Associate ProfessorAsef Nazari

Asef Nazari is an Associate Professor in the School of Information Technology at Deakin University, where he also serves as Senior Graduate Research (HDR) Coordinator and an elected member of the SEBE Faculty Board. He is an applied mathematician and data scientist with formal training at all levels (BSc, MSc, PhD) in applied mathematics, specialising in optimisation. In his leadership roles, he provides strategic oversight of HDR programs, supervises and mentors research students and academics, and contributes to university‑level governance and research culture.

His research spans optimisation, operations research, AI, and ML, with particular strengths in large‑scale and nonsmooth optimisation, mixed‑integer programming, deep learning, anomaly detection, and time series analysis. He has an extensive and sustained publication record, including more than 50 refereed journal and conference papers in leading Q1/Q2 journals and CORE‑ranked venues. His research is highly interdisciplinary and application‑driven, addressing real‑world challenges across energy systems, electricity markets, supply chains and logistics, mining operations, agriculture, sustainability, cybersecurity, and data‑driven decision‑making, often in close collaboration with industry and government partners.

With over 20 years of teaching and academic experience across Australian and international universities, Associate Professor Nazari is recognised for leadership and innovation in curriculum design, course convening, and blended and multi‑campus delivery. He has supervised multiple HDR completions and currently mentors a large and diverse cohort of PhD and Honours students. His work is underpinned by a strong commitment to research translation, industry engagement, and capacity building, and he is widely regarded as a collaborative leader, strategic communicator, and dedicated coach of academics and students, focused on achieving high‑quality research outcomes with real‑world impact.

The list of his research interests is as follows.

• Optimisation (Optimization) and Operations Research
  • Non-smooth Optimisation and Integer Programming
  • Large-scale Optimisation and Decomposition Techniques
  • Supply Chain Management, Project Scheduling, and Manufacturing
• Artificial Intelligence and Machine Learning
  • Image Processing
  • Causal Inference and Cryptocurrency Markets
  • Randomised Numerical Linear Algebra and Big Data
  • Anomaly Detection (in Cyber and Energy Security)
  • Time Series Analysis
• Renewable Energy Management: Adapting New Technologies
  • Microgrids
  • Integration of EVs and Large Storage Units
  • New Storage Technologies
  • False Data Injection Detection
  • Novel Optimisation Techniques for Large-scale Energy Problems