جست‌وجوی استاد و پژوهشگر

• Plasma Physics
• Laser-Plasma Acceleration
• Nanomaterials in Accelerators
• +۳ مورد دیگر

Aakash Sahai is an Assistant Research Professor in the CEDC-Electrical Engineering department at the University of Colorado Denver - Denver Campus. His research focuses on advancing plasma physics, laser-plasma interactions, and nanoplasmonic technologies for high-energy particle acceleration. He is actively involved in designing novel accelerator concepts, such as nanostructure-based plasmonic accelerators capable of achieving extreme electric fields (PetaVolts/meter). His work bridges theoretical, computational, and experimental approaches to address challenges in high-gradient acceleration, plasma wakefields, and extreme nanoscience. Key research interests include laser-driven plasma acceleration, plasmonic field enhancement in nanostructures, and applications of particle beams in medical and high-energy physics. He collaborates on projects like the EuPRAXIA design study, aiming to develop compact, cost-efficient particle sources. His contributions span experimental setups, computational modeling, and innovative methodologies for radio transmission through plasmas and particle beam processing. Notable achievements include pioneering studies on relativistic surface plasmons, PetaVolt plasmonics, and optimizing laser-plasma interactions for proton/ion acceleration. His research has implications for next-generation accelerators, compact X-ray sources, and advanced plasma diagnostics. Sahai’s interdisciplinary approach integrates electrical engineering, material science, and high-energy physics to push the boundaries of accelerator technology. Advising and grants: No formal advisees or grant details listed. His work is supported by collaborations and institutional resources, including participation in national and international initiatives like Snowmass workshops. Labs/Teams: Active contributor to the EuPRAXIA consortium and affiliated with plasma physics and accelerator research groups at University of Colorado Denver.

Tor Söderströmمشاهده پروفایل

استاد

• Sports Science
• Educational Technology
• Athlete Development
• +۳ مورد دیگر

Tor Söderström is a Professor at the Department of Education at Umeå University. His research focuses on learning and development in sports, computer simulation training, and professional knowledge development in police education. He has contributed extensively to understanding talent identification in sports, particularly in Swedish football, and the efficacy of simulation-based training methodologies in law enforcement education. His research spans topics including athlete development trajectories, the impact of physiological testing on elite athlete performance, and the role of childhood athletic ability in long-term sports participation. Notable projects include a study on dropout processes in Swedish football talent systems and analyses of Swedish gym-goers' training patterns over two decades. Publications highlight interdisciplinary approaches, combining sports science, sociology, and educational technology. Recent work explores children’s rights in sports through scoping reviews and examines athlete retention strategies from adolescence into adulthood. His research often emphasizes practical applications in training design and policy development for both sport and professional education sectors. No scientific awards are explicitly mentioned in the provided texts. His work has involved collaborations with institutions like the Swedish Football Association and police education programs, focusing on scenario-based training methodologies and competency development in high-stakes professions.

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۵ مورد دیگر

Johanna Pirker serves as an Associate Professor at the Institute of Human-Centred Computing, Graz University of Technology, where she holds teaching authorization in Applied Computer Science. Her work bridges academic research with practical applications in interactive technologies, maintaining active consultation hours for students every Monday morning. Her research centers on human-centered computing with emphases on virtual/augmented reality systems, serious game design, and AI-driven interactive experiences. She investigates player behavior, user experience optimization, and therapeutic/educational applications of immersive technologies across diverse contexts including rehabilitation, engineering education, and social platforms. Recent 2025 publications reveal strong trends in AI integration for gaming ecosystems (toxicity detection, dialogue systems), VR-based educational tools across disciplines, and cross-cultural analyses of gaming communities. Her work consistently combines experimental user studies with novel system development to address real-world challenges. While specific grant details and student advising records aren't documented in source materials, her extensive publication output across venues like FDG and iLRN indicates active leadership in interdisciplinary collaborations focused on advancing immersive technologies for societal benefit.

Sibel Alumur Alevمشاهده پروفایل

دانشیار

• Supply Chain Management
• Logistics Network Design
• Operations Research
• +۴ مورد دیگر

Sibel Alumur Alev is an Associate Professor and Associate Chair of Graduate Studies at the University of Waterloo. Her research focuses on logistics network design, hub location optimization, and sustainable transportation systems. She actively contributes to the fields of operations research and supply chain management, with a strong emphasis on addressing uncertainty in network design and strategic infrastructure planning. Her work spans applications in autonomous mobility systems, electric vehicle charging infrastructure, healthcare logistics, and pandemic response. She has published extensively on hub-and-spoke network models, reverse logistics for environmental sustainability, and multi-period resource allocation strategies. Notable areas of interest include the integration of stochastic and robust optimization methodologies into real-world logistics challenges. Dr. Alev’s research also bridges academic and industrial needs, addressing practical problems such as optimal testing center locations during pandemics and strategic freight hub expansions. Her contributions have been featured in peer-reviewed journals and conference proceedings, reflecting her commitment to advancing both theoretical and applied aspects of logistics and operations research.

Roy Peaمشاهده پروفایل

استاد

• Brain and Learning Sciences
• Child Development
• Collaborative Learning
• +۹ مورد دیگر

Professor Roy Pea is the David Jacks Professor of Education & Learning Sciences at Stanford University, with a courtesy appointment in Computer Science. He served as Director of the H-STAR Institute (2007-2021) and founded Stanford’s PhD program in Learning Sciences and Technology Design. His research focuses on technology-enhanced learning, social foundations of human learning, and interdisciplinary applications of digital tools. Stanford University, School of Education Graduate School of Education Department Courtesy appointment in Computer Science His work spans complex domains like concussion education, climate change learning, and AI-driven mental health interventions. He co-authored the 2010 National Education Technology Plan and co-edited key texts including Video Research in the Learning Sciences and AI in Education . His NSF-funded LIFE Center (2004-2014) advanced learning science theories. Recent publications address: (1) linguistic framing of concussions and reporting behavior, (2) AI chatbots for mental health, (3) "engineering fiction" to reduce climate change abstractness, and (4) immersive AR/LLM learning experiences. His research integrates data science, psychology, and educational technology. Fellow, American Academy of Arts and Sciences (2019) Inaugural Fellow, International Society of the Learning Sciences (2018) Honorary Doctorate, The Open University (2018) Best Bridging Paper, EDM 2014 LAK13 Best Paper Award (2013) Roy mentors doctoral and master’s students in learning sciences, advising on topics related to technology, cognition, and equity. He contributes to digital education policy through roles on advisory boards for organizations like NSF, NIH, and the Joan Ganz Cooney Center. His patents include methods for digital video analysis and collaborative learning systems.

Mary Jeanette Ignacioمشاهده پروفایل

دانشیار

• Stress Management
• Cognitive Integration
• Collaborative Learning
• +۷ مورد دیگر

Associate Professor Mary Jeanette Ignacio is a faculty member at the Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore (NUS), where she has served since 2008. She currently serves as the Undergraduate Programmes Director for Year 1 and specializes in simulation-based pedagogy. Doctor of Philosophy (Health Professions Education), Maastricht University Bachelor of Science (Nursing), St Dominic Savio College, Philippines Doctor of Medicine (MD), University of Santo Tomas, Philippines Bachelor of Science (Psychology), University of the Philippines Diliman Her research focuses on stress management , cognitive integration , collaborative learning , and human factors in healthcare education. She has pioneered simulation-based teaching strategies across undergraduate and postgraduate nursing curricula and developed innovative approaches to link pathophysiology with clinical practice. Recent publications highlight her work in virtual reality simulations , systematic reviews on gender discrimination in nursing , and interprofessional educational interventions . She has received multiple teaching excellence awards and secured grants for simulation-based learning projects. NUS Teaching Excellence Award (Individual) 2023 NUS Long Service Award 2023 National University of Singapore Teaching Excellence Award 2017 Her funded projects include grants from the Centre for Development of Teaching and Learning (NUS), Singapore Millennium Foundation, and Sigma Theta Tau International Honors Society of Nursing Research. She actively contributes to advancing simulation pedagogy , virtual hospital design , and affective fidelity in simulations to improve clinical learning outcomes.

• Causal Inference
• Machine Learning
• Global Health
• +۴ مورد دیگر

Dr. Laura B. Balzer is an Associate Professor of Biostatistics at the University of California, Berkeley. Her work focuses on causal inference, machine learning, and addressing methodological challenges in both randomized trials and observational studies, particularly in global health contexts. She leads collaborations in East Africa, focusing on HIV elimination and community health in rural regions. Her research emphasizes translating academic findings into real-world impact. Education: PhD in Biostatistics, UC Berkeley (2015) MPhil in Computational Biology, University of Cambridge (2009) BS in Applied Mathematics, University of Vermont (2008) Research Interests: Dr. Balzer’s work addresses causal inference in complex settings, including semi-parametric methods, measurement challenges, and dependence structures. Her global health projects target HIV prevention, tuberculosis transmission, and hypertension management in sub-Saharan Africa. She designs interventions like the SEARCH Dynamic Choice model, which offers flexible HIV prevention options, and evaluates community health worker programs. Publications highlight her contributions to HIV/AIDS research, including studies on PrEP uptake, viral suppression in adolescents, and tuberculosis-HIV co-infection. Methodologically, she advances causal inference frameworks to handle missing data and clustered designs. Awards: While no specific awards are listed, her work has been funded by initiatives like the SEARCH trials, reflecting its scientific and public health significance. Advising & Grants: Balzer collaborates with multidisciplinary teams in Uganda and Kenya, focusing on translational research. Her grants support interventions linking statistical innovation to healthcare delivery improvements in resource-limited settings. Labs/Teams: Her research is embedded within global health partnerships, particularly within the SEARCH trials network, which integrates biostatistics with clinical and community-based implementation.

• Artificial Intelligence
• Computational Biology
• Graph Neural Networks
• +۵ مورد دیگر

Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.

Joseph Mooreمشاهده پروفایل

استادیار

• Aerial Robotics
• Computational Control
• Learning-based Control
• +۳ مورد دیگر

Dr. Joseph Moore is an Assistant Professor in the Department of Mechanical Engineering at Johns Hopkins University (JHU), serving as Director of the Agile and Intelligent Robotics (AIRO) Laboratory. He is affiliated with the Laboratory for Computational Sensing and Robotics (LCSR), the Institute for Assured Autonomy (IAA), and holds a Bridging Faculty appointment in the Research and Exploratory Development Department (REDD) at JHU/APL. His research focuses on computational control, machine learning, and robotics to enable agile systems operating in complex environments. Dr. Moore previously served as Robotics Group Chief Scientist at JHU/APL, leading projects on hybrid unmanned aerial-aquatic vehicles and aerobatic fixed-wing systems. He has secured funding as Principal Investigator (PI) for ONR, DARPA, and ARL programs, particularly in post-stall maneuvering control and multi-robot coordination. His work emphasizes robust control strategies for autonomous systems in constrained environments. Research interests include aerial robotics, optimization, and learning-based control. Notable contributions involve NMPC-based systems, UAV navigation, and adaptive control for uncertain environments. His recent articles highlight advancements in swarm coordination, morphing-wing UAVs, and PAC-NMPC frameworks. Dr. Moore advises students such as Mark Gonzales and Adam Polevoy. Key grants include ONR/DARPA-funded projects on post-stall flight control and Army-funded multi-robot coordination efforts. His lab (AIRO) and collaborations (LCSR, IAA) drive applied and theoretical robotics research.

Ambarish Kulkarniمشاهده پروفایل

استادیار

• Multi-scale molecular modeling
• Data science for materials discovery
• Catalysis
• +۵ مورد دیگر

Ambarish Kulkarni is an Assistant Professor in the Department of Chemical Engineering at the University of California, Davis. His research focuses on multi-scale molecular modeling, data science for materials discovery, catalysis, and separations. He combines quantum chemistry methods (e.g., wave function theory, density functional theory) with classical simulations and machine learning to design novel materials for applications in catalysis, energy storage, and environmental remediation. Specific areas of interest include methane activation, CO 2 capture, and heterogeneous electrocatalysis. His work bridges theory and experiment, collaborating with experimental groups to validate computational findings. Notable projects include: Developing catalysts with atomically dispersed metals for enhanced reactivity Designing zeolite materials for selective chemical transformations Creating machine learning workflows to accelerate material discovery Recent research highlights the role of water in CO 2 adsorption mechanisms, the dynamic behavior of confined nanoparticles, and redox-cycling phenomena in zeolite-embedded catalysts. His computational tools like the Multiscale Atomic Zeolite Simulation Environment (MAZE) enable detailed analysis of complex material behaviors. No scientific awards are explicitly listed in the provided information. His advising activities and grants are not detailed in the current data, but his extensive publication record indicates active research collaboration and funding support.

Zeljko Panticمشاهده پروفایل

دانشیار

• Electrified Transportation
• Power Converters Design and Control
• Wireless Power Transfer Systems
• +۵ مورد دیگر

Zeljko Pantic is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from NC State (2013) and B.S./M.S. degrees from the University of Belgrade (1998/2007). Before joining NC State in 2019, he served as an Assistant Professor and Associate Director of the Electric Vehicle and Roadway Research Facility at Utah State University. He is actively involved in editorial roles for IEEE Transactions on Transportation Electrification and serves on the IEEE IAS Transportation Systems Committee. Education: Ph.D., Electrical Engineering, North Carolina State University (2013) M.S., Electrical Engineering, University of Belgrade (2007) B.S., Electrical Engineering, University of Belgrade (1998) Research: Dr. Pantic specializes in electrified transportation systems, wireless power transfer (WPT), power converter design, and DC microgrid technologies. His work addresses challenges in EV charging infrastructure, magnetic circuit optimization, and energy conversion principles for transportation electrification. Recent projects include autonomous wireless charging systems for UAVs, marine DC microgrids, and road-embedded DWPT solutions. Awards & Recognition: 2019 IEEE JESTPE Second Prize Paper Award 2017 Outstanding Teacher of the Year (USU) 2012 NC State Mentored Teaching Assistantship Award Advisees & Grants: While specific student names are not listed, Dr. Pantic has advised graduate students on projects spanning WPT systems, EV infrastructure, and battery management. His work has been supported by grants focusing on dynamic charging, magnetic materials, and autonomous observatory nodes. Labs & Facilities: He leads research at NC State's Electric Vehicle and Roadway facility, focusing on roadway-integrated wireless charging and high-power WPT systems. Collaborations include ocean observatory development and autonomous system integration.

Hui Caoمشاهده پروفایل

استاد

• Condensed Matter Physics
• Photonics
• Optics
• +۴ مورد دیگر

Hui Cao is the John C. Malone Professor of Applied Physics, Professor of Physics, and Professor of Electrical Engineering at Yale University. Her research focuses on mesoscopic physics, complex photonic materials, nanophotonics, and biophotonics, with experimental investigations into unconventional lasers, coherent light control, and disordered photonic systems. She leads a lab exploring applications in speckle-based imaging, deep-tissue optics, and chip-scale spectrometers. Education: Ph.D. in Physics from Stanford University (1997). Awards include the William E. Lamb Medal (2015), Guggenheim Fellowship (2013), and fellowships from the American Physical Society and Optical Society of America (2007). Research emphasizes random lasers, microcavity lasers, and wavefront shaping to control light in diffusive media. Key innovations include a disordered photonic chip spectrometer and methods to suppress nonlinear instabilities in fiber amplifiers. Awards: 12 major honors including AAAS Fellowship and multiple endowed professorships Patents: 3 core photonic technologies including random laser imaging and fiber amplifier control systems Lab Activities: Developing novel optical devices leveraging disorder and nonlinear effects

• Technology in Education
• Virtual and Extended Realities
• Artificial Intelligence in Assessment
• +۴ مورد دیگر

Professor Edward Palmer is a faculty member in the School of Education at the University of Adelaide, serving as Director of the Unit of Digital Education and Training and Acting Deputy Head of School. His research focuses on technology's role in education and training, particularly in virtual/extended realities, AI-driven assessment, and personalized learning approaches. He has secured over $3 million in funding from government and industry partners, with projects addressing AI ethics, VR applications in medical training, and MOOC design. Education & Roles: Holds academic leadership positions and directs digital education initiatives. Research: Investigates AI in education, VR for situational awareness, and innovative assessment methods. Active in collaborative projects with industry and defense sectors. Grants & Funding: Secured significant grants for ventures in AI ethics, VR training simulations, and digital health hubs. Labs/Teams: Leads the Unit of Digital Learning and Society, collaborating with postdocs like Daniel Lee on AI and VR projects. His work emphasizes practical applications, such as medical procedure training in VR and adaptive learning systems. He mentors HDR students and postdocs in AI-driven training scenarios and digital education innovation.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Tuğba Dalyan is an Associate Professor in the Department of Computer Engineering at Istanbul Bilgi University, Faculty of Engineering and Natural Sciences. She holds a Ph.D. in Computer Engineering from Yıldız Technical University (2014), an MSc from Kocaeli University (2007), and dual BSc degrees in Mathematics and Computer Science and Business Administration (Minor) from Istanbul Bilgi University (2003). She has been a faculty member since 2016 and previously served as a Teaching Staff member and Research Assistant at the same institution. Her research focuses on Natural Language Processing , Machine Learning , Deep Learning , Text Mining , Data Science , and Big Data Analytics . Her work spans computational linguistics, sentiment analysis, author profiling, machine translation, and smart systems. She has led and contributed to numerous research projects, particularly in AI-driven urban solutions and health technologies. The most recent publications show a strong trend in Turkish NLP, zero-shot classification, multimodal AI (image captioning), emotional robotics, and decision support systems using fuzzy logic. Her work combines theoretical rigor with practical applications in smart cities, education, and healthcare. Best Paper Award , CICLing 2012 TÜBİTAK 2209-A student project awards (2022–2024) Horizon2020 Eşik Üstü Ödülü , MIMOSCSA 2024 TÜBİTAK 2242 competition: 2nd and 3rd place (2016, 2018) She has advised numerous student research projects, many of which have received national recognition. She has directed multiple TÜBİTAK and institutional research grants, including projects on smart homes, blockchain crowdfunding, mental health, and AI for social polarization. Her leadership roles include Head of Department, Vice Dean, and Director of Graduate Programs. Tuğba Dalyan leads research in AI and NLP with a strong emphasis on Turkish language technologies. She is involved in interdisciplinary teams working on emotional robots, smart city platforms, and citizen science ecosystems. Her lab activities focus on neural networks, text analysis, and intelligent systems development.

• Nonlinear Optics
• Topological Photonics
• Photonic Crystals
• +۳ مورد دیگر

Jicheng Jin is a Postdoctoral Researcher in the Department of Physics and Astronomy at the University of Pennsylvania, affiliated with the College of Arts & Sciences. His research focuses on topological photonics, nonlinear optics, and advanced photonic materials like AlScN and lithium niobate. Key interests include Floquet engineering, topological insulators of light, and novel optical devices. Research Interests: Nonlinear optical phenomena in photonic crystals Floquet topological phases and their experimental realization CMOS-compatible materials for integrated photonics Surface-emitting THz/FIR sources Bound states in the continuum and high-Q resonances Recent studies emphasize topological edge states, nonlinear frequency conversion, and device applications of 2D van der Waals heterostructures. His work bridges theoretical predictions with experimental validations in advanced photonic systems. Notable trends in publications include exploration of AlScN-based electro-optic phase shifters, Floquet Chern insulators, and geometric phase effects in magnon polaritons. No scientific awards are explicitly mentioned in the provided materials. Advising and grants remain unspecified in the current dataset. Lab affiliations include collaborations related to the Department's photonics and condensed matter physics groups.