Abigail O'Neill

VLDB 2026 Research / reviewers in the wild / expert

dblp:371/6758 · also Abby O'Neill · DBLP profile ↗

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10ranked-venue papers

3as first author

10since 2021 · last 2026

0009-0005-1700-3130ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 8 · 2 first-author · 8 since 2021Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Systems, architecture and hardware · 2 · 1 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Year	Publication	Venue	Position
2026	Instructors' Perspectives on LLM-Generated Programming Formative Feedback abstract We study instructor perspectives on LLM-generated programming feedback in an introductory Python course. LLM tutors predominantly offered debugging help, while human instructors preferred more diverse feedback types, including conceptual reminders, revisiting the problem, and examples. Cases where LLM tutor feedback diverged from human instructors' intent required major edits with different feedback types, while cases with closer alignment needed only minor changes with similar feedback types. Findings highlight the need for LLM tutors to reflect on instructor intent to ensure pedagogically aligned feedback. Rose Niousha, Samantha Boatright Smith, Abigail O'Neill, J. D. Zamfirescu-Pereira, John DeNero, Narges Norouzi	SIGCSE (2)	3
2026	Misconception-Aware LLM Programming Tutor: Lessons Learned from Student-Tutor Interactions abstract Large Language Models (LLMs) are increasingly used as programming tutors, but their feedback is often generic and prone to solution leakage. To address these issues, we present MisconceptionTutor, which grounds feedback in common student misconceptions. Through both pre-deployment analyses and a real-classroom deployment, we find that even simple prompting frameworks can meaningfully steer tutor behavior to be more pedagogically oriented and noticeably more satisfying to students. Rose Niousha, Samantha Boatright Smith, Abigail O'Neill, J. D. Zamfirescu-Pereira, John DeNero, Narges Norouzi	SIGCSE (2)	3
2025	Modeling Student Knowledge Progression Across Concepts in Intelligent Tutoring Interactions Kanav Mittal, Abigail O'Neill, Hanna Schlegel, Gireeja Ranade, Narges Norouzi	AIED (2)	2
2025	LLM-KCI: Leveraging Large Language Models to Identify Programming Knowledge Components abstract Identifying Knowledge Components (KCs) in computer science education improves curriculum design and teaching strategies. We introduce a framework using Large Language Models to identify KCs from programming assignments automatically. Our framework helps educators align assignments with course objectives. GPT-4 identifies relevant KCs well, though there's a low match with expert-generated KCs at the course level. At the problem level, performance is lower, but key KCs are reasonably identified. Rose Niousha, Abigail O'Neill, Ethan Chen, Vedansh Malhotra, Bita Akram, Narges Norouzi	SIGCSE (2)	2
2025	Launching and Enhancing Summer Bridge Programs abstract Summer bridge programs are initiatives aimed at supporting students as they transition into university, helping them build confidence, skills, and a sense of belonging. Many existing programs focus on serving less experienced and/or historically underrepresented students, striving to broaden participation in STEM fields. We aim to bring together colleagues who share a common interest in Computer Science and Engineering Bridge programs. We invite both those interested in starting bridge programs at their institutions and those with knowledge of existing programs, including best practices and lessons learned. We will collaborate to discuss the most effective approaches to these programs and challenges that arise in their implementations, including exclusivity in the selection of admits, target audience, funding limitations, outreach, and more. We aim to have participants leave with resources, including information about other programs, points of contact for networking, and a collaborative document from the discussion, as well as a website for hosting resources of existing programs. Abigail O'Neill, Stella Kaval, Mallika Reddy, Alvaro Monge, Colleen M. Lewis, Narges Norouzi	SIGCSE (2)	1
2025	From Code to Concepts: Textbook-Driven Knowledge Tracing with LLMs in CS1 abstract Gauging a student's understanding of course concepts, at an arbitrary point during a course, can be challenging. Standardized exams offer only a snapshot of performance rather than a deep understanding of progress. However, with Large Language Models (LLMs) now deployed at scale in CS1 courses, we can track multiple attempts from each student for every homework problem. This data provides insights into how students learn and deploy concepts over time, presenting a unique opportunity to rethink how we track changes in individual student knowledge. Traditional Knowledge Tracing (KT) methods often lack explainability and are computationally expensive. In contrast, our framework leverages an LLM to identify student progress on labeled, problem-level concepts from a student homework code submission. Our initial results show that the student's knowledge state can be dynamically updated. This knowledge state can then be used to provide more targeted, effective feedback and create tailored study materials. Abigail O'Neill, Samantha Boatright Smith, Aneesh Durai, John DeNero, J. D. Zamfirescu-Pereira, Narges Norouzi	SIGCSE (2)	1
2025	Spotting AI Missteps: Students Take on LLM Errors in CS1 Samantha Boatright Smith, Heather Wei, Abigail O'Neill, Aneesh Durai, John DeNero, J. D. Zamfirescu-Pereira, Narges Norouzi	SIGCSE (2)	3
2024	Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration abstract Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin	ICRA	1
2024	MANIP: A Modular Architecture for Integrating Interactive Perception for Robot Manipulation abstract We propose a modular systems architecture, MANIP, that can facilitate the design and development of robot manipulation systems by systematically combining learned subpolicies with well-established procedural algorithmic primitives such as Inverse Kinematics, Kalman Filters, RANSAC outlier rejection, PID modules, etc. (aka "Good Old Fashioned Engineering (GOFE)"). The MANIP architecture grew from our lab’s experience developing robot systems for folding clothes, routing cables, and untangling knots. To address failure modes, MANIP can facilitate inclusion of "interactive perception" subpolicies that execute robot actions to modify system state to bring the system into alignment with the training distribution and / or to disambiguate system state when system state confidence is low. We demonstrate how MANIP can be applied with 3 case studies and then describe a detailed case study in cable tracing with experiments that suggest MANIP can improve performance by up to 88%. Code and details are available at: https://berkeleyautomation.github.io/MANIP/ Justin Yu, Tara Sadjadpour, Abigail O'Neill, Mehdi Khfifi, Yunliang Chen 0001, Richard Cheng, Muhammad Zubair Irshad, Ashwin Balakrishna, Thomas Kollar, Kenneth Y. Goldberg	IROS	3
2024	Computer Science Kickstart: An Innovative Bootcamp to Ignite Passion in First-Year Female-Identifying University Students abstract The UC Berkeley CS Kickstart initiative represents a novel approach in addressing the underrepresentation of women in the tech industry. This paper evaluates the efficacy of its one-week intensive curriculum in computer science, particularly focusing on first-year female-identifying university students. The 2023 program cohort witnessed the introduction of a dual-track system, accommodating both beginners and students with prior experience. This study emphasizes the findings from enhanced survey methods and involved detailed pre-program and post-program assessments that gauged the initiative's success in imparting foundational knowledge and fostering enthusiasm in computer science. The findings reveal that such targeted, short-term educational programs significantly contribute to advancing diversity and inclusion in computer science education. The results suggest that early intervention at the university level can play a pivotal role in shaping a more diverse future in the tech landscape. Stella Kaval, Mallika Reddy, Abigail O'Neill	SIGCSE (2)	3