Imran Razzak

Imran Razzak

Associate Professor, Computational Biology

Department of Computational Biology

MBZUAI, Abu Dhabi, UAE

Associate Editor- IEEE TNNLS, Neural Network, IEEE TCSS, IEEE JBHI

Short biography

Imran Razzak is an Associate Professor at MBZUAI, UAE, Abu Dhabi. Before joining MBZUAI, he was Assocaite Professor in Human-Centered Machine Learning at School of Computer Science and Engineering at University of New South Wales, Sydney, Australia. Previously, he was as a Senior Lecturer in Computer Science at School of IT, Deakin University, Victoria. His area of research focuses on connecting language and vision for better interpretation of multidimensional data and spans over three broad areas: Machine Learning, Computer Vision, and Natural Language Processing with special emphasis on healthcare and use of natural language to explain the rationale and decision-making process behind the use of machine learning algorithms and models. With a strong background in computer vision and deep learning techniques, he has developed expertise in analysing and interpreting medical images (US, mammogram, CT, PET, MRI, fMRI) to improve patient outcomes. Throughout his career, he has worked on a range of projects that involve developing and implementing machine learning algorithms for medical image analysis. His work has resulted in more than 200 publications citation in leading journals and conferences in the field of medical image analysis. Through his research, he has made significant contributions to the field of medical image analysis, particularly in the area of early diagnosis. Additionally, he has collaborated with medical professionals to develop tools for early diagnosis of various medical conditions, including cancer, neurodegenerative disease, and heart disease.

Research Interests

I'm passionate about Deep Learning, Machine Learning, Computer Vision and Natural Language Processing. My personal mission is to build AI-based solutions that solve high-impact problems for us to simplify our everyday living.

We are excited to announce Multiple open positions for prospective PhD students in cutting-edge fields: LLM+KG towards improving LLM capablities. We are looking for highly self-motivated individuals who are passionate about research. If you are interested, please send me an email with the following documents: your updated Resume and a detailed Research Proposal. Join us on this thrilling academic journey as we explore new frontiers in these exciting areas of study. Looking forward to hearing from you!

Looking for highly self-motivated individuals who are passionate about research in healthcare (early diagnossis and longitudinal analysis) . If you are interested, please send me an email with the following documents: your updated Resume and a detailed Research Proposal. Join us on this thrilling academic journey as we explore new frontiers in these exciting areas of study. Looking forward to hearing from you!

Dual-doctorate degree candidates/visiting students/visiting scholars in the areas of ML/Human-Centered AI are also welcomed

What's new?

[Aug, 2025] 2 papers Paper accepted in EMNLP.
[Aug, 2025] Our Team ranked 1st in AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report.
[July, 2025] 1 Paper accepted in ACMM and one in CIKM
[July, 2025] We released OphNet-3D, the first extensive RGB-D dynamic 3D reconstruction dataset for ophthalmic surgery
[April, 2025] 2 Papers accepted in MICCAI and 2 papers in IEEE SMC
[March, 2025] Paper accepted in CVPR (ORAL) and one in ACL (ORAL)
[March, 2025] 1 Paper accepted in NPJ Digital Medicine.
[March, 2025] Our work "Retinal Pathways to Asthma Diagnosis: AI-Driven Insight" accepted in ARVO. T
[Feb, 2025] 1 main and 4 Companion accepted in WWWW Sydney
[Jan, 2025] Joined MBZUAI, as an Associate Professor of Computational Biology, I am looking for motivated Post Docs and PhDs/MS students
[November, 2024] Three papers accepted in COLING 2025
[Oct, 2024] Two papers accepted in NeurIPS 2024: (1) Construction of Functional Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model (2) BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
[Sept, 2024] Three papers accepted in WISE 2024.
[Sept, 2024] Two papers accepted in ICDM.
[Feb, 2024] Our study on early diagnosis of Migraines using Retina "Sight for sore heads: Using CNNs to diagnose migraines" has been accepted in "ARVO", This is the first work on exploring migraine and identification of new biomarker using retina
[Feb, 2024] Our state of the art LLM The Darwin Large Language Model has been recognized as the world’s most accurate model in Experimental Bandgap Prediction and Metal Classification Model within MatBench. "
[Feb, 2024] **Call for Workshop Proposals**: Submit your workshop proposals by April 10, 2024, and be part of this cutting-edge event. Don't miss out on this opportunity to contribute to this exciting event!
[Jan, 2024] Our paper "Self-Supervised Spatial-Temporal Transformer Fusion Based Federated Framework for 4D Cardiovascular Image Segmentation" has been accepted in "Information Fusion"
[Jan, 2023] : Our work "SGRA:Graph Representation Alignment for Semi-Supervised Action Recognition" Accepted in IEEE Transactions on Neural Networks and Learning Systems
[Dec, 2023] Our paper "'DHGCN: Dynamic Hop Graph Convolution Network for Self-supervised Point Cloud Learning" has been accepted in Information Fusion "
[Dec, 2023] Our paper "'Unsupervised Unpaired Multiple Fusion Adaptation Aided with Self-Attention Generative Adversarial Network for Scar Tissues Segmentation Framework" has been accepted in Thirty-Eighth AAAI Conference on Artificial Intelligence "
[Nov, 2023] Our paper "Two-Stage Self-Supervised Contrastive Learning Aided Transformer for Real-Time Medical Image Segmentation" has been accepted in "IEEE JBHI"
[OCT, 2023] Our paper "Hybrid unsupervised paradigm based deformable image fusion for 4D CT lung image modality" has been accepted in "Information Fusion"
[OCT, 2023] Our paper "LDMRes-Net: Enabling Real-Time Disease Monitoring through Efficient Image Segmentation" has been accepted in "IEEE Jouranl of Biomedical and Health Informatics"
[Sept, 2023] CI: Leveraging Foundation Models for Accelerated Materials Science Research, Microsoft Accelerate Foundation Models Research Program , 20K (access to Azure OpenAI GPT-4 model)
[Sept, 2023] Our paper "AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge" has been accepted in "IEEE Transactions on Medical Imaging"
[Aug, 2023] We are pleased to release first Darwin foundational LLM, meticulously tailored for the scientific domain, particularly for material science, chemistry, and physics.
[July, 2023] : Our work "Support Matrix Machine via Joint ℓ2,1 and Nuclear Norm Minimization Under Matrix completion Framework for Classification of Corrupted Data" Accepted in IEEE Transactions on Neural Networks and Learning Systems
[July, 2023] : Our work "ASBiNE: Dynamic Bipartite Network Embedding for Incorporating Structural and Attribute Information". Accepted in World Wide Web
[June, 2022] : We have recieved ARC Linkag $343K reserch grant to develop smartphone rip-detection tool to improve rip current awareness, We are looking two PhD students with strong background in Computer Vision and Deep Learning!
[June, 2023] : Our work "Multi-classification of retinal diseases using A Pyramidal Ensemble Deep Framework" accepted in ICIP Check out the paper here .!
[May, 2023] : Our work " Solving Complex Sequential Decision-Making Problems by Deep Reinforcement Learning with Heuristic Rules" accepted in ICCS Check out the paper here .!
[May, 2023] : Our work "Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning" accepted in Neural Network Check out the paper here .!
[April, 2023] : Our Team got top position in several ISBI Challenges i.e. 1st in IACTA-EST, .!
[March, 2023] : Our work "High-Density Electroencephalography and Speech Signal based Deep Framework for Clinical Depression Diagnosis" accepted in IEEE TCBB Check out the paper here .!
[Nov, 2022] : We have recieved NSW Clean Technology Research Development & Commercialisation Infrastructure Grant of $460K reserch grant to develop an API integrations with industry and an application hosting environment with at commercialised applications. We are looking two PhD students with strong background in Multidimendional Time Series Data Analytics!
[March, 2023] : Our work "Simple and robust depth-wise cascaded network for polyp segmentation" has been acceped in EAAI Check out the paper here .!
[March, 2023] : Our work "Segmentation of Intra-operative Ultrasound Using Self-supervised Learning Based 3D-ResUnet Model with Deep Supervision" findings of MICCAI Intra-operative ultrasound (iUS) Challenge has been published check out the paper here .!
[Feb, 2023] : Our work "Distributed Optimization of Graph Convolutional Network using Subgraph Variance" accepted in IEEE Trans on Neural Networks and Learning Systems" Check out the paper here .!
[Feb, 2023] : Our findings on Glaucoma- Airgos Challenge "AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge" Check out the paper here .!
[Jan, 2023] : Our work "Hierarchical Convolutional Attention Based Multimodal Framework for Anxiety and Depression Detection on Social Media and Its Impact During Pandemic" accepted in IEEE Journal of Biomedical and Helath Informatics" Check out the paper here .!
[Nov, 2022] : We have recieved close to $1.7M research grant from CSIRO and 6 Industries to train a cohort of 15 HDR students in developing innovative AI-driven medical technologies .!
[Nov, 2022] : We have recieved $166K, Faculty Infrastructure Research Grant "Spectralis Imaging Platform".!
[Nov, 2022] : Our paper "Semi-Supervised 3D-InceptionNet for Segmentation and Survival Prediction of Head and Neck Primary Cancers" has been accepted in Engineering Applications of Artificial Intelligence !
[Oct, 2022] : Our paper "Conv-ERVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer's Disease Diagnosis" has been accepted in IEEE JBHI !
[Oct, 2022] : Excited to share that our paper "COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis" received 2022 Andrew P. Sage Transactions paper award. Congrats to my co-authors !
[Oct, 2022] : Excited to share that our paper "Marine-tree: A Large-scale Marine Organisms Dataset for Hierarchical Image Classification" has been nominated for Best Paper Award. Congrats to my PhD student Tanya and other co-authors !
[Sept, 2022] : Our paper titled "Prompt Deep Light-weight Vessel Segmentation Network (PLVS-Net)" has been accepted in IEEE Transaction on Computational Biology and Bioinformatics (IEEE TCBB). !
[Sept, 2022] : MICCAI-Challege Update: We are ranked 1st in CuRIOUS (Correction of brain shift with Intraoperative Ultrasound segmentation), 4th in CMRxMotion (Cardiac MRI Analysis Challenge under Respiratory Motion) , 6th in ATM'22 Multi-site, Multi-Domain Airway Tree Modeling, and, 9th in Instance in Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT ! and 10th in HECKTOR (Automatic Head and Neck Tumor Segmentation and Outcome Prediction )
[Aug, 2022] : Recieved K CCDI grant led by Prof. Mahbub (Mahbub Hassan, Imran Razzak, Kim Fernandez) to develop "Open-Source Simulator for Light-based Internet of Things" !
[Aug, 2022] : We are oganizing a special issue "Advanced Machine Learning and Artificial Intelligence Tools for Computational Biology: Methodologies and Challenges" in "IEEE Journal of Biomedical and Health Informatics" (A*), Submissions are welcomed !
[Aug, 2022] : Two paper titled "A Mask-based Output Layer for Multi-level Hierarchical Classification" and "Marine-tree: A large-scale hierarchically annotated dataset for marine organism classification "has been accepted in CIKM. !
[July, 2022]: Our research team won the best demo award at the MDM 2022 conference. Check out the paper here!
[July, 2022] : Recieved 39.5K GROW grant jointly with Rahat Masood to develop " Privisor: A User-Oriented Intelligent Privacy Advisor System for Mobile" !
[July, 2022]: We are currently looking for 2 #casual_Research_Assistants (#RAs) for project titled “User-Oriented Intelligent Privacy Advisor System for Mobile”. The project aims to understand users 'attitude towards mobile privacy through series of measurement and user studies and developing an intelligent privacy risk quantification framework. RA-1 (demonstrated #cybersecurity skills) will assist in #modelling_relationship between #user_interfaces (#UI) and user experiences (#XU) of #mobile_privacy controls, whereas the 2nd RA (demonstrated #machine_learning skills) will assist in development of an #intelligent_framework to quantify the risks of personal information leaks..