Find Academic Supervisors

• Signal Processing
• Radar Systems
• Electromagnetic Modelling
• +3 more

Javier Rosado Sanz is an academic at the Universidad de Alcalá, holding the position of Profesor Ayudante Doctor in the Signal Theory and Communications Department. His research focuses on advanced radar systems, particularly passive radar technologies leveraging existing broadcast signals (DVB-T/S, GPS, etc.) for target detection and tracking. He leads work in the AES3 group, developing smart sensor networks for coastal and urban surveillance, emphasizing acoustic and electromagnetic sensing. He earned his PhD from Universidad de Alcalá in 2021 with a thesis on antenna array configurations and electromagnetic target modeling for passive radar enhancement. His technical expertise spans antenna design optimization, clutter mitigation algorithms, and multi-static radar architectures. Recent research emphasizes UAV/drone detection, 2D/4D tracking systems, and adaptive beamforming techniques. His work bridges theoretical signal processing with practical implementations in maritime/coastal surveillance and smart city sensor networks. Key innovations include reflectarray antenna designs and decentralized detection frameworks for distributed sensor arrays. Publications highlight contributions to passive radar performance under challenging environments (e.g., wind farms, urban clutter) and novel applications like oil spill classification using SAR data. Collaborations involve experimental validations of DVB-based radar systems and conductivity analysis of composite materials used in UAVs.

Silvia FerrariView profile

Professor

• Robotics and Autonomy
• Artificial Intelligence
• Neuroscience
• +5 more

Silvia Ferrari is the John Brancaccio Professor of Mechanical and Aerospace Engineering at Cornell University, leading the Laboratory for Intelligent Systems and Controls (LISC) in Ithaca and co-directing the Cornell-Unibo Věho Institute on Vehicle Intelligence at Cornell Tech. She holds academic affiliations with both Cornell Engineering and Cornell Tech, focusing on interdisciplinary research in autonomy, robotics, and intelligent systems. Education: B.S., Aerospace Engineering, Embry-Riddle Aeronautical University (1997) M.A. and Ph.D., Mechanical and Aerospace Engineering, Princeton University (1999–2002) Research Interests: Her work spans computational intelligence, sensorimotor learning, adaptive control, and robotic autonomy. Key areas include active perception, neural network modeling, distributed sensor networks, and decision-making under uncertainty. She has pioneered methods in information-driven path planning and reinforcement learning, with applications to robotics, neuroscience, and environmental monitoring. Awards: Presidential Early Career Award for Scientists and Engineers (2006) NSF CAREER Award (2005) ONR Young Investigator Award (2004) Fellow of ASME and AIAA (2020) Labs & Leadership: As Director of LISC and co-Director of the Věho Institute, she fosters cross-campus collaborations in autonomy and urban technology. Her recent initiatives include organizing the Autonomy and Mobility Workshop, advancing Cornell’s ‘One Cornell’ research strategy.

Subramaniam PennathurView profile

Professor

• Nephrology
• Diabetic Kidney Disease
• Lipid Metabolism
• +3 more

Subramaniam Pennathur is the Norman Radin Professor of Nephrology, Professor of Internal Medicine, and Section Head of Molecular and Integrative Physiology at the University of Michigan Medical School. He leads research in lipid metabolism, diabetic kidney disease, and nanofluidic technologies. Affiliated with the Caswell Diabetes Institute, MM-PKUHSC Joint Institute, and Center for Computational Medicine and Bioinformatics. Research focuses on mechanisms linking lipid dysregulation to kidney and vascular complications in diabetes. Recent work includes metabotype analysis for cardiorenal risk prediction, nanofluidic biosensors for DNA analysis, and multi-omics integration in kidney disease. Over 20 peer-reviewed publications since 2023 highlight contributions to diabetic nephropathy, environmental toxin effects, and precision medicine approaches. Active in translational research, his lab develops diagnostic tools like optical glucose sensors and investigates therapeutic targets such as β-hydroxybutyrate for metabolic reprogramming. Collaborates across disciplines including biomedical engineering, epidemiology, and computational biology.

• Robotics
• Control Systems
• Aerial Vehicles
• +3 more

Rita Cunha is an Associate Professor at the Institute of Systems and Robotics (ISR) within the University of Lisbon. Her academic role includes teaching courses such as Introduction to Robotics, Signals and Systems, and Control Engineering. Her research focuses on advanced robotics and control systems, with particular emphasis on multirotor drones, formation control, trajectory planning, and autonomous systems. She has contributed significantly to the development of control strategies for unmanned aerial vehicles (UAVs), sensor-based navigation, and distributed multi-agent systems. Her work integrates theoretical control methodologies with practical applications, addressing challenges in aggressive drone maneuvers, collision avoidance, and sensor network deployment for wildfire surveillance. She leads research in collaborative robotics, including multi-drone cinematography and cooperative load transportation systems. Her contributions span both algorithm design and experimental validation, leveraging tools like the Pegasus simulator and physics-informed neural networks. Key Research Areas: Autonomous Systems, Aerial Robotics, Formation Control, Trajectory Optimization, Sensor Networks Teaching: Introduction to Robotics, Control Systems, Signals and Systems Labs/Teams: ISR (Institute of Systems and Robotics) - active in UAV control and autonomous systems research Her over 50 publications since 2013 demonstrate sustained innovation in robotics control, with recent emphasis on high-order consensus algorithms, adaptive control under uncertainties, and real-time trajectory generation for confined spaces. Current projects include LiDAR-based autonomous inspection systems and distributed mission execution frameworks for aerial cinematography.

Niklas KlümperView profile

Researcher

• Uro-oncology
• Immuno-oncology
• Precision Oncology
• +5 more

Dr. Niklas Klümper is an advanced clinician-scientist at the University of Bonn's Faculty of Medicine, affiliated with both the Department of Urology and the Institute for Experimental Oncology (IEO). Supported by the BMBF-funded ACCENT program, he leads a translational research group focused on improving treatment outcomes in uro-oncology through innovative approaches to immunotherapy and antibody-drug conjugates (ADC). Faculty of Medicine, University of Bonn Department of Urology and Pediatric Urology Institute for Experimental Oncology (IEO) ACCENT program fellow His research emphasizes precision oncology , using longitudinal serum analysis of CRP and albumin to predict immunotherapy responses while employing ultra-deep quantitative proteomics for unbiased biomarker discovery. The team investigates ADC resistance mechanisms and develops 3D cell culture models like the CAM assay to identify synergistic treatment combinations. Recent publications highlight his contributions to NECTIN-4 expression patterns , FGFR inhibition resistance , and surfactant protein autoimmunity in treatment-related pneumonitis. His work bridges clinical practice with experimental research, demonstrating how established laboratory markers can be integrated into daily oncology care. Scientific Awards: ACCENT Program Fellow (BMBF-funded) As ACS representative on the ACCENT Executive Board (2023-2024), he contributes to early-career researcher initiatives while maintaining active clinical and laboratory roles.

Samir RawashdehView profile

Associate Professor

• Robotics
• Computer Vision
• Embedded Systems
• +3 more

Samir Rawashdeh is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. He directs research in robotics, computer vision, and autonomous systems through his DAIR Lab. His educational background includes a Ph.D. and M.Sc. in Electrical Engineering from the University of Kentucky, and a B.Sc. from the University of Jordan. Research Focus: Dr. Rawashdeh leads projects in autonomous navigation, humanoid robotics, and sensor systems. His lab develops computer vision algorithms for visual odometry, object tracking, and event-based perception. Current projects include autonomous snowplows, humanoid manipulation systems, and unmanned aerial vehicles, with applications in transportation, space systems, and healthcare. Publications: His research spans computer vision, robotics, and aerospace systems, with recent work focusing on high-temporal-resolution tracking using hybrid vision sensors and lightweight odometry algorithms for mobile robots. His publications demonstrate consistent themes of real-time processing, sensor fusion, and practical implementation in autonomous systems. Awards and Recognition: Advised student teams to win top positions in AUVSI IGVC and ION Snowplow competitions Honorable Mention in Frank J. Redd Student Scholarship (2011, 2012) First Place in Kentucky Academy of Sciences Graduate Research Competition (2008) IEEE Computer Society Design Competition Honorable Mention (2006) Research Infrastructure: Manages robotics facilities including a full-size humanoid robot, autonomous vehicle platforms, and sensor systems. Develops open-source tools like the Lightweight Visual Tracking (LVT) package and maintains public datasets for event-based vehicle detection.

Roberto CattaneoView profile

Professor

• Virology
• Molecular Medicine
• RNA Viruses
• +12 more

Roberto Cattaneo, Ph.D., is a Professor of Biochemistry and Molecular Biology at Mayo Clinic in Rochester, Minnesota, with a primary appointment in the Department of Molecular Medicine and joint appointments in the Department of Biochemistry and Molecular Biology and the Department of Immunology. He is actively engaged in research on enveloped RNA viruses, including measles virus and SARS-CoV-2, with a focus on molecular mechanisms of pathogenesis, viral entry, membrane fusion, and immune evasion. Professional Role: Professor, Vice-chair for Education (Department of Molecular Medicine, 2014–present), Director of Virology and Gene Therapy Track (2004–2015) Research Leadership: Principal Investigator on multiple NIH-funded grants from NIAID and NCI Honors: Richard O. Jacobson Professor of Molecular Medicine (2023), START Career Development Award (Swiss National Science Foundation) Dr. Cattaneo’s research centers on understanding how RNA viruses such as measles and SARS-CoV-2 interact with host cells to cause disease. His team discovered the role of nectin-4 as a host-exit receptor for measles virus, a breakthrough published in Nature . His work explores viral tropism, membrane fusion activation, genome adaptation across tissues, and the development of viral vectors for oncolytic therapy. He has contributed significantly to understanding how measles virus spreads in the brain and how SARS-CoV-2 transmission dynamics can be monitored through wastewater epidemiology. His recent publications demonstrate a strong trend in virology, with emphasis on measles virus pathogenesis, SARS-CoV-2 surveillance, and oncolytic virotherapy. These works span disciplines including molecular biology, immunology, neuroscience, and public health, reflecting a highly interdisciplinary research program. Key subfields include neurotropic viral infections, collective infectious units, receptor-mediated entry, and viral immune evasion. Scientific awards and recognitions include: Richard O. Jacobson Professor of Molecular Medicine, Mayo Clinic (2023) START Career Development Award, Swiss National Science Foundation (1991–1998) Leadership roles in the American Society of Gene & Cell Therapy, including Board Member and Committee Chair Dr. Cattaneo has advised numerous students and researchers, many of whom are co-authors on his recent publications. His research is supported by multiple long-term grants, including an ongoing PhD Training Program in Virology and Gene Therapy funded by NIAID. He leads a dynamic research laboratory focused on fundamental virology with direct translational applications in cancer therapy and infectious disease control.

Sumeetpal SinghView profile

Senior Lecturer

• Computational Statistics
• Monte Carlo Methods
• Bayesian Inference
• +7 more

Sumeetpal Singh serves as a Senior Lecturer in Engineering Statistics (Associate Professor) at the University of Cambridge within the Department of Pure Mathematics and Mathematical Statistics (DPMMS). He holds dual affiliations as an Affiliated Lecturer of the Statistical Laboratory and a Fellow of Churchill College. His office is located in BE3-25 with contact number +44 1223 764 139. His research centers on Computational Statistics, specializing in Monte Carlo methods including Particle Filters and Markov Chain Monte Carlo for Bayesian inference and Maximum Likelihood estimation. Application domains span multi-target tracking systems, sensor networks for industrial condition monitoring, online gaming analytics (cheat detection and player strategy inference), and signal processing for single molecule fluorescence microscopy. This interdisciplinary work bridges theoretical statistics with real-world engineering challenges. As an integral member of the Statistical Laboratory research group, Dr. Singh contributes to advancing statistical methodology while maintaining active industry collaborations. His professional resources include a personal homepage at https://www.dpmms.cam.ac.uk/~sss40 and primary contact via sss40@cam.ac.uk.

• Cancer Research
• Neuro-oncology
• DNA Methylation Analysis
• +4 more

Dr. V. Hovestadt is a prominent researcher at the German Cancer Research Center (DKFZ) in Heidelberg, Germany, affiliated with the Hopp Children's Cancer Center (KiTZ) and University Hospital Heidelberg. As a key contributor to the Heidelberg Brain Tumor Classifier project, Dr. Hovestadt leads the Computational Oncology Research Group focusing on DNA methylation analysis for tumor classification and molecular diagnostics. Dr. Hovestadt's research spans computational oncology, neuro-oncology, and molecular tumor classification, with particular expertise in developing and benchmarking computational workflows for DNA methylation data analysis. Their work addresses critical challenges in pediatric neuro-oncology, including improving classification rates for difficult cases of pediatric low-grade gliomas through in silico purification methods and developing explainable AI approaches for clinical tumor diagnostics. Analysis of Dr. Hovestadt's recent publication record (2023-2025) reveals a strong focus on translating computational methods into clinical applications for brain tumor diagnosis and classification. Their work bridges the gap between bioinformatics methodology development and practical clinical implementation, with significant contributions to understanding tumor biology through DNA methylation profiling. Developed benchmarking frameworks for computational workflows in methylation analysis ('Pipeline Olympics') Created in silico purification methods to improve pediatric low-grade glioma classification Contributed to explainable AI approaches for the Heidelberg Brain Tumor Classifier Enhanced copy-number variation analysis from DNA methylation arrays (conumee 2.0) Investigated cellular hierarchies in embryonal tumors with multilayered rosettes Dr. Hovestadt maintains active collaborations with neuropathology, clinical neuro-oncology, and basic science research groups at DKFZ and University Hospital Heidelberg. Their computational expertise is integral to the multi-omics approach to brain tumor classification that has become standard in modern neuropathology. The research group continues to develop innovative computational methods to improve molecular tumor diagnostics and understand the epigenetic mechanisms driving brain tumor development.

• Cancer Immunology
• Neuro-oncology
• Glioma Research
• +3 more

Dr. Prins, R. M. is a researcher at the German Cancer Research Center (DKFZ) in Heidelberg, Germany, specializing in cancer immunology with a focus on neuro-oncology and brain tumor research. Their work primarily investigates the complex interactions between the immune system and gliomas, particularly exploring mechanisms of immunotherapy resistance and developing novel immunotherapeutic approaches for brain tumors. Research interests include T cell immunotherapy, tumor microenvironment characterization, antigen presentation mechanisms in brain tumors, and the development of cellular therapies for malignant gliomas. Dr. Prins' work bridges basic immunological research with translational applications, focusing on how to overcome the immunosuppressive nature of brain tumors to improve therapeutic outcomes. The publication record demonstrates a consistent focus on understanding and manipulating T cell responses in brain tumors, with recent work emphasizing TCR-T cell therapy development, spatial immune profiling of glioblastoma, and mechanisms of T cell dysfunction in the tumor microenvironment. Their research has appeared in high-impact journals including Nature Communications, Cancer Cell, and Science Immunology. While specific awards are not documented in the provided materials, the quality and impact of the research suggest recognition within the neuro-oncology and cancer immunology communities. The collaborative nature of the work is evident through numerous multi-institutional publications across various aspects of brain tumor immunology.

• Medical Robotics
• Endoscopy
• Colonoscopy
• +3 more

Nanda van der Stap serves as a Lecturer in Technical Medicine at the University of Twente, specializing in the integration of robotics and imaging technologies for minimally invasive surgical procedures. Her academic appointment focuses on advancing endoscopic techniques through engineering solutions. She completed her doctoral studies at the University of Twente, earning a PhD in Technical Medicine in 2016 with her dissertation "Image-based endoscope navigation and clinical applications" that established her research trajectory in medical robotics. Van der Stap's research program centers on medical robotics and computer-assisted endoscopy , with particular emphasis on colonoscopy automation , robotic steering mechanisms , and real-time image processing for gastrointestinal procedures. Her work bridges engineering precision with clinical requirements, developing systems that enhance procedural accuracy while reducing patient discomfort. Current investigations explore virtual reality interfaces for surgical teleoperation, incorporating thermal feedback to create more intuitive human-robot interactions during complex endoscopic maneuvers. Analysis of her publication history (2012-2022) reveals a consistent research trajectory toward autonomous endoscopic systems. Her most significant contributions include real-time tracking algorithms for flexible endoscopy platforms and feasibility studies on automated lumen centralization, demonstrating how robotics can standardize colonoscopy procedures. The 2022 work on thermal feedback in VR teleoperation represents an emerging focus on multi-sensory interfaces that could transform surgeon training and remote surgical assistance. Her scholarly impact is quantified by 77 total citations and an h-index of 5, reflecting growing recognition in medical robotics despite the specialized nature of her work. These metrics indicate meaningful contributions to niche but clinically significant areas of surgical technology. As a Lecturer, van der Stap contributes to academic instruction in Technical Medicine while maintaining active research collaborations. Her projects show consistent interdisciplinary engagement with clinical partners, suggesting grant support from both institutional and national funding bodies focused on healthcare innovation. The research output demonstrates sustained collaboration with engineering and medical teams across multiple institutions. Her work operates within the University of Twente's TechMed Centre ecosystem, an interdisciplinary research environment that connects engineers, computer scientists, and medical professionals. This collaborative framework enables the development of integrated solutions where robotic platforms, imaging systems, and clinical protocols evolve in concert to address real-world surgical challenges.

Gloria EcheverriaView profile

Assistant Professor

• Triple Negative Breast Cancer
• Mitochondrial Biology
• Cancer Metabolism
• +4 more

Gloria Echeverria, Ph.D., serves as an Assistant Professor in the Department of Molecular and Cellular Biology at Baylor College of Medicine. As Principal Investigator of the Echeverria Lab, she leads research focused on understanding the molecular evolution of triple negative breast cancer (TNBC), with particular emphasis on mitochondrial biology, metabolic adaptations, and mechanisms of therapy resistance. Her research program investigates how TNBC evolves and adapts during therapy resistance and metastasis, with a specialized focus on mitochondrial dynamics, metabolic reprogramming, and intra-organellar communication. The lab leverages state-of-the-art technologies in clinically relevant experimental models, particularly human patient-derived xenograft (PDX) models and human tumor cell lines, to discover critical mechanisms underlying resistance to cancer treatments. This work directly informs the development of mitochondria-targeting therapies with the ultimate goal of translating laboratory findings into clinical trials for TNBC patients. Analysis of Dr. Echeverria's recent publication record (2021-2025) reveals a consistent research trajectory centered on TNBC biology, with increasing sophistication in methodology. Her work has evolved from characterizing basic mechanisms of chemotherapy resistance and metastasis to developing advanced computational tools (like Xenomake) for spatial transcriptomic analysis and AI-based approaches for mitochondrial morphology assessment. A notable trend is the growing integration of multi-omics approaches with sophisticated preclinical models to deconvolute complex treatment responses in TNBC. Dr. Echeverria has demonstrated commitment to collaborative science through participation in multi-institutional research efforts and has pioneered frameworks for integrating patient advocates into cancer research. Her laboratory serves as a hub for translational breast cancer research, bridging fundamental biological discoveries with potential clinical applications specifically targeting the mitochondrial vulnerabilities of therapy-resistant TNBC.

Michal NachmanyView profile

Visiting Professor

• Climate Policy
• Environmental Law
• Global Governance
• +3 more

Michal Nachmany is a Visiting Fellow at the London School of Economics (Grantham Research Institute on Climate Change and the Environment). She leads the Climate Change Laws of the World project, a comprehensive global resource on climate legislation, and project manages the investor-led Transition Pathway Initiative (managing $18T+ in assets). Her work bridges academic research with policy implementation, focusing on climate governance, legislative diffusion, and low-carbon transitions. Education: PhD in Climate Policy Research (London School of Economics), MA in Energy Policy (University of Haifa), LLB in Law, and MBA (Hebrew University of Jerusalem) Research Focus: Global climate legislation trends, transnational policy networks, polycentric governance, and corporate climate accountability Media Engagement: Regularly appears on BBC, CNN, and Sky News for climate policy analysis, including discussions on COP outcomes, climate strikes, and emission reduction strategies Recent Publications: Highlight trends in climate legislation, transnational networking impacts, and alignment of national/international climate targets. Collaborations include work with United Nations Environment Programme and investor groups.

• Particle Physics
• High Energy Physics
• Experimental Physics
• +3 more

Davide Malito is a Research Fellow in the ATLAS research group at the Department of Physics, Royal Holloway, University of London, specializing in experimental particle physics with the ATLAS detector at CERN's Large Hadron Collider (LHC). His work focuses on cutting-edge analyses of collider data and computational systems for high-energy physics experiments. Education: PhD in Physics from the University of Calabria (awarded July 28, 2022) Research Interests: Malito's expertise spans particle physics phenomenology and detector software development. His research targets supersymmetry searches through higgsino decays and optimization of track reconstruction systems for LHC Run 3. He specializes in photon/b-jet identification, detector performance analysis, and computational methods for handling high-luminosity collision data, contributing to the advancement of collider physics techniques. Publication Trends: His 2024 publications reveal a dual focus on physics analysis and computational infrastructure. The higgsino search demonstrates innovative approaches to supersymmetry detection using multi-object final states, while the track reconstruction study addresses critical software challenges for upcoming LHC operations, highlighting his integration of theoretical physics with practical computing solutions. Scientific Awards: No awards were documented in the provided materials. Grants and Projects: Malito serves as a core researcher on the STFC-funded "Experimental Consolidated Grant for RHUL 2024" (October 2025–September 2029), led by Dr. Vincent Boisvert. This major project supports Royal Holloway's ATLAS program with co-investigators including Prof. Teixeira-Dias and Dr. Cowan, focusing on detector operations, physics analysis, and software development for the LHC experimental program. Research Environment: As an integral member of Royal Holloway's ATLAS group, he collaborates within the international ATLAS collaboration at CERN, contributing to hardware, software, and physics analysis efforts. The group maintains strong ties to the Centre for Particle Physics and Astronomy, facilitating cross-disciplinary research in nuclear and particle physics phenomena.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Jiaqi Liu is an Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology in Shenzhen, China. His research spans multiple domains within artificial intelligence, with a particular focus on autonomous systems, multimodal learning, and computer vision. He maintains active collaborations with researchers across China and internationally, particularly with Jian Sun and Peng Hang on autonomous driving projects. Dr. Liu's research interests encompass a broad spectrum of AI and computer science topics. His work in autonomous driving involves developing sophisticated decision-making frameworks for cooperative vehicle systems. In multimodal learning, he has pioneered approaches for handling missing modalities and creating adaptive fusion networks. His signal processing research includes innovative methods for sleep staging and EEG analysis using dynamic mode decomposition techniques. Additional research areas include photonic computing accelerators, graph neural networks, and quantum neural networks. The publication trend shows significant growth in output quality and quantity, with numerous papers in top-tier venues including IEEE Transactions, CVPR, AAAI, and IJCAI. His work demonstrates a strong interdisciplinary approach, bridging computer science with applications in healthcare, robotics, and telecommunications. Recent publications indicate increasing focus on the integration of large language models with reinforcement learning for autonomous systems. Dr. Liu has received recognition through publications in high-impact journals and conferences including: IEEE Transactions on Intelligent Transportation Systems IEEE Robotics and Automation Letters Expert Systems with Applications CVPR (Computer Vision and Pattern Recognition) AAAI Conference on Artificial Intelligence His research program involves collaborations across multiple domains, with current projects focusing on language-guided autonomous driving, multimodal sentiment analysis, and advanced signal processing techniques. He leads research efforts in developing novel deep learning architectures for complex real-world problems, particularly in the domains of autonomous systems and healthcare applications.