Despite significant advancements in medical science and an increasing emphasis on precision medicine, the majority of medical diagnoses are still made after patients exhibit noticeable symptoms. Early diagnosis, detection, and phenotyping of diseases could afford patients and their caregivers the opportunity for timely interventions, potentially leading to improved disease management, better prognoses, and more efficient utilisation of healthcare resources. The rising adoption of personalised medicine by global health systems, coupled with recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technologies, presents a significant opportunity to leverage AI in personalised medicine. This approach aims to lower the barriers to the clinical integration of personalised medicine, addressing a critical unmet need in the healthcare sector. Dr. Rokny is a multidisciplinary scientist dedicated to employing state-of-the-art AI and ML methodologies in conjunction with multimodal omics data to unravel the mechanisms underlying genetic diseases and cancers. In his presentation, Dr. Rokny will first outline the scope of work undertaken by his Biomedical Machine Learning Laboratory (BML), which spans the application of ML and AI in genomics and spatial transcriptomics to the more recent deep graph representation learning models. He will delve into a specific projects, “TENNIS” and “SemanticST”, offering detailed insights. Additionally, he will discuss the challenges, limitations, and potential biases inherent in the deployment of AI in individualised medicine, refining its role in transforming healthcare.
Speaker Bio
Dr. Rokny is a Scientia Senior Lecturer (e.q., Associate Professor) at UNSW Sydney and Adj Associate Professor at Concordia University, CANADA, obtaining his Ph.D in 2020 at UNSW Sydney. Dr. Rokny is also the Founder and Director of Australian Society of BioMedical Machine Learning with more than 4000 members. He is also the health data analytics program leader at the Macquarie Centre for Applied AI. During his career, he developed a deep cross-disciplinary expertise in Artificial Intelligence (AI), Bio Machine Learning, and Genomics. Dr. Rokny joined UNSW on a highly prestigious and competitive UNSW Scientia Program Fellowship in October 2019 and currently is the Director of UNSW BioMedical Machine Learning laboratory (BML), where he is supervising a group consisting of 20 researchers including HDR and post-doc supervision, further demonstrating his comprehensive leadership and project management capabilities. Dr. Rokny has held continuous peer reviewed salary and grant support for his entire career, including but not limited to two WA Department of Health Merit Fellowships, two UNSW Scientia Fellowship, Tyree Foundation Institute of Health Engineering, and ARC DECRA. Totally, throughout his career, he has received 28 prizes/awards and also was able to secure over $5.2M peer-reviewed grants as Chief Investigator (CI) including $3.75M as either sole CI or leading CI. Additionally, Dr. Rokny has produced over 80 peer-reviewed publications in AI method development and their applications in genomics, half of which are in the top 10% of the fields (45 as the leading author), evidencing his impact with over 4,100 citations and a h-index of 36. He has also a strong relationship with industry, in which his expertise in AI and ML has been funded >$10M by his industrial collaborators. Dr. Rokny has also a demonstrated experience in leadership through his contribution to the University and school level committees. He has been invited to serve as keynote speaker and program committee member of national and international conferences (including HUGO 2020) and was also an editorial board member of Communications Biology and Neurocomputing. In addition to his research activities, Dr. Rokny is the course director of AI in Biology, Biomedical Informatics and Digital Medicine at UNSW Sydney.
More Details
- When: July 18 2025, at 1-2pm (Brisbane time)
- Speaker: Hamid Alinejad Rokny (Scientia Senior Lecturer in UNSW)
- Host: Yujun Cai
- Venue: 78-411
- Zoom: https://uqz.zoom.us/j/83638226838
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