👥 We recognise that partnerships make our work stronger. We collaborate with other researchers and organisations who bring their own content and technical expertise to tackle complex social challenges. Follow us to stay in touch! #MonashUni
(10/10)

📈 We appreciate that evidence alone isn't enough. Our team (and partners) support organisations to implement and scale evidence-informed programs — from initial planning through to measuring outcomes. Together, we're closing the research-to-practice gap #ImpSci
(9/10)

🧠 We use cutting-edge deep learning methods to build data-driven decision support tools that help practitioners identify and manage risks for vulnerable children and young people.
(8/10)

📊 We have expertise in using linked administrative data in the evaluation of complex social welfare programs. Our specialty? Applying #causalinference methods to understand the real impact of policies and interventions.
(7/10)

🔍 We're passionate about #whatworks in social welfare. Our #evidencesynthesis work ranges from #systematicreviews to component network #metaanalysis, helping unpack what makes complex interventions effective.
(6/10)

🤝 Our work spans #evidencesynthesis, #programevaluation and developing predictive decision support tools. We build long-term partnerships to create lasting change.
(5/10)

🔨 We do practical applied research. Working closely with funders in government agencies, service providers & philanthropic organisations, we translate research and data into actionable insights that can be used to improve policy and practice in the real world.
(4/10)

🧩 We understand that behind every data-point is a real person. We apply our lived experience of social service systems directly to how we work with data — from the questions we ask to how we analyse and interpret findings.
(3/10)

🧮 We're applied researchers from diverse backgrounds: social welfare, #publichealth, AI, engineering, biology & economics 📈📉. Our strength? Combining content expertise in social work policy & practice with rigorous quantitative methods.
(2/10)