جست‌وجوی استاد و پژوهشگر

Xiaojun "Gene" Shanمشاهده پروفایل

دانشیار

• Operations Research
• Healthcare Systems Engineering
• Game-Theoretic Modeling
• +۵ مورد دیگر

Dr. Xiaojun (Gene) Shan is an Associate Professor of Engineering Management at the University of Houston-Clear Lake (UHCL), within the College of Science and Engineering. He holds a Ph.D. in Industrial and Systems Engineering from the University at Buffalo (SUNY-Buffalo) and an M.A. in Cognitive Psychology and Psycholinguistics from the same institution. His research focuses on applying operations research to healthcare systems, disaster risk management, and infrastructure resilience, with notable work in game-theoretic modeling of complex systems. Dr. Shan teaches courses in logistics management, engineering management planning, and business analytics. His research interests include healthcare delivery optimization, electric grid reliability, hurricane risk mitigation, and strategic defense against adversarial threats. He has been recognized with awards such as the Society for Risk Analysis Travel Award and finalist distinctions in INFORMS competitions. Dr. Shan’s work bridges theoretical models (e.g., multi-period defensive resource allocation) with practical applications like staffing optimization in healthcare and fraud detection frameworks. Education: Ph.D. in Industrial & Systems Engineering, SUNY-Buffalo (2008) M.A. in Cognitive Psychology & Psycholinguistics, SUNY-Buffalo (2008) His publications span journals like Reliability Engineering & System Safety and European Journal of Operational Research , with recent work addressing patient scheduling systems and green food recommendation algorithms. Dr. Shan collaborates on interdisciplinary projects, including disaster risk modeling and smart grid cybersecurity. He actively contributes to improving healthcare operations through data-driven approaches and lean methodologies. Awards: Society for Risk Analysis Travel Award (2011, 2012) INFORMS Decision Analysis Society Student Paper Award Finalist (2012, 2010) 11th Annual IIE Doctoral Colloquium Attendee (2012) Dr. Shan’s advising and grants focus on healthcare analytics and infrastructure resilience, with projects funded by organizations emphasizing operational excellence and risk mitigation. He leads efforts to standardize care management processes and optimize staffing models in clinical settings.

Afef Fekihمشاهده پروفایل

دانشیار

• Control Systems
• Fault-Tolerant Control
• Nonlinear Control
• +۵ مورد دیگر

Afef Fekih is an Associate Professor of Electrical and Computer Engineering at the University of Louisiana at Lafayette, where she founded and directs the Advanced Control Systems Laboratory (since 2005). Her research spans control theory applications in aircraft systems, automotive control, renewable energy, and communications with emphases on fault tolerance, nonlinear dynamics, and robust design. She has received significant honors including: Harold Callais Memorial/BORSF Professorship (2013-2016) Chevron/BORSF Professorship (2011-2014) Outstanding Academic Advisor Award (2007) Favorite Teacher Award (2007) Recent publications (2019-2025) demonstrate strong focus on renewable energy control, neural networks in robotics, power electronics, and cybersecurity for medical systems. Trends include AI-driven optimization, sustainable energy solutions, and adaptive control frameworks. She maintains affiliations as a Senior IEEE member and visiting professor at the University of Valenciennes, France.

Mike Kiebishمشاهده پروفایل

استاد مهمان

• Lipidomics
• Metabolomics
• Metabolic Diseases
• +۷ مورد دیگر

Mike Kiebish serves as a Visiting Scholar in the Biology Department at Boston College and Vice President of Platform and Translational Sciences at BPGbio. His multidisciplinary expertise spans molecular cardiology, pharmacology, bioorganic chemistry, and lipidomics/metabolomics, positioning him at the intersection of academic research and biotechnology innovation. His educational background includes: Postdoctoral Training at Washington University School of Medicine in Molecular Cardiology and Pharmacology, Bioorganic Chemistry, Systems Biology, Biophysics, Membrane Biochemistry, and Lipidomics/Metabolomics Ph.D. in Biology from Boston College focusing on Metabolic Neurochemistry, Neuro-oncology, Neurodegeneration, and Lipid biochemistry B.S. from Villanova University with majors in Biology and Comprehensive Science, and minors in Business and Russian Kiebish's research centers on high throughput omic technologies applied to therapeutic development for oncology, metabolic diseases, rare diseases, and neurological disorders. His pioneering work has identified novel lipokines with potent biological functions, developed spatial omic technologies for single-cell metabolite tracking, and created screening tools for protein degradation assessment. His team's implementation of Bayesian analytics has advanced target discovery and biomarker development, resulting in over 100 publications and 10+ issued patents. Analysis of his recent publications reveals a cohesive research trajectory focused on lipid signaling molecules (particularly 12,13-diHOME and related compounds) and their roles in metabolic regulation, cardiovascular function, and cancer biology. His work increasingly integrates AI-driven approaches with experimental biology, exemplified by BPGbio's NAi Interrogative Biology Platform which performs comprehensive multi-omics analysis using Bayesian causal AI models. Kiebish's leadership has contributed to multiple clinical trials for conditions including Glioblastoma, Primary CoQ10 Deficiency, Pancreatic Cancer, Epidermolysis Bullosa, and Sarcopenia. His Protein Homeostasis program, built around a First-In-Class ubiquitin conjugating enzyme (E2) based targeted protein degradation platform, represents a novel therapeutic approach with significant potential.

Julie H. Simpsonمشاهده پروفایل

دانشیار

• Neuroscience
• Genetics
• Molecular Biology
• +۵ مورد دیگر

Julie H. Simpson is an Associate Professor in the Department of Molecular, Cellular, and Developmental Biology (MCDB) at the University of California, Santa Barbara (UCSB). She leads the Simpson Lab, which investigates neural circuits governing motor sequence control using Drosophila as a model system. Her work focuses on understanding how the nervous system organizes complex behaviors like grooming, integrating genetic, optogenetic, and computational approaches. Education: AB in Molecular Biology from Princeton University, PhD in Neurobiology from UC Berkeley, postdoctoral research at UW-Madison and the Janelia Research Campus. Joined UCSB in 2015. Research emphasizes dissecting neuronal circuits using genetic screens, optogenetics, and machine vision. Key projects include studying command neurons (e.g., Mago-no-te), leg projection neurons (LegPN), and sensory integration mechanisms. The lab employs 3D reconstruction tools like Neuroglancer and collaborates on connectome projects. Notable achievements include an NSF CAREER Award for motor sequence research. Active in training undergraduates and graduate students through hands-on research programs, with opportunities in neuroanatomy, microscopy, and computational neuroscience. Labs and affiliations: Principal Investigator at Simpson Lab, affiliated with UCSB Neuroscience and MCDB. Collaborates with institutions like KITP and participates in systems biology initiatives.

Stephen Kilgusمشاهده پروفایل

استاد

• School Mental Health
• Intervention Development
• Behavioral Assessment
• +۳ مورد دیگر

Stephen Kilgus is a Professor in the School Psychology Area at the University of Wisconsin – Madison’s School of Education. His research focuses on school mental health, including intervention development (e.g., the Resilience Education Program and Check In/Check Out) and assessments like the SAEBRS screening tool. He holds a PhD in Educational Psychology from the University of Connecticut (2011) and has been honored with awards such as the Lightner Witmer Award (APA, 2016) and the Article of the Year (Journal of School Psychology, 2014). Dr. Kilgus’ work emphasizes evidence-based practices in mental health screening and intervention, with a focus on equity and cultural responsiveness. His research spans topics like diagnostic accuracy of screening tools, teacher burnout impacts, and functional behavioral assessment. Key contributions include the SAEBRS, widely used for universal screening, and interventions addressing internalizing concerns. He actively engages in professional service, including roles at the National Association of School Psychologists and Society for the Study of School Psychology. Awards reflect his scholarly impact, including the Top Faculty Achiever (2017) and Early Career Scholar recognition (2013). While specific grant details are not listed, his research has been supported by institutions like the National Center for School Mental Health. Collaborations with colleagues like Dr. Katie Eklund and Dr. Nathaniel von der Embse further highlight his leadership in advancing school mental health practices.

• Retail Economics
• Labor Economics
• Game Theory
• +۵ مورد دیگر

Zhen He is an Assistant Professor in the Department of Economics at McMaster University, where he focuses on retail economics, labor markets, and applied microeconomics. His interdisciplinary research bridges economic theory with advanced sensing technologies, particularly in human activity recognition and wearable devices. Key teaching areas: Intermediate Microeconomics, Public Sector Economics, Labour Economics Recent work explores sedentary behavior mapping in aging populations and acoustic sensing applications His scholarly output spans both traditional economic studies and cutting-edge technological implementations, suggesting a unique cross-disciplinary approach. While primarily affiliated with McMaster's Economics department, his research extends into domains like wireless sensing systems and edge computing analytics. Notable trends in his publications include: Acoustic sensing innovations on commodity hardware mmWave radar applications for health monitoring Behavioral pattern analysis in commercial environments

Yuval Elaniمشاهده پروفایل

دانشیار

• Biochemical Technologies
• Biomedical Engineering
• Medical Biotechnology
• +۳ مورد دیگر

Dr. Yuval Elani is an Associate Professor in Biochemical Technologies at Imperial College London's Department of Chemical Engineering (Faculty of Engineering). He leads the Bioinspired Technologies Group, which focuses on synthetic cells, microfluidics, and biohybrid systems. His roles include Co-Director of the Membrane Biophysics Platform and Co-Founder of fabriCELL BIO. Elani earned his BA/MA from the University of Cambridge and his MRes/PhD from Imperial College London. His research spans synthetic cell design, biomimetic technologies, and therapeutic applications, with emphasis on biodegradable nanoparticles, cell-free systems, and microfluidic platforms. Education: BA/MA, University of Cambridge; MRes/PhD, Imperial College London Affiliations: Institute for Molecular Science and Engineering, fabriCELL BIO, Leverhulme Cellular Bionics Centre Elani's research interests include creating bioinspired systems for diagnostics, therapeutics, and energy. His group engineers biomolecular robots and synthetic cells capable of self-repair, decision-making, and environmental interaction. Key achievements include pioneering lipid nanoparticle design and securing grants from industry partners like GSK and AstraZeneca. His work has been recognized with awards such as the Roscoe Medal (2015), Rita & John Cornforth Medal (2017), and UKRI Future Leaders Fellowship (2020). Current projects recruit postdocs and PhD students in synthetic cell development and microfluidic technologies. The lab emphasizes interdisciplinary collaboration, diversity, and fostering early-career researchers. Notable Projects: Synthetic cell preservation strategies, vesicle-based molecular robots, and biohybrid engineered systems Elani's group actively collaborates with clinicians and industries to translate fundamental research into applications like drug delivery and biosensors. His vision is to bridge synthetic biology with engineering to solve global challenges in healthcare and sustainability.

Thomas Beckersمشاهده پروفایل

استادیار

• Control Systems
• Machine Learning
• Robotics
• +۶ مورد دیگر

Thomas Beckers is an Assistant Professor of Computer Science at Vanderbilt University's School of Engineering, joining in January 2023. He holds a Ph.D. in Electrical Engineering from the Technical University of Munich (2020), and B.Sc./M.Sc. degrees from Technical University of Braunschweig (2010/2013). His research bridges machine learning and control theory to develop safe, robust algorithms for complex systems like autonomous vehicles and soft robotics. He is a DAAD AInet Fellow and recipient of the Rhode & Schwarz Outstanding Dissertation Award. His work focuses on physics-enhanced learning and safe control strategies, leveraging Bayesian methods and port-Hamiltonian systems. Recent projects include developing uncertainty-aware control frameworks and Python tools like PyGpPHs for modeling physical systems. He advises students Peilun Li and Kaiyuan Tan, and has organized workshops at major conferences such as ACC and CDC. His lab explores topics ranging from PDE system identification to geometric control of aerial robots. Notable achievements include organizing large-scale workshops (e.g., 2023 CDC workshop with 80+ participants) and securing RAMP awards for data-driven control research. His publications emphasize safety, interpretability, and integration of physical priors into machine learning models.

Hongkai Zhaoمشاهده پروفایل

استاد

• Applied and Computational Mathematics
• Inverse Problems
• Imaging
• +۴ مورد دیگر

Professor Hongkai Zhao is a Chancellor’s Professor and Chair of the Mathematics Department at the University of California, Irvine (UCI), with a joint appointment in Computer Science. He holds a B.S. from Peking University, M.S. from USC, and Ph.D. from UCLA, all in Mathematics. His research focuses on computational and applied mathematics, including moving interface problems, inverse problems, imaging, and Hamilton-Jacobi equations. He is renowned for developing the Fast Sweeping Method (FSM) for solving hyperbolic PDEs efficiently. Education: B.S. (Peking University, 1990), M.S. (USC, 1992), Ph.D. (UCLA, 1996). Employment: UCI faculty since 1999, Szego Assistant Professor at Stanford (1996–1999). Research interests span computational fluid dynamics (moving interfaces), inverse problems (tomography, wave propagation), and imaging (medical imaging, computer vision). Key contributions include numerical methods for surfactant-laden drops, fast algorithms for Hamilton-Jacobi equations, and 4D cone-beam CT reconstruction. Awards include the Feng Kang Prize (2007), Sloan Fellowship (2002–2004), and Chancellor’s Professorship (2016–present). He has authored over 100 publications on numerical methods, inverse problems, and PDEs. Lab affiliations: Active in interdisciplinary research at UCI, leading projects in computational mathematics and imaging science. Current work includes high-frequency wave propagation, multi-scale modeling, and geometric data analysis.

Thilo Womelsdorfمشاهده پروفایل

دانشیار

• Neuroscience
• Cognitive Neuroscience
• Systems Neuroscience
• +۷ مورد دیگر

Thilo Womelsdorf is an Associate Professor in the Departments of Psychology, Computer Science, and Biomedical Engineering at Vanderbilt University, School of Engineering. He leads the Attention Circuits Control Lab , investigating neural mechanisms of attention and learning in nonhuman primates and humans using advanced neurophysiological and computational methods. His work integrates 3D virtual reality paradigms, multi-area electrophysiological recordings, and neuromodulation techniques to study brain-behavior relationships. Education : PhD from German Primate Center (Göttingen University), postdoctoral training at Donders Institute (Netherlands) and Robarts Research Institute (Canada). Recipient of the prestigious E.W.R. Steacie Memorial Fellowship (2017) for bridging cellular and network-level neuroscience. Research : Focuses on how neural circuits implement attentional control and adaptive learning. Key areas include reinforcement learning modeling, causal circuit analysis via electrical/molecular interventions, and translational applications for neuropharmacology. His lab employs cutting-edge tools like spike artifact removal algorithms and multi-neuromodulator measurements. Grants & Collaborations : Active in developing neuro-oscillation biomarkers for neuropsychiatric treatments, and creating multi-task cognitive assessment platforms. Collaborates across disciplines to advance translational neuroscience. Labs/Teams : Directs the Attention Circuits Control Lab, fostering interdisciplinary research between engineering, computer science, and neuroscience. Involved in developing neurotechnology tools like the Quaddles object set and USE experimental suite.

Pierre Payeurمشاهده پروفایل

استاد

• Machine Vision
• 3D Sensing and Modeling
• Human Motion Capture
• +۴ مورد دیگر

Pierre Payeur is a Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a PhD from Université Laval (1999) and has been a faculty member since then. He leads the SMART Research Group and co-founded the VIVA Research Laboratory. His roles include Graduate Programs Coordinator for the Ottawa-Carleton Institute for Electrical and Computer Engineering and faculty advisor for the ARISE student robotics club. Education: Ph.D. in Electrical Engineering from Université Laval (1999) Research interests focus on machine vision, 3D sensing, human motion capture, tactile sensing, robotic navigation, and computational intelligence. His work integrates industry collaboration and has been funded by major granting agencies. Recent articles explore domain adaptation in human action recognition, multi-agent robotics, and soil temperature prediction using AI. His contributions include over 150 technical papers and supervision of 70+ researchers. Awards: Best Paper Awards at IROS 2020 and ISVC 2021 Advising and grants: Over 70 supervised researchers, grants from major agencies. Active in professional roles like co-chair of IEEE TC-28 and organizer of the ROSE conference series. Labs: SMART Group and VIVA Lab, focusing on robotics, sensing, and autonomous systems.

• Digital Signal Processing
• Radar Imaging
• Terahertz Time Domain Spectroscopy
• +۳ مورد دیگر

Associate Professor Brian Ng is an academic at the University of Adelaide, affiliated with the School of Electrical and Mechanical Engineering within the Faculty of Sciences, Engineering and Technology. His primary role is as an Associate Professor/Reader, focusing on teaching and research in electrical engineering and signal processing. He holds a position in the Department of Electrical and Electronic Engineering and is eligible to supervise Masters and PhD students in areas such as radar systems, signal processing, and terahertz technology. His research expertise includes digital signal processing, wavelet systems, pattern recognition, radar imaging (including 3D ISAR and maritime applications), and terahertz time-domain spectroscopy. He has led or co-investigated several grants, such as the SmartSat CRC Project (2021-22), DST Group-funded research on 3D ISAR classification, and terahertz sensing projects. His work spans defense applications, environmental monitoring, and material science. Ng teaches courses like Digital Signal Processing, Radar Principles, and Signal Processing Applications. He has contributed to educational initiatives, including e-learning projects and curriculum design. His professional activities include editorial roles in radar conferences and collaborations with defense and industry partners. Key facilities and labs include those supporting terahertz fiber optics and radar systems at the University of Adelaide's North Terrace campus. His research emphasizes practical applications of signal processing in aerospace, maritime, and biomedical contexts.

Magnus Janssonمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Navigation and Positioning
• +۳ مورد دیگر

Magnus Jansson is a Professor of Signal Processing at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Information Science and Engineering. He holds a Ph.D. from KTH (1997) and has held academic positions at KTH since 1998, progressing from Assistant Professor (1998-2003) to Associate Professor (2003-2013) before becoming a full Professor in 2013. His research focuses on statistical signal processing, machine learning, navigation systems, sensor array processing, and system identification, with applications in radar, communication systems, and sensor fusion. Education: M.Sc. (1992), Licentiate (1995), and Ph.D. (1997) in Electrical Engineering (Automatic Control) from KTH. Postdoctoral work at the University of Minnesota (1998-1999). Served as an editor for IEEE Signal Processing Letters, Elsevier Signal Processing, and EURASIP Journal on Advances in Signal Processing. Current roles include Associate Editor for Elsevier Signal Processing (since 2024). Research emphasizes model selection, low-rank matrix reconstruction, and Bayesian methods, with recent contributions to drone classification via CNNs, robust model selection in high-dimensional data, and navigation algorithms leveraging inertial and vision systems. His work bridges theoretical signal processing with practical applications in robotics, wireless localization, and radar systems. Teaching responsibilities include courses on estimation theory, stochastic signals, and adaptive signal processing. Active in collaborative research on positioning systems and sensor networks, with contributions to IMU-camera calibration and visual-inertial navigation. No specific grants or awards listed, but recognized for editorial roles and academic leadership.

Chris Phillipsمشاهده پروفایل

دانشیار

• Internet Technologies
• Optical Networking
• Network Security
• +۵ مورد دیگر

Dr. Chris Phillips is a Reader in the School of Electronic Engineering and Computer Science at Queen Mary University of London. He holds a BEng in Telecommunications Engineering and a PhD in Telecommunications from Queen Mary. His roles include Director of Recruitment and Admissions for EECS, Programme Director of Computer Systems Engineering, and member of the Networks Research Group. His research focuses on Internet Technologies, Optical Networking, Network Security, and Energy-Efficient systems, with emphasis on resilient communication, wireless sensor networks, and nomadic computing. Education: BEng Telecommunications Engineering (1st Class Honours), Queen Mary University of London (1984–1987); PhD in Telecommunications, Queen Mary University of London (1987–1991). Professional experience includes roles at Bell Northern Research, Siemens–Roke Manor Research, Nortel Networks, and academic leadership at Queen Mary since 2000. He has supervised over 20 PhD students and contributed to multiple research grants, including projects on energy management in wired networks and resilient overlay systems. Research Interests: Internet protocols, optical networking, network security, energy efficiency, wireless sensor networks, and resilient communication systems. His work spans theoretical advancements and practical implementations, with applications in healthcare, agriculture, and vehicular systems. Recent publications explore IoT optimization, cooperative routing in MANETs, and energy-efficient optical networks. Grants and Awards: Secured funding from EPSRC, Royal Society, Nortel, Nokia, and the UK-China Government Office for Science. Notable grants include the EPSRC-funded project on machine learning in optical networks and Royal Society collaborations on intelligent connection management. Teaching: Currently teaches Computer Architecture and Networks (Postgraduate) and Microprocessor Systems Design (Undergraduate). Previously lectured on topics like MPLS networks, G-MPLS, and real-time transport protocols. His teaching integrates practical lab work with theoretical frameworks, emphasizing hands-on experience with microcontrollers and network protocols. Labs/Teams: Active member of the Networks Research Group, collaborating with institutions like Technical University of Denmark and University of Girona. His research team focuses on developing innovative solutions for network resilience, energy efficiency, and distributed systems.

Khalid Rajabمشاهده پروفایل

مدرس

• Millimeter-Wave Radar
• Sensing for Health and Wellbeing
• IoT Sensors
• +۳ مورد دیگر

Dr. Khalid Rajab is a Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London (QMUL). His research focuses on millimeter-wave radar systems, IoT sensors for health monitoring, and machine learning applications in ambient sensing. He holds a PhD and is a Fellow of the Higher Education Academy (FHEA) and a Chartered Engineer (CEng). His research interests include radar-based activity recognition, health and wellbeing sensing, and sensor fusion technologies. He leads projects in wearable-free healthcare monitoring and has contributed to innovations in radar localization and non-invasive health screening. Dr. Rajab has secured a £396,049 grant from EPSRC for developing smart sensors in 'virtual wards' using mm-wave radar. His work spans interdisciplinary collaborations, including with the Centre for Networks, Communications and Systems at QMUL. His publications highlight advancements in radar calibration, multi-target tracking, and metamaterials. He is affiliated with research centers focusing on electronics, networks, and brain-behavior interfaces.