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Simo SärkkäView profile

Professor

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +7 more

Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.

Erik B. SudderthView profile

Professor

• Machine Learning
• Computer Vision
• Statistical Inference
• +3 more

Erik B. Sudderth is a Professor of Computer Science and Statistics and Chancellor's Fellow at the University of California, Irvine (UCI). He leads the Learning, Inference, & Vision Group and directs multiple research centers, including the UCI Center for Machine Learning and Intelligent Systems and the HPI Research Center in Machine Learning and Data Science. He previously served as an Associate Professor at Brown University. Education: B.S. (summa cum laude) in Electrical Engineering from UC San Diego (1999), M.S. and Ph.D. in EECS from MIT (2002, 2006). His research focuses on statistical methods for scalable machine learning, Bayesian nonparametrics, probabilistic graphical models, and applications in computer vision, AI, and environmental science. Key areas include nonparametric clustering, deep generative models, and particle-based inference algorithms. Research interests span diverse topics: advancing Bayesian nonparametric models for medical time series, scalable variational inference, and AI ethics. Notable contributions include the NET-VISA seismic monitoring system (ISBA Mitchell Prize, 2014), the BNPy toolbox (NSF CAREER Award), and work on diverse particle max-product algorithms for continuous inference. Scientific awards include the NSF CAREER Award, ISBA Mitchell Prize, and recognition as one of "AI's 10 to Watch" (IEEE). He has served as editor for top journals (JMLR, IEEE PAMI) and conference chairs (NeurIPS, CVPR). His work bridges theory and practice, with applications in robotics, climate science, and healthcare. Labs/Teams: UCI Learning, Inference, & Vision Group; UCI Center for Machine Learning; CREATE Technology Center. Grants include NSF funding for visually impaired collaboration tools and soil biogeochemical modeling.

Rui NiView profile

Associate Professor

• Experimental Fluid Mechanics
• Turbulence and Turbulent Multiphase Flow
• Dusty Flow and Particle-Laden Flow
• +5 more

Rui Ni is an associate professor in the Department of Mechanical Engineering at Johns Hopkins University, directing the Fluid Transport Lab. His research focuses on experimental fluid mechanics, turbulence, multiphase flows, and their applications in energy systems, environmental engineering, and physiological processes. He holds a PhD in Physics from the Chinese University of Hong Kong (2011), followed by postdoctoral work at Yale and Wesleyan Universities. Before joining JHU, he held the Kenneth Kuan-Yun Kuo Early Career Professorship at Penn State University. His research interests include dusty flows, Lagrangian particle tracking, and animal collective behaviors. Notable projects include collaborations with NASA on plume-surface interaction and the development of advanced diagnostic tools like physics-informed machine learning and 3D particle tracking. He has received prestigious awards, including the NSF CAREER Award and ACS-PRF New Investigator Award, and leads studies on turbulence modulation by deformable bubbles, fish schooling efficiency in turbulent environments, and interfacial mass transfer dynamics. Key Projects: Plume-Surface Interaction (NASA collaboration), Fish Aquarium with Turbulent Environment (FATE) facility, V-ONSET multiphase flow facility. Grants: Gordon and Betty Moore Foundation’s Experimental Physics Investigators Initiative Grant. Lab Focus: Experimental and computational studies of multiphase flows, physiological flows, and complex systems. Ni’s work bridges fundamental fluid dynamics with practical applications, such as improving energy efficiency and understanding biological systems like fish schooling and nasal drug delivery mechanisms.

• Autonomous Systems
• Robotics
• Motion Planning
• +3 more

Javier Alonso-Mora is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Learning & Autonomous Control. His research focuses on autonomous systems, robotics, motion planning, and transportation logistics, with applications in mobile manipulation, dynamic environments, and urban mobility. He leads key projects such as INTERACT (Intuitive Interaction for Robots among Humans) and ACT (Perceptive Acting Under Uncertainty), exploring human-robot interaction, autonomous vehicles, and healthcare robotics. Notable achievements include an ERC Starting Grant (2022) and a Veni Grant (2017). His work addresses challenges in robot navigation, control systems, and fleet optimization, with contributions to both theoretical advancements and practical implementations. Projects like TRiLOGy focus on sustainable water transportation, while HARMONY advances assistive robotics in healthcare. Alonso-Mora’s research leverages geometric fabrics for motion planning, probabilistic modeling for dynamic environments, and multi-agent coordination. He collaborates internationally and contributes to open-source frameworks for robotics. His recent publications emphasize safety-aware control, instance-aware semantic mapping, and adaptive systems for cluttered environments.

Steven RogakView profile

Professor

• Aerosol Science
• Nanoparticle Transport
• Combustion Processes
• +5 more

Steven Rogak is a Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science. He holds a P.Eng. license and degrees including a B.A.Sc. in Mechanical Engineering from UBC, and M.Sc. and Ph.D. from Caltech. P.Eng., University of British Columbia B.A.Sc., University of British Columbia M.Sc., Ph.D., California Institute of Technology His research focuses on aerosol science, particularly solid nanoparticles from combustion processes, their climate and health impacts, and mitigation strategies. Key areas include: Soot morphology and transport properties Engine emission reduction via fuel injectors Indoor air filtration systems Membrane-based energy exchangers Atmospheric particulate analysis The 15 most recent articles span experimental and theoretical studies on soot characterization, membrane technologies, and aerosol dynamics, with applications in climate modeling, healthcare ventilation, and sustainable materials. Collaborations include Westport Innovations and interdisciplinary teams. Rogak leads the Aerosol Laboratory at UBC, where he applies fluid mechanics and heat transfer fundamentals to address environmental and health challenges. He emphasizes experimental rigor and welcomes graduate students with expertise in these areas.

• Additive Manufacturing
• Aerospace Engineering
• Thermofluids
• +1 more

Associate Professor Ng Bing Feng leads research in Additive Manufacturing, Aerospace Engineering, and Thermofluids at Nanyang Technological University's School of Mechanical & Aerospace Engineering. As Cluster Director for Smart & Sustainable Building Technologies at NTU's Energy Research Institute, he oversees projects spanning energy-efficient cooling technologies, advanced filtration systems, and bio-inspired materials. His interdisciplinary work integrates advanced manufacturing with environmental engineering, focusing on sustainable solutions for air quality management and thermal regulation. Current research explores radiative cooling technologies, acoustic agglomeration for emissions control, and crashworthy structures via biomimetic design. Professor Ng's publications demonstrate consistent innovation in multi-scale manufacturing and fluid dynamics, with growing emphasis on sustainable urban technologies. Methodologies combine experimental fluid dynamics with computational modeling and AI-driven design. Leadership Roles: Cluster Director, Energy Research Institute @ NTU Principal Investigator for multiple industry collaborations in sustainable materials Research Output: 42+ publications in high-impact journals Multiple patents in filtration technologies and cooling systems

Peter K. KitanidisView profile

Professor

• Groundwater Flow
• Hydrologic Modeling
• Inverse Problems
• +3 more

Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling

Edoardo CharbonView profile

Professor

• Ultra High-Speed Optical Sensors
• 3D Optical Sensors
• LiDAR
• +10 more

Edoardo Charbon is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering, where he leads the Advanced Quantum Architecture Lab (AQUA). He also serves on the School Council STI and is Co-Director of STI-SSIQ Administration. Previously, he was a full professor and chair at Delft University of Technology from 2008 to 2016. Charbon received his Elektrotechnik Diploma from ETH Zurich, M.S. from UC San Diego, and Ph.D. from UC Berkeley, all in electrical engineering. His career spans industry experience at Cadence Design Systems and Canesta Inc. before joining EPFL in 2002. His research focuses on ultra high-speed and 3D optical sensors, with applications in LiDAR, FLIM (Fluorescence Lifetime Imaging Microscopy), PET (Positron Emission Tomography), FCS (Fluorescence Correlation Spectroscopy), and NIROT (Near-Infrared Optical Tomography). He has pioneered deep-submicron CMOS SPAD technology, which is now mass-produced and used in smartphones, telemeters, and medical diagnostics. His recent work bridges cryo-CMOS circuits for quantum computing with advanced optical sensing techniques. Analysis of his recent publications reveals a strong trend toward integrating quantum technologies with practical imaging applications. His work spans from fundamental device development (SPAD sensors, cryo-CMOS circuits) to applied systems (LiDAR engines, medical imaging devices), with increasing integration of machine learning techniques for real-time processing. 2023 IISS Pioneering Achievement Award Fellow of the IEEE Distinguished visiting scholar, W. M. Keck Institute for Space at Caltech Fellow, Kavli Institute of Nanoscience Delft Distinguished lecturer, IEEE Photonics Society Professor Charbon has authored or co-authored over 500 papers and two books, and holds 27 patents. His research has been supported by collaborations with organizations including Bosch, X-Fab, Texas Instruments, Maxim, Sony, Agilent, and the Carlyle Group. He has driven significant innovation in CMOS SPAD technology, which is now commercially deployed in various applications. He leads the Advanced Quantum Architecture Lab (AQUA) at EPFL, which focuses on the development of advanced sensor systems combining quantum technologies with conventional electronics. The lab has been instrumental in creating SPAD-based imaging systems that push the boundaries of time-resolved optical detection.

Mathieu JoergerView profile

Associate Professor

• Navigation Systems
• Autonomous Systems
• Dynamics and Control
• +6 more

Dr. Mathieu Joerger is an Associate Professor in the Aerospace & Ocean Engineering Department at Virginia Tech, leading the Assured Vehicle Autonomy (AVA) Lab. He holds a Ph.D. (2009), M.S. (2002), and Diplôme d’Ingénieur (2002) from Illinois Institute of Technology and INSA Strasbourg. His research focuses on navigation safety, multi-constellation GNSS, and autonomous system integrity. He serves as Technical Editor for IEEE Transactions on Aerospace and Electronic Systems and co-leads the CARNATIONS initiative for resilient PNT systems. Notable awards include the ION Early Achievement Award (2015) and Bradford W. Parkinson Award (2009). Research interests include GNSS augmentation, LiDAR/IMU integration, and safety quantification for autonomous vehicles. His lab collaborates with industry/government on projects like CAAMS and develops methods to detect GNSS interference using UAS. Key publications address integrity monitoring in SLAM, particle filtering, and Kalman filter applications. Education: Ph.D. Mechanical & Aerospace Engineering, Illinois Tech (2009); M.S. Mechanical Engineering, Illinois Tech (2002); Diplôme d’Ingénieur, INSA Strasbourg (2002). Awards: ION Early Achievement Award, Outstanding NAVIGATION Reviewer, Bradford W. Parkinson Award. Professional Roles: Senior Editor for IEEE Transactions, ARAIM Standards Contributor, CARNATIONS Co-Director. Advises multiple PhD/Master’s students and oversees lab activities involving 20+ researchers. Projects include R-PNT systems, UAS-based RFI localization, and automotive GNSS safety. Active in international conferences like ION GNSS+ and AIAA forums.

• Numerical Modeling
• Computational Simulation
• Materials Science
• +5 more

Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.

Giorgio GrisettiView profile

Professor

• Mobile Robotics
• SLAM
• Robot Localization
• +3 more

Giorgio Grisetti is a Full Professor at Sapienza University of Rome within the Department of Systems and Computer Science, maintaining active research roles in the RoCoCo lab at Sapienza since November 2010 and the Autonomous Intelligent Systems Lab at Freiburg University where he previously served as a Post Doc under Wolfram Burgard starting in 2006. His educational background includes a M.Sc. in Computer Engineering from the University of Rome (2001) and a Ph.D. from Sapienza University of Rome's Intelligent Systems Lab (2006), supervised by Daniele Nardi. His doctoral thesis focused on SLAM using Rao-Blackwellized particle filters. Dr. Grisetti's research centers on mobile robotics with emphasis on robust solutions for autonomous navigation systems. His work spans theoretical and practical advancements in Simultaneous Localization and Mapping (SLAM), robot localization, path planning, and sensor fusion, particularly leveraging LiDAR and multi-sensor configurations. Recent publications demonstrate strong focus on optimization techniques, sensor calibration, and real-time performance for autonomous systems operating in complex environments. His publication trends reveal deep specialization in LiDAR-based SLAM (7 of 15 recent articles), bundle adjustment methods (4 articles), and sensor calibration/perception (3 articles), with consistent contributions to top robotics venues like IEEE Robotics and Automation Letters and ICRA. Key recognitions include: Nomination for the best IROS paper award (2010) Open Source achievement award from Willow Garage (2010) Best paper award at the International Conference and Exhibition on Unmanned Areal Vehicles (2010) Best Paper award at ICRA 2009 (2009) His research is conducted through the RoCoCo lab at Sapienza University of Rome and the Autonomous Intelligent Systems Lab at Freiburg University, focusing on developing foundational algorithms for mobile robot autonomy. Current projects emphasize robust perception systems, optimization frameworks for sensor fusion, and practical implementations for real-world navigation challenges.

Fay CouceiroView profile

Professor

• Environmental Pollution
• Microplastics Research
• Contaminant Fate and Transport
• +4 more

Professor Fay Couceiro is a Professor of Environmental Pollution in the School of Civil Engineering and Surveying at the University of Portsmouth. She leads the Microplastics Research Group and the 'Evaluating change across the plastics lifecycle' theme for the Revolution Plastics Institute, focusing on pollution sources, interventions, and collaborations with industry. She holds editorial roles at Cambridge Prisms: Plastics and peer reviews for multiple journals and funding bodies. Education: BSc in Marine Biology (Queen's University Belfast), PhD in Biogeochemistry (funded project on Strangford Lough), postdoctoral research at the University of Plymouth. Research Interests: Contaminants' fate, microplastics' environmental and health impacts, nutrient dynamics, heavy metals, and organic pollutants like PAHs. Her work integrates pure science with civil engineering solutions. Publications span 2007–2025, emphasizing microplastic toxicity, soil-oil interactions, and pollution mitigation strategies. Over 27 peer-reviewed articles highlight her contributions to environmental science. Advising & Grants: Supervises MSc/PhD students and collaborates with companies like Southern Water. Active in STEM outreach and UK research policy, including the EU's HR Excellence in Research accreditation process. Labs/Teams: Microplastics Research Group, Revolution Plastics Institute, and interdisciplinary teams in environmental technology and resilience.

Ruchi ChoudharyView profile

Professor

• Urban Energy Systems
• Building Energy Modeling
• Sustainable Cities
• +7 more

Professor Ruchi Choudhary is a Professor in Architectural Engineering at the University of Cambridge, Department of Engineering, within the School of Technology. She leads the Energy Efficient Cities initiative (EECi), a cross-disciplinary research project focused on strengthening the UK's capacity to address energy demand reduction and environmental impact in cities through research in building and transport technologies, district power systems, and urban planning. Her research interests span urban energy systems, building energy modeling, sustainable cities, geothermal energy systems, and data-driven energy modeling . She has pioneered work in digital twins for energy systems, urban subsurface thermal modeling, and building-integrated agriculture. Her research group develops numerical tools to improve energy efficiency of cities, with particular focus on modeling energy consumption of large building sets at multiple time and spatial resolutions. Analysis of her recent publications reveals a strong trend toward integrating machine learning with physics-based modeling for energy systems, with increasing emphasis on uncertainty quantification, digital twins, and value of information analysis for decision-making. Her work bridges the gap between theoretical modeling and practical urban implementation, with significant focus on city-scale geothermal potential, underground climate change impacts, and energy equity considerations. Professor Choudhary has supervised numerous PhD students who have gone on to prominent positions at institutions including UCL, University of Cambridge, BEIS, Arup, and various international universities. Her research group includes faculty members, research associates, graduate students, and international collaborators from institutions worldwide. Her current research focuses on two parallel investigations: one on using multi-scale multidisciplinary models to address energy use questions in the built environment, and second, on quantifying uncertainties in model outcomes. Current projects include integration of food production in urban environments, analysis of underground transport systems as energy sources, large-scale integration of ground source heat pumps, and distributed energy networks.

• Bayesian methodology
• Variable selection and model averaging
• Nonparametric regression models
• +3 more

Scientia Professor Robert Kohn is a distinguished academic at the University of New South Wales, holding a position in the School of Economics within the UNSW Business School. With a career spanning several decades, Professor Kohn has established himself as a leading expert in statistical methodology and econometric modeling. His research has significantly contributed to Bayesian statistics and computational methods for complex data analysis. Professor Kohn's research focuses on advanced statistical methodologies including Bayesian methodology, variable selection and model averaging, nonparametric regression models, time series modeling, multivariate Gaussian and non-Gaussian regression, and Markov chain Monte Carlo simulation algorithms. His work bridges theoretical statistics with practical applications across economics, finance, and cognitive science. His research demonstrates a consistent trajectory toward developing more efficient computational methods for complex statistical models, with recent work emphasizing variational Bayesian methods, particle filtering techniques, and applications to time series analysis. Analysis of his recent publications (2022-2025) reveals a strong focus on advancing computational statistical methods, particularly in Bayesian inference for complex models. His work shows increasing integration of machine learning techniques with traditional statistical methods, especially in handling high-dimensional data and complex time series structures. Professor Kohn has made significant contributions to variational inference methods, particle-based computational techniques, and applications to financial time series and cognitive modeling. Professor Kohn has maintained an exceptionally productive research career with continuous publication output since the 1970s, demonstrating remarkable longevity and adaptability in his research focus as statistical methodologies have evolved. His work shows strong international collaboration, particularly with researchers in Australia, the United States, and Europe, reflecting his standing in the global statistical community.

Martin LarssonView profile

Professor

• Mathematical Finance
• Stochastic Analysis
• Probability Theory
• +4 more

Martin Larsson is a Professor in the Department of Mathematical Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. He holds a Ph.D. from Cornell University and completed a postdoctoral appointment at the Swiss Finance Institute at EPFL, Lausanne, Switzerland. His research focuses on Mathematical Finance, stochastic analysis, probability, and statistics, with emphasis on affine and polynomial processes, stochastic portfolio theory, and sequential statistics. Key research domains include modeling interest rate term structures, large-scale equity market dynamics, and statistical testing in online settings. He serves as the Departmental representative for the Master of Science in Computational Finance (MSCF) program at CMU. Larsson has received the Bruti-Liberati Visiting Fellowship from the University of Technology Sydney. His work bridges theoretical probability with applications in finance, including contributions to stochastic volatility modeling, optimal contracts in trading, and robust portfolio optimization under uncertainty. Publications span topics such as martingale exit times, Wasserstein distance convergence, and ergodic control in stochastic systems, reflecting his interdisciplinary approach to mathematical finance and probability theory. His research often combines analytical techniques with stochastic control and geometric flows.