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Xilu Wang

Lecturer / Researcher  ·  University of Surrey
Trustworthy AI & Data-Driven Optimization

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My research focuses on fair, privacy-preserving, and trustworthy machine learning and data-driven optimization, with growing interests in the efficiency of large language and audio models.

21
Journal Papers
10
Conference Papers
480+
Top Citations
5
Funded Projects
4
PhD/Master Students

Education

PhD, Computer Science
University of Surrey, Guildford, UK
Jan 2019 – Jun 2022  ·  Supervisors: Prof. Yaochu Jin; Dr. Sebastian Schmitt & Dr. Markus Olhofer (Honda Research Institute Europe)
M.S., Electronics and Communication Engineering
Xidian University, Xi'an, China
Sep 2016 – Jun 2019  ·  GPA 4/5 Rank 35/590 National Scholarship 2019
B.S., Electronic and Information Engineering
Harbin Institute of Technology, China
Sep 2012 – Jul 2016  ·  GPA 4/5

Research Interests

🎯

Multi-Objective Optimization

Bayesian optimization, surrogate-assisted evolutionary algorithms, expensive black-box optimization

🔒

Trustworthy AI

Federated learning, fair and privacy-preserving optimization, differential privacy

🧠

Efficient LLMs

Parameter-efficient training, speculative decoding, large language model optimization

🎵

Audio AI

Audio deepfake detection, audio language models, self-evolving audio systems

Funded Projects

Federated Self-supervised Learning Framework for Diatom Classification
Surrey–Adelaide Partnership Fund  ·  £10,000
Apr 2025 – Feb 2026

Novel federated self-supervised learning combining privacy-preserving federated learning with self-supervised pre-training for marine diatom classification. Role: Principal Investigator

Fair, Privacy-Preserving, and Trustworthy ML & Data-Driven Optimization
University of Surrey  ·  £20,000
Jan 2019 – Apr 2022

Development and application of fair, privacy-preserving, and trustworthy machine learning and data-driven optimization algorithms. Role: Principal Investigator

Surrogate-Based Runtime Difference Mitigation in Asynchronous Multi-Disciplinary Search
Honda Research Institute Europe  ·  £107,099
Jan 2019 – Apr 2022

Novel surrogate-based approaches for multi-objective car design optimization with asynchronous evaluations and varying time complexities. Role: Lead Researcher

Multi-Source Side Information Fusion Assisted Bayesian Optimization
Royal Society  ·  £12,000  ·  Grant IEC\NSFC\170279
Jan 2019 – Mar 2022

Knowledge transfer and expert insights for turbine engine energy efficiency optimization using ML surrogates for expensive numerical simulations. Role: Algorithm Lead

Autocrane: Level 3 Autonomy on a Log Yard Crane
PSIORI GmbH & Bielefeld University  ·  €16,800
Jan 2023 – Sep 2023

AI system for 350-ton timber loading crane automation. Federated learning for real-time object detection and segmentation (unique worldwide scale). Role: Co-PI & Supervisor

Publications

Recent Advances in Bayesian Optimization
Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
ACM Computing Surveys, 55(13s):1–36, 2023  ·  IF 16.6
JCR Q1480 citations
Evolutionary Optimization of High-Dimensional Multiobjective and Many-Objective Expensive Problems Assisted by a Dropout Neural Network
Dan Guo, Xilu Wang, Kailai Gao, Yaochu Jin, et al.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(4):2084–2097, 2021  ·  IF 8.7
JCR Q1161 citations
An Adaptive Bayesian Approach to Surrogate-Assisted Evolutionary Multi-Objective Optimization
Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Information Sciences, 519:317–331, 2020  ·  IF 8.1
JCR Q1148 citations
A Modified Whale Optimization Algorithm for Large-Scale Global Optimization Problems
Yongjun Sun, Xilu Wang, Yahuan Chen, Zujun Liu
Expert Systems with Applications, 114:563–577, 2018  ·  IF 8.5
JCR Q1253 citations
LOST: Low-rank and Sparse Pre-training for Large Language Models
Jiaxi Li, Lu Yin, Li Shen, ..., Xilu Wang
arXiv preprint arXiv:2508.02668, 2025
A Large-Scale Expensive Optimization Algorithm with a Multi-View Synthetic Sampling
Kaili Zhao, Xilu Wang, Chaoli Sun, Yaochu Jin
IEEE Transactions on Evolutionary Computation, 2025  ·  IF 14.3
JCR Q1
Efficient Large-Scale Expensive Optimization via Surrogate-Assisted Sub-Problem Selection
Kaili Zhao, Xilu Wang, Chaoli Sun, Yaochu Jin, Asad Hayat
IEEE Transactions on Evolutionary Computation, 2025  ·  IF 14.3
JCR Q1
Towards Fairness-Aware Multi-Objective Optimization
Guo Yu, Lianbo Ma, Xilu Wang, Wei Du, Wenli Du, Yaochu Jin
Complex & Intelligent Systems, 11(1):50, 2025  ·  IF 14.3
JCR Q1
Distilling Ensemble Surrogates for Federated Data-Driven Many-Task Optimization
Xilu Wang, Yaochu Jin
IEEE Transactions on Evolutionary Computation, 2024  ·  IF 14.3
JCR Q1
DP-FSAEA: Differential Privacy for Federated Surrogate-Assisted Evolutionary Algorithms
Yuping Yan, Xilu Wang, Péter Ligeti, Yaochu Jin
IEEE Transactions on Evolutionary Computation, 2024  ·  IF 14.3
JCR Q1
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023  ·  IF 8.7
JCR Q1
Federated Many-Task Bayesian Optimization
Hangyu Zhu, Xilu Wang, Yaochu Jin
IEEE Transactions on Evolutionary Computation, 2023  ·  IF 14.3
JCR Q1
Personalized Bayesian Optimization for Noisy Problems
Xilu Wang, Yaochu Jin
Complex & Intelligent Systems, 2023  ·  IF 5.8
JCR Q2
Secure Federated Evolutionary Optimization — A Survey
Qiqi Liu, Yuping Yan, Yaochu Jin, Xilu Wang, et al.
Engineering, 2023  ·  IF 12.8
JCR Q1
Transfer Learning Based Surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Different Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer, Richard Allmendinger
Knowledge-Based Systems, 227:107190, 2021  ·  IF 8.8
JCR Q1
Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Evolutionary Computation, 30(2):221–251, 2022  ·  IF 6.8
JCR Q1
Knowledge Transfer Based on Particle Filters for Multi-objective Optimization
Xilu Wang, Yaochu Jin
Mathematical and Computational Applications, 28(1), 2023  ·  IF 1.9
JCR Q2
FocusNet-CL: A Deep Learning Approach for Clarity Assessment of Laser Direct Writing Blank Samples
Liang He, Xilu Wang, Jingsong Wei
Optics Communications, 2025
Efficient Federated Bayesian Optimization with Symbolic Regression Model
Xilu Wang, Kaifeng Yang, Peng Liao, Mengxuan Zhang, Yaochu Jin
IEEE Congress on Evolutionary Computation (CEC), 2025
Conference
Surrogate-Assisted Evolutionary Neural Architecture Search with Architecture Knowledge Transfer
Peng Liao, Xilu Wang, Yaochu Jin, Chaoli Sun, Wenli Du
IEEE Congress on Evolutionary Computation (CEC), 2025
Conference
MO-EMT-NAS: Multi-objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasets
Peng Liao, Xilu Wang, Yaochu Jin, Wenli Du
European Conference on Computer Vision (ECCV), 2024
Conference
A Graph Neural Network Assisted Evolutionary Algorithm for Expensive Multi-Objective Optimization
Xiangyu Wang, Xilu Wang, Yaochu Jin, Rückert Ulrich
IEEE Congress on Evolutionary Computation (CEC), 2024
Conference
Federated Bayesian Optimization for Privacy-Preserving Neural Architecture Search
Shiqing Liu, Xilu Wang, Yaochu Jin
IEEE Congress on Evolutionary Computation (CEC), 2023
Conference
Transfer Learning for Gaussian Process Assisted Evolutionary Bi-objective Optimization for Objectives with Different Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Genetic and Evolutionary Computation Conference (GECCO), 2020
Conference29 citations
Overview Members News Activities
TAIL Lab Logo

Trustworthy AI Lab

We are a research group at the University of Surrey dedicated to building fair, privacy-preserving, and reliable AI systems. Our work spans data-driven optimization, federated learning, efficient large language models, and audio AI. We collaborate with leading institutions including Honda Research Institute Europe, Bielefeld University, and the University of Adelaide.

Research Directions

🔒

Privacy & Fairness

Federated learning, differential privacy, fairness-aware optimization

🎯

Expensive Optimization

Surrogate-assisted evolutionary algorithms, Bayesian optimization, multi-task learning

🧠

Efficient AI Models

Parameter-efficient training for LLMs, speculative decoding, model compression

🎵

Audio AI

Deepfake detection, self-evolving audio systems, audio language models

Lab Photos

🏛️ Lab / Office Replace with lab1.jpg
👥 Team Photo Replace with lab2.jpg
🎤 Conference / Talk Replace with lab3.jpg
🔬 Research Activity Replace with lab4.jpg

We are recruiting!

We welcome motivated PhD students, postdocs, and visiting researchers interested in trustworthy AI and data-driven optimization. Please send your CV and research statement to wangxilu@surrey.ac.uk.

Lab Members

Principal Investigator

📷
Xilu Wang
Principal Investigator
University of Surrey

PhD Students

📷
Jiaxi Li
PhD Student
Efficient Machine Learning · May 2024–

Alumni

SL
Scheu Louis Lamar
Master (Alumni)
Federated Learning in Object Detection · Bielefeld 2023–2024
AA
Abel Alexander
Master (Distinction, Alumni)
Bayesian Algorithms · Surrey 2021–2022 · Now at Bank of America
AK
Adrian Kruse
Bachelor (Alumni)
Bayesian Multi-objective Optimization · Bielefeld 2023

Lab News

Apr
2025
New project funded: Federated Self-supervised Learning for Diatom Classification
Surrey–Adelaide Partnership Fund awarded £10,000 for collaboration between University of Surrey and University of Adelaide.
2025
Paper accepted: LOST — Low-rank and Sparse Pre-training for LLMs
New work on efficient LLM pre-training published as arXiv preprint.
2025
Two papers accepted at IEEE CEC 2025
Papers on Federated Bayesian Optimization and Surrogate-Assisted NAS accepted.
Jul
2024
Invited Talk at IEEE WCCI 2024, Yokohama
Presented "Bayesian Optimization for Expensive Multi-objective Heterogeneous Optimization" and chaired two sessions.
2024
Paper accepted at ECCV 2024
MO-EMT-NAS: Multi-objective Continuous Transfer of Architectural Knowledge accepted at the 18th European Conference on Computer Vision.
Jun
2023
Outstanding Presentation Award at ABCP 2023
Received award for presentation on "Federated Many-Task Bayesian Optimization" at the Annual Conference PhD & Postdoc Forum.

Lab Activities

Conference Organisation

Chair — Special Session on ML Assisted Heuristics for Combinatorial Optimization
IEEE World Congress on Computational Intelligence 2024, Yokohama, Japan
Chair — Workshop on Privacy-Preserving and Fairness-Aware Optimization
IEEE World Congress on Computational Intelligence 2024, Yokohama, Japan
Chair — Special Session on Security, Privacy Protection, and Fairness Optimization
IEEE Congress on Evolutionary Computation 2023, Chicago, USA
Chair — Workshop on Data-Driven Optimization in Machine Learning
AJCAI Australasian Joint Conference on Artificial Intelligence 2023
Publication Chair — 5th International Conference on Data-Driven Optimization of Complex Systems (DOCS'2023)
Tianjin, China, 2023

Editorial Roles

Guest Editor — Special Issue on Data-Driven Evolutionary Computation
ACM Transactions on Evolutionary Learning and Optimization · 15 submissions managed

Invited Talks

Bayesian Optimization for Expensive Multi-objective Heterogeneous Optimization
IEEE World Congress on Computational Intelligence 2024 · Jul 2024
Federated Many-Task Bayesian Optimization Outstanding Presentation Award
ABCP 2023 Annual Conference (PhD & Postdoc Forum, AI & Cyber Security Track) · Jun 2023
Knowledge Transfer Based on Particle Filters for Multi-objective Optimization
PPSN 2022 Workshop on Parallelism in Knowledge Transfer · Sep 2022
Transfer Learning for Gaussian Process Assisted Evolutionary Bi-objective Optimization
Genetic and Evolutionary Computation Conference (GECCO) 2020 · Jul 2020

Teaching Experience

Apr 2022 – Apr 2024
Scientific Researcher
Bielefeld University — NICE Research Group (Prof. Yaochu Jin)
Apr – Jul 2023
Tutor — Evolutionary Optimization and Learning (392124)
Bielefeld University · 20 students
Apr – Aug 2022
Teaching Assistant — Privacy-Preserving Federated Learning (392274)
Bielefeld University
Sep – Dec 2022
Teaching Assistant — Evolutionary Optimization and Learning (392124)
Bielefeld University
Sep – Dec 2020
Teaching Assistant — Programming for Computational Intelligence (COM3013)
University of Surrey · 35 students

Student Supervision

Jiaxi Li  PhD · Ongoing
Efficient Machine Learning · University of Surrey · May 2024–
Scheu Louis Lamar  Master
Federated Learning in Object Detection · Bielefeld University · Sep 2023 – Sep 2024
Adrian Kruse  Bachelor
Bayesian Multi-objective Evolutionary Optimization · Bielefeld University · Jan – Sep 2023
Abel Alexander  Master · Distinction
A Comparative Study of Bayesian Algorithms on Expensive Optimization · University of Surrey · Sep 2021 – Sep 2022 · Now at Bank of America

Leadership & Service

Vice Chair — Task Force: Data-Driven Evolutionary Optimization of Expensive Problems
The Evolutionary Computation Technical Committee (ECTC)
Chair — Special Session on Machine Learning Assisted Heuristics for Combinatorial Optimization
IEEE World Congress on Computational Intelligence 2024, Yokohama, Japan · 10+ papers reviewed and presented
Chair — Workshop on Privacy-Preserving and Fairness-Aware Optimization
IEEE World Congress on Computational Intelligence 2024, Yokohama, Japan
Chair — Special Session on Security, Privacy Protection, and Fairness Optimization
IEEE Congress on Evolutionary Computation 2023, Chicago, USA
Guest Editor — Special Issue on Data-Driven Evolutionary Computation
ACM Transactions on Evolutionary Learning and Optimization · 15 submissions
Chair — Workshop on Data-Driven Optimization in Machine Learning
AJCAI Australasian Joint Conference on Artificial Intelligence 2023
Publication Chair — 5th International Conference on Data-Driven Optimization of Complex Systems (DOCS'2023)
Tianjin, China

Invited Talks

Jul 2024
Bayesian Optimization for Expensive Multi-objective Heterogeneous Optimization
IEEE World Congress on Computational Intelligence 2024
Jun 2023
Federated Many-Task Bayesian Optimization  Outstanding Presentation Award
ABCP 2023 Annual Conference — PhD & Postdoc Forum (AI & Cyber Security Track)
Sep 2022
Knowledge Transfer Based on Particle Filters for Multi-objective Optimization
PPSN 2022 Workshop on Parallelism in Knowledge Transfer
Jul 2020
Transfer Learning for Gaussian Process Assisted Evolutionary Bi-objective Optimization
Genetic and Evolutionary Computation Conference (GECCO) 2020