Rate My Professor Novi Quadrianto

Quadrianto's research centres on trustworthy machine learning, encompassing ethical aspects such as auditing and mitigating bias against protected subgroups, enhancing interpretability of algorithmic systems, developing safe and robust models for extreme situations, and advancing interactive machine learning between users and algorithms. His interdisciplinary work includes a causality toolbox applied to evidence-based interventions in post-natal care and an AI landscape modelling tool, LIMMMA, analysing trade-offs in peri-urban agriculture for urban sustainability policy. Major funding includes a €1.5 million European Research Council Starting Grant for BayesianGDPR (2019-2024) integrating GDPR principles into machine learning, EU Horizon RIA TANGO for human-centric AI decision support, and ERC Proof of Concept Act.AI for bias mitigation via statistical matching. Earlier honours comprise a £100,000 Newton International Fellowship (2012-2014), Wolfson College Junior Research Fellowship (2012-2015), Microsoft Research Fellowship (2009), and ANU scholarships. Key publications feature 'Okapi: Generalising Better by Making Statistical Matches Match' (NeurIPS 2022), 'Discovering Fair Representations in the Data Domain' (CVPR 2019), 'A Very Simple Safe-Bayesian Random Forest' (IEEE TPAMI 2015), 'Kernelized Sorting' (IEEE TPAMI 2010), and 'Estimating Labels from Label Proportions' (JMLR 2009). He serves as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence since 2016, Area Chair for NeurIPS 2015, and reviewer for top journals and conferences including ICML, ICCV, and AAAI. His contributions, reflected in an h-index of 26, advance ethical AI and interdisciplinary applications.