World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
10141
World Ranking
9552
National Ranking
4050

Research.com Recognitions

  • 2010 - ACM Fellow For contributions to the design and implementation of object-oriented programming languages
  • 2006 - ACM Distinguished Member

Overview

David Ungar is affiliated with Apple in the United States and has contributed to the field of medicine, particularly oncology. Their research work predominantly covers areas such as CAR-T cell therapy research, lymphoma diagnosis and treatment, monoclonal and polyclonal antibodies research, HER2/EGFR in cancer research, viral infectious diseases and gene expression in insects, chronic lymphocytic leukemia research, and toxin mechanisms and immunotoxins.

Ungar's publication record includes recent work focused on treatments for relapsed or refractory diffuse large B-cell lymphoma (DLBCL). Notable papers include:

  • Phase 3 randomized study of loncastuximab tesirine in combination with rituximab (Lonca-R) versus immunochemotherapy in patients with R/R DLBCL (LOTIS-5), 2022, Journal of Clinical Oncology
  • Long-term survival projections of loncastuximab tesirine-treated patients in relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL), 2022, Journal of Clinical Oncology
  • ABCL-334 Long-Term Survival Projections of Loncastuximab Tesirine-Treated Patients in Relapsed or Refractory Diffuse Large B-Cell Lymphoma, 2022, Clinical Lymphoma Myeloma & Leukemia
  • Poster: ABCL-334 Long-Term Survival Projections of Loncastuximab Tesirine-Treated Patients in Relapsed or Refractory Diffuse Large B-Cell Lymphoma, 2022, Clinical Lymphoma Myeloma & Leukemia

Ungar frequently collaborates with several researchers, including Mehdi Hamadani, Carmelo Carlo-Stella, Christopher N. Graham, Laura Liao, and Kate Zhang, indicating a tendency towards interdisciplinary research partnerships.

The primary venues for Ungar's publications are the Journal of Clinical Oncology and Clinical Lymphoma Myeloma & Leukemia, reflecting a focus on clinical and translational oncology research.

Ungar's recognized fields of study encompass oncology, pathology and forensic medicine, radiology, nuclear medicine and imaging, molecular biology, and genetics. The variety of subfields indicates a broad expertise in medical science and cancer research.

Awards received by Ungar include the ACM Fellow honor in 2010 for contributions to the design and implementation of object-oriented programming languages, and the status of ACM Distinguished Member awarded in 2006.

Best Publications

  • Self: The power of simplicity

    David Ungar;Randall B. Smith

  • Generation Scavenging: A non-disruptive high performance storage reclamation algorithm

    David Ungar

  • Generation Scavenging: A non-disruptive high performance storage reclamation algorithm

    Unknown

  • Optimizing Dynamically-Typed Object-Oriented Languages With Polymorphic Inline Caches

    Urs Hölzle;Craig Chambers;David Ungar

  • An efficient implementation of SELF a dynamically-typed object-oriented language based on prototypes

    C. Chambers;D. Ungar;E. Lee

  • Debugging optimized code with dynamic deoptimization

    Urs Hölzle;Craig Chambers;David Ungar

  • Optimizing dynamically-dispatched calls with run-time type feedback

    Urs Hölzle;David Ungar

  • Animation: from cartoons to the user interface

    Bay-Wei Chang;David Ungar

  • The design and evaluation of a high performance Smalltalk system

    David Michael Ungar

  • Mirrors: design principles for meta-level facilities of object-oriented programming languages

    Gilad Bracha;David Ungar

  • Making pure object-oriented languages practical

    Craig Chambers;David Ungar

  • Customization: optimizing compiler technology for SELF, a dynamically-typed object-oriented programming language

    C. Chambers;D. Ungar

  • Organizing programs without classes

    David Ungar;Craig Chambers;Bay-Wei Chang;Urs Hölzle

  • Iterative type analysis and extended message splitting; optimizing dynamically-typed object-oriented programs

    Craig Chambers;David Ungar

  • Architecture of SOAR: Smalltalk on a RISC

    David Ungar;Ricki Blau;Peter Foley;Dain Samples

  • Tenuring policies for generation-based storage reclamation

    David Ungar;Frank Jackson

  • A third-generation SELF implementation: reconciling responsiveness with performance

    Urs Hölzle;David Ungar

  • Method and apparatus of translating and executing native code in a virtual machine environment

    David Ungar

  • Reconciling responsiveness with performance in pure object-oriented languages

    Urs Hölzle;David Ungar

  • Method and apparatus for optimizing exact garbage collection using a bifurcated data structure

    Mario I. Wolczko;David M. Ungar

  • Programming as an Experience: The Inspiration for Self

    Randall B. Smith;David Ungar

Frequent Co-Authors

Craig Chambers
Craig Chambers Google (United States)
Urs Hölzle
Urs Hölzle Google (United States)
James R. Kraemer
James R. Kraemer IBM (United States)
Robert R. Friedlander
Robert R. Friedlander IBM (United States)
David A. Patterson
David A. Patterson University of California, Berkeley
Mark N. Wegman
Mark N. Wegman IBM (United States)
Edward A. Lee
Edward A. Lee University of California, Berkeley
David Grove
David Grove IBM (United States)

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