With a PhD from IST I can ...
According to Merriam-Webster, informatics means simply “information science.” For IST students pursuing the PhD, it’s much more. They’re applying informatics to topics they’re passionate about, from ensuring AI systems are fair and transparent to making legal jargon easier to understand. Here’s a look at how some of our doctoral students are working to make a positive impact on the world.
Siyu Wu
“My goal is to improve human-machine collaboration by designing AI systems that embed transparent knowledge representations within powerful neural networks.”
Wu’s research focuses on neural-symbolic artificial intelligence, with an emphasis on developing decision support systems that integrate cognitive architectures with machine perception and reasoning.
“I am especially interested in combining symbolic and sub-symbolic semantic resources with data-driven approaches to enhance machines' understanding of both physical and digital environments,” she said.
Faculty adviser: Frank Ritter
Bonam Mingole
“I want to use machine learning to solve real-world problems while ensuring AI systems are robust, fair, and transparent.”
Mingole’s research focuses on promoting responsible ways of building, deploying, and using artificial intelligence algorithms, with an emphasis on understanding the social and ethical implications of these activities.
“As a data scientist and responsible AI researcher, I aim to bridge technical innovation with ethical considerations, contributing to AI advancements that benefit society and align with human values.”
Faculty adviser: Amulya Yadav
Nan Zhang
“I’m interested in building efficient expert AI models and in accelerating LLMs so more people can use them to do great things.”
From an efficiency standpoint, Zhang has pruned large language models toward medical and legal domains for domain-specific compression of LLMs.
Toward expert models, Zhang has publications on medical summarization and retrieval- augmented generation toward multi-hop reasoning tasks. Retrieval-augmented generation means to retrieve relevant information to feed into models for more trustworthy outputs.
Faculty advisers: Prasenjit Mitra (retired) and Rui Zhang (Engineering)
Suhas Bettapalli Nagaraj
“I’m developing systems that help both patients and health care providers.”
With applied machine learning and human-computer interaction for health care, Nagaraj has worked on privacy-protecting speech analysis systems that keep sensitive data on users’ devices rather than sending it to the cloud.
During industry internships, he’s addressed real-world challenges like speaker separation technology during clinician-patient chats, multimodal cuff-less blood pressure monitoring, and detecting AI hallucinations in medical text summarization.
Faculty adviser: Saeed Abdullah
Zinan “Zina” Zhang
“I focus on studying children’s favorite games—primarily Roblox—to uncover potential harms.”
Gen Z and Gen Alpha actively discuss video games—especially Roblox and Minecraft—in their daily lives. Zhang’s research has identified numerous safety concerns, including players harming others and game developers targeting children.
“My goal is to conduct impactful research that sheds light on the risks posed by industry practices and contributes to safer, more ethical design,” Zhang said.
Faculty adviser: Yubo Kou
Qiurong Song
“My research focuses on human-computer interaction, privacy, online safety, and health care technologies.”
Song’s research on how privacy and safety concerns manifest in virtual child-focused spaces like user-generated gaming platforms addresses critical online safety issues, particularly in child- oriented virtual spaces.
She also examines privacy challenges in reproductive technologies, such as period and fertility tracking apps, and investigates broader privacy concerns, including users’ understanding of personally identifiable information.
Faculty advisers: Xinning Gui and Yubo Kou
Pranav Narayanan Venkit
“My goal is to foster a future where AI systems are not only powerful and efficient but also ethical, equitable, and aligned with the needs of all members of society.”
Venkit’s research is deeply invested in the field of sociotechnical AI—an interdisciplinary approach that examines the societal implications of artificial intelligence and seeks to develop more trustworthy and safer human language technologies.
“At the core of my research is a focus on identifying and addressing harmful interactions and biases in AI systems, especially as they impact marginalized and minority populations, such as individuals with disabilities,” Venkit said.
Faculty adviser: Shomir Wilson
Aashish Anantha Ramakrishnan
“My research focuses on understanding how image-text relationships are distributed in domain-specific discourses and assessing if MLLMs can learn these linkages.”
Multi-modal large language models are good at extracting information. Humans, however, use multi-modal information as part of a discourse, and discourses like social media posts and news articles often communicate different kinds of messages using different modalities.
“I aim to develop and deploy AI models capable of utilizing a wide variety of multi-modal information for complex, real-time decision making,” Ramakrishnan said, “making them more linguistically grounded and improving their alignment in intents with human users.”
Faculty adviser: Dongwon Lee
Ruyuan Wan
“My research explores human-computer interaction, natural language processing, and social computing, focusing on communication dynamics in human-human and human-AI interactions.”
Wan’s research interests include understanding social dynamics in language (e.g., social bias, propaganda), designing user engagement in online communities for social good, and developing explainable human-centered language technologies. She recently explored how women on social media proactively reappropriate hashtags to predict and manage their audience reach.
“This practice highlights how users can reclaim agency over content distribution on recommendation-driven platforms, offering insights into self-governance within algorithmic-centered power structures,” she said.
Faculty adviser: Ting-Hao ‘Kenneth’ Huang
Shikha Soneji
“I’m on a mission to crack the code of legal jargon and transform it into a language that truly speaks to users.”
By fusing cutting-edge NLP with the transparency of explainable AI, Soneji has devised a system that automatically dissects dense terms of service and privacy policies, distilling them into clear, compelling narratives.
“Imagine a future where the intimidating fine print becomes a gateway to understanding your digital rights,” Soneji said. “This is the transformative vision driving my work.”
Faculty adviser: Jonathan Dodge
Weijieying Ren
“My scientific goal is to advance the integration of machine learning into clinical practice by designing and enhancing clinical AI systems that are informative, interactive, and affordable.”
Ren's research lies at the intersection of artificial intelligence, clinical informatics, and decision support, with a focus on developing conversational systems that assist in diagnosis prediction and lab test recommendation by grounding AI reasoning with clinical evidence.
“At the core of my work is the belief that AI in health care should serve as a trustworthy collaborator—one that can interpret structured EHR data, reason over clinical logic, and support clinicians through transparent, context-aware guidance,” Weijieying said.
Faculty adviser: Vasant Honavar
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