جست‌وجوی استاد و پژوهشگر

Mark Leeمشاهده پروفایل

استاد مدعو

• Human Factors Engineering
• Healthcare Safety
• Data Analytics in Manufacturing
• +۳ مورد دیگر

Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.

Changhuei Yangمشاهده پروفایل

استاد

• Biophotonics
• Microscopy
• Computational Imaging
• +۳ مورد دیگر

Changhuei Yang is the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering at California Institute of Technology, serving as Executive Officer for Electrical Engineering and Investigator at Heritage Medical Research Institute. He holds a Ph.D. and three master's degrees from MIT, with appointments at Caltech since 2003. Research focuses on: Advanced microscopy techniques including Fourier Ptychography Wavefront shaping for biological tissue imaging Optical phase conjugation for deep-tissue applications Compact medical devices for cerebral monitoring Publications demonstrate leadership in computational imaging, with recent advances in stain-free embryo analysis, portable cerebral blood flow monitors, and high-resolution volumetric imaging techniques using neural representations. Honored as National Academy of Inventors member. Research applications span deep-tissue biochemical imaging, incisionless surgery, and optogenetic activation systems.

Yuvraj Agarwalمشاهده پروفایل

استاد

• Systems
• Embedded Systems
• Security
• +۷ مورد دیگر

Yuvraj Agarwal is a Professor in the School of Computer Science at Carnegie Mellon University , where he leads the SYNERGY Labs . His research focuses on Systems and Networking with emphasis on Embedded Systems , Security , and Energy Efficiency in computing environments. He has been recognized with the NSF Expeditions in Computing Award for Computational Decarbonization research and multiple best paper awards. Education : PhD in Computer Science from University of California, San Diego Research Leadership : Founder/Director of SYNERGY Labs; Executive Director of NSF Expeditions in Variability (2010-2013) Research Trajectory : Recent publications highlight his work on Privacy-preserving smart classroom systems (EduSense, ClassID) IoT security labeling frameworks Audio privacy protection mechanisms Computational decarbonization of infrastructure His research combines hardware-software co-design with societal impact considerations. Scientific Recognition : NSF Expeditions in Computing Award for CoDec (2024) Ubicomp/IMWUT Distinguished Paper Award (2024) CHI Best Paper Honorable Mention (2022) Advising & Collaboration : Mentors students in IoT systems , privacy research , and green computing . Collaborates with institutions including University of Massachusetts Amherst and industry partners like Johnson Controls. Grants include NSF funding for multi-university research initiatives.

• Embedded Machine Learning
• High-Performance Computing
• Computer Architecture
• +۶ مورد دیگر

Holger Fröning is a full professor at Heidelberg University’s Institute of Computer Engineering (ZITI), where he leads the Hardware and Artificial Intelligence (HAWAII) Lab. His research focuses on embedded machine learning , high-performance computing , and hardware-software co-design , with emphasis on resource efficiency, power optimization, and emerging architectures like analog , photonic , and resistive memory systems. He has held leadership roles including Managing Director of ZITI (2023–present) and Dean of Studies for Computer Science (2019–2022) , and has collaborated with institutions such as NVIDIA Research, Chinese Academy of Sciences, and Graz University of Technology. Research Trends : His recent publications explore Bayesian neural networks , green machine learning , analog computing noise mitigation , and GPU/FPGA optimization . Articles highlight photonic computing for AI , memory-efficient training , and hardware-aware DNN compression . Scientific Awards : 2025 HiPEAC Paper Award (Nature Computational Science) 2014 Google Faculty Research Award Multiple Best Paper Awards (IPDPS, ICPP, ECML-PKDD workshops) Leadership & Service : Organized workshops (WEML, ITEM, F4HD), chaired tracks at EuroPar and ISC, and served on program committees for ICPR, ECAI, and FPL. Education & Affiliations : PhD and MSc from University of Mannheim (2007/2001). Sponsors include DFG, FWF, FFG, NVIDIA, SAP, and XILINX.

Amy R. Wuمشاهده پروفایل

دانشیار

• Human-Robot Interaction
• Human Biomechanics
• Locomotion
• +۴ مورد دیگر

Amy R. Wu serves as Associate Professor in Mechanical and Materials Engineering at Queen's University and holds the Mitchell Professorship in Bio-inspired Robotics. She leads the Biomechanics x Robotics Laboratory (BxRL) and contributes to the Ingenuity Labs Research Institute. Her academic credentials include: Ph.D. in Mechanical Engineering, University of Michigan Postdoctoral Research, Biorobotics Laboratory, EPFL, Switzerland Dr. Wu's research bridges biomechanics and robotics to enhance legged mobility through innovations in human-robot interaction , exoskeleton design , and gait assistance . Her work focuses on developing robotic systems that adapt to human movement patterns, particularly for spinal cord injury rehabilitation and stability augmentation during walking. Analysis of her 2019-2023 publications reveals consistent advancement in exoskeleton control algorithms, gait stability metrics, and human adaptation studies. Key trends include modular exoskeleton systems (Symbitron), neuromuscular controllers for ankle assistance, and haptic feedback integration for motor learning enhancement. No scientific awards are documented in the provided materials. Dr. Wu mentors graduate students through BxRL, where her team conducts human subject testing and robotic development. Research is supported by institutional resources at Ingenuity Labs, though specific grant details remain unspecified. The Biomechanics x Robotics Laboratory operates within Queen's Ingenuity Labs Research Institute, facilitating interdisciplinary collaboration on projects like outdoor stability assessment, winter walking adaptations, and teleoperation interfaces for drone control.

Dylan Hadfield-Menellمشاهده پروفایل

استادیار

• AI Alignment
• Human-AI Interaction
• Reinforcement Learning
• +۳ مورد دیگر

Dylan Hadfield-Menell is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, holding the Bonnie and Marty (1964) Tenenbaum Career Development Professorship. His research focuses on AI alignment and human-AI interaction within MIT's School of Engineering. His research interests center on agent alignment problems in AI systems, particularly examining uncertainty in objective optimization for human-robot teams and societal oversight of machine learning systems. Key areas include the principal-agent alignment problem , assistance games frameworks , and robust preference learning that accounts for hidden contextual factors in reinforcement learning from human feedback. His recent publications reveal strong trends toward multi-agent cooperation , formal contract mechanisms for resolving social dilemmas, and advanced evaluation methodologies for AI safety. The research spans theoretical frameworks like open-universe assistance games while addressing practical challenges in language model alignment and cultural bias assessment. Scientific awards include: AI2050 Early Career Fellowship from Schmidt Futures Berkeley Fellowship NSF Graduate Research Fellowship C.V. Ramamoorthy Distinguished Research Award His work bridges theoretical computer science with real-world AI governance challenges, as demonstrated through MIT's participation in AI policy white papers. Current research directions include developing frameworks for transparent AI systems and addressing fundamental limitations in aligning recommender systems with human values through interdisciplinary synthesis.

Frede Blaabjergمشاهده پروفایل

استاد

• Power Electronics
• Wind Turbines
• Photovoltaic (PV) Systems
• +۶ مورد دیگر

Frede Blaabjerg is a Professor at Aalborg University (AAU Energy) , affiliated with the Faculty of Engineering and Science . Since 1998, he has pioneered power electronics research in applications such as wind turbines , photovoltaic (PV) systems , reliability engineering , and Power-2-X technologies. Education : PhD in Electrical Engineering (1995, Aalborg University) Honorary Degrees : Honoris Causa at University Politehnica Timisoara (2017) and Tallinn Technical University (2018) His research focuses on power electronics control , system optimization , and reliability for renewable energy and electric mobility . Recent work includes grid-forming converters , virtual synchronous generators , and smart EV charging systems. Key publication trends span 15+ years , with over 3,733 peer-reviewed articles and 900+ journal papers in power electronics , renewables , and energy storage . Notable book series: Control of Power Electronic Converters and Systems (4 volumes, Elsevier). Scientific Awards : 46 IEEE Prize Paper Awards 2020 IEEE Edison Medal 2019 Global Energy Prize 2014 IEEE William E. Newell Power Electronics Award Leadership Roles : Editor-in-Chief, IEEE Transactions on Power Electronics (2006–2012) Chairman, Danish Council for Research and Innovation Policy (2020–) President, IEEE Power Electronics Society (2019–2020)

Norbert Lutkenhausمشاهده پروفایل

استاد

• Quantum Communication
• Quantum Key Distribution (QKD)
• Quantum Information Processing
• +۴ مورد دیگر

Prof. Norbert Lütkenhaus is a Professor and Executive Director of the Institute for Quantum Computing (IQC) at the University of Waterloo, cross-appointed to the Department of Applied Mathematics. He holds affiliations with Perimeter Institute and the Centre for Applied Cryptographic Research. His research focuses on quantum communication theory, particularly quantum key distribution (QKD) and quantum repeaters. He has pioneered methods to bridge abstract quantum protocols with practical optical implementations, emphasizing secure key rate calculations and overcoming quantum channel limitations. Education: PhD (2003) in Physics from University Erlangen-Nürnberg, MSc (1993) and BSc (1990) from Ludwig-Maximilians-Universität München and RWTH Aachen, respectively. Awards include the 2015 American Physical Society Outstanding Referee Award and a 2009 University of Waterloo Excellence Award. Research interests span QKD protocols (e.g., decoy-state BB84, phase-error mitigation), quantum repeater architectures, and entanglement verification. He develops numerical tools for key rate analysis and addresses implementation security loopholes. His work includes theoretical frameworks for long-distance quantum communication and practical QKD system optimizations. Teaching includes courses on quantum information processing (PHYS 768/QIC 890) and mechanics (PHYS 115). He contributes to international standards via ETSI’s QKD-ISG and the QCrypt steering committee. His patents cover QKD system designs and phase-randomization techniques.

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

• Numerical Modeling
• Computational Simulation
• Materials Science
• +۵ مورد دیگر

Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.

Qing Liمشاهده پروفایل

دانشیار

• Photonics
• Nanofabrication
• Quantum Information
• +۳ مورد دیگر

Qing Li is an Associate Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. He holds a B.E. in Electronics Engineering from Tsinghua University (2006) and a Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2013). Prior to CMU, he worked as a postdoctoral researcher at the National Institute of Standards and Technology (NIST), where he developed quantum frequency conversion and microresonator-based optical systems. His research focuses on light-matter interactions in integrated photonics, emphasizing nonlinear optics and quantum information processing. He has pioneered silicon carbide and aluminum nitride platforms for chip-scale quantum technologies and optical metrology. Dr. Li has been recognized with prestigious awards including the Darpa Young Faculty Award (2019), OSA Paul F. Forman Team Engineering Excellence Award (2020), and Sigma Xi Best Ph.D. Thesis Award (Georgia Tech). His work bridges classical and quantum information systems, with applications in secure communication, atomic systems interrogation, and high-precision frequency synthesis. He actively contributes to the Pittsburgh Quantum Institute (PQI), advancing regional quantum engineering initiatives. His research group’s key projects include developing compact optical frequency synthesizers, soliton microcombs for communication grids, and entangled photon pair sources for quantum networks. Grants and collaborations support his exploration of novel photonic materials and devices, targeting advancements in both fundamental science and applied technologies.

Qimin Liuمشاهده پروفایل

استادیار

• Psychological Sciences
• Quantitative Methods
• Clinical Science
• +۴ مورد دیگر

Qimin Liu is an Assistant Professor at Boston University, serving as Lab Director of the Quantitative Psychopathology Laboratory. He holds a PhD in Psychological Sciences from Vanderbilt University with specializations in Clinical Science and Quantitative Methods. His research focuses on emotional disturbances across development, statistical methodology development, and health equity with an emphasis on intersectional marginalization. He has expertise in analyzing intensive longitudinal data and has published extensively on topics like irritability, suicidality, and mental health disparities among sexual and gender minority populations. Dr. Liu’s work frequently integrates advanced statistical techniques such as latent variable modeling, network analysis, and machine learning. His recent studies explore the temporal dynamics of affect, the impact of stigma on mental health, and the role of childhood adversity in psychiatric outcomes. Notable contributions include developing methods for analyzing zero-inflated longitudinal data and creating algorithms for digital phenotyping of mood disorders through mobile device usage patterns. His scholarship emphasizes bridging clinical phenomena with rigorous quantitative approaches, addressing gaps in understanding how social determinants and individual differences shape mental health trajectories. He has collaborated on large-scale datasets like the Collaborative Psychiatric Epidemiological Surveys and contributed to interdisciplinary research on public health outcomes among aging sexual minority men. Dr. Liu’s methodological innovations include the DACF framework for ceiling/floor effect data and the lamme package for log-analytic multiplicative effects modeling. He actively publishes in high-impact journals such as Psychological Methods and Journal of Abnormal Psychology , focusing on both empirical findings and statistical best practices.

Massimo Mischiمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Diagnostic Imaging
• Ultrasound Technology
• +۴ مورد دیگر

Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.

Bharat Bhuvaمشاهده پروفایل

استاد

• Computer-Aided Design
• Radiation Effects on Electronics
• Semiconductor Device Modeling
• +۳ مورد دیگر

Professor Bharat Bhuva is a faculty member in the School of Engineering at Vanderbilt University, holding the position of Professor of Electrical Engineering and Computer Engineering . His research focuses on radiation effects on integrated circuits, semiconductor device modeling, and VLSI design, with an emphasis on advancing the resilience of nanoscale electronics against single-event effects and total-ionizing-dose damage. He also investigates emerging technologies like FinFET and FDSOI for improved radiation hardness and performance. Education: Ph.D. in Electrical Engineering, North Carolina State University M.S. in Electrical Engineering, North Carolina State University B.S. in Electrical Engineering, Maharaja Sayajirao University Research Interests: Professor Bhuva’s work spans computer-aided design tools, semiconductor process modeling, and the mitigation of radiation-induced failures in advanced integrated circuits. His studies address challenges posed by scaling to smaller technology nodes (e.g., 3-nm FinFET) and the impact of environmental factors like temperature, bias conditions, and neutron exposure on circuit reliability. Key areas include multicell upsets, single-event upset (SEU) cross-section analysis, and the efficacy of radiation-hardened-by-design (RHBD) techniques. Awards & Recognition: None explicitly listed in the provided text. However, his extensive publications in top-tier journals like IEEE Transactions on Nuclear Science highlight his contributions to the field. Grants & Advising: Advises on projects related to advanced semiconductor technologies and radiation effects. His research has been supported by grants from institutions focusing on space electronics and nanotechnology. No specific grant details or student advisees are listed. Labs & Teams: Likely affiliated with Vanderbilt’s Cyber-physical Systems and Nano Science and Technology research neighborhoods, though specific lab names are not mentioned in the text.

• Medical Statistics
• Biostatistics
• Epidemiology
• +۳ مورد دیگر

Ruth Keogh is a Professor of Biostatistics and Epidemiology at the London School of Hygiene & Tropical Medicine (LSHTM), affiliated with the Medical Statistics Department within the Faculty of Epidemiology and Population Health. She is Co-Director of the Centre for Data and Statistical Science for Health (DASH) and serves as Departmental Research Degrees Coordinator. Her academic career has spanned roles from Lecturer (2012–2015) to Associate Professor (2015–2019) before attaining her current rank in 2019. Keogh holds advanced degrees including a DPhil in Medical Statistics/Epidemiology (University of Oxford, 2007), MSc in Applied Statistics (Oxford, 2003), and BSc in Mathematics and Statistics (University of Edinburgh, 2002). Her research focuses on causal inference, clinical trial emulation using real-world data, and applications in cystic fibrosis, infectious diseases, and public health. She leads projects on lung function trajectories, vaccine efficacy, and healthcare policy analysis. Her work integrates biostatistical methods with epidemiological studies, emphasizing rigorous analysis of observational data to inform clinical decisions. Notable areas include evaluating antibiotic treatments for cystic fibrosis patients, assessing diagnostic test accuracy for dengue and leptospirosis, and modeling vaccine effectiveness during the COVID-19 pandemic. She teaches courses in survival analysis, electronic health records, and health data science at LSHTM. Keogh has held leadership roles in the International Biometric Society and the STRATOS Initiative, and she has delivered keynote addresses at international conferences on trial emulation and biostatistical methods. Her contributions bridge methodological innovation and practical health challenges, with over 190 publications and active engagement in global health research networks.