Dr Peter Brown
Postdoctoral Fellow

Dr Peter Brown

Doctor of Philosophy, School of Information and Communication Technology, Griffith University, 2019
Research area: Structural Alignments for Similarity Detection in Bioinformatics
Supervisors: Professor Yaoqi Zhou, Dr Wayne Pullan

Bachelor of Information Technology (Honours I), Griffith University, 2014

Grad Sch: Biomedical Eng

Dr Peter Brown is a postdoctoral fellow at the Graduate School of Biomedical Engineering. He completed his doctoral degree in Prof Yaoqi Zhou's lab at Griffith University prior to joining UNSW in 2021. His research interests are in the field of data driven telehealth services, and has a background in full-stack software engineering contributing to and maintaining the codebases powering critical remote patient monitoring telehealth mobile applications. As the academic coordinator of the Tyree Foundation Institute of Health Engineering (Tyree IHealthE) Connected Health Network Laboratory (CHANL), he is responsible for developing the scope and schedule of new projects, offering guidance and assistance including the delivery of training workshops on health application development, regulatory requirement expectations, and barriers constraining the design and development of mobile health applications. He is currently supervising around 30 undergraduate thesis students working with CHANL on biomedical software engineering projects.

LG09 Samuels Building (F25) UNSW Sydney NSW 2052 AUSTRALIA
  • Journal articles | 2019
    Brown P; Zhou Y; Tan AC; El-Esawi MA; Liehr T; Blanck O; Gladue DP; Almeida GMF; Cernava T; Sorzano CO; Yeung AWK; Engel MS; Chandrasekaran AR; Muth T; Staege MS; Daulatabad SV; Widera D; Zhang J; Meule A; Honjo K; Pourret O; Yin CC; Zhang Z; Cascella M; Flegel WA; Goodyear CS; van Raaij MJ; Bukowy-Bieryllo Z; Campana LG; Kurniawan NA; Lalaouna D; Hüttner FJ; Ammerman BA; Ehret F; Cobine PA; Tan EC; Han H; Xia W; McCrum C; Dings RPM; Marinello F; Nilsson H; Nixon B; Voskarides K; Yang L; Costa VD; Bengtsson-Palme J; Bradshaw W; Grimm DG; Kumar N; Martis E; Prieto D; Sabnis SC; Amer SEDR; Liew AWC; Perco P; Rahimi F; Riva G; Zhang C; Devkota HP; Ogami K; Basharat Z; Fierz W; Siebers R; Tan KH; Boehme KA; Brenneisen P; Brown JAL; Dalrymple BP; Harvey DJ; Ng G; Werten S; Bleackley M; Dai Z; Dhariwal R; Gelfer Y; Hartmann MD; Miotla P; Tamaian R; Govender P; Gurney-Champion OJ; Kauppila JH; Zhang X; Echeverría N; Subhash S; Sallmon H; Tofani M; Bae T; Bosch O; Cuív PO; Danchin A; Diouf B; Eerola T; Evangelou E; Filipp F; Klump H; Kurgan L; Smith SS; Terrier O; Tuttle N, 2019, 'Large expert-curated database for benchmarking document similarity detection in biomedical literature search', Database, 2019, pp. 1 - 67, http://dx.doi.org/10.1093/database/baz085
    Journal articles | 2017
    Brown P; Yang Y; Zhou Y; Pullan W, 2017, 'A heuristic for the time constrained asymmetric linear sum assignment problem', Journal of Combinatorial Optimization, 33, pp. 551 - 566, http://dx.doi.org/10.1007/s10878-015-9979-2
    Journal articles | 2017
    Brown P; Zhou Y, 2017, 'Biomedical literature: Testers wanted for article search tool', Nature, 549, pp. 31, http://dx.doi.org/10.1038/549031c
    Journal articles | 2016
    Brown P; Pullan W; Yang Y; Zhou Y, 2016, 'Fast and accurate non-sequential protein structure alignment using a new asymmetric linear sum assignment heuristic', Bioinformatics, 32, pp. 370 - 377, http://dx.doi.org/10.1093/bioinformatics/btv580

IHealthE Catalyst Award (CIA), "A Smart Mouthguard for Sleep Monitoring", 2021

UNSW Research Infrastructure Scheme (CI), "Connected HeAlth Network Laboratory (CHANL)", 2022-2023

TCC-Cardiac is an NSW Health-funded study that will examine the comparative efficacy of the TeleClinical Care - Cardiac solution comprising of a smartphone application, backend clinician dashboard and associated model of care, and its messages component as standalone, as an adjunct to usual standard care in patients who are being discharged home following an acute cardiac event. The system is designed to support patients who have recently been admitted with a heart attack or heart failure. It allows for the delivery of educational messages, the monitoring of physiological parameters (such as heart rate, blood pressure, weight and oxygen levels in the blood) by a team of hospital-based clinicians and provides a virtual cardiac rehabilitation exercise programme.

TCC-Stroke is an NHMRC MRFF-funded study with the goal of detecting early onset of atrial fibrillation using a deep learning algorithm to better treat stroke patients. The system is designed to support patients who have recently been admitted with a stroke, or a Transient Ischaemic Attack (TIA). It allows for the delivery of educational messages, the monitoring of physiological parameters (such as heart rate, blood pressure, and electrocardiogram) by a team of hospital-based clinicians. The app also assists with medication adherence and provides a virtual stroke rehabilitation exercise programme.