Find Academic Supervisors

• Biomedical Engineering
• Electronic Instrumentation
• Analog Electronics
• +3 more

Romano Giannetti is a Professor at the Department of Electronics, Automation and Communications within the Higher Technical School of Engineering (ICAI) at Universidad Pontificia Comillas . Holding a PhD in Electronics and Computer Engineering from the University of Padua (1993), he has served at Universidad Pontificia Comillas since 1998 and currently acts as Head of Department since 2021. Previously, he held administrative roles including Vice-Rector for International Affairs (2009-2012). Research Interests focus on Measurement Instrumentation and Methodology Biomedical Instrumentation Noise Measurement Techniques Low-Noise Electronic Circuits Metamaterial-Based Sensors Non-Invasive Biomedical Systems His work spans portable measurement systems, microelectronic support instrumentation, and wireless neurophysiological monitoring. Recent publications emphasize machine learning integration with sensors, triboelectric nanogenerators, and microwave metamaterials for liquid characterization. Scientific Awards include Four certified Spanish Sexenio research periods (1990-2020) Asociación Nacional de Ingenieros del ICAI Prize for Best Final Year Project (2023-24) Cross4Health Hackathon Prize (2019) Academic Leadership involves supervising 4 Ph.D. theses and teaching courses like Electronics and Biomedical Instrumentation and Electronic Systems . He has participated in 16 funded projects, including the AgroMaster IoT agricultural system and Sepsis Detection devices.

Pierre BoivinView profile

Researcher

• Combustion
• Fluid Dynamics
• Numerical Analysis
• +3 more

Pierre Boivin is a Chargé de Recherche at CNRS with a Habilitation à Diriger des Recherches (HDR), affiliated with the Thermodynamique Ondes Numérique Interfaces Combustion team at M2P2 Institute (Aix-Marseille University). His work bridges theoretical and applied combustion research, focusing on numerical methods for reactive flows and hydrogen safety. Key Research Areas: Combustion physics, hydrogen flame modeling, lattice Boltzmann methods (LBM), compressible flow simulation, and detonation/ignition prediction. Scientific Contributions: 2024 CNRS Bronze Medal for advancing hydrogen combustion theory; developed novel LBM solvers for compressible flows and reduced-order models for self-ignition risks. Collaborations: Frequent co-authorship with Song Zhao, Pierre Sagaut, and teams at M2P2 and CNRS. Article Trends: Recent publications (2025–2021) emphasize LBM for hydrogen combustion, shock-wave interactions, and safety modeling. Key subfields include reactive flow stability, thermodiffusive instabilities, and hybrid numerical algorithms. Scientific Awards: CNRS Bronze Medal (2024) for hydrogen combustion breakthroughs.

Dhakal RajendraView profile

Associate Professor

• Wearable Devices
• RF/Microwave Integrated Sensors
• Machine Learning
• +9 more

Dhakal Rajendra is an Associate Professor at the Department of Computer Science and Engineering, Sejong University (2024–present), previously serving as an Assistant Professor (2018–2024). He earned a B.E. (2007) from Pokhara University and a Ph.D. (2016) from Kwangwoon University. His career includes postdoctoral work (2016–2017) at Kwangwoon University and lecturer roles (2009–2012) at the Institute of Engineering, Nepal. Education B.E. (2007), Pokhara University Ph.D. (2016), Kwangwoon University His research focuses on Wearable Devices , RF/Microwave Integrated Sensors , Machine Learning , and Glucose Biosensors , with innovations in flexible sensors, capacitive pressure systems, and self-powered biomimetic designs for health monitoring. Recent work includes AI-driven bleeding detection and deep learning-enhanced laryngeal cancer classification. Key publications (2023–2025) demonstrate expertise in Advanced Science and Chemical Engineering Journal , covering topics like glycerol@phosphoric acid nanofibers for posture recognition and Mn–Ag electrochemical sensors for athlete rehabilitation. His work has attracted significant citations and international collaborations. Scientific contributions include a Best Paper Award at IEEE CCWC 2019 and editorial roles at Micromachines (2021–present). He has chaired conference sessions and contributed to numerous academic boards, emphasizing microwave sensors and health monitoring systems.

Thomas HacklView profile

Researcher

• NMR Spectroscopy
• Biological Chemistry
• Metabolomics
• +3 more

Dr. Thomas Hackl is a senior NMR spectroscopy specialist at the Department of Chemistry , University of Hamburg, MIN Faculty. He leads the Food-NMR working group since 2014 and serves as Academic Council member since 2009, focusing on solution-state NMR applications for food authentication and metabolomics research.

• machine translation
• analogy
• multilingual alignment
• +7 more

Yves Lepage is a Professor at Waseda University's Faculty of Science and Engineering, specifically within the Graduate School of Information, Production, and Systems. He maintains an active research laboratory (lepage-lab.ips.waseda.ac.jp) and teaches courses including Example-based machine translation/NLP, Natural language processing, and Master's/Doctoral thesis supervision for the 2025 academic year. His research focuses on the application of analogical reasoning to natural language processing problems, particularly machine translation. Lepage's work spans formal analogy between strings, sentence-level analogies, morphological analysis, and multilingual systems. His research interests include machine translation, analogy, multilingual alignment, multilingual large language models, and foreign language aids. He has made significant contributions to understanding analogical density in corpora and developing methods to leverage analogies for translation, especially in low-resource scenarios. His publication record shows consistent output through 2024, with research evolving from foundational work on proportional analogy to sophisticated applications with neural networks. Recent work explores masked prompt learning for analogies, fuzzy analogies for translation, and organizing lexica into analogical grids for morphological generation across languages. Waseda University Teaching Award (Spring semester 2016) Lepage has successfully led multiple research projects funded by the Japan Society for the Promotion of Science, including "Theoretically founded algorithms for the automatic production of analogy tests in NLP" (2021-2024) and "Self-explainable and fast-to-train example-based machine translation using neural networks" (2018-2021). His work has involved international collaboration, including a 2023-2024 research period at the University of Montreal. He serves as Concurrent Researcher at the Waseda Research Institute for Science and Engineering (2024-2026) and has been active in professional organizations including the Information Processing Society of Japan and the Japanese Natural Language Processing Association.

Tapomayukh BhattacharjeeView profile

Assistant Professor

• Robotics
• Human-Robot Interaction
• Haptic Perception
• +5 more

Tapomayukh Bhattacharjee is an Assistant Professor in the Department of Computer Science at Cornell University, where he directs the EmPRISE Lab (Empowering People through Intelligent Systems and Embodied Interaction). His research focuses on enabling robots to assist individuals with mobility limitations in performing Activities of Daily Living (ADLs) through innovations in human-robot interaction, haptic perception, and robot manipulation. Ph.D. in Robotics from Georgia Institute of Technology NIH Ruth L. Kirschstein NRSA Postdoctoral Fellow at the University of Washington (Computer Science & Engineering) His work addresses the fundamental challenge of leveraging robot-world physical and social interactions in unstructured human environments to develop intelligent, real-world robotic systems. Key research themes include: Human-Robot Interaction : Designing intuitive and safe interactions for caregiving Haptic Perception : Using tactile and thermal sensing for rapid environmental understanding Robot Manipulation : Mastering tasks like feeding and dressing with deformable objects AI for Healthcare : Developing algorithms for adaptive and user-centric assistive robotics His research has been recognized with prestigious awards including the NSF CAREER Award '23 , TRI Young Faculty Researcher Award '24 , and multiple best paper awards at top robotics conferences like RSS'25 , HRI , and IROS . Media outlets such as the New York Times and BBC have featured his work, including Fast Company 's selection of his robot-assisted feeding system as one of the best interactive designs of 2019. The EmPRISE Lab emphasizes full-stack robotics development, combining sensing, perception, planning, and control to create systems evaluated with real users. Current projects include the PhyRC Challenge (Physical Robotic Caregiving Challenge), which includes tracks for robot-assisted dressing and bed bathing, with real-world evaluation phases at major conferences like ICRA 2025 in Atlanta, GA.

• Rate-Independent Systems
• Damage Processes
• Crack Propagation
• +6 more

Prof. Dr. Dorothee Knees is a mathematician at the University of Kassel, holding the Professorship for Analysis since 2014. Her research focuses on variational calculus, non-smooth evolutionary equations, and mathematical models for damage processes, crack propagation, magnetic phenomena, and free surfaces in structural mechanics. She has led collaborative projects such as "Rate-independent systems in solid mechanics" with Jörn Mosler and participated in interdisciplinary initiatives like SPP 2256 on variational methods in engineering. Her work bridges mathematical analysis and computational mechanics, emphasizing physical relevance and numerical efficiency. University of Kassel (2014–present): Professorship for Analysis WIAS Berlin (2008–2010): Head of Leibniz Group "Modelling of Damage Processes" University of Stuttgart (2001–2005): Research Associate in Institute for Applied Analysis and Numerical Simulation Her research spans rate-independent systems, non-convex energies, and optimal control in dissipative materials. Key projects include analysis of phase-field fracture models, balanced viscosity solutions, and adaptive numerical methods for crack evolution. She has co-authored foundational papers on micromorphic continua, stress regularity in composites, and J-integrals for cracks in power-law materials. Scientific awards include a scholarship from the Robert Bosch Foundation's Fast Track Program (2005–2008). As an editor, she has contributed to special issues on PDEs and nonsmooth continuum mechanics.

• Cryptography
• Security
• Algorithms
• +7 more

Paul Stankovski Wagner is an Associate Professor and Senior Lecturer at the Department of Electrical and Information Technology, Faculty of Engineering (LTH), Lund University. He serves as Project Manager for ELLIIT (Linköping-Lund initiative on IT and mobile communication) and contributes to the LTH Profile Area: AI and Digitalization as well as the Secure and Networked Systems initiative. Roles: Senior Lecturer, Associate Professor, Project Manager, Researcher, Assistant Supervisor Institutions: Lund University, Linköping University, Pufendorf IAS His research focuses on cryptography, security, and quantum-resistant algorithms. Key areas include Learning with Errors (LWE), post-quantum cryptographic implementations, and blockchain applications in cybersecurity and pharmaceutical R&D. He explores side-channel vulnerabilities, key management systems, and privacy-preserving protocols like PAPR for anonymous credentials. Recent publications (2021-2025) highlight his work on BKW-style LWE solvers, healthcare IT security architectures, and blockchain-based data-sharing frameworks. Articles span journals like ACM Transactions on Computing for Healthcare and International Review of Law, Computers & Technology , with keywords reflecting cryptography, algorithms, and data privacy. He supervises doctoral research (e.g., Amanda Nilsson’s work on post-quantum cryptographic side channels) and collaborates on multidisciplinary projects such as SMARTY (secure software updates for smart cities) and LUTAX (tax research with technological applications).

Esin ÖZTÜRK IŞIKView profile

Associate Professor

• Magnetic Resonance Imaging
• Magnetic Resonance Spectroscopic Imaging
• Medical Imaging
• +5 more

Esin ÖZTÜRK IŞIK is an Associate Professor at Boğaziçi University's Biomedical Engineering Institute, where she also serves as the Director of the Computational Imaging Laboratory. She has held this position since September 2014, following her tenure as an Assistant Professor at Yeditepe University from December 2009 to September 2014. Prior to her academic appointments in Turkey, she completed her postdoctoral fellowship at the University of California, San Francisco, where she had also earned her Ph.D. through the Joint Graduate Program in Bioengineering between UC Berkeley and UCSF. Dr. ÖZTÜRK IŞIK's research primarily focuses on magnetic resonance imaging and spectroscopic imaging techniques, with particular emphasis on developing novel computational methods for MR image analysis. Her work spans multiple areas including parallel imaging theories, fast data acquisition methods, phosphorus MR spectroscopic imaging, and machine learning applications for disease characterization. She has made significant contributions to multimodality medical imaging for cancer diagnosis, treatment planning, and disease monitoring. Her research output shows a clear trend toward increasingly sophisticated computational methods applied to medical imaging problems, with recent publications focusing on compressed sensing techniques for accelerated phosphorus MRSI, machine learning approaches for tumor classification, and multi-parametric MRI analysis for cancer grading. The majority of her work centers on brain tumor imaging, with growing interest in prostate cancer applications as well. Bogazici University Academic Encouragement Award (2014) TUBITAK EU Project Submission Encouragement Award (2010) Educational Stipend Awards from ISMRM conferences (2004-2006) High Field Workshop Trainee Award (2007) UCSF Graduate Division Earle C. Anthony Travel Award (2006) UCB Graduate Division Conference Travel Grant (2006) Dr. ÖZTÜRK IŞIK has secured significant research funding including a TUBITAK 1001 Grant for 'Determination of Multimodality Magnetic Resonance Imaging Based Biomarkers for Mild Cognitive Impairment in Parkinson Disease' (2015-2018), a Bogazici BAP Grant for 'Investigation of the Human Brain Metabolism in-vivo in Chronic Liver Failure' (2015-2018), and a Marie Curie International Reintegration Grant for 'Phosphorus MR Spectroscopic Imaging of Brain Tumors at 3T' (2010-2014). She serves as an active reviewer for numerous prestigious journals including Magnetic Resonance in Medicine and Journal of Magnetic Resonance Imaging. As Director of the Computational Imaging Laboratory at Boğaziçi University, she leads a research team focused on developing advanced computational methods for medical image analysis. Her lab specializes in translating technical innovations into clinical applications, particularly in the areas of brain tumor characterization and cancer imaging. She also serves as the ERASMUS coordinator and Summer internship coordinator for the Biomedical Engineering Institute.

Nathan WeiseView profile

Associate Professor

• Power Electronics
• Power and Energy Conversion
• Electric Drives
• +7 more

Nathan Weise serves as an Associate Professor in the Electrical and Computer Engineering Department within Marquette University's Opus College of Engineering. He established the Electric Machines and Power Electronics at Marquette (EMPOWER) laboratory in 2014, developing it into a world-class research facility. His academic journey includes a Ph.D. (2011), M.S. (2007), and B.S. (2005) in Electrical/Computer Engineering from the University of Minnesota. Dr. Weise specializes in power electronics with research spanning Power and Energy Conversion , Electric Drives , Vehicular Power Systems , and Control of Renewable Energy Sources . His work focuses on high-power-density GaN-based systems for electric aircraft, ultra-fast EV charging infrastructure, and DC circuit breakers. He has secured two highly competitive U.S. Department of Energy ARPA-E grants addressing critical energy challenges. His publication portfolio reveals strong emphasis on GaN semiconductor applications , thermal management systems , and advanced control algorithms for multilevel converters. Recent work shows progression toward aerospace electrification and ultra-high-power systems, with 2025 publications indicating cutting-edge research in electric aircraft propulsion. Way Klingler Young Scholar Award (2020-2021) Marquette Teacher of the Year (2019) IEEE Eta Kappa Nu Beta Omicron Chapter Award Dr. Weise has developed industry-critical courses in digital control of renewable energy, power electronics, and hands-on lab experiences. His future research plans include establishing a world-leading power electronics lab, creating a medium-voltage facility, and authoring the textbook Digital Control of Power Electronics with accompanying lab materials.

• Programming Languages
• Formal Verification
• Compilers
• +6 more

Clément Pit-Claudel is an Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he heads the SYSTEMF lab. Previously, he was a PhD candidate at MIT under Adam Chlipala and a Senior Applied Scientist at Amazon AWS. His research focuses on programming languages, compilers, and formal verification, with broader interests in systems engineering, hardware design languages, security, performance engineering, and type theory. Key research areas include extensible compilation, hardware design languages verification, and tooling for proof assistants. Recent publications span topics like JavaScript regular expression verification ( ICFP 2024 ), relational compilation ( PLDI 2024 ), and foundational integration verification for cryptographic servers ( PLDI 2024 ). Scientific Awards: Distinguished Artifact, ACM SIGPLAN International Conference on Software Language Engineering (2020) William A. Martin Memorial Thesis Award for Outstanding Thesis in Computer Science, MIT (2016) Frederick C. Hennie III Teaching Award, MIT (2016) As an educator, he teaches Software Construction (undergraduate) and Interactive Theorem Proving (graduate) at EPFL. His teaching philosophy emphasizes hands-on learning, oral examinations, and project-based assignments that lead to tangible artifacts. The SYSTEMF lab focuses on building small, fast, and completely verified systems components through machine-checked proofs, hardware-software co-design, low-level compiler engineering, and theorem prover tooling.

Joar BaggeView profile

Research Fellow

• Numerical Analysis
• Machine Learning
• High-Performance Computing
• +3 more

Joar Bagge is a Postdoctoral Fellow at the Center for Numerical Analysis within the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin, where he joined in September 2023 to collaborate with Prof. George Biros and Prof. Gunnar Martinsson on computational engineering projects. He earned his PhD in Applied and Computational Mathematics from KTH Royal Institute of Technology, Stockholm, Sweden, in 2023 under the supervision of Prof. Anna-Karin Tornberg, focusing on special quadrature methods and fast summation techniques for boundary integral equations and Stokes flow. Bagge's research integrates numerical analysis, machine learning, and high-performance computing to develop efficient algorithms for microscale fluid simulations and elliptic PDEs. His work emphasizes special quadrature methods, fast direct solvers, and boundary integral equation formulations, with practical applications in scientific computing. His scientific recognition includes: Peter O'Donnell, Jr. Postdoctoral Fellowship in Computational Engineering and Sciences Bagge actively contributes to the Parallel Algorithms for Data Analysis and Simulation Group at the Center for Numerical Analysis, developing open-source software like the Spectral Ewald Unified package (SE_unified_v2) for fast summation methods in particle simulations.

• Smart Grids
• Demand Response
• Optimisation Modelling
• +3 more

Dr. Dora He is a Postdoctoral Research Fellow at The Australian National University's School of Engineering. She holds a PhD and specializes in smart grid technologies, demand response strategies, and optimization modeling. Her research focuses on battery scheduling, stochastic scheduling, and the integration of electric vehicles (EVs) into energy systems. Research Interests: Smart Grids and Energy Storage Solutions Optimization of Residential and Commercial Energy Demand Virtual Power Plants and Community Battery Systems Real-Time Pricing Mechanisms and Scalable Algorithms Cybersecurity in Smart Grid Infrastructure Her recent work includes analyzing potential benefits of neighborhood batteries and comparing them with virtual power plants, as well as developing fast algorithms for large-scale demand scheduling under real-time pricing. A notable project (2023-2024) explores maximizing consumer benefits from community batteries through targeted research and knowledge sharing. Advising and Grants: Project: 'Keeping the community in community batteries' funded from June 2023 to April 2024 Labs/Teams: Engaged with energy systems research teams at ANU, focusing on grid optimization and renewable energy integration.

• Biomedical Electromagnetics
• Microwave Imaging
• Medical Diagnostics
• +5 more

Andreas Fhager is an Associate Professor in Biomedical Electromagnetics at Chalmers University of Technology, where he leads the research group of the same name. His work focuses on developing microwave-based imaging diagnostics for breast cancer, stroke, and other biomedical applications, encompassing system design, signal processing, electromagnetic modeling, and optimization. He co-founded Medfield Diagnostics AB to commercialize microwave diagnostic equipment, demonstrating strong translational research capabilities. His research centers on biomedical electromagnetics with emphasis on microwave imaging for medical diagnostics. Key areas include breast cancer detection through tomographic systems, stroke diagnosis using ultra-wideband technology, and traumatic injury monitoring via wearable devices. He develops advanced electromagnetic models, optimization algorithms, and signal processing techniques to improve diagnostic accuracy while reducing hardware complexity. His work bridges theoretical innovation with practical clinical applications, particularly targeting prehospital care settings where rapid diagnosis is critical. Analysis of his recent publications (2021-2025) reveals three dominant trends: hardware simplification (reducing transmission channels, frequency points, and system components), noise/multipath mitigation (using lossy gels, dielectric antennas, and asymmetry detection), and clinical translation (wearable abdominal injury monitors, stroke triage tools, and muscle rupture diagnostics). His research increasingly focuses on real-world implementation, with studies using porcine models and phantom testing to validate systems for emergency medical applications. No scientific awards were mentioned in the provided text. Fhager co-founded Medfield Diagnostics AB, indicating active engagement in research commercialization and likely related grant acquisition. While specific grants aren't detailed, his leadership of a research group and extensive publication record suggest successful funding from sources like the Swedish Research Council or EU programs. He teaches Electromagnetic Field Theory, Medical Signals and Systems, and Diagnostic Imaging, contributing to academic training in biomedical engineering. His entrepreneurial activity demonstrates effective translation of academic research into medical technology solutions. He leads the Biomedical Electromagnetics research group at Chalmers University of Technology, which develops end-to-end microwave diagnostic systems from electromagnetic modeling to prototype validation. The group's work includes antenna design (e.g., dielectric rod antennas), computational methods (e.g., discrete dipole approximation), and clinical testing (e.g., porcine models for abdominal injuries). Current projects focus on wearable prehospital diagnostics and stroke triage tools, with future directions likely expanding into point-of-care applications and integration with AI-driven analysis.

Arghya PalView profile

Lecturer

• Causality
• Generative Models
• Computer Vision
• +3 more

Dr. Arghya Pal is a Lecturer at the Malaysia School of Information Technology, Monash University. His research focuses on causality, generative models, and computer vision, with notable contributions in medical imaging optimization, bias detection in AI systems, and cryptographic applications of autoencoders. He has led or co-investigated projects such as Æinstein: Adversarial AI amongst Materials Discovery Domains (2024–2026) and Memory Efficient Fast Sampling of Diffusion Model (2023–2024). Research Interests: Causality and bias analysis in machine learning Generative models for multimedia applications Computer vision techniques in medical imaging Cryptography and AI-driven security systems Awards: Magna cum laude (2022) Grants/Projects: Primary Investigator: Memory Efficient Fast Sampling of Diffusion Model (2023–2024) Chief Investigator: Æinstein Project (2024–2026)