2025 IEEE International Conference on AI and Data Analytics
(ICAD 2025)
24 June 2025 - Tufts University School of Engineering Graduate Programs, Medford, Massachusetts USA
Session 2 Workshops
Workshop 1: Beyond Accuracy: Exploring Responsible AI in Emotion Recognition and Mental Healthcare
Dr. Jingyao Wu
Abstract: As AI continues to permeate various sectors, ensuring that technology is both fair and sensitive to human emotions is crucial. This workshop will explore the challenges AI faces in understanding and responding to the complexity of human emotions, with a focus on the concepts of ambiguity, subjectivity and fairness. The talk will introduce how these factors influence AI decision-making in a variety of applications, from emotion recognition to mental health applications. Through real-world examples and case studies, participants will learn how to create AI models that are not only accurate but also ethical, responsible, and sensitive to human variability.
Dr. Jingyao Wu: Jingyao Wu is a Postdoctoral Associate at the MIT Media Lab and a recipient of the MIT – Novo Nordisk Artificial Intelligence Postdoctoral Fellowship. She received her BE (Hons) degree in Engineering (Telecommunications) and her PhD in speech signal processing from the University of New South Wales, Sydney, Australia, in 2020 and 2024, respectively. She is the lead author of the Best Paper at ACII 2023. Her research interests include affective computing, AI in mental healthcare, speech processing, and deep learning.
Workshop 2: Interpretable Model Learning for Taskable AI Systems
Dr. Pulkit Verma
Abstract:
Taskable AI systems are increasingly expected to operate in diverse, user-specific environments and perform tasks tailored to individual needs, all while improving over time. A critical challenge is determining whether these AI systems can safely and effectively fulfill the specific tasks users have in mind.
In this workshop talk, I will present my work on addressing this challenge, including methods for conducting independent, post-deployment differential assessments of AI systems. I will discuss the necessary requirements these systems must meet to enable such assessments. A key aspect of my approach involves a personalized AI assessment module that allows the AI to execute instruction sequences in simulators and respond to user queries about these executions. Our findings demonstrate that even a basic query-response interface can efficiently produce a user-interpretable model of an AI system’s capabilities.
Dr. Pulkit Verma: Pulkit Verma is a Postdoctoral Associate MIT, where he works with Prof. Julie Shah. His research focuses on the safe and reliable behavior of taskable AI agents. He investigates the minimal set of requirements in an AI system that would enable a user to assess and understand the limits of its safe operability. He received his Ph.D. in Computer Science from Arizona State University, where he worked with Prof. Siddharth Srivastava. Before that, he completed his M.Tech. in CSE at IIT Guwahati. He was awarded the AAAI/ACM SIGAI Innovative AI Education Award at AAAI’s EAAI Symposium in 2025, the Graduate College Completion Fellowship at ASU in 2023, and has received the Best Demo Award at the AAMAS 2022 Conference. Website: https://pulkitverma.net
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