Arslan Chaudhry
email: arslanch@deepmind.com

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I am a Research Scientist at DeepMind in Mountain View. My research focus is on machine learning models that can accrue knowledge over time and in doing so become more sample and compute efficient. Towards this, I study continual-, transfer-, meta- and multitask learning. I did my Phd in Machine Learning with Prof. Philip H. S. Torr at the University of Oxford as a Rhodes Scholar. My Phd thesis was continual learning for efficient machine learning. Before that, I studied Electrical Engineering at the University of Engineering and Technology, Lahore.

• Research Scientist, Google DeepMind, Mountain View (Apr 2021 - Present)
  
  • Adaptivity and Reliability Team (ART).
  • Working on multimodal foundation models.
 
• Research Internships (during PhD)
  
  • Google Research, New York (Jun 2020 - Oct 2020)
    • Worked with Srikumar Ramalingam and Andreas Veit.
    • Project: Dataset Efficiency in Deep Models
  • Facebook AI, Menlo Park (Jun 2019 - Sep 2019)
    • Worked with David Lopez-Paz and Albert Gordo
    • Project: Hindsight Experience Replay in Continual Learning
  • Facebook AI Research, London (October 2018 - February 2019)
    • Worked with Marc'Aurelio Ranzato, Marcus Rohrbach and Mohamed Elhoseiny
    • Project: Experience Replay in Continual Learning
  • Facebook AI Research, Menlo Park (Jun 2018 - Sep 2018)
    • Worked with Marc'Aurelio Ranzato, Marcus Rohrbach and Mohamed Elhoseiny
    • Project: Efficient Lifelong Learning
 
• Software Developer (Jun 2013 - Aug 2016), Mentor Graphics, Lahore
  
  • Project: Worked on the design and development of Mentor Embedded Hypervisor..

• Tutorials, Machine Learning, Trinity 2018, Hilary 2020, Stanford House
• Tutorials, Networking, Trinity (2018, 2019), University of Oxford
• Tutorials, Operating Systems, Hilary (2018, 2019), University of Oxford
• Lab, Software Engineering, Hilary (2017, 2018, 2019), University of Oxford
• Teaching Assistant, Operating Systems, Spring 2013, UET

• Continual Learning in Deep Neural Networks, July 2020, KAUST
• Continual Learning in Deep Neural Networks, July 2020, Google Research
• Continual Learning in Deep Neural Networks, April 2020, Google DeepMind
• Machine Learning Introduction, December 2017, UET Lahore

• Conference Reviewer: ICML (2019-2021), NeurIPS (2019-2022), ICLR (2021-2023), CVPR (2019-2020), ICCV (2019), ECCV (2020)
• Journal Reviewer: IEEE TPAMI, TMLR, IEEE Transaction on Multimedia