World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
108802
World Ranking
1627
National Ranking
840

Research.com Recognitions

  • 2017 - Fellow of Alfred P. Sloan Foundation

Overview

Ali Farhadi is a researcher affiliated with the University of Washington in the United States. Their primary domain is Computer Science, with a particular concentration on Computer Vision and Pattern Recognition. Within this domain, Farhadi has contributed extensively in related subfields such as Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Civil and Structural Engineering, and Aerospace Engineering.

Their scholarly output encompasses topics that include Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, Human Pose and Action Recognition, Advanced Neural Network Applications, Topic Modeling, Natural Language Processing Techniques, and Advanced Image and Video Retrieval Techniques.

Frequent collaborators of Ali Farhadi include Aniruddha Kembhavi, Hannaneh Hajishirzi, Aditya Kusupati, Vivek Ramanujan, and Luca Weihs.

Farhadi's publications appear predominantly in the venue arXiv (Cornell University), with additional contributions to Lecture Notes in Computer Science, the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), the Bulletin of the Seismological Society of America, and Leibniz-Zentrum für Informatik (Schloss Dagstuhl).

Selected recent publications illustrate the range of their research:

  • "A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)" (2024), published by Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Automated Indent Scanning: Artificial intelligence assisted scanning of large regular and irregular nanoindentation arrays in scanning electron microscopes" (2024), published in Zenodo (CERN European Organization for Nuclear Research)
  • "Robust fine-tuning of zero-shot models" (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping" (2020), available on arXiv (Cornell University)

In 2017, Ali Farhadi was recognized as a Fellow of the Alfred P. Sloan Foundation.

Best Publications

  • You Only Look Once: Unified, Real-Time Object Detection

    Joseph Redmon;Santosh Divvala;Ross Girshick;Ali Farhadi

  • YOLO9000: Better, Faster, Stronger

    Joseph Redmon;Ali Farhadi

  • YOLOv3: An Incremental Improvement.

    Joseph Redmon;Ali Farhadi

  • XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

    Mohammad Rastegari;Vicente Ordonez;Joseph Redmon;Ali Farhadi;Ali Farhadi

  • Describing objects by their attributes

    Ali Farhadi;Ian Endres;Derek Hoiem;David Forsyth

  • Unsupervised deep embedding for clustering analysis

    Junyuan Xie;Ross Girshick;Ali Farhadi

  • Bidirectional Attention Flow for Machine Comprehension

    Min Joon Seo;Aniruddha Kembhavi;Ali Farhadi;Hannaneh Hajishirzi

  • Target-driven visual navigation in indoor scenes using deep reinforcement learning

    Yuke Zhu;Roozbeh Mottaghi;Eric Kolve;Joseph J. Lim

  • Every picture tells a story: generating sentences from images

    Ali Farhadi;Mohsen Hejrati;Mohammad Amin Sadeghi;Peter Young

  • Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding

    Gunnar A. Sigurdsson;Gül Varol;Xiaolong Wang;Ali Farhadi;Ali Farhadi

  • From Recognition to Cognition: Visual Commonsense Reasoning

    Rowan Zellers;Yonatan Bisk;Ali Farhadi;Yejin Choi

  • HellaSwag: Can a Machine Really Finish Your Sentence?

    Rowan Zellers;Ari Holtzman;Yonatan Bisk;Ali Farhadi

  • Objaverse: A Universe of Annotated 3D Objects

    Unknown

  • AI2-THOR: An Interactive 3D Environment for Visual AI

    Eric Kolve;Roozbeh Mottaghi;Daniel Gordon;Yuke Zhu

  • OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge

    Kenneth Marino;Mohammad Rastegari;Ali Farhadi;Roozbeh Mottaghi

  • Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

    Unknown

  • Recognition using visual phrases

    Mohammad Amin Sadeghi;Ali Farhadi

  • Understanding egocentric activities

    Alireza Fathi;Ali Farhadi;James M. Rehg

  • Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks

    Junyuan Xie;Ross B. Girshick;Ali Farhadi

  • IQA: Visual Question Answering in Interactive Environments

    Daniel Gordon;Aniruddha Kembhavi;Mohammad Rastegari;Joseph Redmon

  • Defending Against Neural Fake News

    Rowan Zellers;Ari Holtzman;Hannah Rashkin;Yonatan Bisk

  • Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

    Jesse Dodge;Gabriel Ilharco;Roy Schwartz;Ali Farhadi

Frequent Co-Authors

Roozbeh Mottaghi
Roozbeh Mottaghi University of Washington
Hannaneh Hajishirzi
Hannaneh Hajishirzi University of Washington
Abhinav Gupta
Abhinav Gupta Carnegie Mellon University
Yejin Choi
Yejin Choi Stanford University
Dieter Fox
Dieter Fox University of Washington
David Forsyth
David Forsyth University of Illinois at Urbana-Champaign
Derek Hoiem
Derek Hoiem University of Illinois at Urbana-Champaign
Ross Girshick
Ross Girshick Facebook (United States)
Xiaolong Wang
Xiaolong Wang University of California, San Diego
Min Sun
Min Sun National Tsing Hua University

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