Our Team

Ian Manchester

• Director
• Professor
• Institution: University of Sydney
• Twitter: ian__manchester
• LinkedIn: ianmanchester

Ian is the Hub Director and will oversee the full scope of research and demonstrator programs along with Deputy Directors Niko Suenderhauf and Stefan Williams. Within the Hub, his personal research activities will mainly be in achieving robust autonomy in challenging conditions, for example, marine robots in strong currents or UAVs in high winds; incorporating high-dimensional data streams (such as video and laser scans) into real-time perception and control algorithms; learning dexterous mobile manipulation from demonstration and interaction; and scalable algorithms for planning under uncertainty.

Bio: Ian Manchester is a Professor of Mechatronic Engineering at the University of Sydney, Australia, and specialises in the field of control engineering and robotics. His fundamental research work has been motivated by problems in the control of walking robots involving transformative technology which has many applications in both the engineered and natural world. These include transportation systems, chemical processes, aerospace, biological systems and intelligent robotic systems.

After receiving his PhD degree in electrical engineering from the University of New South Wales, Ian worked as a post-doctoral researcher at Umea University, Sweden, and then as a Research scientist at the Massachusetts Institute of Technology (MIT). In 2019, Ian was identified as a future research leader under the University of Sydney Research Accelerator (SOAR) Fellowship scheme. Currently, he is also the Director of the Australian Centre for Field Robotics (ACFR), one of the largest robotics research organisations in the world, internationally renowned for its fundamental research in robot perception and control and the development of autonomous systems operating in complex outdoor environments.

In the past five years, Ian has been invited to give seminars at top universities around the world including: Harvard, Caltech, MIT, University of California, Lund University and KTH Stockholm.

Research Expertise: Ian’s main research is in algorithms for control, estimation, and learning of complex dynamical systems in robotics and other application domains. A major current research focus is novel machine-learning algorithms with guarantees of safety and stability, and applications in safety-critical robotic systems.

Peter Corke

• Strategic Advisor
• Distinguished Professor
• Institution: Queensland University of Technology (QUT)
• Twitter: @petercorke59
• LinkedIn: petercorke

Bio: Peter is a robotics researcher and educator. He is the distinguished professor of robotic vision at Queensland University of Technology and was director of the ARC Centre of Excellence for Robotic Vision (2014-2020). His research is concerned with robotic perception using vision and force, dynamics and control, and the application of robots to mining, agriculture and environmental monitoring. He created widely used open-source software for teaching and research, wrote the bestselling textbook “Robotics, Vision, and Control”, created several MOOCs and the Robot Academy, and has won national and international recognition for teaching including 2017 Australian University Teacher of the Year.

Peter is the Chief Scientist of Dorabot (Shenzhen) and on the advisory boards of Emesent and LYRO. He is a fellow of the IEEE, the Australian Academy of Technology and Engineering, the Australian Academy of Science; former editor-in-chief of the IEEE Robotics & Automation magazine; founding editor of the Journal of Field Robotics; founding multi-media editor and executive editorial board member of the International Journal of Robotics Research; member of the editorial advisory board of the Springer Tracts on Advanced Robotics series; recipient of the Qantas/Rolls-Royce and Australian Engineering Excellence awards; and has held visiting positions at Oxford, University of Illinois, Carnegie-Mellon University and University of Pennsylvania. Prior to QUT, he founded and led CSIRO’s Autonomous Systems Laboratory (2004-2009). He received his undergraduate and master’s degrees in electrical engineering and PhD in mechanical and manufacturing engineering, all from the University of Melbourne.

Research Expertise: Peter is interested in how robots can use sensory information such as vision, force and touch to increase the breadth and reliability of tasks they do in our everyday world. He has a particular interest in the sense of vision, and human hand-eye coordination is wonderful motivating example that highlights the stark difference between human and current robot capability. Some specific topics of interest include:

• The use of visual information for controlling robot motion, a technique known as visual servoing.
• Very wide field-of-view cameras based on fisheye lens and lens/mirror (catadioptric) optical systems.
• Optical flow, how images from a moving robot can be used to infer the world’s 3D structure and the robot’s motion
• Computer architectures for implementing computer vision algorithms in real time
• Stereo vision, using information from one or more cameras to create the 3D world structure.
• The combination arm and mobile robots to create mobile manipulation systems
• Vision processing within networks of cameras.
• Super-fast hand-eye coordination

Niko Sünderhauf

• Deputy Director - Research
• Professor
• Institution: Queensland University of Technology (QUT)
• Twitter: https://twitter.com/nikosuenderhauf
• LinkedIn: nikosuenderhauf

Bio: Professor Niko Suenderhauf is Chief Investigator and member of the Executive Committee of the QUT Centre for Robotics (QCR) where he leads the Visual Learning and Understanding program. Between 2017 and 2020 Niko was Chief Investigator and Project Leader of the Australian Centre of Excellence for Robotic Vision (ACRV).

Niko received his PhD from Chemnitz University of Technology, Germany in 2012. In his thesis, he developed new and outlier-robust mathematical methods for robotic localisation and mapping, as well as general probabilistic estimation problems.

Niko’s research vision and expertise has been recognised by internationally competitive awards such as a Google Faculty Research Award and an Amazon Research Award.

Research Expertise: Niko conducts research in robotic vision, at the intersection of robotics, computer vision, and machine learning. His research focuses on scene understanding, SLAM, localisation, the effective combination of machine learning with classical methods, and reliable and safe machine learning in robotics.

In the context of the Hub, Niko wants to investigate with the Hub’s partners how new implicit representations can improve the efficiency and effectiveness of large-scale mapping over long time horizons; how robots can effectively combine machine learning with hand-written algorithms, demonstrations, or analytical controllers to perform complex tasks such as mobile manipulation on an underwater platform; how machine learning can be made more reliable, robust and adaptable to new situations; and how the combination of learning-based and analytical methods for control can allow us to give safety and performance guarantees.

Stefan Williams

• Deputy Director - Technology & Impact
• Professor
• Institution: University of Sydney
• LinkedIn: stefan-williams-88216b23

Bio: Prof. Stefan B. Williams is the Professor of Marine Robotics at the University of Sydney’s Australian Centre for Field Robotics where he leads Australia’s Integrated Marine Observing System Autonomous Underwater Vehicle Facility.  He is also the Head of the University of Sydney’s Digital Sciences Institute and the Co-Director of the NSW Space Research Network.  Stefan was the Head of School of Aerospace, Mechanical and Mechatronic Engineering from 2016-2021 and an ARC Future Fellow.

Stefan’s research over the past two decades has focused on marine robotic systems. He has introduced fundamental new insights in the areas of navigation, planning, control and machine learning to allow these platforms to operate in challenging environments. In addition to contributions to the engineering sciences that underpin these advances, he has led major national programs aimed at introducing Autonomous Underwater Vehicle (AUV) systems in support of applications in ecology, archaeology and geoscience, developing close international networks of collaborators.

Stefan has been awarded multiple distinctions for his work including the 2020 VC Award for Outstanding Teaching and Research, the 2019 Payne Scott Professorial Distinction and in 2018 he was awarded Distinguished Lecturer by the IEEE Oceanographic Engineering Society.

Research Expertise: Stefan’s research interests include Simultaneous Localisation and Mapping (SLAM) in unstructured underwater environments, autonomous navigation and control and classification and clustering of large volumes of data collected by robotic systems. He has led research cruises to sites around Australia and overseas deploying AUV systems as part of studies in marine engineering, ecology, geoscience and archaeology.

Stefan will lead the Capability Demonstrator program for the Hub and is particularly interested in exploring autonomous intervention techniques that allow robots to build detailed maps of infrastructure and to use these to complete manipulation tasks in an autonomous manner.  This will require advances in perception, mapping, control and reasoning in order to allow these systems to interact safely with their immediate environment.

Stephan has led projects with Thales, Mitsubishi Heavy Industries, the Schmidt Ocean Institute, IMOS, AIMS, NSW DPI Fisheries, Parks Australia, Department of Natural Resources and Environment Tasmania, SmartSat CRC, Mission Systems, Blueprint Lab, Advanced Navigation, Hullbot, Accenture, NASA Jet Propulsion Lab, DLR (German Aerospace Agency), Investment NSW, NSW Office of the Chief Scientist and Engineer, CSIRO, Woods Hole Oceanographic Institution, SARDI.

Related Articles

• Prestigious Title Awarded to Professor Stefan Williams

Hanna Kurniawati

• Theme Lead - Planning and Control (ANU Node Lead)
• Professor
• Institution: Australian National University (ANU)
• Twitter: -
• LinkedIn: -

Bio: Hanna Kurniawati is a Professor with ANU Futures Fellowship at the Australian National University (ANU). She leads the Robust Decision-Making and Learning Lab, part of the Planning and Optimisation group at the ANU School of Computing. She is also a deputy lead for the ANU Humanising Machine Intelligence Grand Challenge Project.

Hanna receives a PhD for work on high dimensional motion planning from the National University of Singapore. Before joining the ANU, Hanna was a faculty member at the University of Queensland. From 2013 to 2020, she was also a Research Affiliate at the Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT). In 2019, Hanna received an ANU Futures Fellowship –a highly competitive fellowship to attract and retain world-leading early and mid-career researchers at the ANU.

Research Expertise:

Hanna’s research focuses on robust decision-making and motion planning under uncertainty. Her fundamental research has received multiple awards, including the Robotics: Science and Systems 2021 Test of Time Award for pioneering work on robust planning in the non-deterministic and partially observable world.

In addition to fundamental research, Hanna is leading collaborative projects with several industry partners on the applications of planning under uncertainty in robotics and related domain, such as AI-based co-pilot for Helicopter Emergency Medical Services.

In this hub, Hanna will lead the planning and control theme. She is particularly interested in investigating highly scalable and adaptable planning and learning under uncertainty approaches. Such approaches would enable robots to accomplish inspection and intervention tasks safely and robustly, despite substantial uncertainty, including partial observability, non-deterministic effects of actions, information gaps, limited data, and changing and unpredictable nature of the operating environments.

Don Dansereau

• Theme Lead - Sensing and Perception
• Senior Lecturer
• Institution: University of Sydney
• Twitter: https://twitter.com/dgdvision
• LinkedIn: donald-dansereau

Bio: Dr Donald Dansereau leads the Robotic Imaging Lab at the University of Sydney. His work explores how computational imaging can help robots see and do, encompassing the design, fabrication, and deployment of new imaging technologies. Don authored the Light Field Toolbox for Matlab and his industry experience includes physics engines for video games, computer vision for microchip packaging, and chip design for automated electronics testing. His industry collaborators have included Intel Corporation, Ford Motor Company, and start-up companies EPI Imaging LLC, Integral Scopes, and most recently Project ARIA, making a medical prosthetic for the visually impaired. His collaborations have helped address the challenges of designing, deploying, and maintaining novel sensing payloads in demanding commercial applications.

In 2004 he completed an MSc at the University Calgary where he received the Governor General’s Gold Medal for his pioneering work in light field processing. In 2014 he completed a PhD on underwater robotic vision at the Australian Centre for Field Robotics, followed by postdoctoral appointments at the Queensland University of Technology and Stanford University.

Research Expertise: Don’s work focuses on novel camera and imaging system design, characterisation, and interpretation to allow robots to see in news ways, more robustly, and in previously prohibitive conditions. Including:

• Computational imaging: joint design of optics and algorithms to break camera trade-offs and measure new forms of visual information; bespoke hyperspectral, event, time of flight, and plenoptic camera systems
• Low-level robotic vision: algorithms for making sense of light specifically for robotic applications
• Novel camera calibration: how to effectively calibrate, rectify, and interpret imagery from a range of emerging imaging technologies
• Novel representations: spherical light fields, radiance fields, and related plenoptic representations for high-fidelity visual sensing and reconstruction
• Light field image processing: exploiting the rich information captured by this powerful imaging technology to deliver low-power, low-latency, hardware-friendly robotic imaging and perception

Don also works within Sydney Uni’s Nano Institute on the fabrication and integration of custom optical elements with the goal of enabling new kinds of cameras to see in challenging conditions such as poor water quality or rain. This involves the fabrication of micron-scale diffractive and refractive custom optics as well as dicing and mounting of micro-optics for integration into imaging systems.

Viorela Ila

• Theme Lead - Mapping and Insights
• Doctor
• Institution: University of Sydney
• LinkedIn: viorela-ila

Bio: Dr Viorela Ila received the Engineering degree in Industrial Engineering and Automation from the Technical University of Cluj-Napoca, Romania, in 2000 and the Ph.D. in Information Technologies from the University of Girona, Spain, in 2005. After the PhD studies, she joined the Robotics group at the Institut de Robótica i Informàtica Industrial, Barcelona, Spain. In 2009 she was awarded the MICINN/FULBRIGHT post-doctoral fellowship which allowed her to join the group of Prof. Frank Dellaert at College of Computing, Georgia Tech, Atlanta US. In 2010, Viorela joined the robotic group at LAAS-CNRS, Toulouse, France to work in the ROSACE project founded by RTRA-STAE. Between 2012 and 2014 she was a research scientist at Brno University of Technology in Czech Republic. From 2015-2018, Viorela was research fellow with the ARC Centre of Excellence for Robotic Vision at the Australian National University (ANU).

Research Expertise: Viorela’s research lies at the intersection of two fields, robotics and computer vision. Her research focuses on delivering foundation methodologies and algorithms to enable robotic systems to build real-time detailed 3D representation of the environment, a problem known as simultaneous localization and mapping (SLAM). This problem is of key importance in enabling robotic systems to progress from the structured setups such as labs and factories to operating in real world environment. Her research approach draws from recent advances in fundamental fields such as stochastic systems, information theory, high performance computing, machine learning and linear and nonlinear algebra and develops principled techniques and algorithms further integrated into robotic SLAM systems.

Dimity Miller

• Lecturer
• Institution: QUT
• Twitter: @DimityMiller

Bio: Dr Dimity Miller is a Chief Investigator with the QUT Centre for Robotics, and a Lecturer with the QUT School of Electrical Engineering and Robotics. From 2021-2022, Dimity was a postdoctoral research fellow jointly across the CSIRO Machine Learning and Artificial Intelligence Future Science Platform,  the CSIRO Robotics and Autonomous Systems Group, and the QUT Trusted Networks Lab. She obtained her PhD in 2021 from QUT, where she worked within the ARC Centre of Excellence for Robotic Vision (ACRV). Her thesis developed methods for extracting uncertainty from object detection models used for robotic perception. In particular, she focused primarily on developing object detectors that express high uncertainty in open-set conditions, where they encounter novel object classes. Dimity’s commitment to excellence in research has been recognised by an Executive Dean’s Commendation for Outstanding Doctoral Thesis Award in 2022, as well as a Vice-Chancellor’s Performance award in 2017.

Research Expertise:  Dimity’s research expertise is in reliable robotic vision – operating at the intersection of deep learning, computer vision, and robotics. In particular, she is passionate about understanding when and why computer vision models may fail in robotic applications, and developing techniques to mitigate this. Her current research spans across topics including uncertainty estimation, object detection, novelty/anomaly detection, continuous learning, deep learning introspection, metrics for assessing model performance, and spatiotemporal learning.

Ian Manchester

• Director
• Professor
• Institution: University of Sydney
• Twitter: ian__manchester
• LinkedIn: ianmanchester

Ian is the Hub Director and will oversee the full scope of research and demonstrator programs along with Deputy Directors Niko Suenderhauf and Stefan Williams. Within the Hub, his personal research activities will mainly be in achieving robust autonomy in challenging conditions, for example, marine robots in strong currents or UAVs in high winds; incorporating high-dimensional data streams (such as video and laser scans) into real-time perception and control algorithms; learning dexterous mobile manipulation from demonstration and interaction; and scalable algorithms for planning under uncertainty.

Bio: Ian Manchester is a Professor of Mechatronic Engineering at the University of Sydney, Australia, and specialises in the field of control engineering and robotics. His fundamental research work has been motivated by problems in the control of walking robots involving transformative technology which has many applications in both the engineered and natural world. These include transportation systems, chemical processes, aerospace, biological systems and intelligent robotic systems.

After receiving his PhD degree in electrical engineering from the University of New South Wales, Ian worked as a post-doctoral researcher at Umea University, Sweden, and then as a Research scientist at the Massachusetts Institute of Technology (MIT). In 2019, Ian was identified as a future research leader under the University of Sydney Research Accelerator (SOAR) Fellowship scheme. Currently, he is also the Director of the Australian Centre for Field Robotics (ACFR), one of the largest robotics research organisations in the world, internationally renowned for its fundamental research in robot perception and control and the development of autonomous systems operating in complex outdoor environments.

In the past five years, Ian has been invited to give seminars at top universities around the world including: Harvard, Caltech, MIT, University of California, Lund University and KTH Stockholm.

Research Expertise: Ian’s main research is in algorithms for control, estimation, and learning of complex dynamical systems in robotics and other application domains. A major current research focus is novel machine-learning algorithms with guarantees of safety and stability, and applications in safety-critical robotic systems.

Anna Paradowska

• Chief Investigator
• Professor
• Institution: University of Sydney
• Twitter: @AnnaParadowska1
• LinkedIn: anna-paradowska-717b349

Bio: Prof Anna Paradowska is Conjoint Professor in Advanced Structure Materials and Industrial Engagement Manager at the Australian Centre for Neutron Scattering (ACNS) at Australian Nuclear Science and Technology Organisation (ANSTO). Anna completed her MSc degree at the Wroclaw University of Technology, Poland, where she specialized in Materials Technology and Monitoring Systems. She received her PhD at the Department of Mechanical Engineering at Monash University, Australia. Her thesis research focused on the investigation of residual stress in welds using neutron and synchrotron diffraction. Prior to joining ACNS, Anna was working at ISIS at Rutherford Appleton Laboratory, UK, where she was an Instrument Scientist on the materials and engineering beamline called ENGIN-X.

Anna has published over 150 articles in journals and conference proceedings. She has won several grants and publication awards both in Australia and overseas. She actively serves on a number of national and international advising and reviewing committees. Anna is Deputy Chair of National Committee of Applied Mechanics – Engineers, Australia. Anna is a passionate advocate for women in STEMM, and actively promotes the value of research and collaboration to the community at every opportunity.

Research Expertise: Anna is an international expert in neutron diffraction stress analysis, which she uses to advance manufacturing procedures in additive manufacturing and welding structures.

Niko Sünderhauf

• Deputy Director - Research
• Professor
• Institution: Queensland University of Technology (QUT)
• Twitter: https://twitter.com/nikosuenderhauf
• LinkedIn: nikosuenderhauf

Bio: Professor Niko Suenderhauf is Chief Investigator and member of the Executive Committee of the QUT Centre for Robotics (QCR) where he leads the Visual Learning and Understanding program. Between 2017 and 2020 Niko was Chief Investigator and Project Leader of the Australian Centre of Excellence for Robotic Vision (ACRV).

Niko received his PhD from Chemnitz University of Technology, Germany in 2012. In his thesis, he developed new and outlier-robust mathematical methods for robotic localisation and mapping, as well as general probabilistic estimation problems.

Niko’s research vision and expertise has been recognised by internationally competitive awards such as a Google Faculty Research Award and an Amazon Research Award.

Research Expertise: Niko conducts research in robotic vision, at the intersection of robotics, computer vision, and machine learning. His research focuses on scene understanding, SLAM, localisation, the effective combination of machine learning with classical methods, and reliable and safe machine learning in robotics.

In the context of the Hub, Niko wants to investigate with the Hub’s partners how new implicit representations can improve the efficiency and effectiveness of large-scale mapping over long time horizons; how robots can effectively combine machine learning with hand-written algorithms, demonstrations, or analytical controllers to perform complex tasks such as mobile manipulation on an underwater platform; how machine learning can be made more reliable, robust and adaptable to new situations; and how the combination of learning-based and analytical methods for control can allow us to give safety and performance guarantees.

Stefan Williams

• Deputy Director - Technology & Impact
• Professor
• Institution: University of Sydney
• LinkedIn: stefan-williams-88216b23

Bio: Prof. Stefan B. Williams is the Professor of Marine Robotics at the University of Sydney’s Australian Centre for Field Robotics where he leads Australia’s Integrated Marine Observing System Autonomous Underwater Vehicle Facility.  He is also the Head of the University of Sydney’s Digital Sciences Institute and the Co-Director of the NSW Space Research Network.  Stefan was the Head of School of Aerospace, Mechanical and Mechatronic Engineering from 2016-2021 and an ARC Future Fellow.

Stefan’s research over the past two decades has focused on marine robotic systems. He has introduced fundamental new insights in the areas of navigation, planning, control and machine learning to allow these platforms to operate in challenging environments. In addition to contributions to the engineering sciences that underpin these advances, he has led major national programs aimed at introducing Autonomous Underwater Vehicle (AUV) systems in support of applications in ecology, archaeology and geoscience, developing close international networks of collaborators.

Stefan has been awarded multiple distinctions for his work including the 2020 VC Award for Outstanding Teaching and Research, the 2019 Payne Scott Professorial Distinction and in 2018 he was awarded Distinguished Lecturer by the IEEE Oceanographic Engineering Society.

Research Expertise: Stefan’s research interests include Simultaneous Localisation and Mapping (SLAM) in unstructured underwater environments, autonomous navigation and control and classification and clustering of large volumes of data collected by robotic systems. He has led research cruises to sites around Australia and overseas deploying AUV systems as part of studies in marine engineering, ecology, geoscience and archaeology.

Stefan will lead the Capability Demonstrator program for the Hub and is particularly interested in exploring autonomous intervention techniques that allow robots to build detailed maps of infrastructure and to use these to complete manipulation tasks in an autonomous manner.  This will require advances in perception, mapping, control and reasoning in order to allow these systems to interact safely with their immediate environment.

Stephan has led projects with Thales, Mitsubishi Heavy Industries, the Schmidt Ocean Institute, IMOS, AIMS, NSW DPI Fisheries, Parks Australia, Department of Natural Resources and Environment Tasmania, SmartSat CRC, Mission Systems, Blueprint Lab, Advanced Navigation, Hullbot, Accenture, NASA Jet Propulsion Lab, DLR (German Aerospace Agency), Investment NSW, NSW Office of the Chief Scientist and Engineer, CSIRO, Woods Hole Oceanographic Institution, SARDI.

Related Articles

• Prestigious Title Awarded to Professor Stefan Williams

Mitch Bryson

• Chief Investigator
• Doctor
• Institution: University of Sydney

Bio: Dr. Mitch Bryson is currently the Director for the undergraduate mechatronics program and a lecturer in the school of Aerospace, Mechanical and Mechatronic Engineering. He is a research expert in aerial and marine robotic navigation, mapping and sensor fusion, working in applications in marine science and forestry using computer vision, hyperspectral imaging and laser scanning. He leads the ACFR Forestry research group that performs research into innovative technologies and techniques for measuring and managing forest resources using state-of-the-art sensors, autonomous systems and intelligent processing algorithms.

Research Expertise:

• aerial and marine robotic navigation,
• mapping and sensor fusion,
• working in applications in marine science and forestry using computer vision,
• hyperspectral imaging,
• laser scanning,
• point cloud perception
• deep learning

Hanna Kurniawati

• Theme Lead - Planning and Control (ANU Node Lead)
• Professor
• Institution: Australian National University (ANU)
• Twitter: -
• LinkedIn: -

Bio: Hanna Kurniawati is a Professor with ANU Futures Fellowship at the Australian National University (ANU). She leads the Robust Decision-Making and Learning Lab, part of the Planning and Optimisation group at the ANU School of Computing. She is also a deputy lead for the ANU Humanising Machine Intelligence Grand Challenge Project.

Hanna receives a PhD for work on high dimensional motion planning from the National University of Singapore. Before joining the ANU, Hanna was a faculty member at the University of Queensland. From 2013 to 2020, she was also a Research Affiliate at the Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT). In 2019, Hanna received an ANU Futures Fellowship –a highly competitive fellowship to attract and retain world-leading early and mid-career researchers at the ANU.

Research Expertise:

Hanna’s research focuses on robust decision-making and motion planning under uncertainty. Her fundamental research has received multiple awards, including the Robotics: Science and Systems 2021 Test of Time Award for pioneering work on robust planning in the non-deterministic and partially observable world.

In addition to fundamental research, Hanna is leading collaborative projects with several industry partners on the applications of planning under uncertainty in robotics and related domain, such as AI-based co-pilot for Helicopter Emergency Medical Services.

In this hub, Hanna will lead the planning and control theme. She is particularly interested in investigating highly scalable and adaptable planning and learning under uncertainty approaches. Such approaches would enable robots to accomplish inspection and intervention tasks safely and robustly, despite substantial uncertainty, including partial observability, non-deterministic effects of actions, information gaps, limited data, and changing and unpredictable nature of the operating environments.

Don Dansereau

• Theme Lead - Sensing and Perception
• Senior Lecturer
• Institution: University of Sydney
• Twitter: https://twitter.com/dgdvision
• LinkedIn: donald-dansereau

Bio: Dr Donald Dansereau leads the Robotic Imaging Lab at the University of Sydney. His work explores how computational imaging can help robots see and do, encompassing the design, fabrication, and deployment of new imaging technologies. Don authored the Light Field Toolbox for Matlab and his industry experience includes physics engines for video games, computer vision for microchip packaging, and chip design for automated electronics testing. His industry collaborators have included Intel Corporation, Ford Motor Company, and start-up companies EPI Imaging LLC, Integral Scopes, and most recently Project ARIA, making a medical prosthetic for the visually impaired. His collaborations have helped address the challenges of designing, deploying, and maintaining novel sensing payloads in demanding commercial applications.

In 2004 he completed an MSc at the University Calgary where he received the Governor General’s Gold Medal for his pioneering work in light field processing. In 2014 he completed a PhD on underwater robotic vision at the Australian Centre for Field Robotics, followed by postdoctoral appointments at the Queensland University of Technology and Stanford University.

Research Expertise: Don’s work focuses on novel camera and imaging system design, characterisation, and interpretation to allow robots to see in news ways, more robustly, and in previously prohibitive conditions. Including:

• Computational imaging: joint design of optics and algorithms to break camera trade-offs and measure new forms of visual information; bespoke hyperspectral, event, time of flight, and plenoptic camera systems
• Low-level robotic vision: algorithms for making sense of light specifically for robotic applications
• Novel camera calibration: how to effectively calibrate, rectify, and interpret imagery from a range of emerging imaging technologies
• Novel representations: spherical light fields, radiance fields, and related plenoptic representations for high-fidelity visual sensing and reconstruction
• Light field image processing: exploiting the rich information captured by this powerful imaging technology to deliver low-power, low-latency, hardware-friendly robotic imaging and perception

Don also works within Sydney Uni’s Nano Institute on the fabrication and integration of custom optical elements with the goal of enabling new kinds of cameras to see in challenging conditions such as poor water quality or rain. This involves the fabrication of micron-scale diffractive and refractive custom optics as well as dicing and mounting of micro-optics for integration into imaging systems.

Viorela Ila

• Theme Lead - Mapping and Insights
• Doctor
• Institution: University of Sydney
• LinkedIn: viorela-ila

Bio: Dr Viorela Ila received the Engineering degree in Industrial Engineering and Automation from the Technical University of Cluj-Napoca, Romania, in 2000 and the Ph.D. in Information Technologies from the University of Girona, Spain, in 2005. After the PhD studies, she joined the Robotics group at the Institut de Robótica i Informàtica Industrial, Barcelona, Spain. In 2009 she was awarded the MICINN/FULBRIGHT post-doctoral fellowship which allowed her to join the group of Prof. Frank Dellaert at College of Computing, Georgia Tech, Atlanta US. In 2010, Viorela joined the robotic group at LAAS-CNRS, Toulouse, France to work in the ROSACE project founded by RTRA-STAE. Between 2012 and 2014 she was a research scientist at Brno University of Technology in Czech Republic. From 2015-2018, Viorela was research fellow with the ARC Centre of Excellence for Robotic Vision at the Australian National University (ANU).

Research Expertise: Viorela’s research lies at the intersection of two fields, robotics and computer vision. Her research focuses on delivering foundation methodologies and algorithms to enable robotic systems to build real-time detailed 3D representation of the environment, a problem known as simultaneous localization and mapping (SLAM). This problem is of key importance in enabling robotic systems to progress from the structured setups such as labs and factories to operating in real world environment. Her research approach draws from recent advances in fundamental fields such as stochastic systems, information theory, high performance computing, machine learning and linear and nonlinear algebra and develops principled techniques and algorithms further integrated into robotic SLAM systems.

Patrick Haslum

• Chief Investigator
• Associate Professor
• Institution: Australian National University (ANU)

Bio: Associate Professor Patrick Haslum is part of the Planning and Optimisation group in the School of Computer Science at the ANU. Patrik Haslum received his Ph.D. in computer science from Linköping University in 2006. His main area of research is AI planning, with a focus on problem modelling and bridging planning and optimisation.

Research Expertise: His research interests are in artificial intelligence, with a focus on reasoning and optimisation problems over deterministic models. His work focuses mainly in the areas of automated planning, scheduling and diagnosis, developing knowledge-based technologies that combine problem-independent algorithmic techniques with declaratively specified, algorithm-independent problem knowledge. This approach enables application of these technologies to a wide range of different problem domains.

Patrik has multiple industry experiences in the application of planning, including as an investigator in a DARPA SAIL-ON (Science of Artificial Intelligence and Learning for Open-world Novelty) project.

Guodong Shi

• Chief Investigator
• Associate Professor
• Institution: University of Sydney
• LinkedIn: guodong-shi-50b77616a

Bio: Dr. Guodong Shi (Member, IEEE) received his B.Sc. degree in mathematics and applied mathematics from the School of Mathematics, Shandong University, Jinan, China, in 2005, and a Ph.D. degree in systems theory from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, in 2010. From 2010 to 2014, he was a Postdoctoral Researcher with the ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm. From 2014 to 2018, he was with the Research School of Engineering at Australian National University, as a Lecturer, and then, a Senior Lecturer, and a Future Engineering Research Leadership Fellow. In 2019, he joined the Australian Centre for Field Robotics as a Senior Lecturer.

Research Expertise: Guodong Shi’s research aims to grasp the structural and functional aspects of multiple genres of networks motivated by engineering, social, and quantum network systems, using tools ranging from control theory and optimisation to computational complexity and stochastic decision-making. His research outputs have impacted:

• the understanding in the roles of adversaries and biases in social opinion dynamics,
• developing fundamental distributed control and optimisation algorithms,
• designing novel privacy-protection mechanisms, and
• advancing scalability for quantum information processing.

Felipe Werndl Trevizan

• Chief Investigator
• Doctor
• Institution: Australian National University (ANU)

Bio: Dr. Felipe Trevizan is a Senior Lecturer at the School of Computing at ANU.  Previously, Felipe was a Senior Research Scientist at NICTA (now Data61/CSIRO).  Felipe earned his Ph.D. (2013) from Carnegie Mellon University (CMU). Along with colleagues and students, Felipe is the co-recipient of the 2016 best paper award from the Transport Research Board and the best paper award at the International Conference on Automated Planning and Scheduling (ICAPS) in 2016 and 2017.

Research Expertise: Felipe’s research interests lie at the intersection of Artificial Intelligence, Operations Research and Machine Learning including automated planning and scheduling, reasoning under uncertainty, heuristic search, and reinforcement learning.

Salah Sukkarieh

• Chief Investigator
• Professor
• Institution: University of Sydney
• LinkedIn: salah-sukkarieh

Bio: Salah Sukkarieh is the Professor of Robotics and Intelligent Systems at the University of Sydney. He was awarded the NSW Science and Engineering Award for Excellence in Engineering and Information and Communications Technologies in 2014; one of Australia’s Most Innovative Engineers by Engineers Australia in 2016; in 2017 the CSIRO Eureka Prize for Leadership in Innovation and Science, one of 11 Launch Food Innovators worldwide, and the AFR True Leaders Game Changer; and in 2019 was nominated as one of the 2019 NSW Australian of the Year, as well as being recognised by Engineers Australia’s as one of their Centenary Heroes. Salah is a Fellow of Australian Academy of Technological Sciences and Engineering (ATSE) and has over 500 academic and industry publications in robotics and intelligent systems.

From 2019-2022, Salah was the CEO of Agerris, an Agtech start-up company spun out of the ACFR, where he led the manufacturing and commercialisation of on-farm robotic solutions to improve agricultural productivity and environmental sustainability. He was the Director Research and Innovation at the Australian Centre for Field Robotics from 2007-2018, where he led the strategic research and industry engagement program in the world’s largest field robotics institute. Salah has received over $70m in university-industry-government research grants in the last ten years and is recognised as an international expert in the research, development and commercialisation of field robotic systems. He has led several robotics and intelligent systems R&D projects in logistics (Patrick Stevedores), commercial aviation (Qantas), aerospace (various national and international agencies), education (Australian Government), environment monitoring (Australian Government), agriculture (horticulture, grains, meat and livestock) and mining (Rio Tinto).

Research Expertise: Salah research focusses on autonomous systems science, specifically in the link between data fusion, optimisation and control, and new approaches and techniques for large-scale machine learning and data analytics. His research has provided impactful solutions to barriers in important large-scale engineering operations across various industries. Examples include:

• Navigation systems for autonomous air and ground robots used across various Australian industries.
• Machine learning techniques implemented on industry and government funded aerial surveillance systems for environment monitoring.
• Multi-Agent autonomous mathematical paradigms and algorithms used in the mining, agriculture, security and logistics industries.
• Robotic and data analytic systems for crop and animal monitoring systems for Australian agriculture
• Machine optimisation and learning systems for commercial aviation.

Aude Vignelles

• Board Member
• Institution: Australian Space Agency
• Twitter: @OzAude
• LinkedIn: audevignelles

Aude Vignelles is the Chief Technology Officer of the Australian Space Agency. As part of the senior executive team, Aude supports the Australian Space Agency with strategic technology advice and program delivery to grow a trusted and respected Australian space capability, using advanced insight and engagement with the Australian and international space sector.

Her office sets strategic priorities by developing and updating technology roadmaps across the Agency seven priority areas; identifies and develops domestic and international space programs, and support their deliveries; represents the Agency at international technology forums; maintains awareness of the state of the art for the space sector as the Technology Authority for the Agency and sustains strong engagement with the local industry and scientific community.

Prior to this role, Aude was the Executive Manager, Satellite & Fixed Wireless Operations at NBN.

Aude is a space and aeronautics engineer who started her career at the European Space Agency (ESA) in the Netherlands. She became a source of expertise for the test campaign of all scientific programmes run by ESA from their early phase, such as Rosetta. She was appointed Test Manager for the X-ray Multi Mirror mission (XMM), then the largest scientific space program ever carried out by ESA. She then moved to London where she started a career in the broadcast industry and successfully integrated the first digital terrestrial broadcast centre in the world. She continued this career in broadcast and media here in Australia with Foxtel and Austar. She later was appointed Vice President of Technicolor covering all programs in the APAC region.

Aude has been living in Australia for the past 22 years and has contributed to the Australian space community through White Papers, events at conferences with the growing start up community in Australia, and promotion for Women in Space and Engineering at universities.

Gaurav Sukhatme

• Board Member
• Professor
• Institution: University of Southern California
• Twitter: @gauravsukhatme
• LinkedIn: gaurav-sukhatme-9b6420b

Gaurav S. Sukhatme is Professor of Computer Science and Electrical & Computer Engineering at the University of Southern California (USC). He holds the Fletcher Jones Foundation Endowed Chair in Computer Science and serves as the Executive Vice Dean at the USC Viterbi School of Engineering. He is currently also an Amazon Scholar. He served as Chairman of the Computer Science department at USC from 2012-17. Sukhatme received his undergraduate education at IIT Bombay (B. Tech. in Computer Science & Engineering 1991), and M.S. (1993) and Ph.D. (1997) degrees in Computer Science from USC. After a brief postdoc, he joined the USC faculty. He is the co-director of the USC Robotics Research Laboratory and the director of the USC Robotic Embedded Systems Laboratory, which he founded in 2000.

Sukhatme’s research is in networked robots, learning robots and field robotics. He has published extensively in these and related areas. He has served as the Principal Investigator on numerous NSF, DARPA and NASA grants. He is a Fellow of the AAAI, the IEEE and a recipient of the NSF CAREER award and the Okawa foundation research award. He was named a distinguished alumnus by IIT Bombay in 2020. Sukhatme is one of the founders of the Robotics: Science and Systems conference. He was program chair of the 2008 IEEE International Conference on Robotics and Automation and the 2011 IEEE/RSJ International Conference on Robots and Systems. He is the Editor-in-Chief of the Springer Nature journal Autonomous Robots and has served as Associate Editor of the IEEE Transactions on Robotics and Automation, the IEEE Transactions on Mobile Computing, and on the editorial board of IEEE Pervasive Computing.

Mike Zimmerman

• Board Member
• Institution: Main Sequence Ventures
• Twitter: @mikezim25
• LinkedIn: mikezim

Mike Zimmerman is a founding partner at Main Sequence, a deep tech venture capital firm co-founded with CSIRO that invests in and creates companies solving Planetary Challenges with world-leading Australian science and engineering.  At Main Sequence he leads the Supercharge Industrial Productivity Challenge, and also invests in the Feed 10 Billion and Decarbonise the Planet challenges. Previously, he spent 20+ years as an entrepreneur, executive and investor in Australia and the US.

Mike also sits on the Board of the Australian Investment Council and the Board of the Stanford Graduate School of Business Alumni Association.  He has an MBA from Stanford University and a BA (honors) from Amherst College.

Robert Mun

• Executive Director Engineering and Information Science
• Doctor
• Institution: Australian Research Council (ARC)
• LinkedIn: @robert-mun-42926691

Dr Robert Mun joined the ARC in May 2018 as Executive Director for Engineering and Information Sciences.

Dr Mun has a strong technical background as a researcher, and has had an extensive career in the Australian Government. Prior to joining the ARC, Dr Mun was Branch Head at the Defence Science and Technology Group with the Australian Department of Defence, and previously was Scientific Advisor to Navy and to the Defence Material Organisation, and Senior Scientist leading systems engineering research, also with the Department of Defence. With research expertise commencing in the area of fluid mechanics, through his subsequent Australian Government roles, his experience has extended to complex systems integration, focusing on the integration and networking of multiple Defence systems. His research team’s specialty was including the human element within the engineering solution.

Throughout his career, Dr Mun has been involved in working with a wide range of national and international research specialists, incorporating multiple technologies into emerging solutions. This work has included managing Defence capability demonstrator grant programs.

Dr Mun has authored numerous Australian Government reports, and has contributed to a wide range of policy and capability outcomes. Prior to joining the Australian Government, he published numerous journal and book articles on fluid mechanics.

Bridget de Pelleport

• Hub Manager
• none
• Institution: University of Sydney
• LinkedIn: bridget-de-pelleport-24a13615a

As Hub Manager, Bridget is responsible for the business operations of the Research Hub and provides strategic advice to the Leadership Team. She has extensive experience managing ARC Hubs, having previously worked at an ARC Research Hub on energy storage at UNSW. Prior to this, Bridget was a membership engagement advisor to the Committee for the Economic Development of Australia (CEDA) and spent 13 years in Paris, France, where she had the privilege of working at the Organisation for Economic Co-operation and Development (OECD).

Overall, Bridget enjoys supporting academics, professionals and organisations that are making a significant impact in their fields and that provide substantial benefit to the broader society.