World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
5416
World Ranking
10817
National Ranking
681

Overview

Adrian Weller is affiliated with the University of Cambridge in the United Kingdom and primarily focuses on research within the field of Computer Science. Their work encompasses a broad range of topics within artificial intelligence and its applications.

The main areas of study in their research include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Safety Research
  • Signal Processing
  • Radiology, Nuclear Medicine and Imaging

Adrian Weller has contributed extensively to several prominent topics, with particular attention to:

  • Explainable Artificial Intelligence (XAI)
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Data Classification
  • Domain Adaptation and Few-Shot Learning
  • Ethics and Social Impacts of AI
  • Topic Modeling
  • Artificial Intelligence in Healthcare and Education

The scientist has published extensively, with notable papers including:

  • "Leveraging Data Science to Combat COVID-19: A Comprehensive Review," 2020, IEEE Transactions on Artificial Intelligence
  • "Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims," 2020, Data Archiving and Networked Services (DANS)
  • "How transparency modulates trust in artificial intelligence," 2022, Patterns
  • "Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims," 2020, arXiv (Cornell University)
  • "Rethinking Attention with Performers," 2020, arXiv (Cornell University)

Frequent co-authors include:

  • Umang Bhatt
  • Katherine M. Collins
  • Krzysztof Choromański
  • Valerii Likhosherstov
  • Mateja Jamnik

Their works appear regularly in several well-known publication venues, such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Apollo (University of Cambridge)
  • Patterns
  • Nature Machine Intelligence

Adrian Weller's research output reflects a sustained focus on advancing understanding across computer science, particularly artificial intelligence, with emphasis on trustworthy AI, machine learning techniques, and the social and ethical implications of technology.

Best Publications

  • Explainable machine learning in deployment

    Umang Bhatt;Alice Xiang;Shubham Sharma;Adrian Weller

  • A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices

    Till Speicher;Hoda Heidari;Nina Grgic-Hlaca;Krishna P. Gummadi

  • Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

    Nina Grgic-Hlaca;Elissa M. Redmiles;Krishna P. Gummadi;Adrian Weller

  • Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning

    Rowan McAllister;Yarin Gal;Alex Kendall;Mark van der Wilk

  • Rethinking Attention with Performers

    Krzysztof Marcin Choromanski;Valerii Likhosherstov;David Dohan;Xingyou Song

  • Leveraging Data Science to Combat COVID-19: A Comprehensive Review

    Siddique Latif;Muhammad Usman;Sanaullah Manzoor;Waleed Iqbal

  • Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning

    Nina Grgic-Hlaca;Muhammad Bilal Zafar;Krishna P. Gummadi;Adrian Weller

  • Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty

    Umang Bhatt;Javier Antorán;Yunfeng Zhang;Q. Vera Liao

  • Transparency: Motivations and Challenges.

    Adrian Weller;Adrian Weller

  • From Parity to Preference-based Notions of Fairness in Classification

    Muhammad Bilal Zafar;Isabel Valera;Manuel Gomez Rodriguez;Krishna P. Gummadi

  • Towards Principled Disentanglement for Domain Generalization

    Unknown

  • The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making

    Nina Grgić-Hlača;Muhammad Bilal Zafar;Krishna P. Gummadi;Adrian Weller

  • Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

    Miles Brundage;Shahar Avin;Jasmine Wang;Haydn Belfield

  • Rethinking Attention with Performers

    Krzysztof Choromanski;Valerii Likhosherstov;David Dohan;Xingyou Song

  • Challenges for Transparency

    Adrian Weller

  • Evaluating and Aggregating Feature-based Model Explanations

    Umang Bhatt;Umang Bhatt;Adrian Weller;Adrian Weller;José M. F. Moura

  • Synthetic Data -- what, why and how?

    Unknown

  • Structured Evolution with Compact Architectures for Scalable Policy Optimization

    Krzysztof Choromanski;Mark Rowland;Vikas Sindhwani;Richard E. Turner

  • One-Network Adversarial Fairness

    Tameem Adel;Isabel Valera;Zoubin Ghahramani;Adrian Weller

  • Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

    Xiang Bai;Hanchen Wang;Liya Ma;Yongchao Xu

  • “Explaining” machine learning reveals policy challenges

    Diane Coyle;Adrian Weller;Adrian Weller

  • You Shouldn't Trust Me: Learning Models Which Conceal Unfairness from Multiple Explanation Methods.

    Botty Dimanov;Umang Bhatt;Mateja Jamnik;Adrian Weller

  • Discovering Interpretable Representations for Both Deep Generative and Discriminative Models

    Tameem Adel;Zoubin Ghahramani;Adrian Weller

  • Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

    Nina Grgić-Hlača;Elissa M. Redmiles;Krishna P. Gummadi;Adrian Weller

  • Getting a CLUE: A Method for Explaining Uncertainty Estimates

    Javier Antoran;Umang Bhatt;Tameem Adel;Adrian Weller

  • Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers

    Krzysztof Choromanski;Valerii Likhosherstov;David Dohan;Xingyou Song

  • Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty

    Umang Bhatt;Yunfeng Zhang;Javier Antorán;Q. Vera Liao

Frequent Co-Authors

Krishna P. Gummadi
Krishna P. Gummadi Max Planck Institute for Software Systems
Vikas Sindhwani
Vikas Sindhwani Google (United States)
Tony Jebara
Tony Jebara Columbia University
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Jinwoo Shin
Jinwoo Shin Korea Advanced Institute of Science and Technology
Richard E. Turner
Richard E. Turner University of Cambridge
Michael Chertkov
Michael Chertkov University of Arizona
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Kush R. Varshney
Kush R. Varshney IBM (United States)

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