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Introduction:
The Hong Kong University of Science and Technology (Guangzhou) is actively recruiting global PhD talent for Senior Lecturer and Lecturer positions within its College of Future Technology. This presents a unique opportunity for cybersecurity, IT, and AI professionals to transition into academia. The role requires not only deep technical expertise but also the ability to mentor students through project-based learning in cutting-edge fields like edge computing, AI security, and IoT. Below is a breakdown of the core technical competencies and hands-on tutorials aligned with HKUST(GZ)s research thrusts.
Learning Objectives:
- Understand and demonstrate proficiency in Edge AI and efficient embedded AI systems.
- Master differential privacy techniques and auditing frameworks for AI models.
- Execute federated learning deployments and resource-constrained model optimization.
- Implement cloud and system hardening strategies using CIS benchmarks and zero-trust principles.
- Learn to assess and exploit AI system vulnerabilities through ethical hacking and red teaming.
You Should Know:
- Edge AI & Efficient Embedded AI System Deployment
Edge AI focuses on embedding lightweight artificial intelligence into distributed computing infrastructures to enable low-latency, privacy-preserving intelligence at the network edge. Research at HKUST(GZ)s IoT Thrust emphasizes edge computing, intelligent caching, and resource-efficient machine learning across adaptive sensor networks and IoT devices. Dr. Huangxun Chen, Assistant Professor in IoT Thrust with joint appointment in AI Thrust, conducts research on efficient edge/embedded AI systems and sensing/perception security. She won the Best Paper Award at ACM SIGCOMM 2022 for her work in this domain.
Step-by-step guide: Deploying a quantized DNN on a microcontroller.
1. Set up the environment on Linux:
sudo apt-get update && sudo apt-get install cmake ninja-build python3 -m pip install tensorflow tensorflow-model-optimization
- Train and quantize a model (example using TensorFlow Lite):
import tensorflow as tf converter = tf.lite.TFLiteConverter.from_saved_model(‘model.pb’) converter.optimizations = [tf.lite.Optimize.DEFAULT] tflite_quant_model = converter.convert()
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Compile for ARM Cortex-M using ARM GCC toolchain:
arm-none-eabi-gcc -mcpu=cortex-m4 -mthumb -O2 main.c -o output.elf
4. Flash the microcontroller:
pyocd flash -t <your_mcu> quantized_model.bin
This guide demonstrates adapting a DNN for deployment on constrained hardware — a key skill for mentoring Red Bird MPhil students in project-based learning.
2. Differential Privacy & Algorithmic Synthesis for GNNs
Privacy protection is essential as AI systems process increasingly sensitive data. Differential Privacy (DP) guarantees that algorithm outputs change very little when any single persons data is modified, while still allowing accurate population-level analysis. HKUST(GZ) hosts regular seminars on rigorous approaches to data privacy, including techniques like node-level private learning using subgraph sampling for Graph Neural Networks (GNNs), achieving significantly higher accuracy than prior methods. The university also engages in research projects on continuous graph pattern counting under DP, with public code repositories available.
Step-by-step guide: Implementing node-level DP on a GNN.
1. Install required libraries (Windows/Linux):
pip install torch torchvision torchaudio —index-url https://download.pytorch.org/whl/cu118 pip install dgl torch-geometric opacus
2. Implement subgraph sampling for node-level privacy:
from opacus import PrivacyEngine privacy_engine = PrivacyEngine( model, batch_size=64, sample_size=5000, noise_multiplier=1.0, max_grad_norm=1.0, ) privacy_engine.attach(model)
- Audit privacy leakage by modeling it as a “bits transmission” problem leveraging mutual information to deliver tighter privacy loss estimates compared to classical statistical audits.
4. Clone the reference implementation:
git clone https://github.com/hkustDB/Dynamic-Join.git cd Dynamic-Join/Code python run_dp_gnn.py —dataset cora —epsilon 0.1
3. Federated Learning (FL) & Heterogeneous On-Device Models
Federated Learning is a decentralized machine learning framework enabling multiple clients to collaboratively solve tasks without sharing their raw data. HKUST(GZ)s research includes federated fine-tuning combining LoRA with FL to enable collaborative fine-tuning of global models with edge devices, leveraging distributed data while ensuring privacy. The university has developed frameworks like FedZKT (zero-shot knowledge transfer for heterogeneous models) and GPT-FL (integrating foundational models into edge-device training pipelines).
Step-by-step guide: Implementing FedZKT with heterogeneous devices.
1. Launch the coordinator on the central server:
python server.py —rounds 100 —clients 5 —model resnet18
2. On each edge device, start local training:
from fedzkt import HeterogeneousClient client = HeterogeneousClient(device_id=1, local_model=‘mobilenet_v2’) client.load_dataset(path=‘./client1_data’) client.train(local_epochs=5, learning_rate=0.01)
- Perform zero-shot knowledge transfer between heterogeneous models using feature anchors to establish shared feature spaces:
python knowledge_transfer.py —source mobileNet —target efficientNet —method ‘anchor’
4. Monitor aggregated updates:
tensorboard —logdir=./logs/fedzkt
This approach enables privacy-preserving collaborative learning across devices with varying computational capabilities — directly applicable to mentoring Red Bird MPhil projects on IoT and edge intelligence.
4. Cloud Hardening & System Security Configuration
Cloud hardening involves establishing and maintaining secure configurations for cloud systems to reduce risk and mitigate vulnerabilities. HKUST(GZ) maintains a Private Cloud Platform Usage Manual and offers cybersecurity trainings, including phishing awareness and guest network maintenance. The IoT Thrusts Security Research Lab advances secure and privacy-preserving sensing and computation for protecting AI and cyber-physical technologies. Red Bird MPhil students have also achieved recognition in cybersecurity competitions, including winning first place in the Hong Kong Digital Asset Society Hackathon 2023.
Step-by-step guide: Hardening a cloud instance against common attack vectors.
1. Remove non-essential services and close unused ports:
netstat -tulpn | grep LISTEN sudo systemctl disable —now <unused_service> sudo ufw default deny incoming sudo ufw allow ssh sudo ufw enable
- Implement IAM with principle of least privilege using role-based access control (RBAC), enforce MFA, and regularly audit access logs for anomalies.
3. Apply CIS Benchmarks using an automated tool:
git clone https://github.com/CISOfy/lynis cd lynis sudo ./lynis audit system —tests-from-group malware,authentication,networking
- Set up CSPM policies and implement encryption-at-rest for databases using AES-256, removing default credentials and restricting administrative access.
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AI Security & Red Teaming for AI Systems
As AI systems become pervasive, the need for “white-hat hacker” style security research grows. HKUST(GZ) faculty research involves identifying and tackling fundamental cybersecurity problems arising from omnipresent interconnected and intelligent systems. The universitys curriculum includes an “AI Security and Privacy” course. Research also explores red teaming side-channel attacks toward cloud AI platforms to recover private images — demonstrating vulnerabilities where attackers can reconstruct private inputs via side channel analysis. IoT Thrust Professor Long Yan describes the groups work as “white-hat hacker” security research, proactively identifying potential security vulnerabilities before malicious exploitation occurs.
Step-by-step guide: Performing a red-team assessment on an AI inference API.
- Reconnaissance: Gather model metadata using API endpoint probing and documentation review.
2. Craft adversarial examples:
import adversarial as adv attack = adv.FastGradientMethod(model) adversarial_image = attack.generate(x_test, eps=0.1)
- Perform side-channel timing analysis to infer model architecture:
time curl -X POST https://target-ai-api.com/predict \ -H “Content-Type: application/json” \ -d ‘{“input”: “sample_data”}’ -
Mitigate discovered vulnerabilities by implementing rate limiting, input sanitization, and model hardening techniques.
What Undercode Say:
- Mastering these five domains — Edge AI, Differential Privacy, Federated Learning, Cloud Hardening, and AI Red Teaming — aligns directly with HKUST(GZ)s interdisciplinary research environment and the mentorship expectations for Senior Lecturer roles in the College of Future Technology.
- The Red Bird MPhil Programs project-based learning model demands educators who can translate cutting-edge research into hands-on student projects. Candidates who demonstrate practical command of these technical areas through portfolio work, published code repositories, or industry experience will stand out in the recruitment process for these global PhD talent positions.
Prediction:
- Increased integration of AI security and privacy modules into university curricula, driven by demand for professionals who understand both AI development and adversarial threats.
- Expansion of interdisciplinary research initiatives combining federated learning with edge computing, lowering barriers to privacy-preserving AI deployment in resource-constrained environments.
- Growth in industry-academia partnerships for red teaming and penetration testing of AI systems, as organizations seek to identify vulnerabilities before exploitation.
- Shortage of qualified educators who can bridge the gap between theoretical AI research and practical security implementation may create bottlenecks in training the next generation of cybersecurity professionals.
- HKUST(GZ)s strategic investment of RMB 1.68 billion in its Embodied Intelligence Institute positions it as a regional leader in robotics and AI research, likely attracting top talent to faculty positions in the coming years.
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