جست‌وجوی استاد و پژوهشگر

Loutfouz Zamanمشاهده پروفایل

دانشیار

• User research in gaming
• Game analytics and data science
• Game evaluation and testing
• +۲ مورد دیگر

Dr. Loutfouz Zaman is an Associate Professor in the Game Development and Interactive Media department at Ontario Tech University , part of the Faculty of Business and Information Technology . He holds a PhD in Computer Science with a focus on Human-Computer Interaction from York University. His research explores visual programming interfaces, game analytics, and extended reality technologies, with a focus on practical applications such as automated bug detection and user experience optimization. Dr. Zaman has collaborated on projects funded by MITACS, NSERC, and industry partners, addressing challenges in healthcare incident management, language learning gamification, and air traffic control simulation training. He teaches courses ranging from introductory game math to graduate-level topics in human-computer interaction and machine learning for game analytics. Education: Bachelor of Science in Computer Science (Software Systems) – Concordia University Master of Science in Computer Science (Human-Computer Interaction) – York University PhD in Computer Science (Human-Computer Interaction) – York University Research Interests: Dr. Zaman’s work spans user research in gaming, game evaluation methodologies, and emerging technologies like AR/VR. His team focuses on developing tools for visual game analytics and automated testing, with recent projects including mixed reality fitness gaming interfaces and deep learning-based bug detection systems. Grants & Collaborations: His industry partnerships include projects on healthcare incident management during pandemics, CRM gamification, and pet identification systems using computer vision. He actively seeks doctoral candidates to expand research in visual analytics, XR technologies, and automated bug detection. Labs & Affiliations: Dr. Zaman is affiliated with the Software and Informatics Research Centre (SIRC) and contributes to the university’s efforts in bridging academic research with real-world applications.

Guowei Yangمشاهده پروفایل

مدرس ارشد

• Software Engineering
• Machine Learning
• Programming Languages
• +۱ مورد دیگر

Dr. Guowei Yang is a Senior Lecturer at the School of Electrical Engineering and Computer Science , The University of Queensland, Australia. He transitioned to this role in 2021 after serving as a tenured Associate Professor at Texas State University's Department of Computer Science. His research focuses on the intersection of Software Engineering Machine Learning Programming Languages Cybersecurity with an emphasis on system reliability and security. His recent publications (2022-2025) analyze: Android API compatibility challenges Symbolic execution optimizations Fuzzing techniques for JVM security Moving target defense strategies Automated patch generation Constraint classification in code analysis He leads a research group seeking students with backgrounds in software engineering, formal methods, and AI. Current funding includes a 2023-2026 Oracle Corporation grant for DevSecOps analysis. Dr. Yang supervises multiple PhD and Master's projects, often as principal advisor, focusing on topics like deep learning testing, IoT analysis, and DevSecOps integration.

Alex K. Jonesمشاهده پروفایل

استاد

• Quantum Computing
• Computer Architecture
• Nanoscale Magnetic Memory Systems
• +۵ مورد دیگر

Dr. Alex K. Jones is a Professor in the Department of Physics & Astronomy at the University of Pittsburgh, with a secondary appointment. His research focuses on quantum computing architectures, sustainable computing, and emerging memory technologies. He leads projects funded by NSF, DARPA, NSA, ARO, and industry, emphasizing energy-efficient computing systems and fault-tolerant designs. Dr. Jones' work spans quantum system co-design (e.g., superconducting qubit architectures), nanoscale magnetic memories (racetrack/STT-MRAM), and carbon lifecycle modeling for computing systems. He has pioneered concepts like GreenChip sustainability tools and CORUSCANT processing-in-memory frameworks. His IEEE Fellowship and Best Paper Award (IGSC 2018) highlight his impactful contributions. Key Research Areas: Quantum Computing, Non-Volatile Memories, Sustainable Computing, Fault Tolerance Funding Agencies: NSF, DARPA, NSA, ARO, Industry Collaborations Major Projects: Quantum circuit decomposition, racetrack memory applications, carbon modeling for datacenters His 200+ publications include seminal works on fault tolerance in domain-wall memories, energy-efficient architectures, and cross-disciplinary innovations at the intersection of physics and computer engineering. Current efforts explore carbon depreciation models and real-time accelerator customization for edge computing.

• Computer-aided drug design (CADD)
• Molecular dynamics simulations
• Structural biology
• +۳ مورد دیگر

Associate Professor Jacob D. Durrant leads the Durrant Lab at the University of Pittsburgh’s Department of Biological Sciences, affiliated with the Dietrich School of Arts and Sciences. His research focuses on computational biology, particularly computer-aided drug design (CADD), molecular dynamics simulations, and structural biology. The lab develops novel algorithms and software tools like AutoGrow, NNScore, and MolModa, emphasizing open-source platforms for drug discovery. Key projects include identifying small-molecule ligands for cancer, neurological, and infectious disease targets, as well as advancing molecular visualization through Blender and virtual reality (ProteinVR). Education includes a PhD in Chemistry, though specific details are not provided. Current graduate students include May Ahmed (machine learning in drug discovery), Nacef Guessaymi (amyloid fibril dynamics), and Joshua Mercado (LARP1 structural biology). Undergraduates and postdocs contribute to projects ranging from virtual screening to GPU-accelerated simulations. Research emphasizes ligand identification via ensemble docking, molecular visualization techniques, and the application of AI/machine learning to drug design. Recent work includes studies on SARS-CoV-2 treatments (RECOVERY trial), TEM-1 β-lactamase inhibition, and allosteric drug targets for hexokinase 2. Lab tools like FPocketWeb and PCAViz enable collaborative, browser-based molecular analysis. Advisees and collaborations extend across computational biology programs at Pitt and CMU. The lab prioritizes translational research, bridging in silico simulations with experimental validation for clinical applications. Future directions include AI-driven ligand optimization, VR-enhanced molecular exploration, and open-source software development for global research accessibility.

Tim Pearceمشاهده پروفایل

استاد

• Machine Olfaction
• Neuromorphic Engineering
• Neuroscience
• +۴ مورد دیگر

Tim Pearce is a Reader in Bioengineering at the School of Engineering, University of Leicester. He holds a PhD from Warwick University and has served as a Research Assistant Professor at Tufts University Medical School. His work focuses on machine olfaction, neuromorphic engineering, and interdisciplinary applications of chemical sensing technologies. Key contributions include the development of electronic noses, biomimetic infochemical communication systems, and neuromorphic implementations inspired by insect olfactory pathways. Research Interests: Machine olfaction and sensor arrays Neuromorphic engineering and bio-inspired computation Integration of neuroscience principles into engineering systems Applications in environmental monitoring, healthcare, and security Publications highlight advancements in odor classification, spatio-temporal signal processing, and AI-driven world modeling. Notable collaborations include EU projects like Neuro-IT and AMOTH, which translated biological olfactory principles into real-world technologies. He has authored over 100 articles, including the seminal Handbook of Machine Olfaction . Awards include Fellowships from the Institute of Physics and the Higher Education Academy. Editorial roles span Frontiers in Neuromorphic Engineering and Connection Science . He actively contributes to global initiatives in neuroengineering and computational neuroscience.

Santiago Badiaمشاهده پروفایل

استاد

• Computational Science and Engineering
• Numerical Analysis
• Numerical Linear Algebra
• +۸ مورد دیگر

Santiago Badia is a Professor of Computational Mathematics at Monash University's School of Mathematics since 2019. Previously, he held roles at UPC (Universitat Politècnica de Catalunya) as Professor of Computational Science and Engineering (2009–2019) and was an adjunct researcher at CIMNE (Barcelona), leading the Large Scale Scientific Computing Department. He earned his PhD in 2006 from UPC, with prior work at Politecnico di Milano (2006) and Sandia National Labs (2007–2008). His research focuses on numerical approximation of PDEs using finite element methods, with applications in fluid/solid mechanics, electromagnetics, and multiphysics problems. He leads high-performance computing projects like FEMPAR (scalable PDE solvers) and Gridap (Julia-based PDE framework). His work includes perfect weak scalability on supercomputers and solving PDEs with up to 60 billion unknowns. Research interests span computational science, numerical analysis, parallel computing, and numerical linear algebra. Projects address challenges in fusion energy, additive manufacturing, and nuclear waste repositories. He actively supervises PhD students through funded projects, emphasizing computational modeling and simulation. Collaborations include international teams in geophysical fluid dynamics and stochastic inverse problems. His software frameworks (FEMPAR, Gridap) are widely used for industrial and academic research, emphasizing scalability and usability.

• Mechanical Engineering
• Composite Materials
• Additive Manufacturing
• +۴ مورد دیگر

Ana Muñoz Sánchez is an Associate Professor in the Department of Mechanical Engineering at University Carlos III of Madrid, where she conducts research in composite materials, additive manufacturing, and manufacturing processes. Her work bridges theoretical modeling with practical applications, particularly in drilling processes, ballistic protection, and engineering education. Her research focuses on several key areas within mechanical engineering and materials science: Composite materials machining, particularly carbon fiber reinforced polymers (CFRP) Tool wear analysis and optimization of drilling parameters Numerical modeling of manufacturing processes Additive manufacturing applications in medical and educational contexts Ballistic protection systems Engineering education tools and methodologies Dr. Muñoz Sánchez's publication record demonstrates consistent contributions to both fundamental manufacturing research and practical educational applications. Her recent work shows a strong emphasis on improving manufacturing processes for composite materials while simultaneously developing innovative educational tools, including specialized resources for visually impaired students. Her research combines computational methods with experimental validation to address complex engineering challenges. She has secured funding from multiple Spanish research agencies for projects including "DIGITDRILL - Digitalization of industrial drilling process" and "DISEÑO AVANZADO, FABRICACIÓN Y ENSAYO DE PROTECCIONES BALÍSTICAS DE CABEZA (PROTECT_BAL)". Her work has practical applications in aerospace, medical simulation, and inclusive education.

Jyh-Charn Liuمشاهده پروفایل

استاد

• Real-time distributed computing systems
• Behavior modeling and simulation
• Cyber physical systems security
• +۵ مورد دیگر

Jyh-Charn 'Steve' Liu is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on real-time distributed systems, cyber-physical security, and interdisciplinary applications such as mathematical expression analysis and GNSS spoofing mitigation. Education: Ph.D., Electrical & Computer Engineering, University of Michigan (1989) M.S., Electrical Engineering, National Cheng Kung University (1981) B.S., Electrical Engineering, National Cheng Kung University (1979) Research Interests: Real-time distributed computing systems Cyber-physical systems security Behavior modeling and simulation Mathematical expression analysis and tools for STEM education GNSS spoofing detection and mitigation Blockchain-based supply chain management Key Publications Trends: Recent work emphasizes AI-driven solutions for mathematical document processing (e.g., LaTeX conversion from images), cybersecurity in navigation systems, and interdisciplinary applications like STEM education tools. Early-career contributions include real-time scheduling algorithms and embedded systems design. Awards: Senior Member, IEEE Computer Society (2014) Nominated for ACM Eugene L. Lawler Award (2013) Conference leadership roles (RTAS 2006/2007) Lab & Collaborations: Leads the Real Time Distributed Systems Lab, exploring areas such as verifiable credentials, urban navigation systems, and medical image analysis. Collaborates on projects like DIME (mathematical expression tool) and MOP (mathematical PDF labeling).

Jonathan Ajo-Franklinمشاهده پروفایل

استاد

• Applied Geophysics
• Seismic Imaging
• Timelapse Seismology
• +۵ مورد دیگر

Jonathan Ajo-Franklin is a Professor in the Department of Earth, Environmental and Planetary Sciences at Rice University, specializing in applied geophysics. His research focuses on seismic acquisition and monitoring to address subsurface challenges in energy systems, CO2 mitigation, and water resource management. He leads the Ajo-Franklin Lab, leveraging distributed fiber optic sensing (DAS) and dark fiber infrastructure for dense seismic data collection. Education: B.A. in Computer Science/History (Rice University, 1998), M.S. and Ph.D. in Geophysics (Stanford University, 2003–2004). His work spans geothermal energy, carbon storage, and cryosphere dynamics, with projects like the EGS Collab initiative and collaborations with DOE and industry partners. Recent publications emphasize DAS applications in fracture monitoring, geothermal systems, and subsurface imaging. Key research areas include timelapse seismology, rock physics, and the integration of seismic data with environmental challenges. Ongoing projects explore geothermal reservoir imaging, CO2 leakage detection, and permafrost thaw monitoring using DAS. The lab’s tools include dark fiber networks, permanent seismic sources, and ambient noise analysis.

Joseph C. Ayoobمشاهده پروفایل

دانشیار

• Computational Biology
• Educational Program Development
• Mentoring Strategies
• +۳ مورد دیگر

Joseph C. Ayoob, PhD, is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh School of Medicine. He holds roles as Associate Director of the UPMC Hillman Academy and Faculty Fellow of the University of Pittsburgh Center for Mentoring. His work focuses on innovative training programs for students at all academic levels, including the NSF-funded TECBio REU program (since 2010) and the CompBio Academy high school program (since 2011). He also directs the CPCB MetaSchool, a professional development series for graduate students. Dr. Ayoob earned his PhD in Neuroscience from Johns Hopkins University School of Medicine. His educational efforts span undergraduate, graduate, and postdoctoral trainees, emphasizing lab-based skills and professional development. He is a Principal Facilitator for the Entering Mentoring and Entering Research curricula, recognized as a National Research Mentoring Network (NRMN) Master Mentor and Mentor of the Month. Research interests include STEM education innovation, mentorship best practices, and computational biology training. He has pioneered programs like the CPCB Presentation Course and CoBB Professional Development course. Awards include recognition for mentorship excellence and contributions to mentored research training. Publications highlight his work in educational frameworks, undergraduate research effectiveness, and molecular mechanisms in neurodegenerative diseases. His lab supports interdisciplinary training through summer programs targeting underrepresented students in STEM fields.

Hans Martin Kjerمشاهده پروفایل

دانشیار

• Ultrasound Imaging
• Super-Resolution Imaging
• Biomedical Image Analysis
• +۷ مورد دیگر

Hans Martin Kjer is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the UltraSound and Biomechanics group within the Visual Computing Center and the Center for Fast Ultrasound Imaging. His research bridges engineering and medical imaging, with a strong emphasis on developing and validating advanced ultrasound techniques for biomedical applications. Research Interests: His work focuses on super-resolution ultrasound imaging, microvascular analysis, 3D reconstruction of biological structures, and image registration. He applies computational methods to improve the resolution and accuracy of ultrasound, particularly in renal and lymph node vasculature imaging. His research contributes to the UN Sustainable Development Goals in health and well-being through innovative diagnostic tools. Publication Trends: Over the past several years, Kjer has consistently published in high-impact journals and conferences in biomedical engineering and imaging. His recent work emphasizes the validation of super-resolution ultrasound against micro-CT, realistic 3D blood flow simulation, and the application of AI in enhancing imaging resolution. These studies reflect a strong trend toward quantitative, reproducible, and clinically relevant imaging solutions. Scientific Contributions: While no specific awards are listed, his leadership in major research projects and frequent collaborations with leading experts in ultrasound (e.g., Jørgen Arendt Jensen) underscore his significant role in the field. Advising and Funding: Kjer serves as a supervisor and principal investigator in several funded research initiatives, including AI for Extreme Super-Resolution CT , 3DIM: 3D Imaging Center , and QIM: Center for Quantification of Imaging Data from Max IV . He mentors PhD students and collaborates across disciplines, contributing to both biomedical and materials science imaging projects. Laboratories and Teams: He is an integral member of the Center for Fast Ultrasound Imaging and the Visual Computing Center at DTU. These teams focus on cutting-edge ultrasound technologies, image processing algorithms, and multimodal imaging integration, positioning Kjer at the forefront of computational biomedical imaging in Denmark.

Esther Ulitzschمشاهده پروفایل

دانشیار

• Psychometric Model Development
• Bayesian Latent Variable Models
• Small-Sample Estimation Techniques
• +۳ مورد دیگر

Esther Ulitzsch is an Associate Professor at the University of Oslo 's Centre for Educational Measurement (CEMO) . Her research focuses on advancing psychometric models, particularly Bayesian latent variable techniques for small-sample conditions, and analyzing digital interaction data from simulated learning environments. She holds a PhD from Freie Universität Berlin and previously worked as a Research Associate at the IPN – Leibniz Institute for Science and Mathematics Education in Kiel, Germany. Education: PhD in Educational Measurement (Freie Universität Berlin) Research Associate at IPN Kiel Research Interests: IRT model development for aberrant response detection Efficient estimation in small samples Clickstream analysis for student behavior Test-taking engagement dynamics Publications: Over 20 peer-reviewed articles since 2017, including work on mixture models for careless responding, neural networks for IRT estimation, and Bayesian factor modeling. Recent contributions address response time analysis, cross-country measurement invariance, and sequential process mining in interactive tasks. Affiliations: Active in the CREATE (Research on Equality in Education) and FREMO (Frontier Research in Educational Measurement) groups at CEMO.

Antonio García Cabotمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Educational Technology
• Natural Language Processing
• +۵ مورد دیگر

Antonio García Cabot is an Associate Professor in the Department of Computer Science at the University of Alcalá. His research focuses on artificial intelligence, educational technology, natural language processing, and mobile computing. He leads the INTELIA research group (Interaction Technologies and Artificial Intelligence Lab) and previously contributed to the PMI group (Intelligent Mobile Platforms). He earned his Doctorate in Computer Science from the University of Alcalá in 2013 with a thesis titled *Propuesta de un sistema multi-agente para la adaptación de contenidos docentes a las competencias, contexto y dispositivo del usuario*. His work emphasizes accessibility in digital education, gamification in MOOCs, and software engineering innovations like automated code repair using LLMs. Research trends in his 2024-2025 publications include: Advancing AI applications in educational assessment (e.g., automated question generation, distractor design) Improving mobile app usability through gesture-based interfaces and sensor integration Developing large language model-based tools for vulnerability repair and teacher simulation analysis Systematic reviews on digital competence ecosystems and multilingual AI systems His contributions to open educational resources (OER) and accessible virtual campuses have been widely adopted in Latin America. Current research extends into embedded systems education and olfactory stimuli impacts on mobile app performance.

• Artificial Intelligence
• Educational Technology
• Mobile Applications
• +۳ مورد دیگر

Eva García López is a Professor in the Department of Computer Science at the University of Alcalá. She holds a PhD from the same institution, completing her thesis on improving the usability of interfaces for teaching objects in m-learning in 2013. Her research focuses on usability engineering, artificial intelligence, and educational technology, with emphases on mobile applications, accessibility, and gamification. She is affiliated with the INTELIA research group (Interaction Technologies and Artificial Intelligence Lab) and previously contributed to TIFyC (Information Technologies for Training and Knowledge) and PMI (Intelligent Mobile Platforms). Eva’s work spans developing adaptive learning systems, enhancing digital content accessibility, and leveraging large language models for educational tools. Her projects include MOOC development, social gamification experiments, and usability evaluations of mobile instant messaging apps. She has also explored the impact of olfactory stimuli on user performance and the application of NLP in automated question generation. Her contributions include guidelines for accessible digital content creation and frameworks for personalized learning paths in web engineering curricula. Her research integrates interdisciplinary approaches, combining machine learning, human-computer interaction, and educational theory to address challenges in digital learning ecosystems. Notable contributions include frameworks for curriculum development, methodologies for usability evaluation, and tools for automated code vulnerability repair.

• Computational Neuroscience
• Mathematical Neuroscience
• Brain Physics
• +۱۲ مورد دیگر

Gaute Einevoll is a Professor in the Department of Physics at the Norwegian University of Life Sciences (NMBU), Faculty of Science and Technology. His academic work bridges the disciplines of physics and neuroscience, with a strong focus on computational and mathematical modeling of brain function. Position: Professor of Physics Institution: Norwegian University of Life Sciences (NMBU) Department: Department of Physics Research Focus: Brain Physics, Computational Neuroscience His research interests lie at the intersection of physics and neuroscience, particularly in understanding the biophysical mechanisms underlying neural signals such as local field potentials (LFPs), EEG, and spike trains. He develops and applies mathematical models to interpret experimental data from multielectrode arrays and laminar recordings. His work includes modeling cortical circuits, thalamocortical networks, and the transition from grid cells to place cells in spatial navigation. He also investigates the biophysical basis of signal propagation and filtering in neural tissue. The 15 most recent publications reflect a consistent focus on computational modeling of neural activity, with strong emphasis on biophysical realism, data analysis methods, and tool development. Articles span topics from single-neuron contributions to EEG, astrocytic ion dynamics, and large-scale neural recordings. Keywords across these works include Computational Neuroscience, Biophysics, Neural Modeling, and Electrophysiology, with subfields covering LFPs, spike-train analysis, cortical circuits, and network dynamics. A recurring theme is the development of analytical and computational tools to bridge experimental data with theoretical understanding. No scientific awards are mentioned in the provided text. Gaute Einevoll has supervised research and taught courses such as FYS388/488 Computational Neuroscience and FYS102 Thermal Physics and Electromagnetism. He has contributed to major collaborative efforts like the Human Brain Project. While specific grants are not listed, his extensive publication record in high-impact journals suggests sustained research funding. He has also authored educational materials, including a textbook on physics and an edited volume on natural sciences. He is associated with research groups focused on computational neuroscience and brain physics. He has developed the LFPy tool for simulating extracellular potentials, indicating leadership in neuroinformatics tool development. His team likely involves interdisciplinary collaboration between physicists, neuroscientists, and computational biologists working on modeling neural systems.