Timothy Hospedales

Professor of Artificial Intelligence

Institute of Perception, Action and Behaviour
School of Informatics
The University of Edinburgh
t.hospedales at ed.ac.uk

Head of Centre, Samsung AI Research Centre, Cambridge

I am a Professor within IPAB in the School of Informatics at the University of Edinburgh, where I head the Machine Intelligence Research group; ELLIS fellow; and Head of Centre, Samsung AI Research Centre, Cambridge where I also direct the Machine Learning & Data Intelligence Programme.

Previously I was Alan Turing Institute Fellow, Reader ('16-'20) at Edinburgh and Senior Lecturer/Lecturer ('12-16) at QMUL within the Risk and Information Management (RIM) group and Centre for Intelligent Sensing, where I founded the Applied Machine Learning Lab. I recieved my PhD in Neuroinformatics from Edinburgh in 2008, working with Sethu Vijayakumar in the Statistical Machine Learning and Motor Control group, and my BA in Computer Science from the University of Cambridge in 2002.

My research focuses on efficient and robust AI using techniques such as meta-learning and lifelong transfer-learning, in both probabilistic and deep learning contexts. I have looked at a variety of application areas including computer vision, vision and language, reinforcement learning for robot control, finance and beyond.


10/2017: Program co-chair of BMVC2018!

02/2018: Nine papers accepted at CVPR'18!

07/2018: Our Meta Learning Active Learning paper awarded Best Paper Prize at ICML AutoML'18!

08/2018: Congratulations Kunkun Pang! Dynamic Ensemble Active Learning wins Best Student Paper Award @ ICPR2018!

06/2019: Congratulations Carl Allen! Analogies Explained paper wins Honorable Mention @ ICML'19!

08/2019: TuckER model for fast and effective knowledge graph completion accepted at EMNLP2019!

12/2019: Joining T-PAMI as AE!

02/2020: Four papers at CVPR'20!

04/2020: Check out our new Survey on meta-learning!

07/2020: Four papers in ECCV'20!

08/2020: Promoted to Full Professor.

09/2020: Paper in Siggraph Asia 2020!

09/2020: Paper on online meta-RL in NeurIPS'20!

02/2021: Our MetaQDA achieves 3rd place in AAAI Meta-Learning competition.

02/2021: Four papers at CVPR'21!

07/2021: Associate Program Co-Chair of AAAI'22!

11/2021: Tim recognised as an ELLIS fellow.

12/2021: Our PMF places second in NeurIPS Meta-learning competition!

01/2022: Three papers in ICLR'22!

04/2022: Check out our new book, Visual Adaptation in the Deep Learning Era!

05/2022: Check out our SPM tutorial on self-supervision!

01/2023: Five papers at ICLR'23!

07/2023: New multi-task meta-learning benchmark at CVPR23: Meta-Omnium.

09/2023: Best Paper Prize at AutoML'23! Congratulations Linus and Da!

08/2016: Co-chairing BMVA symposium on Transfer Learning in Computer Vision

09/2016: Guest Editor - IET CV - Special Issue on Deep Learning for Computer Vision

10/2016: Keynote on transfer learning with paramaterised tasks and domains at ECCV 2016 TASK-CV workshop!

10/2016: Giving the zero-shot learning part of ACM Multimedia '16 tutorial Emerging topics in learning from noisy and missing data.

10/2017: Program co-chair of BMVC 2018

11/2017: Hiring one 3.5yr Postdoc on Machine Learning for Robotics & AI. Closing date: 14th Dec 2017. Advert. Apply.

03/2018: Keynote at Vision & Language Conference.

03/2018: Talk on sanity guarantees for deep learning at Deep Learning in Finance Summit London 2018.

09/2018: Hiring a postdoc research associate on deep learning, 2.5yr post, international applicants welcome. Closing date 4th October 2018. Apply.

05/2019: Teaching the Advanced Topics in Deep Learning Summer School, May 2019 in Verona.

05/2020: Keynote @ DIRA workshop in CVPR2020

08/2020: Tutorial on Domain Adaptation @ ECCV2020

09/2020: Open positions for PhD and Postdoc researchers.

10/2020: Check out our Special Issue on Learning with Fewer Labels in IEEE PAMI.

12/2020: Invited Talk at the Meta Learning Workshop in NeurIPS-20

02/2021: Co-organizing learning-to-learn workshop at ICLR-21! Submission deadline Feb 26.

02/2021: Co-organizing learning-to-learn workshop at ICRA-21!

01/2022: Talk at IBM Neuro-symbolic AI workshop 2022.

10/2022: Seminar on meta-learning at Deep Learn 2022 Summer School.

12/2022: Co-organizing Domain Generalisation Workshop at ICLR23.

06/2023: Keynote at IbPRIA 2023.

Sample code for active learning & discovery (PAKDD'11,TKDE'11,ECCV'12)

Unsegmented sports news dataset available (ICDM'11). Includes reference extracted STIP features and ground-truth multi-label annotation.

Sample code for MCTM (ICCV'09, IJCV'11) and WSJTM (ACCV'10, PAMI'11)

Attribute Dataset for Heterogeneous Face Recognition (ACCV'14)

Code and Data for BMVC04 Fine-grained SBIR paper.

02/2015: Code for the famous ELF descriptor in re-id. First ever public release!

06/2015: Ground-truth Anomaly Annotation for ICCV'09 and IJCV'11 papers.

07/2015: Code for Sketch-a-Net BMVC'15 paper.

09/2015: Code for our ICLR'15 Multi-Task/Multi-domain paper

09/2015: Multi-camera video surveillance dataset from our TCSVT'15 paper

03/2016: 800K image+social metadata dataset from our ACM DH'16 paper

05/2017: LIMA dataset for re-id and tracking at CVPR'17

01/2018: Demo code CVPR'18 learning to compare few-shot learning

07/2018: Neural Decision tree Example Code.

05/2019: Hypernetwork KB Completion Code.

05/2019: Tucker Decomposition Link Prediction Code.

06/2021: How Well do Self-Supervised models Transfer? Code.

12/2021: Meta Channel Coding. A new benchmark for meta-learning!

04/2022: P>M>F: State of the art Few-Shot Learning Code.

09/2022: Meta-audio blog and code. Check if your meta-learner works outside of computer vision!

07/2023: Meta-Omnium multi-task meta-learning benchmark.

Visit my Google Scholar Profile.



















Meta Learning

Lifelong and Transfer Learning

Zero-Shot Learning

Learning for Robotics and Control


Active Learning

Visual Attribute Learning

Deep Learning

Machine Learning

Zero-shot learning, Vision and Language


Computer Vision


Senior Members

  • Machine Learning, Finance
  • Reliable Machine Learning, ML Theory
Dr. Da Li
  • Meta-Learning, Domain Generalisation

PhD Students

Raman Dutt (CDT)
  • Medical Image Analysis, Cross-Domain
Yongshuo Zhong (CDT)
  • Multi-view self-supervision
Ruchika Chavhan
  • Multi-Task Meta-Learning
  • Meta-Learning, Data Distillation, Cross-Domain
  • Meta-Learning, Self-Supervised Learning
Panagiotis Eustratiadis
  • Reinforcement Learning, Adversarial Robustness
Boyan Gao
  • Unsupervised Deep Learning, Meta-Learning


  • Few-shot regression.

PhD Students, Co-supervisor

  • Multi-View Learning. With Hakan Bilen.
  • Meta-learning. Anomaly detection.

Postdocs, Alum

Ryan Layne
  • Person Re-identification, Transfer Learning
Dr. Chenyang Zhao
  • Lifelong learning for Robotics and Control
Kunkun Pang
  • Active Learning & Perception

PhD Students, Graduated

Carl Allen (2017-21)
Theoretical Analysis of Embedding Models

Yongxin Yang (2013-17)
Lifelong Learning, Deep Learning
→ Postdoc @ Edinburgh → Faculty @ Surrey → Faculty @ QMUL
Ivana Balazevic (2017-21)
Knowledgebases, Link Prediction
→ Deep Mind
Tanmoy Mukherjee (2015-20)
Vision & Language, Semantic Embeddings
→ Postdoc at University of Cambridge
Kunkun Pang (2015-19)
Active Perception, Active Learning
→ Postdoc at University of Edinburgh
Marija Jegorova (2016-20)
Generative Adversarial Networks
→ Postdoc at University of Edinburgh → FAIR
Chenyang Zhao (2015-19)
Lifelong learning for Robotics and Control
→ Postdoc at Edinburgh
Xueting Zhang (2017-21)
Few Shot, Continual Learning
Miguel Jaques (2018-21)
Physics Informed Deep Networks
Transfer Learning, Zero-shot Learning, Attributes, Learning to Rank
→ Faculty at Fudan University, PRC
Ryan Layne
Person Re-identification, Attributes
Yun Zhou
Transfer Learning in Bayesian Networks
→ Goldsmiths → Faculty at NUDT, PRC
Weakly Supervised Learning, Attributes
→ Imperial College London
Action Recognition, Transfer Learning
→ National University of Singapore
Cross-domain, Sketches, Face Recognition. With Yi-Zhe Song.

Past Visitors

  • Structured Video Analysis, Generative Adversarial Nets
  • Person Identification
  • Generative, Graphical Models in Vision
  • Domain Generalisation. Deep RL.


2018/19: Semester A
  • Introduction to Vision and Robotics, Image and Vision Computing
2017/18: Semester A
  • Introduction to Vision and Robotics, Image and Vision Computing
2016/17: Semester B
  • Inf1 - Object Oriented Programming
2015/16: Semester A
  • Data Mining, Machine Learning, Procedural Programming
2014/15: Semester A
  • Data Mining, Procedural Programming
2014/15: Semester B
  • Java (BUPT)
2013/14: Semester A
  • Data Mining
2013/14: Semester B
  • Semantic Web
2012/13: Semester A
  • Information Systems
2012/13: Semester B
  • Semantic Web