Michael Gleicher

About

Michael Gleicher is a David DeWitt Professor and Associate Chair for Undergraduate Programs in the Department of Computer Sciences at the University of Wisconsin, Madison. His research primarily focuses on Visual Computing, with current interests in robotics and data visualization, emphasizing how these technologies can be made useful for people. His work encompasses areas such as animation, virtual reality, multimedia, and visualization theory, including summarization, uncertainty, and effective visualization design. Gleicher is recognized as an ACM Fellow, a member of the IEEE Visualization Academy, and an IEEE Senior Member, and holds a concurrent position as an Amazon Design Scholar. Throughout his career, Gleicher has contributed to various themes in robotics and visualization, including shared autonomy for robotic inspection, communication of physical interactions between humans and robots, and the development of visualization methods that enhance understanding and decision-making. His research also explores novel sensors for robotics applications, such as Single Photon Avalanche Diode (SPAD) sensors, and investigates how emerging sensing technologies can be integrated into robotic systems. Additionally, he has worked on visualizing robot awareness, understanding human perception principles for visualization design, and improving image and video authoring techniques. Gleicher is actively involved in teaching, offering courses such as CS765 Visualization and CS559…

Research topics

• Computer Science
• Artificial Intelligence
• Information Retrieval
• Natural Language Processing
• Programming language
• Cartography
• Human–computer interaction
• Geography

Selected publications

• MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

  IEEE Robotics and Automation Letters · 2021 · 51 citations

  Recently, there has been a wealth of development in motion planning for robotic manipulation—new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="…

  DOI

• CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

  iScience · 2020 · 46 citations

  Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification of cell clusters by considering the rich hierarchical structure of known cell types. Furthermore, CellO comes pre-trained on a comprehensive data set of human, healthy, untreated primary samples in the Sequence Read Arch…

  PublisherDOI

• Task-Level Authoring for Remote Robot Teleoperation

  Frontiers in Robotics and AI · 2021 · 20 citations

  Remote teleoperation of robots can broaden the reach of domain specialists across a wide range of industries such as home maintenance, health care, light manufacturing, and construction. However, current direct control methods are impractical, and existing tools for programming robot remotely have focused on users with significant robotic experience. Extending robot remote programming to end users, i.e., users who are experts in a domain but novices in robotics, requires tools that balance the r…

  DOI

• Towards 3D Vision with Low-Cost Single-Photon Cameras

  2024-06-16 · 12 citations

  article

  We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process, account for its non-idealities, and adapt neural rendering to reco…

  PublisherDOI

• Human–Robot Collaboration With a Corrective Shared Controlled Robot in a Sanding Task

  Human Factors The Journal of the Human Factors and Ergonomics Society · 2024-08-08 · 6 citations

  article

  OBJECTIVE: Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human-robot collaborative (HRC) sanding task. BACKGROUND: Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semi-autonomously while the human provides real-ti…

  PublisherDOI

Recent grants

• III: Small: Subset Selection for Summary Visualization

  NSF · $532k · 2020–2026

• NRI-Small: Perceptually Inspired Dynamics for Robot Arm Motion

  NSF · $800k · 2012–2018

• Collaborative Research: Retargetable Images and Video

  NSF · $406k · 2004–2011

Frequent coauthors

• Bilge Mutlu

  73 shared

• Daniel Rakita

  Yale University

  31 shared

• Michael Zinn

  University of Wisconsin–Madison

  29 shared

• Michael Hagenow

  22 shared

• Michael Ashikhmin

  20 shared

• Lucas Kovar

  University of Wisconsin–Madison

  20 shared

• Erik Reinhard

  University of Utah

  20 shared

• Robert G. Radwin

  University of Wisconsin–Madison

  20 shared

Education

• Ph.D., Computer Sciences

  University of Wisconsin, Madison

  1995

• M.S., Computer Sciences

  University of Wisconsin, Madison

  1991

• B.S., Computer Sciences

  University of Wisconsin, Madison

  1989

Awards & honors

• David DeWitt Professor of Computer Sciences
• ACM Fellow
• member of the IEEE Visualization Academy
• IEEE Senior Member
• Amazon Design Scholar

Similar researchers at University of Wisconsin-Madison

• Yong Jae Leeh-90
• Somesh Jhah-69
• AnHai Doanh-56