Akram Bayat

Research interests and focus

• Human-centered artificial intelligence
• AI applications in healthcare
• UX/UI design for intelligent systems
• Prompt engineering

Akram Bayat’s research is dedicated to advancing human-centered artificial intelligence that empowers people through intuitive, ethical, and impactful technology. Dr. Bayat focuses on bridging AI innovations with healthcare applications, user experience design, and automation to create systems that enhance human decision-making and well-being. By integrating insights from human-computer interaction and AI, she aims to develop intelligent tools that are both powerful and accessible, driving real-world positive change.

Education

• PhD in Computer Science, University of Massachusetts Boston

Biography

Akram Bayat is an assistant teaching professor at the Khoury College of Computer Sciences at Northeastern University, based in Silicon Valley. An AI scientist and educator passionate about advancing technology and shaping the next generation of innovators whose expertise spans artificial intelligence, machine learning, computer vision, human-computer interaction, and healthcare, she merges cutting-edge research with hands-on teaching to prepare students for real-world impact.

Her research journey began as a Postdoctoral Associate at the MIT Media Lab, where she explored novel intersections between engineering, medical imaging, AI, and HCI to create high-impact, patient-centered solutions. She then served as a Data Science Fellow at Innovate for Health, a joint initiative of UC Berkeley, UCSF, and Johnson & Johnson, applying advanced computational methods to solve critical healthcare challenges. She earned her PhD in Computer Science from the University of Massachusetts Boston, developing innovative machine learning algorithms and deep learning models for real-world applications.

Bayat leads the Human-Centered AI Lab at Northeastern University’s Silicon Valley campus, where her team conducts interdisciplinary research focused on designing AI systems that prioritize human needs and experiences. Students and collaborators interested in research opportunities are encouraged to visit the lab’s website to learn more.

Her teaching philosophy is rooted in empowering students to design human-centered computing solutions, integrating UX/UI design principles with AI-driven innovation. She fosters collaboration, creativity, and critical thinking, equipping students to thrive in the fast-moving technology landscape of Silicon Valley and beyond.

Projects

• SimPath: An AI-powered platform that simulates realistic mental health patients for training clinical students and therapists. It uses a dynamic AI system and a dual-mode interface to enable practice in diagnostic reasoning and conversation. The platform includes automated feedback and is designed to bridge the gap between academic training and real-world clinical practice.
• Multimodal Emotion Recognition: This project aims to enhance emotion recognition in human-computer interactions by combining both speech and text data. By integrating prosodic features from audio with semantic information from text, the system can better capture nuanced emotional states. The goal is to incorporate this technology into a therapy training platform to enable more empathetic and realistic responses.
• User Study on 3D AI Virtual Patients: A user study that evaluates a 3D AI virtual patient simulation designed to train speech-language pathology students on expressive aphasia. The study uses both cooperative and difficult patient scenarios to assess student learning, confidence, and user experience. It employs a mixed-methods approach with surveys and interviews to create a rigorous framework for evaluating the educational impact of AI-enabled training.

Upcoming Projects

• Explainable Emotion Recognition: A project to develop and implement interpretability techniques like SHAP and LIME to explain how emotion recognition models for healthcare dialogues make their predictions from text and audio data.

Recent publications