جست‌وجوی استاد و پژوهشگر

• Neuroscience
• Robotics
• Computational Biology
• +۴ مورد دیگر

Prof. Pavan Ramdya, the DSM-Firmenich Next Generation Chair in Neuroscience at École Polytechnique Fédérale de Lausanne (EPFL), leads the Neuroengineering Laboratory. His research focuses on reverse-engineering biological intelligence in Drosophila melanogaster to inspire neuroprosthetics, robotics, and AI. He holds a PhD in Neurobiology from Harvard University and completed postdoctoral training in robotics (EPFL), neurogenetics (UNIL), and bioengineering (Caltech). University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Life Sciences Academic Rank: Professor His lab employs computational, engineering, genetic, and microscopy approaches to study neural population dynamics, biomechanics, and gene expression in limb-dependent behaviors. Key research trends from his publications include neuromechanical modeling of Drosophila , sensory-motor integration, and AI-robotics synergy for biological discovery. HFSP Career Development Award Swiss National Science Foundation Eccellenza Grant UNIL Young Investigator Award in Basic Science FENS-Kavli Network of Excellence member The lab mentors doctoral researchers such as Sibo Wang, Victor Stimpfling, and Femke Hurtak, alongside postdoctoral fellows like Jasper Phelps and alumni including Victor Lobato Rios. Collaborations span robotics (Auke Ijspeert), microrobotics (Sakar), and computational imaging (Fua).

Abhinav Bhateleمشاهده پروفایل

دانشیار

• High Performance Computing
• Systems
• Networking
• +۴ مورد دیگر

Abhinav Bhatele is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park, and directs the Parallel Software and Systems Group. He holds affiliate roles in the AIM Institute and AMSC Program. His research focuses on high-performance computing, systems, networking, and AI, particularly in parallel computing, distributed AI, and machine learning applications. He has contributed to frameworks like AxoNN and Hatchet, and his work spans performance modeling, network simulation, and scalable deep learning. Education: Ph.D. (2010), M.S. (2007), and B.Tech. (2005) in Computer Science from the University of Illinois at Urbana-Champaign and IIT Kanpur, respectively. Prior to UMD, he was at Lawrence Livermore National Laboratory (2011–2019). Research interests include parallel systems, network design, performance analysis tools, and applying machine learning to optimize HPC workflows. He has received notable awards, including the IEEE TCSC Award (2023), NSF CAREER (2021), and UIUC Early Career Alumni Award (2024). Key achievements include developing open-source tools like Hatchet and AxoNN, optimizing large-scale LLM training, and advancing GPU-based supercomputing. He advises over 10 students and has secured grants totaling millions, focusing on HPC software ecosystems and AI integration. Professional service includes roles at SC, ISC conferences, and editorial work for IEEE TPDS. His lab collaborates on projects like the Exascale Computing Project and DOE INCITE allocations.

• Biomechanics
• Musculoskeletal System
• Human Movement Analysis
• +۳ مورد دیگر

Maarten Afschrift is an Assistant Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Behavioural and Movement Sciences, Department of Biomechanics. He holds additional roles at IBBA and AMS - Rehabilitation & Development. His research focuses on biomechanical modeling of human movement, particularly in musculoskeletal systems and gait analysis. He specializes in predictive simulations of locomotion, exoskeleton assistance, and the biomechanical implications of aging on movement strategies. Research Interests: Biomechanics of human movement and gait Musculoskeletal modeling and simulation Exoskeleton and assistive technology design Effects of aging on motor control and balance Predictive control strategies in human locomotion Recent Research Trends: His work emphasizes optimizing exoskeleton assistance for lower limbs, understanding metabolic energetics during walking, and modeling foot mechanics to improve gait simulations. Recent studies explore trade-offs between accuracy, speed, and stability in stepping tasks across age groups, leveraging computational frameworks like PredSim for predictive analysis. Teaching & Grants: He teaches courses on biomechanics of locomotion and contributes to editorial work in biomechanical journals. No specific grants or awards are listed, but his research is supported through institutional and collaborative projects. Labs/Teams: Active in interdisciplinary teams at IBBA and AMS, focusing on rehabilitation engineering and biomechanical innovations.

Yawen Wangمشاهده پروفایل

استادیار

• Gear Dynamics
• Structural Vibration and Acoustics
• Active Noise Control
• +۳ مورد دیگر

Dr. Yawen Wang is an Assistant Professor of Research in the Mechanical and Aerospace Engineering Department at The University of Texas at Arlington (UTA). He directs the Vibro-Acoustics and Sound Quality Research Laboratory and leads the Hypoid and Bevel Gear Mesh and Dynamic Modeling Consortium. His academic career includes adjunct roles at the University of Cincinnati and teaching positions since 2017. Dr. Wang holds a PhD in Mechanical Engineering from the University of Cincinnati (2017), an MS from the same institution (2013), and a BS from Chongqing University (2010). Educational Background PhD in Mechanical Engineering, University of Cincinnati, 2017 MS in Mechanical Engineering, University of Cincinnati, 2013 Bachelor of Science in Mechanical Engineering, Chongqing University, 2010 Research Expertise Dr. Wang specializes in precision machine elements (gears, bearings), gear noise/vibration, structural acoustics, active noise control, automotive NVH, and data-driven design. His work integrates computational modeling, experimental techniques, and analytical approaches to solve engineering challenges in powertrain systems and vehicle dynamics. Recent projects include optimizing hypoid gear designs, improving drivetrain prognostics via Vibration Resonance Spectrometry (VRS), and reducing electric vehicle noise through advanced materials and control systems. Professional Contributions He has authored/co-authored over 70 journal/conference papers and collaborates with industry leaders like Ford, GM, Daimler, and Toyota. His awards include the InterNoise Hallberg Foundation Award (2015) and NoiseCon Michiko Finegold Award (2014). Dr. Wang serves on ASME committees and chairs sessions at the International Power Transmission and Gearing Conference. Academic Leadership He advises graduate students, including Chia Ching Lin for a dissertation on axle system dynamics. Teaching responsibilities span mechanical vibrations, structural dynamics, and powertrain design. Dr. Wang also oversees grants from sponsors such as Jian’an Industrial Co., advancing gear mesh modeling and dynamic analysis. Labs & Consortia As director of the Vibro-Acoustics Lab, he focuses on sound quality design, active control systems, and vehicle interior acoustics. The Gear Consortium he leads develops tools like HGSim for gear mesh simulation, with recent updates to version 7.8.3 (2024).

• Computer Vision
• High Performance Computing
• Data Science
• +۱ مورد دیگر

Kannappan Palaniappan is a Curators' Distinguished Professor in Electrical Engineering and Computer Science at the University of Missouri. His pioneering work spans computer vision, high-performance computing, and biomedical image analysis, with applications from molecular microscopy to satellite remote sensing. Received prestigious honors including NASA Public Service Medal for scientific visualization innovations and National Academies Jefferson Science Fellowship. Research interests include large-scale image analysis, geospatial visualization, biomedical imaging, and parallel computing algorithms. Developed foundational techniques for the Digital Earth visualization system and co-founded NASA's Visualization and Analysis Lab. Publications demonstrate extensive applications across scales from cellular neuroscience to Earth observation. Recent works focus on neural circuit modeling, high-performance computing frameworks, and interdisciplinary applications in neuroscience and geospatial analysis. NASA Public Service Medal National Academies Jefferson Science Fellowship Air Force Summer Faculty Fellowship Boeing Welliver Faculty Fellowship William T. Kemper Teaching Fellowship Secured research funding from NIH, NSF, NASA, and DoD laboratories. Holds patents for moving object detection and camera pose estimation technologies.

Bin Zhangمشاهده پروفایل

دانشیار

• Prognostics and Health Management
• Fault Detection and Isolation
• Failure Prognosis
• +۸ مورد دیگر

Dr. Bin Zhang is an Associate Professor in the Department of Electrical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. With over 20 years of experience in prognostics and health management, intelligent systems and control, and robotics, he has established himself as a leading researcher in battery management systems, power electronics, and fault-tolerant control systems. Dr. Zhang's educational background includes: Ph.D. in Electrical Engineering from Nanyang Technological University, Singapore M.E. in Mechanical Engineering from Nanjing University of Science and Technology, China B.E. in Mechanical Engineering from Nanjing University of Science and Technology, China His research focuses on active approaches to achieve intelligent smart systems with self-situational-awareness and self-adapting capabilities. Primary interests include prognostics and health management (PHM), which covers fault detection and isolation, failure prognosis, and fault tolerance; robotics and unmanned systems; intelligent systems and control; and dynamic systems design, modeling, simulation and control. His work integrates physics-based models with data-driven techniques and computational intelligence, including pattern recognition and machine learning. Analysis of Dr. Zhang's recent publications reveals a strong emphasis on battery modeling (particularly lithium-ion batteries), power electronics control (including fractional order delay and virtual variable sampling techniques), and deep learning methods (including graph neural networks, deep residual convolutional neural networks, and deep belief networks). His research spans multiple application domains including power grids, batteries, aircraft, helicopters, and manned/unmanned vehicles. Dr. Zhang serves as Associate Editor for prestigious journals including IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Neurocomputing. He is a Senior Member of IEEE and a member of ASME. As director of the Resilient Systems Laboratory, Dr. Zhang advises numerous graduate students working on cutting-edge research in battery technology, power cable insulation, and control systems. His lab is equipped with advanced facilities including an 8-channel ARBIN BT-Smart battery testing system, power electronics control systems, cable/wire testing systems, rotating machinery testing systems, and unmanned vehicles including quadrotors and hexacopters.

• Antenna Engineering
• 6G/5G Communications
• Metamaterials
• +۶ مورد دیگر

Ming Shen is an Associate Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on antennas, millimeter-wave systems, and AI-driven RF sensors with applications in 5G/6G communications, biomedical engineering, and smart systems. His research interests span antenna design (including phased arrays, metamaterials, and compact structures), AI integration in electromagnetic systems, and medical sensor technologies. Recent projects include drone-based electromagnetic signature analysis, vibration energy harvesting for pacemakers, and smart healthcare systems for posture recognition and surgical site infection monitoring. Key projects include DRONES: Drone-Obtained Electromagnetic Signatures (2024–2028), Sensor Intelligence for Healthcare and Sports (2022–2027), and DeepBone (2021–2022), which explored deep learning for surgical infection detection. His work also bridges machine learning and electromagnetic design, with breakthroughs in surrogate modeling and automated antenna optimization. Ming Shen has supervised 6 PhD students and published over 160 peer-reviewed articles. Notable contributions include AI-assisted NLOS sensing, ultra-wideband antenna innovations, and medical applications such as electrical impedance-based bone healing assessment.

Stefan Radevمشاهده پروفایل

استادیار

• Bayesian Inference
• Deep Learning
• Probabilistic Modeling
• +۳ مورد دیگر

Stefan Radev is an Assistant Professor in the Cognitive Science department at Rensselaer Polytechnic Institute . His work focuses on developing Bayesian methods with generative neural networks and computational models for complex systems like cognition and disease outbreaks. He is the core developer of the BayesFlow framework, which enables amortized Bayesian inference using deep learning. Radev's research addresses computational challenges in Bayesian workflows, such as rapid parameter estimation and model validation through neural networks. His primary research interests include Deep Learning , Probabilistic Modeling , and their applications in computational neuroscience and biomedical engineering. He collaborates with the Center for Modeling, Simulation and Imaging in Medicine (CEMSIM) and contributes to open-source projects like BayesFlow, which supports multi-backend frameworks (PyTorch/TensorFlow/JAX). Recent publications highlight advancements in amortized inference, simulation-based calibration, and robust Bayesian workflows. His work spans domains from cognitive modeling to biomedical imaging, emphasizing interdisciplinary applications of Bayesian methods. No scientific awards are explicitly listed, though his contributions to open-source tools and impactful research indicate significant academic recognition.

B. Wayne Bequetteمشاهده پروفایل

استاد

• Biomedical Process Systems
• Chemical Process Control
• Cell and Gene Therapies
• +۴ مورد دیگر

B. Wayne Bequette is a Professor of Chemical and Biological Engineering at Rensselaer Polytechnic Institute and Technology Manager for the Northern Regional Center of CESMII. His research focuses on biomedical and chemical process systems modeling, particularly CAR-T cell therapy automation, diabetes technology, and smart manufacturing. Education: B.S. (1980), M.S.E. (1985), Ph.D. (1986) from University of Arkansas and University of Texas His work addresses critical challenges in cell therapy manufacturing (reducing treatment time/cost) and diabetes technology (automated insulin delivery systems). He pioneers human-in-the-loop control frameworks integrating patient monitoring with cyber-physical systems. Recent publications emphasize AI-driven process monitoring (2025) and smart manufacturing pedagogy (2024). Awards include fellowships from IEEE, AIChE, and AIMBE. Teaches flipped-classroom courses in process systems engineering. Scientific Awards IEEE Fellow (2016) AIMBE Fellow (2014) AIChE Fellow (2008)

• Computational Fluid Dynamics
• Aerodynamic Shape Optimization
• Simulation-Based Design
• +۳ مورد دیگر

Dr. Jason Hicken is an Associate Professor in the Mechanical, Aerospace, and Nuclear Engineering Department at Rensselaer Polytechnic Institute (RPI), and co-director of the Rensselaer Augmented and Virtual Environment. His research focuses on computational fluid dynamics, aerodynamic shape optimization, and simulation-based design. He holds a Ph.D. in Aerospace Engineering from the University of Toronto Institute for Aerospace Studies (2009), with postdoctoral training at Stanford University. Notable awards include the AFOSR Young Investigator Award (2015) and NSF CAREER Award (2016). His research interests span high-order numerical methods, entropy-stable discretizations, and optimization techniques for complex systems. He has contributed to advancing finite-difference operators, summation-by-parts schemes, and multi-disciplinary design optimization. His work bridges computational engineering and applied mathematics, addressing challenges in fluid dynamics, thermal systems, and aerospace applications. His lab, the Optimal Design Lab (http://www.optimaldesignlab.com/), develops innovative tools for engineering design optimization. Key achievements include developing entropy-stable numerical frameworks and integrating machine learning techniques for preconditioning in advection-dominated flows. His research has been applied to electric motor design, thermal management systems, and additive manufacturing. Awards recognize his contributions to foundational methods in computational engineering and aerospace optimization.

Caroline G. Richterمشاهده پروفایل

استادیار

• Intellectual and Developmental Disabilities
• Specific Learning Disabilities
• Williams syndrome
• +۵ مورد دیگر

Caroline G. Richter is an Assistant Professor in the Department of Psychology at the University of Alabama at Birmingham's College of Arts and Sciences. She founded and directs the Science of Child DevelopmenT And NeuRodiverSity (STARS) laboratory, focusing on cognitive and socio-emotional factors affecting academic achievement in neurodevelopmental disorders. Education includes: B.A., Psychology, Universidade Federal de Minas Gerais Certified Cognitive-Behavioral Therapist, Wainer Psicologia Cognitiva M.A., Developmental Psychology, Universidade Federal de Minas Gerais M.S., Experimental Psychology, University of Louisville Ph.D., Experimental Psychology, University of Louisville Postdoctoral Research Associate, University of Connecticut Research focuses on understanding variability in academic outcomes for children with neurodevelopmental disorders including learning disabilities, autism spectrum disorders, Williams syndrome, and Down syndrome. Current projects investigate socio-emotional competencies, mindfulness-based interventions for sleep improvement in Down syndrome, and biopsychosocial outcomes of educational interventions. Publications demonstrate strong focus on emotional well-being measurement, neurodevelopmental disorders, and educational interventions, with recent work expanding into neuroimaging and epigenetic influences. Research trends show increasing integration of physiological measures and technology-based assessments. Awards include the McNulty Civitan Scientist Award from the Civitan International Research Center and funding from NIH, NICHD, NCCIH, Learning Disabilities Foundation of America, and UAB's Center for Engagement in Disability Health and Rehabilitation Sciences. Leads the STARS Lab with multiple graduate students including award-winning researcher Kibryn Lovejoy. The lab utilizes UAB's Multi-Disciplinary Driving Simulator for cognitive research and conducts community outreach including sensory-friendly events with Birmingham Barons baseball.

Chad Dutyمشاهده پروفایل

استاد

• Additive Manufacturing
• Polymer Composites
• Material Extrusion
• +۷ مورد دیگر

Chad Duty is a Professor in the Department of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee (2021-present), previously serving as Associate Professor (2015-2021). His research focuses on Additive Manufacturing (AM), particularly polymer and composite structures, with emphasis on Big Area Additive Manufacturing (BAAM). He leads initiatives in large-scale AM tooling, material development, and process optimization. Duty has held roles at Oak Ridge National Laboratory (ORNL), including Group Leader of Deposition Science & Technology (2013-2015) and Senior Research Scientist at the Manufacturing Demonstration Facility (2011-2015). He earned a PhD from Georgia Tech (2001) and BSME from Virginia Tech (1997). Research interests include anisotropic mechanical behavior, melt flow characterization, and recycling of composite materials. Notable contributions include the development of BAAM for multi-meter structures and novel coating materials. His work bridges academia and industry, with applications in aerospace, automotive, and renewable energy sectors. Awards include the ORNL Director's Award (2014), R&D 100 Awards (2009, 2011), and the Eugene P. Wigner Fellowship (2004). His lab focuses on advancing AM technologies through material innovation and process scalability, collaborating with institutions like ORNL and industry partners.

Sami Al-Aniمشاهده پروفایل

مدرس ارشد

• Pediatric Surgery
• Hand Surgery
• Medical Tourism
• +۳ مورد دیگر

Sami Al-Ani is a Senior Lecturer in Anatomy at Aston Medical School, Aston University. He combines academic work with clinical practice as a Plastic and Hand Surgeon, focusing on pediatric orthopedic injuries, hand surgery, and medical history research. Research Interests: Systematic reviews in pediatric hand trauma Arab-Islamic influence on ophthalmology Medical education innovations Health tourism and cosmetic surgery ethics Article Trends: Recent publications emphasize systematic reviews of pediatric fractures (fifth metacarpal neck, volar plate avulsion), historical medical research on Arab-Islamic contributions to ophthalmology, and critiques of cosmetic medical tourism. External Collaborations: Active in international research networks, with collaborations spanning high-, middle-, and low-resource settings, particularly in surgical education model development.

Paul Savaمشاهده پروفایل

استاد

• Seismic Imaging
• Planetary Geophysics
• Wavefield Tomography
• +۵ مورد دیگر

Paul Sava is a Professor and C.H. Green Chair of Exploration Geophysics at the Colorado School of Mines, serving as Department Head of Geophysics since 2019. He holds a PhD in Geophysics from Stanford University (2005), an MSc from Stanford (1998), and an Engineer Geophysicist degree from the University of Bucharest (1995). His research focuses on seismic and radar wavefield imaging, planetary geophysics, and infrastructure monitoring. Notable contributions include advancements in wave-equation migration and planetary radar tomography. Education: PhD in Geophysics, Stanford University, 2005 MSc in Geophysics, Stanford University, 1998 Engineer Geophysicist, University of Bucharest, 1995 Research interests emphasize wavefield-based methods for subsurface imaging, planetary resource exploration, and autonomous data acquisition. Recent work explores seismic imaging of asteroids and Mars, leveraging orbital radar systems. His publications span seismic inversion, microseismic monitoring, and elastic wavefield tomography. Awards include the Reginald Fessenden Award (2007), T.K. Young Leadership Award (2020), and multiple Top 30 presentations at SEG Annual Meetings. He leads the Center for Wave Phenomena and actively collaborates with NASA and industry consortia on grants totaling over $6 million. Advising and grants involve mentoring over 30 students and postdocs, with grants focused on planetary radar imaging, seismic FWI, and Mars subsurface analysis. Key projects include the Planetary Orbital Radar Processing and Simulation System (PORPASS) and autonomous seismic exploration systems. Labs/Teams: Directly leads the Center for Wave Phenomena and the Seismic Imaging Team at CWP, advancing open-source tools like Madagascar for reproducible geophysical research.

• High-Performance Computing (HPC)
• Parallel Programming
• Performance Modeling
• +۶ مورد دیگر

Felix Wolf is a Full Professor at Technische Universität Darmstadt's Department of Computer Science since 2015 and leads the NHR4CES@TU Darmstadt HPC center. He served as Department Chair and Vice Department Chair at TU Darmstadt, with prior faculty roles at RWTH Aachen University (2006–2015) and adjunct positions at the University of Tennessee (2003–2008). His academic work spans performance analysis, parallel programming, and scalability modeling. Ph.D. in Computer Science, RWTH Aachen University (2003) M.Sc., RWTH Aachen University (1998) Research Interests : Wolf focuses on High-Performance Computing , including performance modeling , parallel programming tools , and I/O optimization . His work addresses GPU acceleration , machine learning integration into HPC systems, and structural plasticity simulation . Key projects involve tools like Scalasca, Score-P, and Extra-P for automated performance analysis. Article Trends : Recent publications emphasize HPC systems for AI-driven workflows , I/O contention reduction , and GPU port validation . His team explores empirical modeling for deep learning and scientific simulations , with applications in engineering and neuroscience .