Ricardo Cannizzaro (He/Him)

Ricardo Cannizzaro (He/Him)

Causal AI and robotics researcher | Final-stage DPhil (PhD) candidate at the Cognitive Robotics Group, Oxford Robotics Institute, University of Oxford | Former Australian Senior Defence Research Scientist (Robotics Specialist)

Ricardo Cannizzaro

About

I am a causal AI and robotics researcher and final-stage DPhil (PhD) candidate with the Cognitive Robotics Group and the Goal-Oriented Long-Lived Systems group at the Oxford Robotics Institute, University of Oxford Department of Engineering Science, supervised by Prof Lars Kunze and Prof Nick Hawes. I have submitted my PhD thesis and am currently awaiting defence.

My doctoral research was funded by the Australian Defence Science and Technology Group, where I previously worked as a Defence Research Scientist on trusted autonomous systems.

During my PhD, I completed two PhD Research Internships at Microsoft Research (MSR) with the AI Interaction and Learning and People-Centric AI teams in Redmond. My work focused on improving consistency and reasoning in foundation and generative AI models operating in interactive, causally complex environments, including counterfactual consistency for image fine-tuning and parallel-world consistency for generative modelling in video game environments.

I have over 10 years’ experience designing and implementing AI/ML-enabled software and hardware systems for learning, inference, and decision-making under uncertainty, spanning real-world robotics deployments and large-scale simulated environments.

Research Focus

My research explores probabilistic generative causal models for encoding structured knowledge and uncertainty, combining domain expertise with data-driven learning. Methodologically, this includes causal representation learning, Bayesian inference, probabilistic programming, and deep generative models such as diffusion models and transformers.

A central focus of my work is learning human-aligned causal representations and developing faithful, counterfactual-based explanations that support understanding and trust by non-technical users. I am particularly interested in human-facing AI systems that must behave consistently, robustly, and transparently in real-world settings.

Research Interests: probabilistic generative causal modelling, Bayesian causal inference, causal representation learning, counterfactual reasoning and explanations, probabilistic programming, foundation and generative models, uncertainty-aware decision-making, and human-centred AI systems.

Currently Seeking

I am currently seeking senior industry research roles advancing reliable and scalable, human-aligned AI systems through learning and causal reasoning with foundation and generative models, and principled decision-making under uncertainty in large-scale, real-world deployments.

Contact

To have a chat about my research or to discuss collaborations, please reach out to me at ricardo@robots.ox.ac.uk or via LinkedIn.

Work Experience & Education

Work Experience

Before my DPhil I was working as a Defence Research Scientist in the Aerial Autonomy group of the Australian Defence Science and Technology Group (2017-2021), where my research focused on decentralised teams of autonomous aerial and ground robots for missions in challenging uncertain and complex environments, such as the urban terrain. My research was at the exciting intersection of AI/ML, software and hardware engineering to develop autonomous behaviours, integrate them into hardware, and experimentally validate the complete autonomous systems through flight trials in real urban environments across Australia, Singapore, Montreal, and New York City.

My AI/ML and robotics research at DSTG has spanned a wide range of robotics and AI/ML topics, including:

Education

DPhil (PhD) Engineering Science

Oxford Robotics Institute, University of Oxford
Thesis submitted; defence expected 2026

Supervisors: Prof Lars Kunze and Prof Nick Hawes
(Cognitive Robotics Group; Goal-Oriented Long-Lived Systems Group)
Funded by the Australian Defence Science and Technology Group

Thesis: Causal Artificial Intelligence for Robust Robot Reasoning under Uncertainty

My doctoral research focuses on uncertainty-aware and probabilistic causal modelling for learning, inference, decision-making, and explanation in complex, partially observable environments. A central theme of my work is learning human-aligned causal representations and developing faithful, counterfactual-based explanations to support understanding and trust by non-technical users.

Methodologically, this work combines causal representation learning, Bayesian inference, probabilistic programming, and deep generative models, applied across both interactive virtual environments and real-world, hardware-integrated robotic systems. This research included, and subsequently extended, work completed through two Microsoft Research PhD internships, each forming a core thesis chapter.

Selected Coursework: Oxford Scientific Entrepreneurship Course; Oxford Language Centre Italian Fast-Track Course (Parts 1–2)

Bachelor of Engineering (Honours) (Robotics & Mechatronics) (First Class Honours)

I completed my Bachelor of Engineering (Honours) (Robotics & Mechatronics) (First Class Honours) in 2016 at the Swinburne University of Technology in Melbourne, Australia, School of Engineering (4 years + industry-based learning year at DSTG). For my honours thesis project I created an autonomous ground robot system for remote chemical detection and localisation, under the supervision of Professor Zhenwei Cao and Dr Jennifer Palmer. I implemented a passive chemical-emitter localisation algorithm and integrated a novel bespoke DSTG chemical detection sensor into an autonomous Clearpath Robotics TurtleBot 2 robot system.

Bachelor of Science (Mechanical Systems)

I completed my Bachelor of Science (Mechanical Systems) in 2012 at the University of Melbourne in Melbourne, Australia, Faculty of Engineering and Information Technology / Faculty of Science. I spent 6 months at KTH Stockholm in 2012 as a visiting student at the Division of Robotics, Perception and Learning and Department of Engineering Mechanics (Aerospace Engineering).

News

Recent updates on publications, awards, and academic activities.

  • 25/01/2026 — I’m excited to announce that my collaboration with Microsoft Research has resulted in a paper accepted at ICLR 2026: Multiverse Mechanica: A Testbed for Learning Game Mechanics via Counterfactual Worlds. The work introduces a causal generative modelling framework for learning and intervening on game mechanics, enabling counterfactually consistent parallel-world generation where only causally relevant factors change. [Paper]
  • 10/10/2025 — I have submitted my Oxford DPhil (PhD) thesis and am currently awaiting my defence (March 2026).
  • 05/09/2025 — I am absolutely delighted to share that our work on causal reasoning for robot manipulation has received the Best Paper Award at the European Conference on Mobile Robots 2025 (ECMR 2025) in Padua, Italy! This recognition means a lot - not just for the paper, but for the broader vision of building trustworthy, explainable, and robust autonomous robot systems. A huge thank you to my brilliant collaborators at the Oxford Robotics Institute, Microsoft Research, and Bristol Robotics Laboratory! [LinkedIn post]🏆🤖🎉
  • 15/08/2025 — I was delighted to return to Microsoft Research (Redmond) to complete an advanced PhD Research Internship, where I extended my earlier work on causal and parallel-world consistency by developing mechanics-aware generative modelling frameworks and an instrumented interactive PyGame environment.
  • 16/08/2024 — I’m pleased to share that I completed my first PhD Research Internship at Microsoft Research, working with the same team on counterfactual consistency objectives for diffusion-based multi-modal generative models and human-aligned image fine-tuning.
  • 15/04/2024 — I’m happy to share our work was published at SEAMS 2024: Aloft: Self-Adaptive Drone Controller Testbed, introducing a testbed for studying self-adaptive control in autonomous aerial systems.
  • 02/10/2023 — I’m excited to share our paper was published at IROS 2023: CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments, which integrates causal modelling into online planning under uncertainty.
  • 10/08/2023 — I am honoured and grateful to have received travel support to attend IROS 2023, supported by the IEEE Robot Learning Technical Committee, IEEE RAS member grants, and St Edmund Hall postgraduate funding.
  • 29/05/2023 — I co-organised the Multidisciplinary Approaches to Co-Creating Trustworthy Autonomous Systems (MACTAS) workshop at ICRA 2023, alongside Prof Lars Kunze. The workshop brought together keynote speakers, spotlight talks, and panel discussions to explore multidisciplinary perspectives on trust and trustworthiness in autonomous systems. [Workshop]
  • 05/01/2023 — I was honoured to give an invited talk at the IROS 2023 workshop on Causality for Robotics, presenting my work on confounded POMDP robot planning.
  • 04/06/2021 — I was honoured to receive the Best Paper Award at the ICRA 2021 workshop on Robot Swarms in the Real World for our work on multi-robot exploration.
  • 30/05/2021 — I’m happy to share our paper was published at ICRA 2021: An Upper Confidence Bound for Simultaneous Exploration and Exploitation in Heterogeneous Multi-Robot Systems, addressing scalable exploration–exploitation trade-offs in multi-robot teams.
  • 18/01/2021 — I’m excited to announce that I have started my DPhil (PhD) at the Oxford Robotics Institute, University of Oxford, with the Cognitive Robotics Group, supervised by Dr Lars Kunze and co-funded by the Australian Defence Science and Technology Group. My research focuses on probabilistic generative causal models for robust robot reasoning and decision-making under uncertainty.
  • 11/11/2019 — I’m pleased to share that our work was published at ACRA 2019, presenting a Random-Finite-Set-based SLAM approach for aerial robots operating with scanning and solid-state LIDARs.
  • 28/02/2019 — I’m happy to share that our paper was published at AIAC 2019, presenting an experimental evaluation of LIDAR and X-band radar sensing in particle-dense environments.
  • 25/05/2018 — I’m excited to share our paper was published at ICRA 2018, presenting scalable swarming and data-ferrying strategies for unmanned aerial systems.