جست‌وجوی استاد و پژوهشگر

Ebrahim Poustinchiمشاهده پروفایل

دانشیار

• Architectural Design
• Robotics in Design
• Computational Design
• +۴ مورد دیگر

Ebrahim Poustinchi is an Associate Professor at the College of Architecture & Environmental Design, Kent State University. He specializes in integrating robotics, computational design, and digital fabrication into architectural practices. His research explores human-robot collaboration, cyber-physical systems, and virtual/augmented reality applications in design. Poustinchi leads projects like the Environmental Science and Design Research Institute, focusing on innovative design methodologies. His work emphasizes parametric design strategies, hardware-software integration for robotic control, and experiential design through mixed-reality interfaces. Notable projects include the PX-Alpha robot operator and the Chameleon Grasshopper plug-in. He has no listed scientific awards but actively contributes to academic discourse through publications and symposiums. No formal advisees or grants are explicitly mentioned in the provided texts. Poustinchi’s research spans architectural theory, computational tools, and cross-disciplinary applications, positioning him at the forefront of digital innovation in design education and practice.

Jose M. Vidalمشاهده پروفایل

استاد

• Multiagent Systems
• Agent-based Modeling
• Algorithmic Game Theory
• +۷ مورد دیگر

Jose M. Vidal is a Professor and Director of Undergraduate Studies in the Computer Science and Engineering department at the Molinaroli College of Engineering and Computing, University of South Carolina . He holds a Ph.D. in Computer Science and Engineering from the University of Michigan (1998), an M.S. in Computer Science from Rensselaer Polytechnic Institute (1991), and a B.S. in Computer Science and Engineering from MIT (1990). His research focuses on multiagent systems , spanning theoretical aspects like coordination mechanisms, algorithmic game theory, and distributed algorithms, as well as implementation in agent-based simulations, web services, and mobile applications. He has authored numerous publications in fields including healthcare process optimization, traffic engineering, and sociological theory construction. 2019: Ad Hoc Vehicle Platoon Formation (Traffic Engineering) 2019: Wikitheoria for Sociological Theory Construction 2018: Email Intent Classification using Deep Learning 2017: Intelligent Transportation Systems using Mobile Data 2016: Healthcare Workflow Simulation Models 2014: Behavioral Modeling from Observational Data He has received significant funding from the NSF, Darpa, and industry partners for projects like Wikitheoria (a collaborative theory-building platform), TargetShare (resource allocation), and mobile healthcare applications. His work bridges theoretical research with practical implementations in domains ranging from traffic systems to hospital operations.

• Complex Systems
• Statistical Physics
• Network Theory
• +۶ مورد دیگر

Hernan Makse is a Distinguished Professor of Physics at the City College of New York (CCNY) , affiliated with the Levich Institute and Department of Physics. He directs the Complex Networks and Data Science Lab , pioneering research in statistical physics approaches to complex systems. His work bridges brain networks, biological synchronization, social media dynamics, and granular materials. Research Focus: Makse's lab develops emergent laws for complex systems using statistical mechanics, network theory, and machine learning. Key areas include: Brain network optimization via Collective Influence algorithms for neural integration and control Symmetry fibrations in biological systems (e.g., C. elegans connectome) COVID-19 contact tracing through superspreader k-core analysis Election/fake news forecasting via Twitter data mining Granular matter phase transitions and jamming physics Publication Trends: His recent articles focus on network-based prediction systems (elections, pandemics), neural synchronization mechanisms, and AI-driven solutions for biological and social systems. Earlier foundational work established fractality and renormalization in complex networks. Awards & Honors: Elected Member, Academia Brasileira de Ciencias (2023) Academic Leadership: He mentors PhD students in physics/data science and collaborates with Memorial Sloan Kettering (brain tumors), University of Vienna (neural symmetry), and Brazilian health agencies (COVID apps). Grants include NSF-CRCNS and NIH Brain Initiative funding. Lab Initiatives: The lab develops open-source tools for COVID-19 contact tracing and neural symmetry analysis. Current projects include MRI-based breast cancer prediction and neurotransmitter dynamics modeling.

• Big Data Analytics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Mahmoud Daneshmand is an Industry Professor at the School of Business and holds a joint appointment in the Department of Computer Science at Stevens Institute of Technology. He has over 40 years of experience in academia and industry, serving as a Distinguished Member of Technical Staff at Bell Labs and AT&T Shannon Labs. His research focuses on Big Data Analytics, Machine Learning, IoT, and Data Mining, with over 300 publications and three books to his name. He co-founded the Business Intelligence & Analytics MS program at Stevens and leads initiatives in IEEE, including steering committees for IoT and Big Data journals. Education: PhD (1976) in Statistics from UC Berkeley; MA (1973) in Statistics from UC Berkeley. Research Interests: Big Data, AI, IoT, Data Mining, Risk Management, and Network Reliability. Dr. Daneshmand has received numerous awards, including IEEE Distinguished Lecturer, AT&T Recognition Awards, and IEEE Standards Committee honors. He chairs international conferences, edits journal special issues, and advises on industry standards. His work spans academia-industry collaboration, innovation in data-driven technologies, and leadership in IEEE initiatives. Grants & Patents: Holds two patents (2009-2010) and led over 60 large-scale Bell Labs projects. PI of the American Bureau of Shipping Grant. Service: Editorial roles in IEEE journals, NJBDA board member, and academic coordinator for Stevens' programs.

Sukanta Basuمشاهده پروفایل

استاد

• Atmospheric boundary layer processes
• Atmospheric optics
• Machine learning
• +۳ مورد دیگر

Sukanta Basu is a Professor of Empire Innovation at the University at Albany, State University of New York. He is affiliated with the Atmospheric Sciences Research Center (ASRC) and the Department of Environmental and Sustainable Engineering . With a career spanning institutions like Texas Tech University, North Carolina State University, and Delft University of Technology, Dr. Basu’s work bridges atmospheric physics and renewable energy applications. Education : PhD in Civil Engineering (2004), University of Minnesota M.A.Sc in Environmental Engineering (2000), University of Windsor B.Tech (Honors) in Civil Engineering (1998), Indian Institute of Technology, Kharagpur Research Interests : Dr. Basu specializes in atmospheric boundary layer processes , optical turbulence modeling , and machine learning applications for wind energy systems. His work spans numerical weather prediction , renewable energy forecasting , and turbulence parameterization for climate models. Article Trends : His recent publications focus on optical turbulence climatology , low-level jet modeling , and machine learning-driven wind gust estimation . These works highlight collaborations with institutions in the Netherlands, UK, and Belgium, emphasizing global energy transition challenges. Scientific Roles : Associate Editor, Journal of Geophysical Research – Atmosphere (2025–present) Coordinator of the Dutch GROW consortium’s met-ocean program (2018–2020) Elected member of the AMS Boundary Layers and Turbulence Committee (2009–2011) Advising & Grants : Dr. Basu has advised PhD students like Bowen Li (2025) and mentored award-winning researchers such as Max Pierzyna . His research has been funded by the US National Science Foundation (NSF-CAREER) , US Department of Energy, Horizon Europe, and the Carbon Trust (UK). Labs & Teams : He leads the Atmospheric Turbulence Research Lab at ASRC and collaborates with the Delft University of Technology (Netherlands) and Wind Energy Science editorial board.

Hadjiefthymiades Stathesمشاهده پروفایل

استاد

• IoT Security
• Cloud Computing
• Machine Learning
• +۷ مورد دیگر

Hadjiefthymiades Stathes is a Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His primary research division focuses on Computer Systems and Applications. He specializes in IoT security, cloud computing, machine learning, and edge computing, with notable contributions to data analytics, trust management, and cybersecurity in distributed systems. Research Interests: His work spans IoT security protocols, optimization of cloud storage systems, anomaly detection in Kubernetes environments, and predictive analytics for sensor networks. He has explored trust models in pervasive computing, energy-efficient node authentication, and hardware-accelerated mixed reality interfaces. His recent projects include frameworks for Earth observation data accessibility and next-generation internet experimentation platforms. Publications: His recent articles highlight advancements in IoT data reduction techniques, trust sustainability in infrastructure, and the application of Markov chains for multivariate event correlation. He has also contributed to the design of experiment description languages for mobile IoT and emergency response platforms for radiological incidents. Awards & Grants: No scientific awards or grants were explicitly mentioned in the provided texts. Labs & Teams: His research involves collaborations on projects like EO4EU-AI (Earth observation with extended reality interfaces) and TRACE (logistics integration systems). He is affiliated with the university's advanced systems research groups focusing on edge computing and secure IoT ecosystems.

Panagiotis PAPADAKISمشاهده پروفایل

دانشیار

• Robotics
• Computer Vision
• 3D Shape Analysis
• +۴ مورد دیگر

Panagiotis PAPADAKIS is an Associate Professor (HDR) in Informatics & Robotics at IMT Atlantique Bretagne/Pays de la Loire, Department of Informatics. He leads the RAMBO team at CNRS Lab-STICC and is an associate member of the IRL CROSSING. His research focuses on cognitive robotics, computer vision, and artificial intelligence with applications in assistive robotics, urban search and rescue (USAR), and 3D environment analysis. He has over €1M in research funding and has co-authored numerous top-tier publications in computer vision and robotics journals/conferences. Educations: PhD in Information Technology, National University of Athens (2009) Informatics and Telecommunications Degree, National University of Athens (2005) Research Interests: Robot Control and Navigation 3D Shape Analysis & Matching Urban Search and Rescue (USAR) Robotics Computer Vision for Assistive Technologies Cognitive Robotics and Human-Robot Interaction Key Projects: Leading RAMBO team in CNRS Lab-STICC Coordinated multi-robot assistance systems for smart environments Development of digital twin-driven smart home solutions Teaching: Postgraduate courses in Computer Vision/Perception at University of Rome Programming courses at ENSTA ParisTech Undergraduate Computer Graphics at University of Athens

Gheorghi Guzunمشاهده پروفایل

دانشیار

• Data Management
• Machine Learning
• Data Compression
• +۴ مورد دیگر

Dr. Gheorghi Guzun is an Associate Professor in the Department of Computer Engineering at San José State University (SJSU), where he has served since 2017. He holds a Ph.D. in Electrical and Computer Engineering from the University of Iowa (2016) and a BSc in Electrical Engineering from the Technical University of Moldova (2010). His research focuses on scalable data management systems, machine learning, and hardware-driven software design, with applications in energy-efficient data centers, infrastructure monitoring, and predictive analytics. He is a recipient of the NSF CAREER Award for his work on efficient AI systems. Education background: Ph.D. in Electrical and Computer Engineering, University of Iowa (2016) BSc in Electrical Engineering, Technical University of Moldova (2010) Research interests span data compression, large-scale indexing, and algorithm optimization for machine learning. His projects emphasize scalability and performance in distributed systems, with recent work addressing energy efficiency and sparsity-driven methods for AI. He also develops educational technologies, such as competitive learning platforms to enhance student engagement. Notable achievements include the NSF CAREER Award and contributions to infrastructure monitoring systems, flood event modeling, and predictive university admission yield estimation. His lab's work is documented at https://g-guzun.github.io/ . Grants and advising: His NSF-funded CAREER project explores scalable sparsity-driven methods for AI systems. He collaborates on projects involving distributed data analytics and maintains an active research lab at SJSU.

Jonas Latzمشاهده پروفایل

مدرس

• Bayesian Inference
• Uncertainty Quantification
• Stochastic Processes
• +۵ مورد دیگر

Jonas Latz is a Lecturer in Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, uncertainty quantification, stochastic processes, and their applications in computational mathematics and inverse problems. He has contributed to areas such as physics-informed neural networks, stochastic gradient methods, and medical imaging modeling. Key research interests include developing robust algorithms for Bayesian inverse problems, analyzing stochastic dynamical systems, and advancing numerical methods for partial differential equations. His work bridges theoretical foundations with practical applications in fields like tumor growth modeling and medical image reconstruction. Recent Achievements : Recipient of the SIAM Activity Group Uncertainty Quantification Early Career Prize (2024) SIAM Student Paper Prize (2020) SIGEST Award (2023) Dr. Latz collaborates internationally on topics such as adversarial machine learning, deep learning methods for PDEs, and stochastic sampling techniques. His research emphasizes rigorous mathematical analysis alongside computational innovation.

• Process Systems Engineering
• Large-Scale Optimization
• High-Performance Computing
• +۲ مورد دیگر

Carl D. Laird is the John E. Swearingen Professor and Department Head of Chemical Engineering at Carnegie Mellon University. He leads an internationally recognized research program in process systems engineering, known for high-performance computing techniques in large-scale nonlinear optimization, parallel scientific computing, and open-source software development. Education: Ph.D. in Chemical Engineering, Carnegie Mellon University (2006) B.S. in Chemical Engineering, University of Alberta (2000) Research Focus: His work solves problems in non-traditional domains including public health, homeland security, critical infrastructure, and energy systems through advanced optimization methodologies. Current research integrates machine learning with optimization for improved decision-making in complex systems. Publication Trends: Recent work focuses on mathematical optimization frameworks, decomposition methods for large-scale problems, integration of machine learning surrogates, and applications in energy systems and chemical manufacturing. Research demonstrates consistent innovation in computational methods for engineering challenges. Awards and Honors: Steven J. Fenves Award for Systems Research INFORMS Computing Society Prize CAST Division Outstanding Young Researcher Award NSF CAREER Award Montague Center Teaching Excellence Award Wilkinson Prize for Numerical Software (for IPOPT development) Leadership and Funding: As director of the Center for Advanced Process Decision-Making, he oversees collaborative research with industry partners. His research has been supported by NSF, DOE, and industrial consortia.

TAY Seng Chuanمشاهده پروفایل

دانشیار

• Remote Sensing
• Image Processing
• Parallel Simulation
• +۳ مورد دیگر

Dr. Tay Seng Chuan is an Associate Professor in the Department of Physics at the National University of Singapore (NUS). He is also the Deputy Director of the Centre for Remote Imaging, Sensing and Processing (CRISP) since 2001. His roles include coordination of the SM2/SM3 Bridging Courses under the Office of the Provost and Warden at Ridge View Residences . PhD in Computer Science from NUS (1999) Master of Science in Computer and Information Sciences from NUS (1994) His research interests span Remote Sensing , Image Processing , Parallel Simulation , Performance Modeling , High Level Architecture (HLA), and Information Technology Applications . He has contributed to Parallel Discrete-Event Simulation , Performance Analysis , and IT in Education . His publications focus on Time Warp Algorithms , Distributed Simulation , Parallel Processing , and Java-based Educational Tools . He has received multiple Meritorious Teaching Awards from NUS. Meritorious Teaching Award (2000) Meritorious Teaching Award (1999) Science Teaching Award (1997) In administration , he has served as Deputy Director at CRISP, Head of Education Division at CITA (1998–2001), and Webmaster of the Physics Department (1996–2001). He has also supervised UROPS projects on topics like Memory Management for Parallel Simulation and Remote Sensing Data Processing .

Sonya Staceyمشاهده پروفایل

دانشیار مدعو

• Clinical Pharmacy
• Pediatric Pharmacology
• Medication Safety
• +۳ مورد دیگر

Dr Sonya Stacey serves as an Adjunct Associate Professor at the School of Pharmacy and Pharmaceutical Sciences , The University of Queensland . Her work focuses on pediatric pharmacy , emphasizing medication safety , pharmacokinetics , and clinical pharmacology in hospital settings. Specializes in pediatric medication safety and adverse drug event analysis Develops clinical training frameworks for pharmacists Investigates antibiotic compatibility and dosing in critical care Active in respiratory pharmacology, particularly for cystic fibrosis Her recent research includes vaccine administration challenges ( 2025 ), antibiotic IV compatibility studies ( 2025 ), and safety evaluations of antifungal therapies ( 2024 ). Earlier work established standards for pharmacist training ( 2015 ) and advanced practice models in Australia ( 2014 ). Collaborative projects span multidisciplinary pediatric medicine safety initiatives and hospital pharmacy service development. As a co-author on over 20 publications, her work impacts healthcare policy and clinical practice. She contributes to the Safe and Effective Medication Collaborative and OPTMED-D research projects, addressing medication information continuity post-discharge.

Amanda Costonمشاهده پروفایل

استادیار

• Algorithmic Fairness
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Amanda Coston is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. Her research focuses on addressing challenges in algorithmic decision support systems and data-driven policy-making, emphasizing equity, validity, and reliability. She earned her PhD in Machine Learning and Public Policy from Carnegie Mellon University, advised by Alexandra Chouldechova and Edward H. Kennedy, and completed a postdoc at Microsoft Research's Machine Learning and Statistics Team. Her work spans causal inference, machine learning, and nonparametric statistics, with applications in criminal justice, healthcare, and public policy. Education: PhD in Machine Learning and Public Policy, Carnegie Mellon University (2019-2022) MS in Machine Learning, Carnegie Mellon University (2019) Bachelor of Science in Computer Science, Princeton University (2013) Research Interests: Her research investigates how algorithms and data systems can perpetuate or mitigate disparities in high-stakes domains. Key areas include counterfactual audits of racial bias in policing, fairness in predictive models, and validity in algorithmic decision-making. She develops methodologies to ensure equitable outcomes in applications like healthcare resource allocation and criminal justice risk assessments. Awards & Honors: 2024 Schmidt Sciences AI 2050 Early Career Fellowship 2023 FAccT Best Paper Award (Counterfactual Prediction Under Outcome Measurement Error) 2023 SaTML Best Paper Award (A Validity Perspective on Evaluating the Justified Use of Algorithms) 2022 Meta Research PhD Fellowship Teaching & Mentorship: She teaches Causal Inference (STAT 156/256) at Berkeley and has mentored students through programs like AI4ALL. Her teaching emphasizes ethics, fairness, and societal impacts of AI. Service: Referee for journals including Nature Human Behaviour, JASA, and Transactions on Machine Learning Research Steering Committee Member for ML4D Workshop (NeurIPS) Program Committee Member for FAccT and AAAI Labs & Collaborations: Amanda collaborates with interdisciplinary teams on projects involving policy design, algorithmic fairness, and healthcare equity. She co-organized the ML4D workshops at NeurIPS 2018-2019 and leads the FEAT reading group at CMU.

Yuhao Zhuمشاهده پروفایل

دانشیار

• Visual Computing
• Imaging
• Human Perception
• +۵ مورد دیگر

Yuhao Zhu is an Associate Professor at the University of Rochester, holding joint appointments in the Department of Computer Science and the Department of Brain and Cognitive Sciences. He is affiliated with the Center for Visual Science and the Goergen Institute for Data Science. His research focuses on visual computing, spanning imaging (optics, image sensors), human perception/cognition, and computer systems (architecture, programming models). He earned his Ph.D. from The University of Texas at Austin and has held visiting positions at Harvard University and Arm Research. Education: Ph.D. in Electrical and Computer Engineering, The University of Texas at Austin, 2017 M.S.E. in Electrical and Computer Engineering, The University of Texas at Austin, 2015 B.S. in Computer Science and Engineering, Beihang University, 2010 Research Interests: Dr. Zhu explores interdisciplinary areas such as computational imaging, human visual perception modeling, and energy-efficient computing systems. His work bridges optics, cognition, and computer architecture, with applications in AR/VR, autonomous systems, and neural rendering. Key themes include optimizing systems using biological principles and developing tools for efficient visual computing. Awards: NSF CAREER Award (2020) University Research Award (2023, 2019) ISCA Hall of Fame (2023) ACM SIGARCH Dissertation Award (2018, Honorable Mention) Teaching and Service: He teaches courses on computer imaging, graphics, and systems architecture. He serves on program committees for top conferences (ISCA, ASPLOS, MICRO) and chairs university-wide committees on AI ethics and international collaborations. His lab, Horizon Lab, actively mentors students and fosters interdisciplinary research. Labs/Teams: The Horizon Lab at URCS focuses on visual computing, with projects in computational art, AR/VR optimization, and robotic computing systems. Collaborations span academia and industry (e.g., Meta, Arm).

Muhammad Awaisمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Trustworthy AI
• Self-Supervised Learning
• +۵ مورد دیگر

Dr Muhammad Awais is a Senior Lecturer in Trustworthy and Responsible AI at the University of Surrey. He leads research on foundation models and self-supervised learning, with affiliations to the Surrey Institute for People-Centred Artificial Intelligence (PAI) and the Centre for Vision, Speech and Signal Processing (CVSSP). His expertise spans AI ethics, audio processing, medical AI, and biometric systems. Education: PhD in AI, MSc in AI, BSc Computer Engineering, BSc Mathematics and Physics. His research focuses on advancing AI through self-supervised learning techniques, with applications in healthcare (e.g., Parkinson’s Disease rehabilitation), transportation, and security systems. He has published extensively on topics like masked autoencoders, audio event classification, and age-invariant face recognition. Key contributions include DailyMAE (fast autoencoder pretraining), ASiT (audio-spectrogram transformers), and AiCareGaitRehabilitation (AI-driven gait rehabilitation). His work emphasizes ethical AI deployment and cross-modal learning.