Email: m.alikhani AT northeastern DOT edu
Malihe Alikhani (She/Her)
I am an assistant professor of AI and social justice at the Khoury School of Computer Science at Northeastern University. I am also a member of the Northeastern Ethics Institute a Cultural Competence in Computing (3C) fellow.
I work towards studying and addressing biases in learning models, especially in applications such as education, health, and social justice. This aspiration has led me to collaborate with educators, healthcare experts, and community leaders to create inclusive technology-enabled experiences.
My research aims to design inclusive and equitable language technologies that communicate effectively across diverse populations, with a particular focus on underserved communities. By integrating insights from cognitive science, neuroscience, philosophy, policy, and social sciences with machine learning techniques, I develop computational models that capture the rich diversity of human interpretation and enhance the effectiveness of language as a communicative tool.
Selected Publications
Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models, Anthony Sicilia, Hyunwoo Kim, Khyathi Raghavi Chandu, Malihe Alikhani, Jack Hessel, The 62nd Annual Meeting of the Association for Computational Linguistics, 2024. PDF
PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners, Anthony Sicilia, Katherine Atwell, Malihe Alikhani, Seong Jae Hwang, The Conference on Uncertainty in Artificial Intelligence (UAI), 2022. Best Paper Award PDF
Including Signed Languages in Natural Language Processing, Kayo Yin, Amit Moryossef, Julie Hochgesang, Yoav Goldberg, and Malihe Alikhani, The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021. Best Theme Paper Award PDF
Current Projects
Harm Measurement for Inclusive Language Technologies
AI-assisted moderation and mediation; harm measurement and ethics of social interactions with AI; oppressive practices & norms; pragmatics of harmful speech
Equitable, Robust, and Trustworthy Language Technologies
Studying the generalization and robustness of NLP models using the tools of learning theory; designing theoretical machine learning frameworks for language generation and dialogue management; reducing overreliance and sycophancy.
Collaborative Conversational AI for Education and Health
Modeling collaboration as shared meaning construction; Modeling common ground in dialogue; Supporting patient and health professionals' interactions with AI through robust interactions.
Sign Language Processing
Sign language understanding and generation; designing interactive AI systems for signers with applications in healthcare and education.
