Rate My Professor Nir Lipovetzky
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Always patient and willing to help.
A true role model for academic success.
Always patient and willing to help.
Makes learning feel rewarding and fun.
Great Professor!
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About Nir
Nir Lipovetzky serves as an Associate Professor in the School of Computing and Information Systems, Faculty of Engineering and Information Technology, at the University of Melbourne. He completed his academic training at Universitat Pompeu Fabra, obtaining a BSc in Computer Science in 2004, MEng in Artificial Intelligence in 2007, and PhD in Artificial Intelligence in 2012 supervised by Prof. Hector Geffner. Additionally, he holds a Graduate Certificate in University Teaching from the University of Melbourne awarded in 2020. Post-PhD, Lipovetzky joined the University of Melbourne as a research fellow for three years under the supervision of Prof. Peter Stuckey and Prof. Adrian Pearce, where he applied automated planning, constraint programming, and operations research to address mining scheduling challenges. He has progressed through academic ranks to his current position and is actively involved in research labs including the Agent Lab, AI Assurance Lab, and Digital Agriculture, Food and Wine lab. He also serves as the Course Director for the Master of Artificial Intelligence (Online).
Lipovetzky's research interests span AI planning, search algorithms, machine learning, verification techniques, constraint programming, operations research, intention recognition, sequential decision-making processes, and their applications to autonomous systems, with a particular emphasis on agriculture. His contributions have been recognized with prestigious awards such as the Pragnesh Jay Modi Best Student Paper Award at the 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2024), Distinguished Program Committee recognition at IJCAI-ECAI 2022 (top 3% of reviewers), Early Career Spotlight at IJCAI 2021, Best System Demonstration (Planimation) at ICAPS 2019, winner and runner-up in Sparkle Planning Competition 2019, winner in IPC-9 Agile and Satisficing Tracks 2018, Best Paper Award at ICAPS 2015, ICAPS Best Dissertation Award 2013, Knowledge Engineering Competition 2016 win, and Distributed and Multiagent Planners Competition 2015 win. Key publications include "Planning for Novelty: Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning" (IJCAI 2021), "Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation" (ICAPS 2025), "MetaPilot: A DRL-Based Controller for Dynamic Adaptation to Shifting Scheduling Objectives in HPC Systems" (Future Generation Computer Systems, 2025), "Applying Organizational Mining to Discover Agent Systems from Event Data" (Information Systems, 2025), and "Process Mining over Sensor Data: Goal Recognition for Powered Transhumeral Prostheses" (Information Systems, 2025).
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