Welcome to the DeepWok Lab

The DeepWok Lab, is an ML research group led by Dr. Aaron Zhao, where the group members are mainly from Imperial College London and the University of Cambridge.

Members


Student Projects

Each year, we run and supervise a number of students for their undergraduate and master projects at Imperial College London and the University of Cambridge. We also run a great number of summer research internships.

We finished hiring for the 2024 summer

Project Proposals

This is an incomplete list of possible projects for summer/PartII/PartIII/Mphil/MSc/FYP students [last update: 17th Dec 2023]. Some of the links may lead to empty Notion pages; these projects do exist, it’s just taking some time to complete the documents. If you are interested, please email me with your transcript and CV, and the following is the list of our project proposals, this year, because of space limitations, we are only consider 3rd or 4th year students.

Note: For Imperial FYP candidates, I am more than happy to discuss self-proposed projects with you

I am also happy to host self-proposed projects if it matches the Lab’s research interests. Feel free to contact a.zhao@imperial.ac.uk if you would like to do a project with us!

Past and Current Students

Academic Year 2023/2024

  • Henry Li (Summer Research Intern, University of Cambridge)
  • Ben Zhang (Summer Research Intern, University of Cambridge)
  • Harry Langford (Summer Research Intern, University of Cambridge)
  • Sanjit Raman (Summer Research/Teaching Intern, Imperial College London)
  • Kevin Lau (Summer Research/Teaching Intern, Imperial College London)
  • Xiandong Zou (Summer Research Intern, Imperial College London)
  • Roshan Aekote(Summer Research Intern, Imperial College London)
  • Li Wang(MSc project, Imperial College London)
  • Charles Jin(MSc project, Imperial College London)
  • Yichen Li(MSc project, Imperial College London)
  • Przemyslaw Forys (MSc project, Imperial College London)
  • Yuhe Zhang (Final Year Project, Imperial College London)
  • Bryan Tan (Final Year Project, Imperial College London)
  • Balint Szekely (Final Year Project, Imperial College London)
  • Derek Lai (Final Year Project, Imperial College London)
  • Bakhtiar Mohammadzadeh (Final Year Project, Imperial College London)
  • TszHang Wong (Final Year Project, Imperial College London)
  • Ben Zhang (Part II Project, University of Cambridge)
  • Bradley Chen (Part II Project, University of Cambridge)
  • Kate Liang (Part II Project, University of Cambridge)

Academic Year 2022/2023

  • David Gyulamiryan (Summer Research Intern, University of Cambridge)
  • Eduard Burlacu (Summer Research Intern, University of Cambridge)
  • Harry Langford (Summer Research Intern, University of Cambridge)
  • Ben Zhang (Summer Research Intern, University of Cambridge)
  • Leah He (Summer Research Intern, University of Cambridge)
  • Junyi Wu (Summer Research Intern, Imperial College London)
  • Harry Ni (Summer Research Intern, Imperial College London)
  • Xiandong Zou (Summer Research Intern, Imperial College London)
  • Anthony Bolton (Summer Research Intern, Imperial College London)
  • Aaron Thomas (Summer Research Intern, Imperial College London)
  • Sudarshan Sreeram (Summer Research Intern, Imperial College London)
  • Diego Van Overberghe (Summer Research Intern, Imperial College London)
  • Bryan Tan (Summer Research Intern, Imperial College London)
  • TszHang Wong (Summer Research Intern, Imperial College London)
  • Aman Vernekar (Summer Research Intern, University of Cambridge)
  • Eduard Burlacu (Summer Research Intern, University of Cambridge)
  • Harry Langford (Summer Research Intern, University of Cambridge)
  • Haoliang Shang (BEng Project, Imperial College London / ETH Zurich)
  • Jacky Choi (BEng Project, Imperial College London / ETH Zurich)
  • Can Xiao (MSc Project, Imperial College London)
  • Sheng Luo (MSc Project, Imperial College London)
  • Chuiyu Wang (MSc Project, Imperial College London)
  • Pedro Gimense (Final Year Project, Imperial College London)
  • Nickolaos Ilioudis (Final Year Project, Imperial College London)
  • Issa Bqain (Final Year Project, Imperial College London)
  • Tobias Cook (Final Year Project, Imperial College London)
  • Peter Barabas (Final Year Project, Imperial College London)
  • Ritvik Shyam(Final Year Project, Imperial College London)
  • Harry Knighton (Part II project, University of Cambridge)
  • Fredrik Ekholm (Part II project, University of Cambridge)
  • Thomas Yuan (Part II project, University of Cambridge)
  • Kyra Zhou (Part II project, University of Cambridge)

Academic Year 2021/2022

  • Eleanor Clifford (Summer Research Intern, from University of Cambridge)
  • Joseph Rance (Summer Research Intern, from University of Cambridge)
  • Victor Zhao (Summer Research Intern, from University of Cambridge)
  • Skye Purchase (Summer Research Intern, from University of Cambridge)
  • Cindy Wu (Summer Research Intern, from University of Cambridge)
  • Guo Yang (Summer Research Intern, from University of Cambridge)
  • Prisha Satwani (Summer Research Intern, from LSE)
  • Jason Brown (Summer Research Intern, from University of Cambridge)

Publication

Year 2024

Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation; Xiangyu Zhao, Zehui Li, Mingzhu Shen, Guy-Bart Stan, Pietro Lio, Yiren Zhao; European Conference on Artificial Intelligence (ECAI 2024)

Unlocking the Global Synergies in Low-Rank Adapters; Zixi Zhang, Cheng Zhang, Xitong Gao, Robert D. Mullins, George Anthony Constantinides, Yiren Zhao; ICML 2024 Workshop ES-FoMo-II

Optimised Grouped-Query Attention Mechanism for Transformers; Yuang Chen, Cheng Zhang, Xitong Gao, Robert D. Mullins, George Anthony Constantinides, Yiren Zhao; ICML 2024 Workshop ES-FoMo-II

HASS: Hardware-Aware Sparsity Search for Dataflow DNN Accelerators; Z Yu, S Sreeram, K Agrawal, J Wu, A Montgomerie-Corcoran, C Zhang, J Cheng, C Bouganis, Y Zhao; The International Conference on Field-Programmable Logic and Applications (FPL 2024)

LQER: Low-Rank Quantization Error Reconstruction for LLMs; C Zhang, J Cheng, G Constantinides, Y Zhao; International Conference on Machine Learning (ICML 2024)

ImpNet: Imperceptible and Blackbox-undetectable Backdoors in Compiled Neural Networks; E Clifford, I Shumailov, Y Zhao, R Anderson, R Mullins; 2nd IEEE Conference on Secure and Trustworthy Machine Learning (SaTML 2024)

Year 2023

Will More Expressive Graph Neural Networks do Better on Generative Tasks?; X Zou, X Zhao, P Lio, Y Zhao; The Second Learning on Graphs Conference (LOG 2023)

Latent Diffusion Model for DNA Sequence Generation; Z Li, Y Ni, T Huygelen, A Das, G Xia, G Stan, Y Zhao; Conference on Neural Information Processing Systems, AI for Science Workshop (NeurIPS 2023, AI for Science Workshop)

MASE: An Efficient Representation for Software-Defined ML Hardware System Exploration; C Zhang, J Cheng, Z Yu, Y Zhao; Conference on Neural Information Processing Systems, Machine Learning for Systems Workshop (NeurIPS 2023, ML for Systems Workshop)

Dynamic Stashing Quantization for Efficient Transformer Training; G Yang, D Lo, R Mullins, Y Zhao; The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023, findings)

Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?; C Zhang, J Cheng, I Shumailov, G Constantinides, Y Zhao; The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)

MiliPoint: A Point Cloud Dataset for mmWave Radar; H Cui, S Zhong, J Wu, Z Shen, N Dahnoun, Y Zhao; Conference on Neural Information Processing Systems (NeurIPS 2023, Datasets and Benchmarks Track)

Revisiting Structured Dropout; Y Zhao, O Dada, X Gao, RD Mullins; The 15th Asian Conference on Machine Learning (ACML 2023)

Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer; Z Li, A Das, WAV Beardall, Y Zhao, GB Stan; The 2023 ICML Workshop on Computational Biology (ICML-WCB 2023, contributed talk, best paper award)

Revisiting Automated Prompting: Are We Actually Doing Better?; Y Zhou, Y Zhao, I Shumailov, R Mullins, Y Gal; Association for Computational Linguistics 2023 (ACL 2023)

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration; X Zhao, H Stärk, D Beaini, P Liò, Y Zhao; ICLR 2023 - Machine Learning for Drug Discovery workshop (ICLR 2023 MLDD workshop)

Augmentation Backdoors; J Rance, Y Zhao, I Shumailov, R Mullins; ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning (ICLR 2023 BANDS Workshop)

Adaptive Channel Sparsity for Federated Learning under System Heterogeneity; X Gao, D Liao, Y Zhao, C Xu; The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2023)

Architectural Backdoors in Neural Networks; M Bober-Irizar, I Shumailov, Y Zhao, R Mullins, N Papernot; The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2023)

Year 2022

Revisiting Embeddings for Graph Neural Networks; S Purchase, Y Zhao, R Mullins; The First Learning on Graphs Conference (LOG 2022)

Wide Attention Is The Way Forward For Transformers; J R Brown, Y Zhao, I Shumailov, R Mullins; All Things Attention: Bridging Different Perspectives on Attention, Oral, (NeurIPS 2022 Workshop)

DARTFormer: Finding The Best Type Of Attention; J R Brown, Y Zhao, I Shumailov, R Mullins; ICBINB, (NeurIPS 2022 Workshop)

Rapid Model Architecture Adaption for Meta-Learning; Y Zhao, X Gao, I Shumailov, N Fusi, R Mullins; Advances in Neural Information Processing Systems 35 (NeurIPS 2022)

DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning; R Hönig, Y Zhao, R Mullins; International Conference on Machine Learning (ICML 2022)