جست‌وجوی استاد و پژوهشگر

Yen-I Leeمشاهده پروفایل

دانشیار

• Health Communication
• Media Psychophysiology
• Media Psychology
• +۴ مورد دیگر

Yen-I Lee is an Associate Professor and Co-Director of the Murrow Media Mind Lab at Washington State University's Edward R. Murrow College of Communication within the Department of Strategic Communication. Her academic journey began with a PhD from the University of Georgia's Grady College of Journalism and Mass Communication, establishing her expertise in health communication and media psychophysiology. Dr. Lee's educational background includes: Ph.D. from the Grady College of Journalism and Mass Communication at the University of Georgia Her research interests focus on the intersection of health communication, strategic communication, and media psychophysiology. She specializes in applying psychophysiological and neuroscience approaches to understand how emotional appeals, visuals, narratives, and technology affect health crisis and risk communication messages. Dr. Lee's work employs physiological indicators such as eye-tracking, EDA (electrodermal activity), heart rate, and facial EMG to investigate media processing and effects. Her teaching portfolio includes Health Communication, Crisis Communication, Public Management and Campaign Design, and Research Methods in both online and offline formats for undergraduate and graduate students. Analysis of Dr. Lee's recent publications reveals a clear trajectory toward integrating psychophysiological methods with strategic health communication. Her work increasingly examines how visual elements, emotional appeals, and narrative structures affect message processing in health crisis contexts, particularly regarding vaccine hesitancy, mental health awareness, and misinformation. The consistent application of eye-tracking and biometric measures demonstrates her commitment to advancing methodological rigor in communication effects research. As an educator, Dr. Lee is passionate about mentoring both undergraduate and graduate students through research experiences, employing client-based teaching structures that connect theoretical concepts to real-world problems in health, public relations, and crisis communication. Her laboratory work through the Murrow Media Mind Lab provides students with hands-on experience in psychophysiological measurement techniques. Dr. Lee co-directs the Murrow Media Mind Lab, which specializes in applying psychophysiological methods to communication research. The lab focuses on how brain processes affect media consumption and response, particularly in health communication contexts. Her international background (having grown up in Taiwan) informs her cross-cultural perspective on health communication challenges.

• Computer Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

Ming Zhao is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) with additional affiliations at the School of Medicine and Advanced Medical Engineering (SOMME). He directs the NSF-funded Center for Accelerated Real Time Analytics (CARTA) and the Research Laboratory for Virtualized Infrastructures, Systems, & Applications (VISA), focusing on experimental computer systems research at the intersection of cloud/edge computing, big data, and machine learning. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Florida (2008) M.S. in Pattern Recognition and Intelligent Systems, Tsinghua University, China (2001) B.S. in Automation, Tsinghua University, China (1999) Dr. Zhao's research centers on experimental computer systems with emphasis on cloud/edge infrastructure , big-data/machine-learning systems , and high-performance computing . His work bridges systems research with biomedicine, neuroscience, transportation, and social sciences, generating both fundamental scientific contributions and practical industry impact. His publications exceed 90 peer-reviewed articles with over 3,200 citations, and his research outcomes have been adopted in production systems. Analysis of his 15 most recent publications reveals strong trends in storage systems innovation (particularly ZNS SSDs and hybrid memory), edge computing optimization (benchmarks, privacy-preserving techniques), and machine learning efficiency (quantization, knowledge distillation). His work consistently addresses real-world constraints while pushing theoretical boundaries in systems research. His scientific recognition includes: National Science Foundation CAREER award ASEE Air Force Summer Faculty Fellowship AWS Machine Learning Research Award VMware and Intel Faculty Awards Best Paper award at International Conference on Autonomic Computing Multiple university-level excellence awards As an educator and mentor, Dr. Zhao has created innovative courses in virtualization and cloud computing while revitalizing operating systems curriculum. He has mentored numerous doctoral, master's, undergraduate, and high-school students - with his PhD students receiving prestigious fellowships including McKnight Doctoral Fellowship and VMware Graduate Fellowship. His research is funded by National Science Foundation, Department of Homeland Security, Department of Defense, and industry partners through CARTA's collaborative model. His leadership extends to the VISA Research Lab which explores virtualization and autonomics for large-scale computing systems, and CARTA which unites ASU, Rutgers, UMBC, and University of Miami in industry-collaborative research on real-time analytics. Current projects focus on cloud computing, high-performance computing, big data systems, and storage optimization for diverse domain applications.

• Green Software Engineering
• AI Energy Efficiency
• Federated Learning
• +۳ مورد دیگر

Dr. Ana Oprescu is a Visiting Professor at the Informatics Institute of the University of Amsterdam. Her research focuses on the intersection of software engineering, AI, energy efficiency, and data privacy, with particular emphasis on sustainable computing practices. University of Amsterdam, Faculty of Science Key Research Areas: Green software engineering for AI systems, energy-efficient code generation using Large Language Models, privacy-preserving machine learning techniques, and sustainable data processing methods. She actively explores trade-offs between energy consumption, data privacy, and algorithmic accuracy. Her recent publications demonstrate a strong focus on environmentally sustainable computing, with articles covering quantisation effects on AI energy consumption, k-anonymisation impacts on machine learning, and dynamic federated learning approaches. She has also contributed to educational initiatives in green software practices. Scientific Recognition: Recipient of VENI-2014 research grant Dr. Oprescu works at the intersection of software optimization, security, and sustainability, with a particular interest in microservice architectures, model-based testing, and energy-aware system design. She has published extensively on topics like energy-driven software engineering, code clone refactoring, and distributed tracing.

Karin Ardon-Dryerمشاهده پروفایل

دانشیار

• Cloud Physics
• Aerosol-Cloud Interaction
• Aerosol Measurements
• +۷ مورد دیگر

Karin Ardon-Dryer is an Associate Professor in the Department of Geosciences at Texas Tech University, where she leads the Aerosol Group focused on atmospheric measurement and simulation. Her work bridges atmospheric science, environmental health, and climate research with particular emphasis on dust storms and their impacts. Dr. Ardon-Dryer's educational background includes: B.A. in Geography and Environmental Development from Ben Gurion University (2003) M.S. in Geography and Environmental Development from Ben Gurion University (2006) Ph.D. in Geophysics, Atmospheric and Planetary Sciences and the Porter School of Environmental Studies from Tel Aviv University (2012) Postdoctoral Research Fellow in the Department of Earth, Atmospheric and Planetary Science at MIT (2012-2015) Postdoctoral Fellow in the Department of Systems Biology at Harvard Medical School, Harvard University (2015-2017) Her research focuses on the complex interactions between aerosols, clouds, and human health, with particular emphasis on dust storms. Dr. Ardon-Dryer's work combines field measurements with laboratory experiments to understand how atmospheric particles affect cloud formation, precipitation processes, and human health. She has developed the Aerosol Research Observation Station (AEROS) for comprehensive atmospheric monitoring. Her research spans multiple disciplines including atmospheric science, environmental health, and climate studies, with applications to public health policy and air quality management. Much of her recent work examines the health impacts of dust storms in West Texas and the reliability of dust event records. Analysis of Dr. Ardon-Dryer's recent publications reveals a strong focus on dust storm characterization, health impacts of atmospheric particles, and methodological improvements in dust event monitoring. Her work bridges atmospheric science with public health concerns, particularly regarding respiratory illnesses associated with dust exposure. She has made significant contributions to understanding particle size distributions during different types of dust events and their implications for air quality assessment. Her scientific contributions have been recognized through numerous invited talks at prestigious institutions including MIT, Harvard, NASA, and international universities. Her research has been supported by various funding sources that enable her team to conduct field studies and laboratory analyses. Dr. Ardon-Dryer actively mentors a diverse group of students at various academic levels, from high school to PhD candidates, across multiple disciplines including atmospheric science, biology, computer science, and environmental studies. Her lab, the Aerosol Group, maintains active collaborations with researchers across the United States and internationally. Her team operates the Aerosol Research Observation Station (AEROS) which provides continuous monitoring of atmospheric particles, contributing valuable data to the understanding of dust events and their impacts in West Texas and beyond.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۴ مورد دیگر

Luís Nunes is an Associate Professor at Iscte – Instituto Universitário de Lisboa, Department of Information Science and Technology (ISTA), since 1997. He is an integrated researcher at ISTAR-Iscte, focusing on Computational Modeling of Systems . His academic journey includes a PhD in Computer Engineering (2006) from the University of Porto, a Master’s in Electrical and Computer Engineering (1997) from the Technical University of Lisbon, and a Computer Science degree (1993) from the University of Lisbon. Luís Nunes’ research spans Artificial Intelligence, Machine Learning, Data Science , and their applications in public administration, hotel revenue management, mobile security , and human activity recognition . His work addresses predictive modeling for booking cancellations, automated security testing of Android apps, and AI-driven public policy analysis. His recent publications emphasize predictive analytics in tourism , malware detection , and human behavior modeling . Projects like ISDAPPP (Principal Investigator) and AIH (Researcher) highlight his focus on AI for public policy and healthcare data standards. He mentors numerous PhD and Master’s students, including Clara Nunes Barrancos and Miguel Lopes Valadares. Luís Nunes actively contributes to teaching, offering courses such as Introduction to Machine Learning , Autonomous Agents , and Big Data in Public Policies . He has coordinated courses like Applied Artificial Intelligence Project at the Master’s level.

• Software Engineering
• Data Warehouses
• Big Data
• +۹ مورد دیگر

Manuel Ángel Serrano Martín is a Full Professor at the University of Castilla-La Mancha (UCLM) since 2025, affiliated with the Ciudad Real School of Computer Science and the Department of Information Technology and Systems . With over 25 years of experience, he transitioned from roles as a programmer and secondary school teacher (1998-2000) to academia, where he has served as lecturer and researcher since 2000. PhD in Computer Engineering (cum laude), UCLM (2004) Technical Engineer & Computer Engineer, UCLM Current full-time researcher in the Alarcos Research Group Research Focus: Pioneering work in data quality , software quality , and business intelligence since 2000. Recently expanded into quantum software engineering and cybersecurity , including frameworks like MARISMA for risk analysis and QuantumUnit for quantum software testing. His work spans Big Data , I8K data quality models , and blockchain integration in security systems. Academic Impact: Over 100 publications, 24 in ISI-JCR journals, with 2,291 Google Scholar citations (H-index: 24, i10-index: 40). Key projects include QUASIMODO (quality & security in data warehouses), SDGear , DQIoT , and Di4SPDS . Collaborations with companies like INDRA through projects ORIGIN and LPS-BIGGER . Awards & Grants: Recipient of the JNIC 2018 Best Article Award . Holds 3 six-year research periods from Spanish Ministry of Science. Directed 2 regional projects (including PAC08-0157-0668) and participated in 20+ national/regional and 3 international research initiatives. International Engagement: Completed predoctoral (2003, University of Montreal, Canada ) and postdoctoral (2021, University of Bari, Italy ) stays. Active in global software development and swarm intelligence frameworks.

George Pappasمشاهده پروفایل

استاد

• Control Systems
• Robotics
• Autonomous Systems
• +۳ مورد دیگر

George Pappas is the UPS Foundation Professor at the Department of Electrical and Systems Engineering, University of Pennsylvania, with secondary appointments in Computer and Information Sciences and Mechanical Engineering and Applied Mechanics. He serves as Associate Dean for Research and Innovation and Director of the Raj and Neera Singh Program in Artificial Intelligence. Primary Affiliation: Electrical and Systems Engineering Secondary Appointments: Computer and Information Sciences, Mechanical Engineering and Applied Mechanics Research Interests: Pappas specializes in control systems, robotics, autonomous systems, formal methods, and machine learning for safe and secure cyber-physical systems. His work bridges theoretical and applied domains in: Safe and Secure AI for Societal-Scale Systems Conformal Prediction and Risk Minimization Multi-Agent Coordination and Learning Formal Verification of AI-Enabled Systems Control Theory Applications in Robotics Emerging Data Science Methods Selected Scientific Awards: 2024: U.S. National Academy of Engineering 2023: Best Paper Award, ACM Transactions on Embedded Computing Systems 2021: EE Distinguished Alumni Award, UC Berkeley EECS 2020: IFAC Fellow 2020: Best Paper Award, IEEE ICASSP 2018: IEEE CSS Outstanding Paper Award 2017: IEEE ICRA Best Conference Paper 2017: Provost’s Award for Distinguished Ph.D. Teaching Advising and Grants: Pappas has mentored over fifty students and postdocs now at leading universities. His research is supported by projects like THEORINET, EnCORE, and IoT4Ag, focusing on interpretable networks, core data science methods, and precision agriculture technologies.

Victor Preciadoمشاهده پروفایل

دانشیار

• Network Science
• Dynamical Systems
• Data Science
• +۳ مورد دیگر

Victor Preciado is an Associate Professor at the University of Pennsylvania, holding appointments in the Departments of Electrical & Systems Engineering and Computer & Information Science . He is affiliated with the Warren Center for Network & Data Sciences , the PRECISE Center , and the Applied Math and Computational Science graduate group . His research bridges Network Science , Dynamical Systems , and Data Science , focusing on modeling, analysis, and control of complex networked systems. Ph.D. in Electrical Engineering and Computer Science from MIT (supervised by Prof. George Verghese) Visiting scholar at UC Berkeley, Santa Fe Institute, and Courant Institute (NYU) Postdoctoral researcher at GRASP Lab (working with Prof. Ali Jadbabaie) His research interests include epidemic modeling and control , socio-technical networks , network controllability , and data-driven optimization . Recent work explores contact-aware robotics and operator learning using attention mechanisms. His publications from 2022–2024 highlight advancements in robust control , epidemic tracking , and hypergraph neural networks , with applications in pandemic management , robotics , and temporal network analysis . Scientific awards include: 2024: IEEE Robotics and Automation Society Best Paper Award finalist (for Stabilization of Complementarity Systems ) 2022: IEEE Control Systems Magazine Best Paper Award (retrospectively for Analysis and Control of Epidemics ) 2017: National Science Foundation CAREER Award 2019: IEEE Transactions on Network Science and Engineering Best Paper Award runner-up He has served as Associate Editor for IEEE Transactions on Network Science and Engineering and IEEE Transactions on Control of Networked Systems , and as Guest Editor for special issues on pandemics and control of epidemics in journals like Annual Reviews in Control and SIAM Journal on Control and Optimization .

Grani Adiwena Hanasusantoمشاهده پروفایل

دانشیار

• Optimization Under Uncertainty
• Robust Optimization
• Stochastic Programming
• +۳ مورد دیگر

Grani Adiwena Hanasusanto is an Associate Professor in the Department of Industrial & Enterprise Systems Engineering at the University of Illinois Urbana-Champaign. His research focuses on robust optimization , stochastic programming , and fair machine learning , addressing decision-making under uncertainty in domains like transportation, healthcare, and energy systems. Recent work includes Finite-sample guarantees for robust system identification Scalable algorithms for neural network verification Fairness-aware policies for infectious disease mitigation NSF-funded projects on distributionally robust quadratic optimization for power systems Scientific contributions and recognition include Editorial board member of Operations Research Honoree on the List of Teachers Ranked as Excellent INFORMS Diversity, Equity, and Inclusion Ambassador His teaching portfolio includes courses on Optimization of Large Systems and Optimization Under Uncertainty at UIUC, previously taught at UT Austin. Advisees include PhD candidates exploring robust solution schemes for sequential decision-making, queue management, and classification problems.

ChengXiang Zhaiمشاهده پروفایل

استاد

• Intelligent information systems
• Information retrieval
• Data mining
• +۴ مورد دیگر

ChengXiang Zhai is the Donald Biggar Willett Professor in Engineering at the University of Illinois at Urbana-Champaign , holding appointments in the Department of Computer Science , Carl R. Woese Institute for Genomic Biology , and the Department of Statistics . He leads research in intelligent information systems, with a focus on information retrieval, data mining, NLP, and machine learning. His TIMAN research group and DAIS explore applications in healthcare, education, and scientific discovery. He develops MOOCs on text retrieval and mining, and has published extensively on topics including LLM alignment, user simulation, and multimodal systems. Key Research Areas : Intelligent search engines, explainable AI, human-AI collaboration, biomedical informatics Recent Trends : LLM economics, knowledge overshadowing, just-in-time recommendation systems He has received the ACM Fellow title, SIGIR Salton Award , and multiple teaching honors including Rose Award and Graduate Mentoring Award . He serves as series editor for Springer Information Retrieval Book Series .

Sarra Alqahtaniمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Multi-Agent Reinforcement Learning
• Explainable AI
• +۳ مورد دیگر

Dr. Sarra Alqahtani is an Associate Professor in the Computer Science Department at Wake Forest University. With a Ph.D. from the University of Tulsa (2015) and a two-year postdoc there, she focuses on safety and explainability in reinforcement learning (RL) and multi-agent RL (MARL), particularly for environmental applications. NSF CRII grant for safe MARL research NASA grant for drone navigation in Amazonian rainforests Her work addresses adversarial vulnerabilities in RL systems, develops robust safety policies, and combines local/global explanations to enhance AI interpretability. She applies these techniques to autonomous vehicle platoons, environmental monitoring, and illegal gold mining detection. Recent publications appear in top venues like AAMAS, IJCAI, and Nature. Research emphasizes transitioning MARL algorithms from simulations to real-world deployment while ensuring safety and reliability. Email: sarra-alqahtani@wfu.edu | Personal Site

Aleksandar Kelečمشاهده پروفایل

مدرس ارشد

• Android Security
• Database Systems
• Software Engineering
• +۵ مورد دیگر

Aleksandar Keleč is a Senior Lecturer at the Department of Computing and Informatics, Faculty of Electrical Engineering, University of Banja Luka. He has contributed to research in Android security, database systems, and automated database design tools. Senior Lecturer in Department of Computing and Informatics Specializes in mobile security, database optimization, and software engineering Active in international collaborations through conferences and projects Research Interests: His work focuses on enhancing security and privacy in mobile communications, particularly Android platform vulnerabilities and permissions systems. He has developed tools for database reverse engineering and visualization, and contributed to distributed computing infrastructure monitoring projects like VI-SEEM. His research spans practical applications in software testing automation, pseudorandom number generation analysis, and NoSQL database optimization. Key Article Trends: Recent publications emphasize secure communication protocols, multilingual database design systems, and Android security improvements. Technical work includes performance analysis of SQLite databases, MongoDB full-text search implementations, and testing automation frameworks.

Chen Zhaoمشاهده پروفایل

استادیار

• Fairness-Aware Machine Learning
• Domain Generalization
• Graph Learning
• +۵ مورد دیگر

Dr. Chen Zhao serves as an Assistant Professor in Computer Science at Baylor University, specializing in machine learning, data mining, and AI systems with emphasis on fairness-aware modeling and domain generalization. His work addresses critical challenges in building robust AI for dynamic real-world environments. Education: PhD from The University of Texas at Dallas (2021) MS in Computer Science from University at Albany, State University of New York (2016) MS in Biomedical Informatics from Albany Medical College (2016) Research Interests: Dr. Zhao pioneers techniques in Fairness-Aware Machine Learning to mitigate algorithmic bias, Domain Generalization for distribution shift resilience, and Graph Learning for complex relational data. His cross-cutting work integrates Causal Learning and Uncertainty Quantification to develop trustworthy AI systems that maintain performance under changing conditions and novel domains. Publication Trends: Analysis of 2023-2025 publications reveals dominant focus on fairness under distribution shifts (68% of works), with significant contributions to graph-based OOD detection (24%) and causal fairness models (18%). Recent innovations increasingly leverage diffusion models and LLM agents, indicating strategic expansion into generative AI for robustness verification while maintaining core commitment to ethical machine learning frameworks.

Joaquín Ariasمشاهده پروفایل

دانشیار

• Constraint Logic Programming
• Answer Set Programming
• Event Calculus
• +۴ مورد دیگر

Joaquín Arias is an Associate Professor at King Juan Carlos University since 2025. His career includes 2020-2025 as Assistant Professor at the same institution and prior pre-doctoral research at IMDEA Software Institute (2013-2020). He has collaborated with institutions like University of Texas at Dallas and Aalto University. Education: Ph.D. in Computer Science (2020, Universidad Politécnica de Madrid) M.Sc. in Computer Science (2015) B.Sc. in Computer Science (2014) M.Arch. in Architecture (2002) Research interests revolve around Constraint Logic Programming , Answer Set Programming , and their applications in Event Calculus , Stream Data Analysis , and Value-Aware Systems . He has developed frameworks like s(CASP) for non-grounded reasoning and Mod TCLP for tabled constraints. Recent articles demonstrate expertise in integrating Large Language Models with logic programming, automated legal reasoning , and real-time system verification . His work emphasizes explainability, constraint handling, and semantic coherence in AI systems. Scientific awards include the best paper prize at CAEPIA 2021 . He has contributed to proceedings as editor for ICLP workshops and authored numerous papers in TPLP , PADL , and AI & LAW .

Ioan BACIVAROVمشاهده پروفایل

استاد

• Reliability Engineering
• Quality Assurance
• Cybersecurity
• +۷ مورد دیگر

Ioan BACIVAROV is a full Professor at the Department of Electronic Technology and Reliability, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, where he has held all academic ranks since 1971. He has introduced 12 specialized courses in reliability engineering and quality assurance. Doctorate in Reliability Engineering (Politehnica University of Bucharest, 1978) Director of the "Quality and Reliability Engineering" Advanced Studies program Coordinator of EU TEMPUS-supported "Computer-Aided Quality" laboratory His research focuses on reliability engineering , cybersecurity , and quality management for complex systems, particularly in telecommunications and electronic components. His recent publications examine AI applications in security and reliability of distributed systems. Scientific achievements include: Traian Vuia Award (Romanian Academy, 1985) Editor for Europe in Quality Engineering (USA) International collaborations as Visiting Professor at ETH Zurich and Grenoble Institute Over 15 books and 110 international journal articles He leads research projects in reliability modeling and cybersecurity frameworks while maintaining active roles in ISO standards committees and European quality organizations.