Computer Science Seminar: Rafał Kocielnik (California Institute of Technology)

January 30, 2025 | 11:00am - 12:00pm

Milstein 402 & Zoom

Add to Calendar 2025-01-30 11:00:00 2025-01-30 12:00:00 Computer Science Seminar: Rafał Kocielnik (California Institute of Technology) Speaker: Rafał Kocielnik (California Institute of Technology) Title: Human-Centered AI: From Clinical Education Support to Enabling End-user Inspection of AI The seminar will be available for in-person and Zoom participation. To participate online, please email inquiry-cs@barnard.edu to receive the Zoom link. Modern generative AI offers significant capabilities, yet integrating these technologies with the practical needs of domain experts and end-users is challenging. Additionally, end-use issues like social bias, stereotyping, and limited explainability can undermine trust, safety, and AI adoption, highlighting critical hurdles in AI deployment in real-world settings. In this talk, I will present my work in Human-Centered AI, focusing on two core projects. First, I will explore the use of unsupervised, explainable Human-AI collaboration techniques. These techniques are applied to discover clinically relevant categorizations of unstructured verbal feedback during live surgery. This work represents a key effort into applying AI in clinical education developed alongside clinicians. Second, I will showcase efforts to enable end-users to examine social biases in large language models (LLMs) through user-friendly interfaces, an initiative crucial for enhancing transparency and trust in AI. I will also highlight my mentorship of undergraduate students who have significantly contributed to these projects. Together, these efforts demonstrate a blend of technical innovation and user-centric design in my AI research approach. Rafał Kocielnik is a Postdoctoral Researcher at Caltech's Department of Computing and Mathematical Sciences, focusing on Human-Centered AI. His work is distinguished by a strong commitment to mentorship, preparing undergraduate students for impactful careers in both industry and academia. His educational background—an M.Sc. in Computer Science from the Polish-Japanese Academy of Information Technology in Poland, a PDEng in Industrial Design from Eindhoven University of Technology in the Netherlands, and a Ph.D. in Human-Centered Design & Engineering from the University of Washington, USA—equips him to address multidisciplinary challenges around AI. During his Ph.D., he focused on designing engaging conversational interactions for reflection and behavior change. His research at Caltech, which earned him a CRA Computing Innovation Fellowship in 2020, includes AI for surgical training, addressing social bias in generative AI, and mitigating toxicity in social media. Rafał has published over 60 peer-reviewed papers and collaborated with industry leaders such as Microsoft, NVIDIA, and Activision, as well as nonprofits like Wikipedia and Cedars-Sinai Medical Center. His contributions have been recognized with best paper awards at major HCI and applied ML venues such as CSCW, Conversational User Interfaces (CUI), and Machine Learning for Health (ML4H), and his work has also appeared in prestigious journals including Nature Digital Medicine and JAMA. This track record underscores his commitment to interdisciplinary research, mentorship, and advancing AI with a human-centered approach. Milstein 402 & Zoom Barnard College barnard-admin@digitalpulp.com America/New_York public
Campus Map

Speaker: Rafał Kocielnik (California Institute of Technology)

Title: Human-Centered AI: From Clinical Education Support to Enabling End-user Inspection of AI

The seminar will be available for in-person and Zoom participation. To participate online, please email inquiry-cs@barnard.edu to receive the Zoom link.

Modern generative AI offers significant capabilities, yet integrating these technologies with the practical needs of domain experts and end-users is challenging. Additionally, end-use issues like social bias, stereotyping, and limited explainability can undermine trust, safety, and AI adoption, highlighting critical hurdles in AI deployment in real-world settings.

In this talk, I will present my work in Human-Centered AI, focusing on two core projects. First, I will explore the use of unsupervised, explainable Human-AI collaboration techniques. These techniques are applied to discover clinically relevant categorizations of unstructured verbal feedback during live surgery. This work represents a key effort into applying AI in clinical education developed alongside clinicians. Second, I will showcase efforts to enable end-users to examine social biases in large language models (LLMs) through user-friendly interfaces, an initiative crucial for enhancing transparency and trust in AI. I will also highlight my mentorship of undergraduate students who have significantly contributed to these projects. Together, these efforts demonstrate a blend of technical innovation and user-centric design in my AI research approach.

Rafał Kocielnik is a Postdoctoral Researcher at Caltech's Department of Computing and Mathematical Sciences, focusing on Human-Centered AI. His work is distinguished by a strong commitment to mentorship, preparing undergraduate students for impactful careers in both industry and academia. His educational background—an M.Sc. in Computer Science from the Polish-Japanese Academy of Information Technology in Poland, a PDEng in Industrial Design from Eindhoven University of Technology in the Netherlands, and a Ph.D. in Human-Centered Design & Engineering from the University of Washington, USA—equips him to address multidisciplinary challenges around AI. During his Ph.D., he focused on designing engaging conversational interactions for reflection and behavior change. His research at Caltech, which earned him a CRA Computing Innovation Fellowship in 2020, includes AI for surgical training, addressing social bias in generative AI, and mitigating toxicity in social media. Rafał has published over 60 peer-reviewed papers and collaborated with industry leaders such as Microsoft, NVIDIA, and Activision, as well as nonprofits like Wikipedia and Cedars-Sinai Medical Center. His contributions have been recognized with best paper awards at major HCI and applied ML venues such as CSCW, Conversational User Interfaces (CUI), and Machine Learning for Health (ML4H), and his work has also appeared in prestigious journals including Nature Digital Medicine and JAMA. This track record underscores his commitment to interdisciplinary research, mentorship, and advancing AI with a human-centered approach.

Computer Science