Aneta Neumann

Aneta Neumann is a Researcher at the School of Computer and Mathematical Sciences within the University of Adelaide. Her work bridges bio-inspired computation with machine learning and computational creativity, focusing on dynamic and stochastic multi-objective optimization for real-world applications in mining, renewable energy, cybersecurity, and public health.

• Education: B.Sc. in Computer Science from Christian-Albrechts-University of Kiel, Germany; Ph.D. from University of Adelaide, Australia.

Her research explores evolutionary diversity optimization to generate innovative solutions for complex problems like the Traveling Thief Problem, Chance-Constrained Knapsack, and Time-Use Planning. She investigates theoretical foundations through runtime analysis and applies these insights to industrial software integration in mining and energy sectors.

Recent work emphasizes AI-based optimization trends, particularly in quantum computing benchmarks (e.g., Maximum Cut) and health outcomes via time-use scheduling. Her publications span top venues like GECCO, AAAI, NeurIPS, and Algorithmica.

• Scientific Awards: ACM-W Scholarship (2018), Hans-Juergen and Marianna Ohff Research Grant, Best Paper Award at GECCO 2024, multiple Best Paper Nominations at GECCO (2019, 2021, 2022).

She co-organizes key conferences (AI-OPT 2025, EMO 2025) and serves as track co-chair for Genetic Algorithms at GECCO. Her teaching includes the Big Data Fundamentals course in the University of Adelaide's MicroMasters program.