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
- Aaron Zhao (Principle Investigator)
- Cheng Zhang (PhD Student, co-supervised with Prof. George Constantinides)
- Victor Zhao (PhD Student, co-supervised with Prof. Pietro Lio)
- Zehui Li (PhD Student, co-supervised with Prof. Guy-Bart Stan)
- Mingzhu Shen (PhD Student, co-supervised with Prof. Christos Bouganis)
- Pedro Gimense (PhD Student, co-supervised with Prof. George Constantinides)
- Tim Clifford, starting in Jan (PhD Student, co-supervised with Prof. Ross Anderson and Prof. Robert Mullins)
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.
Project Proposals
This is an incomplete list of possible projects for summer/PartII/PartIII/Mphil/MSc/FYP students [last update: 26th Jan]. 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:
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Note: For Imperial FYP candidates, project ideas would be updated in late September, however, I am more than happy to discuss self-proposed projects with you:
- Taken
Efficient Self-supervised Learning - Taken
Sparse training with dynamic lottery ticket - Taken
Wide Vision Transformers - Taken
Knowledge Distillation for Vision Transformers - Taken
Model backdoors in Multi-Agent RL - Taken
Stable and Diverse DNA sequence design using Diffusion Models (Co-supervised with Prof. Guy-Bart Stan’s team as part of the AI4EB program) - Taken
Transformer Quantization Framework - Taken
Low precision Vision Transformers - Taken
Neural weather modeling using Graph Representational Learning - Taken
Continuing the Art of Prompting in the Vision Land - Taken
Dataflow Federated Learning - [Taken]
Tricking GPT3 using prompt chaining - [Taken]
GNN Expressiveness and Graph Generative Models - High priority Constrained Hardware Accelerator Design for Machine Learning (Co-supervised with Jianyi Cheng)
- Feature-level Augmentation
- Boosting Model Compression with Unlabelled Data
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 2022/2023
- 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
- Tim 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 2023
MiliPoint: A Point Cloud Dataset for mmWave Radar ; H Cui, S Zhong, J Wu, Z Shen, N Dahnoun, Y Zhao (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)