Meet the Women at Michigan who are Shaping the Field of AI

Elizabeth Bondi-Kelly

Assistant Professor

Elizabeth (Liz) Bondi-Kelly’s work is breaking new ground in the application of AI for social impact. Leading the Realize Lab at Michigan, she engages with nonprofits, impacted and interested communities, and interdisciplinary collaborators, developing and deploying multi-agent systems and machine learning tools for conservation and health. This includes identifying pollutants in the environment and improving access to reliable health information.

Bondi-Kelly’s research has been widely published in distinguished venues, including the proceedings of top conferences such as the AAAI Conference on Artificial Intelligence, the International Joint Conference on Artificial Intelligence, and others. She also founded and leads Try AI, a 501(c)(3) nonprofit setting out to create educational opportunities for more students to explore AI and its role in society.

Joyce Chai

Professor
Associate Director, Michigan Institute for Data Science (MIDAS)

Joyce Chai bridges Computer and Cognitive Science through her efforts to improve communication between humans and embodied agents. She studies how experience with the world and social interaction shapes language learning and language use; and develops human language technology that is sensorimotor-grounded, pragmatically-rich, and cognitively-motivated. She leads the Situated Language and Embodied Dialogue (SLED) lab at Michigan exploring the integration of language, perception, and action, and building embodied AI agents that strive to collaborate with humans through language communication. She brings this ambition to the classroom, teaching core AI courses as well as some on natural language processing and embodied communication.

Chai has served on the executive board of NAACL and as Program Co-Chair for multiple conferences (e.g., ACL 2020, NAACL 2015, IUI 2014). She is a recipient of the NSF CAREER Award and multiple paper awards with her students (e.g., Best Long Paper Award at ACL 2010, Outstanding Paper Awards at EMNLP 2021 and ACL 2023). The SEAGULL team from her lab won the First Place Prize in Amazon Alexa AI Simbot Challenge in 2023.  She is a Fellow of the Association for Computational Linguistics.

Farnaz Jahanbakhsh

Assistant Professor

Farnaz Jahanbakhsh designs, builds, and studies social computing systems that have pro-social outcomes. These systems and studies have applications to social media, education, collaboration, social justice, and the gig economy. One of her main interests is designing systems that give people more autonomy and which democratize power that is otherwise in the hands of a centralized authority. Many of these systems re-imagine the design of social media, the web, and societal algorithms to give users more agency over what content they want to see or avoid. As an example, Jahanbakhsh has explored and field tested the democratization of content moderation for combatting online misinformation as well as for building pluralistic values into social media algorithms and feeds. 

Jahanbakhsh’s work has been broadly published in premier venues, such as the ACM CHI conference on Human Factors in Computing Systems and the ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing.

Danai Koutra

Associate Professor

Danai Koutra focuses on massive, interconnected data representing various phenomena spanning from human interactions and web searches to brain activity -– figuring out how it’s structured, how it changes over time, and how to best represent it and learn from it. Leading the Graph Exploration and Mining at Scale (GEMS) Lab, her work builds a deeper understanding of the data that goes into algorithmic decision-making tools, and contributes practical methods that handle large-scale data in a more nuanced way. Among other things, Koutra designs new, efficient methods for summarizing and understanding the patterns behind facts about the world; works on fusing and learning from multiple data sources and modalities; and develops methods to accomplish feats from speeding up web searches to better understanding the structure and function of the human brain. 

Koutra has served as Program co-Chair of conferences, including ACM SIGKDD 2024, ECML/PKDD 2023, The Web Conference (WWW) 2022, and IJCAI Survey Track 2025. She is the recipient of the NSF PECASE Award, the NSF CAREER Award, an IBM Early Career Data Mining Research Award, the ACM-SIGKDD Rising Star Award, the IEEE Tao Li Award, the 2022 Test-of-Time ICDM paper award, amongst others.

Sindhu Kutty

Teaching Faculty
Director, CSE Teaching Lab

Sindhu Kutty brings her enthusiasm about computer science to her teaching. At Michigan, she focuses on teaching math-based computer science courses including Machine Learning and Foundations of Computer Science. She is also passionate about getting undergraduate students excited about venturing beyond the course curriculum, and works with them to channel that excitement into publishable undergraduate research. While she has published in highly selective conferences in her area of market mechanism design and its connections to statistical machine learning, Kutty is especially proud of the work she has published and presented with her undergraduate students. 

Kutty has been recognized by the American Society for Engineering Education for her work as a Graduate Student Instructor and she has won numerous competitive faculty teaching grants. She is the recipient of the College of Engineering’s Jon R. and Beverly S. Holt Award for Excellence in Teaching and has been recognized with a U-M EECS Outstanding Achievement Award.

Q. Vera Liao

Visiting Associate Professor

Vera Liao will be joining the faculty at CSE in the Fall of 2025. She is currently a visiting faculty with the department and a Principal Researcher at Microsoft Research, where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group. Liao’s research interest include HCI, Human-AI Interaction, Responsible Computing and Experience. She is interested in examining and mitigating risks of emerging technologies, and her most recent work focuses on transparency of AI technologies (e.g. explainability, evaluation, uncertainty communication) and its intersection with important aspects of human experiences such as trust and control. 

Her work has broadly appeared in premier HCI, AI and AI ethics venues, and has been recognized by many best paper and nomination awards, including an ACM CHI 2024 Best Paper.

Maggie Makar

Assistant Professor

Maggie Makar is an Assistant Professor of Computer Science and Engineering at the University of Michigan in Ann Arbor. She works at the intersection of machine learning and causality, adapting ideas from causality to make predictive models more robust and using ideas from machine learning to make causal inference more statistically efficient. She uses machine learning and causal inference tools in service of healthcare applications. Her work has been published in machine learning conferences such as NeurIPS, ICML, AISTATs, AAAI as well as medical venues such as JAMA, and Health Affairs. She received her PhD from CSAIL, MIT. She is the recipient of the NSF CAREER award and the Google Research Scholar award.

Rada Mihalcea

Janice M. Jenkins Collegiate Professor of Computer Science and Engineering
Director, Michigan AI Lab

Rada Mihalcea works on research in natural language processing in her Language and Information Technologies (LIT) lab. Her projects search for patterns in language data collected from around the world – gaining a deeper understanding of linguistic phenomena, as well as the opinions, beliefs, and values of the people behind the language. Her projects address topics in cross-cultural and multilingual text processing, computational sociolinguistics, and multimodal processing. Mihalcea also leads the department’s AI Lab, which promotes and highlights internal and external collaborations and findings in AI. Mihalcea also prioritizes broadening participation in the field of computing by piloting programs and courses aimed at supporting women in engineering and computer science.

She is the recipient of the NSF PECASE Award and the NSF CAREER Award, and is a Fellow of the ACM and the AAAI. She has been recognized with the U-M Sarah Goddard Power Award, the U-M Carol Hollenshead Award, and the U-M Distinguished Faculty Award. In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania. She has received two test of time awards recognizing papers with lasting impact, including from AAAI and ACM ICMI.

Emily Mower Provost

Professor
CSE Sr. Associate Chair for Academic Affairs

Emily Mower Provost‘s research focuses on advancing speech-based machine learning methods and leveraging these innovations to gain new insights into human behavior. Her research focuses on developing robust, perception-centered systems for emotion recognition, mental health modeling, and assistive technologies. 

Mower Provost leads the Computational Human Artificial Intelligence (CHAI) Lab, where her group tackles the challenges of interpreting subtle, ambiguous emotions in real-world settings.  They research new approaches that reflect the nuanced way people perceive and express emotions, embracing variability and improving performance on natural real-world data. The CHAI Lab also develops tools to support mental health care, including systems that monitor mood and behavior patterns in individuals with conditions like bipolar disorder. Through this human-centered approach, the CHAI Lab is building technology that not only senses how people feel but also adapts to individual and contextual differences—paving the way for more empathetic and responsive AI systems.

She has been awarded a Toyota Faculty Scholar Award, an NSF CAREER Award, the Oscar Stern Award for Depression Research, and the College of Engineering Trudy Huebner Service Excellence Award. She is a co-author of multiple award-winning papers in the field of automatic emotion recognition.

Lu Wang

Associate Professor

Lu Wang’s research is focused on natural language processing and machine learning. Specifically, she builds trustworthy language models that produce factual, accurate, and safe content. She has worked on problems of summarization, generation, reasoning, argument mining, as well as creating novel applications to understand narratives and media bias and to support education. 

Wang leads the LAnguage Understanding and generatioN researCH (LAUNCH) Lab, which aims to build efficient systems to improve large language models’ factuality and complex reasoning capabilities, as well as conduct robust evaluation of these models on real-world applications. 

Wang is the recipient of an NSF CAREER Award, and she has won multiple paper awards at top venues (ACL, CHI, and SIGDIAL). She is program co-chair for the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) 2025. She currently serves as Association for Computational Linguistics (ACL) equity director. She is a co-founder and an officer of ACL Special Interest Group on Summarization (SIGSUMM).

Xu Wang

Assistant Professor

Xu Wang leads the Lifelong Learning Lab, which conducts research at the intersection of Human-Computer Interaction, Learning Sciences, and Artificial Intelligence. One of Wang’s research goals is to empower instructors and educators to create effective learning experiences more easily, which in turn supports scalable teaching and learning. An example is providing instructors with on-demand controllable AI assistance while they are designing quiz questions. In addition, she studies ways to help people be more attentive to each other’s ideas during collaboration. 

Wang has developed Augmented Reality-based systems to support physical task learning, including a tool that helps surgeons to quickly search videos and create interactive feedback, saving time while improving educational value for trainees. Wang’s work has been published at many top-tier conferences and journals in Human-Computer Interaction and Educational Technologies.

Jenna Wiens

Associate Professor
Associate Director, Michigan AI Lab
Co-Director, AI & Digital Health Innovation

Jenna Wiens develops the computational methods needed to help organize, process, and transform health data into actionable knowledge. Leading the Machine Learning for Data-Driven Decisions (MLD3) Lab, she and her students work closely with clinicians to improve clinical care with machine learning and artificial intelligence, with an emphasis on human-AI collaboration, reinforcement learning, and causal inference techniques. Wiens describes machine learning as the study of methods for automatically detecting patterns in data. These approaches shine when working with the massive and complex datasets that health data represent. Her ultimate goal is to use these methods to augment clinical practice and improve patient outcomes.

Wiens has been named a Forbes 30 under 30 in Science and Healthcare and one of MIT Tech Review’s 35 Innovators Under 35. She has received an NSF CAREER Award  and a Sloan Fellowship in Computer Science, and was selected for the Carl Friedrich von Siemens Research Award by the Alexander von Humboldt Foundation and for the Sarah Goddard Power Award by the University of Michigan.

Stella Yu

Professor

Stella Yu conducts cutting-edge research on unsupervised representation learning and open long-tailed recognition from natural data.  She is actively extending these approaches to developmental robotics and embodied AI, grounding action and perception by learning sensorimotor contingencies along with world models.  Dr. Yu is interested not only in understanding visual perception from multiple perspectives, but also in using computer vision and machine learning to automate and exceed human expertise in practical applications.

Dr. Yu’s research has three themes: Actionable Representation Learning Driven by Natural Data, Efficient Structure-Aware Machine Learning Models, and Application to Science, Medicine, and Engineering.  She is a recipient of the US NSF CAREER Award and the Clare Boothe Luce Professorship.