Yeely Khoh is a research engineer in metabolic modelling, working on developing and optimising software to build metabolic models of Leishmania parasites to support the investigation of unknown metabolic mechanisms and pathways.

Curtis Wakeling is a bioinformatician at the MACSYS Monash node.

Curtis completed an extended Bachelor of Science (Biochemistry) at Monash University with a research project supervised by Traude Beilharz, and a Master of Science (Bioinformatics) at the University of Melbourne.

Dr. Benjamin Teo is a postdoctoral research fellow in biological data science at MACSYS.

Dr. Teo completed his PhD in Statistics at the University of Wisconsin-Madison, where he worked on statistical and computational techniques to estimate models of continuous trait evolution on phylogenetic networks. He is broadly interested in the application and adaptation of graphical models techniques to biological modeling, and more recently in the statistical analysis of metabolomic data and protein structural phylogenetics.

Liam Zefu Li is a final-year BSc student at The University of Melbourne majoring in Mathematics with a Diploma in Computing. His current research focuses on Parameter Inference in Stochastic Biological Systems via Differentiable Programming. When he’s not obsessing over models, you’ll probably find him at the gym!

Dr. Jiren Zhou is a computational biologist and machine-learning researcher focused on developing deep learning algorithms to predict RNA molecular properties, with particular interests in RNA modifications, RNA secondary structure, and the functional roles of RNAs in disease.

Jiren received his PhD from Northwestern Polytechnical University, where his research combined graph-based modeling and language-model approaches to study RNA interaction networks and disease-relevant regulatory mechanisms. He has experience designing computational pipelines for large-scale biological data, integrating heterogeneous signals (e.g., sequence, structural constraints, and evolutionary information) to support RNA-centric discovery. Overall, his work bridges modern machine learning and biological computation, with the goal of developing scalable, reproducible methods that advance our understanding of RNA biology and accelerate translational applications.

Dr Ke Ding is a computational biologist and machine learning researcher at the Australian National University, working at the intersection of genomics, deep learning, and high-performance computing for large-scale biological data.

Dr Ding completed his PhD in Computational Biology at the Australian National University, where he developed genomic foundation models to predict gene and protein expression from DNA and RNA sequences. His research focuses on large-scale pretraining, distributed GPU computing, and integrating machine learning with biological discovery to advance data-driven genomics.

Jacob Amy is a molecular biologist working in the MACSYS Monash node. His research integrates microbiology and molecular biology to develop a unified growth and sampling strategy for microbial multi-omics.

In MACSYS, Jacob draws on his experience in microbiology and genomics to develop scalable methods of delivering high-quality samples suitable for transcriptomics, proteomics, metabolomics and lipidomics that enable mathematical modelling and inference. His previous work focused on the characterisation of mobile genetic elements in anaerobic bacteria, as well as specialising in single-cell and spatial transcriptomics projects within Monash University’s genomics platform.