جستجوی استاد راهنمای دانشگاهی

Amit Lalمشاهده پروفایل

استاد

• Microsystem Engineering
• MEMS and NEMS
• Ultrasonic Sensors
• +۱۲ مورد دیگر

Amit Lal is a Professor in the School of Electrical and Computer Engineering at Cornell University, with affiliations in Biomedical Engineering, Applied Engineering Physics, and Mechanical and Aerospace Engineering. He is a member of key research centers including Cornell CCMR, NBTC, and KAUST-CU. Education: B.S. in Electrical Engineering, California Institute of Technology, 1990 Ph.D. in Electrical Engineering, University of California, Berkeley, 1996 Prof. Lal's research focuses on the development of integrated microsystems using micro- and nanoscale fabrication. His work spans ultrasonic MEMS, low-power IoT sensors, atomic microsystems, and bio-robotics. He directs the SonicMEMS Laboratory, advancing technologies in GHz ultrasonics, inertial sensing, and chip-scale manipulation of particles. His interdisciplinary interests include biomedical imaging, solid-state devices, nanotechnology, and plasma science. His recent publications highlight innovation in energy harvesting, MEMS gyroscopes, and biologically integrated systems. The works reflect strong trends in autonomous sensing, miniaturized power sources, and hybrid bio-electromechanical systems, particularly for medical and navigation applications. Scientific Awards and Honors: NSF CAREER Award Whitaker Foundation Award Department of Defense Exceptional Service Award Best Program Manager Award, DARPA IEEE Ultrasonics and Frequency Control Symposium Best Paper Award IEEE NEMS Best Paper Award Robert M. Scharf 1977 Professor, Cornell Engineering HHMI Visiting Scientist, Janelia Farms Intel Fellowship (awarded to advisee) Prof. Lal has advised numerous students who have gone on to win awards and publish impactful research. He has secured significant research funding through DARPA and other agencies, managing and initiating multiple high-impact programs. His leadership extends to service on technical committees for IEEE conferences and journals, including Transducers and the IEEE Sensors Council. He has also contributed to academic recruiting within ECE. He leads the SonicMEMS Laboratory , a multidisciplinary research group focused on transforming sensing, communication, and computation at the microscale. The lab fosters collaboration across engineering and life sciences, pushing the boundaries of what integrated microsystems can achieve.

Biyun Xieمشاهده پروفایل

استادیار

• Kinematically Redundant Robots
• Fault Tolerant Robots
• Collaborative Robots
• +۴ مورد دیگر

Biyun Xie is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Kentucky's Stanley and Karen Pigman College of Engineering. Her research focuses on kinematically redundant robots, fault-tolerant robotics, and human-robot interaction, with applications in dangerous environments and collaborative systems. Education : Ph.D. in Electrical Engineering from Colorado State University (2019), Ph.D. in Mechanical Engineering from Beijing University of Technology (2015), and B.S. in Mechanical Engineering and Automation from Beijing University of Technology (2009). Research Interests : Kinematically Redundant Robots Fault Tolerant Robots Collaborative Robots Human-Robot Interaction Publications (2025–2023) highlight advancements in real-time fault-tolerant motion planning for redundant robots, neural network-based motor health monitoring, human-like motion algorithms, and collision-free trajectory optimization. These works intersect robotics, artificial intelligence, and mechanical/electrical engineering. Contact : Biyun.Xie@uky.edu | 859-562-2557

YING-TSONG LINمشاهده پروفایل

استاد

• Applied Ocean Sciences
• Autonomous Ocean Platforms
• Internal Waves and Ocean Mixing
• +۳ مورد دیگر

YING-TSONG LIN is an Acting Professor at the Scripps Institution of Oceanography (SIO), UC San Diego. His research focuses on applied ocean sciences, autonomous ocean platforms, internal waves, ocean acoustics, and instrumentation. He leads projects like the New England Shelf Break Acoustics (NESBA) experiment, emphasizing real-time acoustic modeling and environmental interactions. Research interests include 3D acoustic propagation modeling, ocean mixing dynamics, and seabed characterization. His work integrates high-performance computing and distributed sensor networks for oceanographic studies. Recent studies address underwater explosions, renewable energy impacts, and vessel localization using acoustic coherence. Publications emphasize advancements in hydroacoustic modeling, seabed inversion techniques, and environmental asymmetry effects. His contributions span interdisciplinary areas like bioacoustics and seismic-to-acoustic wave conversions. Labs/Teams: Involved with SIO's Acoustics and Oceanography research groups, focusing on autonomous platforms and global observing systems.

Forest Agostinelliمشاهده پروفایل

استادیار

• Deep Learning
• Reinforcement Learning
• Search Algorithms
• +۷ مورد دیگر

Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.

• Theoretical Chemistry
• Quantum Chemistry
• Relativistic Effects
• +۷ مورد دیگر

Professor Kenneth Ruud is a leading expert in theoretical and computational chemistry at UiT The Arctic University of Norway. He serves as Director General of the Norwegian Defence Research Establishment and leads the Hylleraas Centre for Quantum Molecular Sciences. His research focuses on relativistic quantum chemistry, developing advanced ab initio methods for molecular property calculations, and integrating QM/MM and continuum solvent models. Education: PhD from University of Oslo (1998, supervised by Trygve Helgaker) Postdoc: University of San Diego with Peter Taylor (1998-2000) His work spans relativistic effects in molecular properties, vibronic coupling, and X-ray spectroscopy. He contributes to software development through programs like Dalton, Dirac, ReSpect, and OpenRSP. Recent publications highlight applications in spin-vibronic dynamics, heavy metal L/M-edge XAS, and topological materials. Key scientific contributions include relativistic DFT for nuclear spin-rotation constants, polarizable embedding models for vibrational spectra, and quantum dynamics frameworks. Awards recognize his impact through the Dirac Medal (2008) and multiple academy memberships. Elected to Norwegian Academy of Science and Letters Fellow of American Association for the Advancement of Science (AAAS) Foreign member of Finnish Academy of Science and Letters He actively participates in open science initiatives and serves on boards including Norges Forskningsråd and CAROS center for subsea robotics. Current projects involve quantum molecular science in extreme environments and computational protocol development.

• Plant Developmental Biology
• Epigenetics
• Plant Genetics
• +۵ مورد دیگر

Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement

Kaspar Althoeferمشاهده پروفایل

استاد

• Robotics
• Soft Robotics
• Systems Engineering
• +۷ مورد دیگر

Professor Kaspar Althoefer is a Professor of Robotics Engineering at the School of Engineering and Materials Science , Queen Mary University of London. He leads the Centre for Advanced Robotics @ Queen Mary (ARQ) and serves as Programme Director for BEng/MEng Robotics Engineering. His research focuses on robot autonomy, soft robotics , and tactile sensing with applications in healthcare, nuclear engineering, and manufacturing. Research Highlights: Soft Robotics, Tactile Sensing, Haptic Perception, Minimally Invasive Surgery, Human-Robot Interaction Key Collaborations: St Thomas Hospital London, EU Horizon Europe, EPSRC, Innovate UK Accomplishments: £10M+ as Principal Investigator, 500+ peer-reviewed papers, 7 patent applications His work spans robot autonomy , soft robotics , and machine learning , particularly in modeling tool-environment interactions and developing tactile sensors for surgical and nuclear applications. Recent projects include intelligent endoscopic microsurgery robots and eversion robots for radiation mapping . Scientific Awards: Senior Member, IEEE He supervises a team of 6 PhD students and postdoctoral researchers, with completed projects in soft robotic gloves , pneumatic actuators , and eversion robot navigation .

Maha Hajiمشاهده پروفایل

استادیار

• Systems Engineering
• Ocean Engineering
• Sustainable Offshore Technologies
• +۶ مورد دیگر

Maha Haji serves as Assistant Professor in Cornell University's Sibley School of Mechanical and Aerospace Engineering and Systems Engineering field member since 2021. She directs the Symbiotic Engineering and Analysis (SEA) Lab, pioneering sustainable offshore technologies for Blue Economy advancement and climate change mitigation through interdisciplinary symbiotic system design. Her academic credentials include: B.S. in Mechanical Engineering and B.A. in Applied Mathematics from University of California, Berkeley (2012) M.S. in Oceanographic Engineering from MIT/Woods Hole Oceanographic Institution (2015) Ph.D. in Mechanical and Oceanographic Engineering from MIT/Woods Hole Oceanographic Institution (2017) Professor Haji's research integrates Systems Engineering and Ocean Engineering to develop sustainable offshore solutions, emphasizing Design Optimization for renewable energy systems (offshore wind/wave), mineral extraction from seawater, desalination, and aquaculture. Her work creates synergistic multi-functional platforms that harmonize resource harvesting with ocean ecosystem preservation. Publication analysis reveals consistent focus on symbiotic offshore systems, with 2018-2019 studies demonstrating interdisciplinary approaches to ocean resource utilization—spanning environmental science, engineering design, and economic optimization for critical mineral extraction and renewable energy integration. She secured $2.6M in NOAA Sea Grant funding for the MOLLUSCA microplastic collecting robot project with Professor Sunny Jung, advancing ocean conservation technology. Her mentorship cultivates next-generation engineers in sustainable offshore development through the SEA Lab's research framework. The SEA Lab operates as a multidisciplinary hub developing symbiotic engineering solutions for offshore wind, mineral extraction, and aquaculture systems, emphasizing lifecycle sustainability and climate resilience in ocean technology deployment.

Xiaofeng Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +۵ مورد دیگر

Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.

• Biomedical Imaging
• Medical Imaging Physics
• Motion Compensation in Imaging
• +۵ مورد دیگر

Associate Professor Andre Kyme is an academic staff member in the School of Biomedical Engineering at The University of Sydney. His research focuses on developing enabling technologies for biomedical imaging, including motion compensation in MRI/PET, robotic platforms for image-guided therapy, and cross-disciplinary applications like plant salt uptake analysis using PET. He collaborates with institutions globally and advises students on projects like lameness detection in horses and AI-based motion correction. Research Interests: Kyme's work spans motion correction in medical imaging modalities, medical robotics integration with imaging systems, and innovative applications of imaging technologies in non-traditional fields. His team emphasizes leveraging advancements in computer vision, machine learning, and instrumentation to improve imaging performance and accessibility. Recent Projects: Current research includes MRI-compatible robotic platforms for therapy applications, AI-driven lameness detection in horses, and pediatric neuroimaging improvements. He leads the BREEZE initiative to enhance MRI accessibility for children with cerebral palsy through eye-gaze communication technology. Publications: His work spans 20+ years with over 50 peer-reviewed publications in journals like Physics in Medicine and Biology and IEEE Transactions. Key areas include PET/SPECT/CT motion correction algorithms, robotic systems for medical imaging, and novel imaging applications in plant science. Teaching: Kyme instructs core biomedical engineering courses including thesis supervision and capstone projects at both undergraduate and postgraduate levels. Labs/Teams: Active in the Brain and Mind Centre and Biomedical Imaging, Visualisation and Information Technologies groups at Sydney. Collaborates with industry partners like TeleMedVet and academic institutions including University of California Davis and Chinese University of Hong Kong.

Davide Scaramuzzaمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Autonomous Navigation
• +۴ مورد دیگر

Davide Scaramuzza is a Professor and Director of the Robotics and Perception Group at the University of Zurich. He holds a Ph.D. from ETH Zurich and has conducted postdoctoral research at the University of Pennsylvania and Stanford. His research focuses on autonomous drone navigation using visual and event-based sensors, leading to breakthroughs like AI drones outperforming human pilots in racing (Nature 2023). He pioneered algorithms for Mars helicopter navigation and developed the PX4 autopilot system. Key awards include the Kiyo-Tomiyasu IEEE Technical Field Award (2024), ERC Consolidator Grant (2019), and multiple best paper awards. His entrepreneurial ventures include co-founding Zurich-Eye (later Meta Zurich) and SUIND for agricultural drones. He co-authored the textbook Introduction to Autonomous Mobile Robots , widely used in academia. Research spans event camera algorithms, visual-inertial SLAM, and reinforcement learning for agile flight. His lab's work is featured in IEEE Spectrum, The Guardian, and Forbes. He advises UN initiatives on AI for disaster response and nuclear safety. Current projects include Graph-Generating State Space Models (CVPR 2024) and event-based vision for automotive systems (Nature 2024).

Heikki Handroosمشاهده پروفایل

استاد

• Mechatronics
• Robotics
• Machine Automation
• +۵ مورد دیگر

Heikki Handroos is a Full Professor of Mechanical Engineering at LUT University, leading the Laboratory of Intelligent Machines since 1993. He holds a DSc (Technology) from Tampere University of Technology and has served as Vice-Dean of the Faculty of Technology (2007-2009) and currently chairs the Collegiate Body of LUT University. His research focuses on mechatronics, robotics, control systems, and fluid power, with over 300 publications and 2,400+ citations. He has supervised 34 doctoral theses and 150+ MSc projects, managed R&D projects exceeding €20M, and co-founded four tech startups. His work spans industrial collaborations, digital twin applications, and innovative robotics for nuclear energy (e.g., DEMO reactor maintenance systems). He has held visiting professorships in the U.S., Japan, and Russia, and actively contributes to academic editorial roles and professional societies like ASME and IEEE.

• Radiation Biophysics
• Cancer Radiotherapy
• Ionizing Radiation Effects
• +۴ مورد دیگر

David J. Brenner serves as Higgins Professor of Radiation Biophysics in Radiation Oncology and Environmental Health Sciences at Columbia University Medical Center. He directs both the century-old Center for Radiological Research and the Radiological Research Accelerator Facility (RARAF), leading interdisciplinary teams focused on radiation applications in medicine and safety. BA in Physics from Oxford University (1974) MSc in Radiation Physics from University of London (1976) MA in Physics Philosophy from Oxford University (1979) PhD in Physics from University of Surrey (1980) His research spans dual aspects of radiation: therapeutic applications in cancer treatment and risk assessment across diverse scenarios. Key initiatives include advancing carbon-ion therapy for pancreatic cancer, developing safe far-UVC light for pathogen elimination, and investigating low-dose radiation risks from medical imaging to nuclear terrorism. His team leverages RARAF's unique capabilities for mechanistic studies of radiation effects. Publications reveal dominant themes in radiation biophysics, with significant contributions to biodosimetry (RABiT platform), UV disinfection technology, and cancer risk modeling. Recent work emphasizes translational applications including medical countermeasures for radiation exposure and precision radiation oncology techniques. National Academy of Sciences Nuclear and Radiation Studies Board member National Council on Radiation Protection and Measurements member Radiation Research Society Failla Gold Medal recipient (2011) Oxford University Weldon Prize for mathematical biology (2015) Robert D. Moseley Award for Radiation Protection in Medicine Brenner leads multiple NIH-funded projects including biodosimetry development and UV disinfection research. His mentorship extends through directing Columbia's Radiological Research Accelerator Facility and training programs in radiological sciences. Current lab efforts focus on carbon-ion therapy mechanisms and 222-nm UV applications against drug-resistant pathogens, with active collaborations across oncology, microbiology, and physics disciplines.

• Materials Science and Engineering
• Additive Manufacturing
• Materials Informatics
• +۷ مورد دیگر

S. Mohadeseh Taheri-Mousavi is an Assistant Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. She joined CMU in September 2022 after postdoctoral appointments at MIT and Brown University. Her research is supported by major grants from NASA STRI, DARPA, the Army Research Laboratory, and the Naval Nuclear Laboratory, and she is affiliated with the NextManufacturing Center and the Wilton E. Scott Institute for Energy Innovation. Her educational background includes a Ph.D. from EPFL, Switzerland, and M.Sc. and B.Sc. degrees from Sharif University of Technology, Iran. She was awarded both early and advanced Swiss National Science Foundation fellowships during her postdoctoral studies. Taheri-Mousavi’s research focuses on the intersection of materials science, mechanical engineering, and computer science. She develops multi-scale computational models and AI-driven frameworks—such as AlloyGPT and generative AI agents—to design next-generation structural alloys, particularly for additive manufacturing and extreme environments. Her work emphasizes materials sustainability, industrial decarbonization, and uncertainty quantification in alloy design. The integration of machine learning with Integrated Computational Materials Engineering (ICME) and CALPHAD methods enables rapid exploration of high-dimensional composition and processing spaces. Her recent publications (2023–2025) show a strong trend toward AI/ML applications in alloy discovery, hydrogen embrittlement modeling, and high-temperature aluminum and tungsten alloys. These works reflect a deep commitment to accelerating materials innovation through human-AI collaboration and smart experimental validation. Her scientific honors include prestigious Swiss National Science Foundation fellowships. She has also received seed funding from the Scott Institute for Energy Innovation to study hydrogen embrittlement. She advises a dynamic team of doctoral students and a postdoctoral researcher, working on topics including hydrogen embrittlement, generative AI for welding, and gradient alloys. Her research is funded by high-impact grants from NASA, DARPA, the Army, and the Naval Nuclear Laboratory, supporting transformative projects in structural alloy design. She leads the Taheri-Mousavi Group, which operates within CMU’s Materials Characterization Facility and the NextManufacturing Center. The group focuses on developing novel AI-integrated computational frameworks to guide efficient and intelligent experimentation in alloy development.

Brett J. Borghettiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۱۲ مورد دیگر

Dr. Brett J. Borghetti is a Professor of Computer Science in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), Graduate School of Engineering and Management, Wright-Patterson AFB, OH. He was promoted to Professor in July 2022, following prior appointments as Associate Professor (2017) and Assistant Professor (2008/2013). His expertise lies in artificial intelligence, machine learning, deep learning, cybersecurity, and human-machine teaming. Education: Ph.D. in Computer Science, University of Minnesota, Twin Cities (2008) M.S. in Computer Systems, Air Force Institute of Technology (1996) B.S. in Electrical Engineering, Worcester Polytechnic Institute (1992) Dr. Borghetti's research focuses on applying machine learning to physical science sensors (hyperspectral, seismic, RF), cybersecurity, and enhancing human-machine team performance. He teaches graduate courses in machine learning, AI, data security, and algorithm design, and advises numerous MS and PhD students in areas such as sensor exploitation, cognitive workload, and cyber situational awareness. His recent publications demonstrate strong trends in deep learning for multimodal sensor fusion, nuclear security, and neuroergonomics. Scientific Awards: AETC Educator of the Year (2021, Civilian) AFIT Ezra Kotcher Teaching Award (2021) AFIT Teaching Excellence Award (2019) AF STEM Outstanding Science and Educator Award (2015) Multiple Eta Kappa Nu Outstanding Instructor Awards Air Force Meritorious Service Medal and other military honors Dr. Borghetti has advised numerous graduate students and led research projects with significant funding and applications in defense and national security. He has directed research in AI-driven sensor analysis, cyber defense systems, and adaptive automation. His work often involves collaboration with national labs and DoD agencies. He has contributed to major research initiatives in human factors, cyber intruder detection, and machine learning for operational environments. Labs and Research Teams: His work is associated with AFIT's research in cyber security, sensor exploitation, and human-machine systems. He collaborates with teams working on the Cyber Intruder Alert Testbed (CIAT), neuroergonomic modeling, and machine learning for defense applications.