Anthony Rowe is the Siewiorek and Walker Family Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU) and a Chief Scientist at Bosch Research. His primary affiliation is with the CyLab and the Wireless, Sensing and Embedded Systems (WiSE Lab) at CMU. He specializes in networked embedded systems, sensor networks, and extended reality (XR) technologies. His research emphasizes energy-efficient sensing, real-time localization, and XR integration with physical systems. Research Focus: His work spans XR systems (e.g., AR/VR edge networking in ARENA), mmWave radar for sensing (e.g., tire wear monitoring via Osprey), distributed edge computing (Silverline), and low-power wide-area networking (OpenChirp). Recent efforts include AI-integrated XR platforms (XaiR) and radar tomography (DART). Grants & Projects: Leads the CONIX Research Center ($27.5M NSF/DARPA grant), Bosch-funded edge computing projects, and DOE initiatives on microgrids. Notable projects include ARENA (XR edge architecture), GridBallast (smart grid control), and rural microgrid deployments in Haiti. Awards: Best Student Paper (ISMAR 2024), Best Paper (IPSN 2020), and the Steven J. Fenves Research Award (2015). Recognized for innovations in localization (MobiCom 2021), radar (ICRA 2023), and energy systems (BuildSys 2010). Teaching: Teaches courses on embedded systems (18-349/18-449), real-time systems, and mixed reality (18-453). Courses emphasize hands-on design and real-world applications. Labs & Teams: Directs the WiSE Lab, collaborating with Bosch Research and industry partners. The lab develops open-source frameworks like ARENA and OpenChirp, and contributes to standards for edge computing and sensing.
Mark Foster is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, with a primary appointment in the Whiting School of Engineering. He is also a Fellow of the Hopkins Extreme Materials Institute. His research focuses on developing ultrahigh-speed optical systems at the intersection of photonics and electronics, emphasizing photonic devices and information theory to advance imaging, sensing, and communications technologies. Applications include quantum-optical systems, ultrawide-bandwidth microwave photonics, and terahertz-rate imaging systems. Dr. Foster received his BS (2003), MS (2007), and PhD (2008) in Applied and Engineering Physics from Cornell University. Before joining Johns Hopkins in 2010, he served as a postdoctoral associate there. His work has been funded by the NSF, IARPA, DTRA, and NIH, resulting in over 200 publications and eight patents. He has held leadership roles, including chairing the IEEE Photonics Society’s Baltimore chapter (2011–2014). Research Highlights: World-leading imaging systems achieving terahertz frame rates Quantum-optical platforms and nonlinear photonic materials (e.g., NbTiOx) Secure authentication via physically unclonable functions (PUFs) Applications in fusion energy diagnostics and medical imaging His awards include the NSF CAREER Award (201?), DARPA Young Faculty Award, and ONR Young Investigator Award. Current projects explore machine learning-resistant PUFs, multi-modal imaging systems, and photonics for extreme environments.
Professor Kim Eun-hee is a faculty member in the Department of Defense Systems Engineering at Sejong University, specializing in advanced radar technologies and signal processing. Her work bridges theoretical research and practical applications in defense systems. Ph.D. in Mechanical Engineering (2004), KAIST M.Sc. in Engineering (1996), KAIST B.Sc. in Precision Engineering (1994), KAIST Her research focuses on radar system design, including airborne active phased array radar, automotive radar, broadband noise radar, and over-the-horizon radar. She explores waveform optimization, MIMO architectures, and signal processing algorithms to enhance radar performance in complex environments. Publications highlight her expertise in MIMO radar configurations, Doppler-insensitive waveforms, and machine learning integration for signal analysis. She leads industry-academic collaborations with organizations like Hanwha Systems and LIG Nex1. She contributes to technical committees, including the Sensor and Signal Processing Division of the Korean Society of Military Science and Technology. Her laboratory (Defense Radar Technology Laboratory) focuses on radar design, signal processing, and sensor integration.
Aydin Babakhani is a Professor in the Department of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA), affiliated with the College of Life Sciences. He directs the Integrated Sensors Laboratory (ISL), which focuses on the design and implementation of integrated sensors and systems. His research spans high-speed wireless communication, terahertz technology, medical implants, radar systems, and industrial monitoring solutions. Research Interests: Prof. Babakhani's work integrates silicon-based technologies with applications across multiple domains. Key areas include: Silicon mm-Wave/THz transceivers and on-chip antennas for communication and sensing Wirelessly powered medical implants for biopotential monitoring and neural stimulation THz radar systems for micrometer-resolution imaging and vibration detection Energy harvesting solutions for batteryless sensors in industrial and biomedical applications CMOS-based optoelectronic systems and photonic computing accelerators His recent publications (2021-2025) demonstrate a strong emphasis on terahertz systems, wireless power transfer, and miniaturized medical electronics. Over 80% of his latest articles involve silicon-integrated solutions for biomedical implants or THz sensing, with emerging focus on AI-accelerated photonic computing and multi-Gbps wireless links.
Leif Eriksson is a Professor at Chalmers University of Technology , specializing in Radar Remote Sensing within the Department of Space, Earth and Environment . His career at Chalmers began in 2004, and he was promoted to Professor in 2022 after serving as Group Leader (2012–2017) and Head of Faculty Assembly (2017–2020). His research focuses on developing advanced methods for environmental monitoring using radar data, particularly synthetic aperture radar (SAR) from satellites and aircraft. Leadership Roles: Group Leader (Radar Remote Sensing), Faculty Assembly Head Key Collaborations: Rymdstyrelsen, EU Horizon, VINNOVA, European Space Agency Research Interests : Dr. Eriksson’s work spans forest biomass estimation , sea ice dynamics , and ocean surface current/wind retrieval . He integrates SAR data with in situ observations and climate models to study: Forest degradation (clear cuts, storm damage) via multi-temporal SAR Sea ice concentration, drift patterns, and thickness in Arctic regions Wind vectors and surface currents using interferometric SAR techniques Applications for maritime navigation safety and polar shipping optimization Article Trends : His recent publications emphasize SAR’s role in transport infrastructure monitoring (e.g., Iron Ore Line degradation), pan-Arctic landfast ice stability , and multi-frequency SAR fusion for enhanced sea ice observations. Collaborative work with teams across Europe and the U.S. highlights interdisciplinary approaches to climate and marine research. Projects & Grants : Dr. Eriksson leads or contributes to projects such as: CAISA (2022–2024): Air-ice-sea data assimilation EONav (2016–2019): Copernicus data for maritime navigation SEDNA (2017–2020): Safe Arctic shipping Forest Biomass Monitoring (2017–2018): Spaceborne SAR applications His work is supported by Rymdstyrelsen, EU Horizon, and industry partners like Trafikverket. Labs & Teams : He is central to the Radar Remote Sensing Group at Chalmers, collaborating with institutions like Lund University and international bodies such as ESA. His research often involves satellite campaigns (e.g., TanDEM-X, Sentinel) and field studies in polar regions.
Alexis Berne is an Associate Professor at the Environmental Remote Sensing Laboratory (LTE) within the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL). He co-directs the SSIE-GE program and serves as a member of the CDS (Commission for Doctoral Studies) . His research spans radar meteorology, precipitation microphysics, polar precipitation, and geostatistics, with a focus on mountainous and polar regions. Current Positions Associate Professor, LTE, EPFL (2013–present) Co-Director, SSIE-GE, EPFL PhD Program Committee Member, EDCE-GE, EPFL Research Interests include the remote sensing of precipitation, particularly snowfall and ice production mechanisms, using radar and geostatistical methods. His work addresses atmospheric processes in extreme environments like Antarctica and the Swiss Alps, leveraging machine learning and numerical modeling for climate analysis. Teaching encompasses courses on remote sensing, atmospheric processes, and climate change, emphasizing interdisciplinary approaches and spatiotemporal variability. He advises current PhD students such as Heather Anne Corden and Gionata Ghiggi, alongside mentoring past students like Jacopo Grazioli and Timothy Hugh Raupach.
Jeremy Dahl is a Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine. He directs the Ultrasound Imaging & Instrumentation Lab and serves as Director of Research Academic Affairs in the Department of Radiology since 2020. He holds multiple affiliations across Stanford including Bio-X, the Cardiovascular Institute, Wu Tsai Human Performance Alliance, Maternal & Child Health Research Institute, Stanford Cancer Institute, and Wu Tsai Neurosciences Institute. Dr. Dahl received his B.S. in Electrical Engineering from the University of Cincinnati (1999) and Ph.D. in Biomedical Engineering from Duke University (2004). His research focuses on developing ultrasonic beamforming and image reconstruction methods for diagnostic imaging applications, particularly techniques that generate high-quality images in difficult-to-image patients. His laboratory specializes in B-mode and Doppler imaging techniques that utilize additional information from ultrasonic wavefields to improve image quality and develop real-time imaging systems for clinical applications including cardiac, liver, and fetal imaging. Dr. Dahl's research has led to significant advancements in ultrasound molecular imaging platforms, sound speed estimation, aberration correction, and reverberation noise suppression. His work often bridges engineering innovation with clinical applications for cancer detection and other diseases. His recent publications demonstrate strong focus on machine learning applications in ultrasound, distributed aberration correction, and molecular imaging techniques. Fellow, American Institute of Ultrasound in Medicine (2021) Senior Member, Institute of Electrical and Electronics Engineers (2020) Distinguished Investigator Award, The Academy for Radiology & Biomedical Imaging Research (2018) Outstanding Paper Award, IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (2011) Dr. Dahl serves in editorial roles for major journals including IEEE Transactions on Medical Imaging (2017-2024) and IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2013-Present). His laboratory has successfully translated numerous innovations into clinical applications, with multiple patents including recent developments in pulsed focused ultrasound therapy and speed of sound quantification.
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.
Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.
Anne E. White is the School of Engineering Distinguished Professor of Engineering and associate vice president for research administration at the Massachusetts Institute of Technology (MIT). She serves in the Department of Nuclear Science and Engineering within MIT's School of Engineering and is a key researcher at the Plasma Science and Fusion Center (PSFC). White has held significant leadership roles including NSE department head from 2019 to 2023 and co-chair of the MIT Climate Nucleus from 2021 to 2024. She currently chairs the Fusion Energy Sciences Advisory Committee (FESAC), providing federal advisory input to the U.S. Department of Energy Office of Science. White received her PhD in physics from UCLA, where she conducted research at the Electric Tokamak. Her early career included research positions at the National Spherical Torus Experiment at Princeton Plasma Physics Laboratory and the DIII-D National Fusion Facility at General Atomics before joining MIT as a faculty member. Her educational background laid the foundation for her expertise in plasma physics and fusion energy research. Professor White's research focuses on magnetic fusion energy, specifically on understanding turbulent transport in magnetically confined fusion plasmas. Her work spans diagnostic development, novel experimentation, and validation of nonlinear gyrokinetic codes. She aims to demonstrate nuclear fusion as a practical part of the world's sustainable energy future. Her group develops and uses radiometers, reflectometers, and interferometers to measure fluctuations in plasma density, temperature, and flows in tokamaks. This research is critical for improving predictive capabilities of turbulent transport models, which is essential for developing viable fusion reactors. Analysis of Professor White's recent publications reveals a strong focus on plasma diagnostics and turbulence measurements across multiple tokamak facilities. Her work spans experimental measurements on ASDEX Upgrade, Alcator C-Mod, NSTX, and DIII-D tokamaks, with particular emphasis on electron temperature fluctuations, turbulence characterization, and transport model validation. A significant theme is the development and application of novel diagnostic techniques for simultaneous measurements of multiple plasma parameters. Her research increasingly incorporates computational approaches, including gyrokinetic simulations and machine learning methods, to interpret experimental data and advance predictive capabilities in fusion plasma physics. Professor White has received numerous prestigious awards throughout her career: Fellow, American Physical Society Division of Plasma Physics (2019) Cecil and Ida Green Career Development Professor, MIT (2014) American Physical Society Katherine E. Weimer Award (2014) Fusion Power Associates Excellence in Fusion Engineering Award (2014) Junior Bose Award for Excellence in Teaching, MIT (2014) PAI Outstanding Faculty Award from MIT student chapter of the American Nuclear Society (2013) Norman C. Rosenbluth Career Development Professor, MIT (2012-2014) Department of Energy Early Career Award (2011-2016) Marshall N. Rosenbluth Outstanding Doctoral Thesis Award (2009) As an educator and mentor, Professor White has advised numerous students through MIT's Department of Nuclear Science and Engineering. She has taught courses including Principles of Plasma Diagnostics, Seminar in Fusion & Plasma Physics, and Introduction to Plasma Physics. Her leadership extends to developing educational resources, notably leading a team in 2018 to create a free MITx MOOC focused on nuclear science and engineering for global high school learners. Professor White has secured significant research funding through Department of Energy awards, including the Early Career Award (2011-2016) and various fusion energy fellowships throughout her career. Her research group at MIT's Plasma Science and Fusion Center has contributed to multiple major fusion facilities and has been instrumental in advancing understanding of plasma turbulence and transport. Professor White leads the Fusion and Plasmas Lab at MIT, which focuses on diagnostic development and turbulence measurements in fusion plasmas. Her team has made significant contributions to research on four major tokamaks: Alcator C-Mod, ASDEX Upgrade, DIII-D, and National Spherical Torus Experiment Upgrade. At MIT's Plasma Science and Fusion Center, she previously served as assistant division head for magnetic fusion energy collaborations and ran the Gyrokinetic Simulation Working Group and the Alcator C-Mod Transport Group. Her lab maintains close collaboration between experimental work, theoretical modeling, and computational simulation to advance the understanding of plasma turbulence and transport phenomena critical for fusion energy development.
Professor Athanassios Manikas holds the Chair of Communications & Array Processing in the Department of Electrical & Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He is a Fellow of both the IET and IMA, and has held significant editorial roles including Associate Editor for IEEE Transactions on Aerospace and Electronic Systems. His research focuses on wireless communications, radar systems, antenna array processing, and applied mathematics, with over 50 supervised PhD students and 150+ Masters projects. He leads Imperial's research group in array processing and has extensive industry collaborations, including technical leadership of the University Defence Technology Centre in Signal Processing (2008-2013). Awards include the IEEE PIMRC 2022 Best Paper Award and recognition as an IEEE COMSOC Distinguished Lecturer (2016-2017). His work integrates differential geometry principles with array processing, as detailed in his monograph Differential Geometry in Array Processing . Professor Manikas has served as an expert witness in high-profile cases and contributes to academic governance roles such as the Royal Society's International Fellowship Committee. His research group is affiliated with the Space Lab at Imperial, focusing on innovative applications of array signal processing in aerospace and defense systems.
Dr. Andrea Lecchini Visintini is an Associate Professor at the School of Electronics and Computer Science , University of Southampton. He specializes in systems modelling and control with applications in aerospace engineering and biomedical domains, utilizing Monte Carlo methods for stochastic optimization. Cyber-Physical Systems Research Group Institute for Life Sciences Research Focus: His work bridges computational methods with practical applications in: Neurovascular coupling and brain tissue pulsation analysis Advanced control strategies for aerospace systems Stochastic optimization in machine learning and fault detection Medical imaging and diagnostic protocol development Publication Trends: Recent work emphasizes interdisciplinary approaches combining computational neuroscience with engineering, focusing on brain hemodynamics, MIMO system control, and data augmentation techniques for imbalanced datasets. Supervision: Currently supervising PhD student Xuankun Cai in Computer Science.
Prof. Xiaojing Huang is a Professor of Information and Communications Technology at the University of Technology Sydney (UTS), serving as Head of Discipline for SEDE Communications and Electronics within the School of Electrical and Data Engineering. He leads the Mobile Sensing and Communications program at the Global Big Data Technologies Centre. With over 30 years of experience, he has authored over 300 publications and 31 patents, focusing on wireless communications, signal processing, and antenna technologies. Education: PhD (Electrical Engineering, Shanghai Jiao Tong University, 1989). Previous roles include Principal Research Scientist at CSIRO (2009-2014), Associate Professor at University of Wollongong (2004-2009), and key industry roles at Motorola and Shanghai Yang Tian Science and Technology Corporation. Research interests include full-duplex wireless systems, millimeter-wave and terahertz communications, massive antenna arrays, and mixed-signal processing platforms. His work on the CSIRO Ngara backhaul system earned multiple awards, including the 2012 CSIRO Chairman's Medal and Australian Engineering Innovation Award. Recent grants include $4.2M (AUD) for projects like 'Radio Frequency Camera for Radar Imaging' (ARC DP220101158) and 'Terabit mm-Wave Backbones for Integrated Space Networks' (ARC DP200101532). He has supervised numerous students in high-speed communication systems and full-duplex technologies. Awards include: 2013 CSIRO Leadership Achievement Award, 2012 Australian Engineering Innovation Award, and IEEE Sumner Award (nominee). Active in IEEE standards (802.11/802.15) and collaborations with institutions like Tsinghua University.
Anne H Schistad Solberg is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. She leads research in digital signal processing and image analysis, with a focus on machine learning applications across multiple domains. As co-director of SFI Visual Intelligence, she oversees research on interpretable deep learning models, uncertainty quantification, contextual learning, and self-supervised learning approaches. Her research spans medical imaging (particularly cardiovascular ultrasound), environmental monitoring using satellite imagery, and seabed mapping with sonar technology. Professor Solberg's work demonstrates a consistent trajectory from foundational signal processing techniques to cutting-edge deep learning applications. Her recent publications show increasing specialization in medical image analysis, particularly in echocardiography enhancement and cardiac structure segmentation, while maintaining strong contributions to remote sensing and geophysical applications. She teaches several popular courses including IN2070, IN3310, and IN5400 (Machine Learning for Image Analysis), which is noted as the most popular master's/PhD course on deep learning at the University of Oslo. Professor Solberg serves as principal investigator for the Intelligent Cardiovascular Ultrasound Scanner (INCUS) project, collaborating with GE Vingmed Ultrasound to develop AI-enhanced cardiac imaging systems that improve diagnostic accuracy and productivity in echocardiography. Co-director of SFI Visual Intelligence research center Principal Investigator for the INCUS project (Intelligent Cardiovascular Ultrasound Scanner) Member of the Digital Signal Processing and Image Analysis (DSB) research group Member of the Strategic Research Initiative: Multimodal Medical Imaging and Image Analysis (MEDIMA) Her research group develops algorithms that address real-world challenges in medical diagnostics and environmental monitoring, with a particular emphasis on making deep learning models more interpretable and reliable for critical applications. The INCUS project, funded through User-driven Research-based Innovation (BIA), aims to reduce the time wasted during cardiac ultrasound examinations by implementing intelligent algorithms that learn from expert users and historical data.
Jeffrey Glassberg, MD, is a Professor in both the Department of Emergency Medicine and the Division of Hematology and Medical Oncology at the Icahn School of Medicine at Mount Sinai. His clinical and research focus centers on hematology and emergency medicine, with particular attention to sickle cell disease and its complications. Education MD, Washington University Internship and Residency in Emergency Medicine, Bellevue Hospital Center Research Focus Dr. Glassberg's work explores the intersection of emergency care and hematological disorders, particularly sickle cell disease. His research includes epidemiological studies of ICU utilization and advanced imaging techniques to assess retinal blood flow abnormalities in sickle cell patients. Publications Recent work published in Chest and Ophthalmic Surgery Lasers and Imaging Retina focuses on clinical outcomes and vascular complications in sickle cell disease. Contact Information Phone: 212-824-8074 Address: One Gustave L. Levy Place, Box 1620, New York, NY 10029
