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个人简介
Research Interests
My Intelligent Adaptive Interventions research group aims to transform everyday technology for people into intelligent, perpetually improving systems, by integrating human intelligence with statistical machine learning. Simple examples in education include enhancing the explanations students receive for how to solve problems, and examples in health include personalizing which text messages get people to exercise. Our approach is to deploy randomized A/B comparisons that compare alternative actions, creating these A/B comparisons using insights from human-computer interaction and experimental psychology, as well as harnessing ideas from users and designers through crowdsourcing or 'human computation' to perpetually expand the set of actions/conditions being tested. We then apply algorithms to discover which actions are effective (and for which subgroups of users) and rapidly provide the most effective actions to future users, to enhance and personalize their experience. For example, existing research and active collaborations apply and extend algorithms in reinforcement learning (e.g. multi-armed contextual bandits) along with models from statistics. As shown in the diversity of publication venues, my group bridges research in human-computer interaction, experimental psychology, applied statistics and machine learning.
My Intelligent Adaptive Interventions research group aims to transform everyday technology for people into intelligent, perpetually improving systems, by integrating human intelligence with statistical machine learning. Simple examples in education include enhancing the explanations students receive for how to solve problems, and examples in health include personalizing which text messages get people to exercise. Our approach is to deploy randomized A/B comparisons that compare alternative actions, creating these A/B comparisons using insights from human-computer interaction and experimental psychology, as well as harnessing ideas from users and designers through crowdsourcing or 'human computation' to perpetually expand the set of actions/conditions being tested. We then apply algorithms to discover which actions are effective (and for which subgroups of users) and rapidly provide the most effective actions to future users, to enhance and personalize their experience. For example, existing research and active collaborations apply and extend algorithms in reinforcement learning (e.g. multi-armed contextual bandits) along with models from statistics. As shown in the diversity of publication venues, my group bridges research in human-computer interaction, experimental psychology, applied statistics and machine learning.
研究兴趣
论文共 172 篇作者统计合作学者相似作者
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Conference on Designing Interactive Systemspp.86-101, (2024)
JOURNAL OF AFFECTIVE DISORDERS (2024): 122-130
AAAI 2024no. 21 (2024): 22906-22912
Nele Albers,Amal Abdulrahman,Deborah Richards,Caroline A. Figueroa,Bibhas Chakraborty,Ananya Bhattacharjee, Linwei He,Mark A. Neerincx,Joseph Jay Williams, Nezih Younsi,Tibor Bosse,Annemiek Linn,
International Conference on Persuasive Technologypp.1-7, (2024)
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Naaz Sibia,Giang Bui, Bingcheng Wang, Yinyue Tan,Angela Zavaleta Bernuy,Christina Bauer,Joseph Jay Williams,Michael Liut,Andrew Petersen
Steven Ritter, Stephen E. Fancsali, April Murphy,Neil T. Heffernan, Ben Motz,Debshila Basu Mallick,Jeremy Roschelle,Danielle S. McNamara,Joseph Jay Williams
ACM Conference on Learning @ Scalepp.565-566, (2024)
Ananya Bhattacharjee, Zichen Gong, Bingcheng Wang, Timothy James Luckcock, Emma Watson, Elena Allica Abellan, Leslie Gutman,Anne Hsu,Joseph Jay Williams
CoRR (2024)
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JMIR FORMATIVE RESEARCH (2024): e47360-e47360
Ananya Bhattacharjee, Zichen Gong, Bingcheng Wang, Timothy James Luckcock, Emma Watson, Elena Allica Abellan, Leslie Gutman,Anne Hsu,Joseph Jay Williams
arxiv(2024)
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Ananya Bhattacharjee, Yuchen Zeng, Sarah Yi Xu,Dana Kulzhabayeva, Minyi Ma,Rachel Kornfield,Syed Ishtiaque Ahmed,Alex Mariakakis,Mary P Czerwinski,Anastasia Kuzminykh,Michael Liut,Joseph Jay Williams
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