World's Best Scientists 2026 revealed!
Julie A. Shah

Julie A. Shah

D-Index & Metrics

Computer Science

D-Index
47
Citations
8208
World Ranking
6540
National Ranking
2890

Overview

Julie A. Shah is affiliated with MIT in the United States and has contributed extensively to the field of computer science, particularly within artificial intelligence. Their body of work encompasses multiple subfields, including artificial intelligence, social psychology, control and systems engineering, computer vision and pattern recognition, and surgery. The combined number of publications highlights a diverse research focus spanning computational and human-centric areas.

Their research covers several main topics, with a strong emphasis on explainable artificial intelligence (XAI), reinforcement learning in robotics, human-automation interaction and safety, adversarial robustness in machine learning, robot manipulation and learning, topic modeling, and AI-based problem solving and planning.

Julie A. Shah has authored papers published in various academic venues with repeated contributions, particularly in arXiv (Cornell University), which accounts for the majority of their publications. Other frequent venues include the Proceedings of the AAAI Conference on Artificial Intelligence, IEEE Robotics and Automation Letters, International Journal of Human-Computer Interaction, and IEEE Transactions on Robotics.

Recent publications exemplify the scope of their research, including:

  • Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence (2022), arXiv (Cornell University)
  • Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report (2022), arXiv (Cornell University)
  • The Situation Awareness Framework for Explainable AI (SAFE-AI) and Human Factors Considerations for XAI Systems (2022), International Journal of Human-Computer Interaction
  • Do Feature Attribution Methods Correctly Attribute Features? (2022), Proceedings of the AAAI Conference on Artificial Intelligence
  • What skills and abilities can automation technologies replicate and what does it mean for workers? (2022), OECD social employment and migration working papers

The scientist collaborates regularly with specific co-authors, including Yilun Zhou, Mycal Tucker, Serena Booth, Lindsay Sanneman, and Ankit Shah, indicating a network of partnerships fostering research development across their focal areas.

Best Publications

  • Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence

    Peter Stone;Rodney Brooks;Erik Brynjolfsson;Ryan Calo

  • Analyzing the Effects of Human-Aware Motion Planning on Close-Proximity Human–Robot Collaboration

    Przemyslaw A. Lasota;Julie A. Shah

  • A Survey of Methods for Safe Human-Robot Interaction

    Przemyslaw A. Lasota;Terrence Fong;Julie A. Shah

  • The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

    Been Kim;Cynthia Rudin;Julie A Shah

  • Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams

    Matthew C. Gombolay;Reymundo A. Gutierrez;Shanelle G. Clarke;Giancarlo F. Sturla

  • Improved human-robot team performance using chaski, a human-inspired plan execution system

    Julie Shah;James Wiken;Brian Williams;Cynthia Breazeal

  • Improving Robot Controller Transparency Through Autonomous Policy Explanation

    Bradley Hayes;Julie A. Shah

  • Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks

    Stefanos Nikolaidis;Ramya Ramakrishnan;Keren Gu;Julie Shah

  • Toward safe close-proximity human-robot interaction with standard industrial robots

    Przemyslaw A. Lasota;Gregory F. Rossano;Julie A. Shah

  • Evaluating Effects of User Experience and System Transparency on Trust in Automation

    X. Jessie Yang;Vaibhav V. Unhelkar;Kevin Li;Julie A. Shah

  • Human-robot cross-training: computational formulation, modeling and evaluation of a human team training strategy

    Stefanos Nikolaidis;Julie Shah

  • Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification

    Claudia Perez-D'Arpino;Julie A. Shah

  • An Architecture for Online Affordance-based Perception and Whole-body Planning

    Maurice Fallon;Scott Kuindersma;Sisir Karumanchi;Matthew Antone

  • Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences:

    Matthew C. Gombolay;Anna Bair;Cindy Huang;Julie A. Shah

  • Human-Aware Robotic Assistant for Collaborative Assembly: Integrating Human Motion Prediction With Planning in Time

    Vaibhav V. Unhelkar;Przemyslaw A. Lasota;Quirin Tyroller;Rares-Darius Buhai

  • Optimizing Makespan and Ergonomics in Integrating Collaborative Robots Into Manufacturing Processes

    Margaret Pearce;Bilge Mutlu;Julie Shah;Robert Radwin

  • Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints

    Matthew C. Gombolay;Ronald James Wilcox;Julie A Shah

  • Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

    Matthew C. Gombolay;Ronald J. Wilcox;Julie A. Shah

  • An Empirical Analysis of Team Coordination Behaviors and Action Planning With Application to Human–Robot Teaming:

    Julie A. Shah;Cynthia Breazeal

  • Implicit Coordination Strategies for Effective Team Communication

    Abhizna Butchibabu;Christopher Sparano-Huiban;Liz Sonenberg;Julie Shah

  • Efficient Model Learning for Human-Robot Collaborative Tasks.

    Stefanos Nikolaidis;Keren Gu;Ramya Ramakrishnan;Julie A. Shah

  • Do Feature Attribution Methods Correctly Attribute Features

    Yilun Zhou;Serena Booth;Marco Tílio Ribeiro;Julie Shah

  • Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification

    Claudia Perez D'Arpino;Julie A Shah

Frequent Co-Authors

Been Kim
Been Kim Google (United States)
Ece Kamar
Ece Kamar Microsoft (United States)
Eric Horvitz
Eric Horvitz Microsoft (United States)
Terrence Fong
Terrence Fong Ames Research Center
Bilge Mutlu
Bilge Mutlu University of Wisconsin–Madison
Cynthia Rudin
Cynthia Rudin Duke University
Rachid Alami
Rachid Alami Laboratory for Analysis and Architecture of Systems

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