport, to improve usability through an audio interface and AI-powered interactive avatar. TeleHSupport is a secure, self-service kiosk enabling individuals with limited access to traditional healthcare to receive reliable AI-generated health information and guidance. It delivers safe, evidence-based guidance on chronic disease through text-based conversational AI, with clinically validated content curated by advanced practice nursi...

Evolution of Language-Guided Control in UAV Systems From Search and Rescue (SAR) to intelligence, surveillance and reconnaissance (ISR), the use of small unmanned aerial systems (sUAS) in scientific and military applications has become increasingly more prominent. One of the most important aspects of the use of these devices is how they are directed. This blog takes a look at the evolution of control for these systems from 2017 to 2026, by examining the approach used in the paper, “ 'Fly Like This': Natural Language Interface for UAV Mission Planning ” [1], to the use of the Large Language Models (LLMs) being examined in “ A Universal Large Language Model-Drone Command and Control Interface ” [2]. These papers explore how natural language interfaces (NLI), or more generally, each of these multiple human-system interfaces can be used to dictate the actions of these platforms. Taken together, these publications reflect the technological capabilities present at the time of their r...

Figure 1: A sample prompt used in generating persona using ChatGPT (Figure 1 in   Park et al. ) Introduction: The Unseen Biases of Our AI Companions Large Language Models (LLMs) such as ChatGPT have become ubiquitous in our lives. We rely on them as impartial conveyors of facts for everything ranging from composing emails to solving complex queries. However, if we put these AIs in the hot seat and question them about identity, which is a very human thing, what would be the outcome? What if we ask them about autism? In the recent study “ ‘As an Autistic Person Myself:’ The Bias Paradox Around Autism in LLMs ” ( CHI 25 ), Park et al . went deep to understand what ChatGPT thinks about the condition of being autistic. The results they got weren’t just a malfunction of the system; rather, they were an indication of the way people behave towards the issue of neurodiversity,  going back and forth between one extreme and the other. The artificial mind is going through the so-called " ...

  This summer, I had the privilege of interning with the  Microsoft AI  organization at  Microsoft , located in Redmond, Washington, USA. My internship was a 12-week program that started on May 19th, 2025. During this internship, I worked as a data scientist intern under the supervision of  Yatish Singh . Throughout my internship, I attended weekly meetings with the entire Microsoft Bing team. The weekly meetings were to update my progress, obtain feedback, resolve issues, or improve the solution. I had one-on-one meetings with my mentor, Tom Potier , twice weekly to discuss my progress and any issues I faced. I also met with my manager, Yatish Singh , at least twice a week. My team was responsible for the allocation of cloud resources, such as servers, virtual machines (VMs), and operating systems (OS), to customers and partners across several regions globally. At the time of my internship, the allocation of resources was done manually, so this ...

  Fig 1 from Rawte et al. Taxonomy for Hallucination in Large Foundation Model The rise of large language models (LLMs) has brought about accelerated advances in natural language processing (NLP), enabling powerful results in text generation, comprehension, and reasoning. However, alongside these advancements comes a persistent and critical issue: hallucination. Defined as the generation of content that deviates from factual accuracy or the provided input, hallucination presents a multifaceted challenge with implications across various domains, from journalism to healthcare. This blog post presents insights from three recent comprehensive surveys on hallucination in natural language generation (NLG) and foundation models to provide an understanding of the problem, its causes, and ongoing mitigation efforts. “ Survey of Hallucination in Natural Language Generation ” by Ji et al. (2022) provides a foundational exploration of hallucination in various NLG tasks, including abstractiv...