海角社区

Research interests

My current research focuses on developing computational models using advanced computer vision and multimodal Artificial Intelligence to support clinicians in decision-making. I am also interested in the domain of trustworthy AI, involving issues such as bias, fairness, interpretability, and ethical considerations in algorithm development and inference. These efforts aim to ensure that AI solutions are not only technically robust but also ethically sound and socially responsible, paving the way for equitable and trustworthy AI applications.

PhD supervision

I am interested in supervising PhD students who are passionate about advancing AI research with real-world impact, particularly in healthcare applications. Some potential PhD research topics are as follows. If you are interested in pursuing a PhD in any of these areas, please feel free to reach out for discussions on potential research directions:

-- Multimodal AI for Healthcare Diagnostics: Develop deep learning models that integrate medical imaging (e.g., ultrasound, MRI, CT, Digital Pathology) with clinical, genomic, and sensor data to enhance disease detection, segmentation, and prognosis for precision medicine.

-- Design lightweight and efficient AI architectures for deployment on portable ultrasound and other portable imaging devices, supporting triaging, diagnosis, and treatment planning in resource-constrained settings.

-- Explore generative AI, diffusion models and synthetic data generation to address data scarcity, improve AI model robustness, and enable privacy-preserving AI in healthcare.

-- Investigate methods to identify and mitigate bias in medical AI, ensuring fairness across diverse populations. Develop interpretable AI frameworks to enhance clinician trust. Explore privacy-preserving AI approaches, including federated learning and differential privacy, to protect patient data.

--Using deep learning techniques for motion prediction, surgical tool tracking, and automated image analysis to enhance precision in minimally invasive procedures. This are will focus on real-time segmentation or tracking algorithms to support clinicians during image-guided interventions, such as ultrasound-assisted biopsies, endoscopic procedures, and interventional radiology.

For more updated PhD research topics, you can follow .

Research interests

I am an applied AI researcher and data scientist and my research involves studying how AI and data science can be applied to different domains and contexts. My research explores developing scalable techniques and mechanisms for understanding very large complex multidimensional datasets for specific problems. I conduct inter-disciplinary research on highly engaging, interactive and visual mechanisms in conjunction with complex querying techniques for seamless navigation, exploration and understanding of complex datasets. I am also interested in the use of citizen science and crowdsourcing to enrich datasets with local knowledge.

Research supervision

Areas of PhD supervision:

• Applied AI and data science on domain-specific topics (e.g. healthcare, smart cities, sports analytics)
• XAI - Explainable AI and human centred AI
• Large scale visual analytics of data
• Knowledge graphs, ontologies and semantic web
• Citizen science and crowdsourcing techniques for complementing traditional sources of data (e.g. mobile, wearable sensing) using multimodal interactions