جست‌وجوی استاد و پژوهشگر

Samira Shirzaei Nicholsمشاهده پروفایل

استادیار

• Operations Research
• Public Health
• Gender Equity
• +۴ مورد دیگر

Samira Shirzaei Nichols serves as an Assistant Professor in the Department of CISA (Computer Information Systems and Analytics) at the College of Business, University of Central Arkansas (UCA), with contact details including email sshirzaei@uca.edu, office COB 305D, and phone (501) 450-5331. Her academic role integrates quantitative analytics with real-world problem-solving across public health, social equity, and business operations. Her research spans Operations Research, Public Health, and Gender Equity, employing machine learning, statistical modeling, and simulation to address epidemiological forecasting (HIV/COVID-19), gender pay disparities in academia/business, and service system optimization. Key methodologies include ARIMA/SARIMA time-series analysis, RNN neural networks, mixed-methods approaches, and Society 5.0 simulation frameworks, demonstrating interdisciplinary rigor in translating theoretical models to societal challenges like refugee health access and grocery store operational efficiency. Analysis of her 15 publications (2014-2025) reveals an accelerating focus on public health crises since 2020, with 60% of recent work dedicated to pandemic response modeling and gender equity analysis. While early research centered on supply chain optimization (2014-2018), post-2020 outputs pivot toward urgent health applications—evidenced by 5 COVID-19 forecasting studies—and structural inequities, using comparative data (2018 vs. 2021) to quantify gender pay gaps across academic disciplines. Her work consistently bridges technical analytics with human-centric outcomes. No information is available regarding student advising, grant funding, laboratory facilities, or research teams from the provided sources, though her prolific publication record indicates active scholarly engagement.

Michal Tzurمشاهده پروفایل

استاد

• Online Transportation Problems
• Machine Learning combined with Optimization
• Humanitarian Logistics
• +۳ مورد دیگر

Prof. Michal Tzur is a distinguished faculty member at Tel Aviv University's Industrial Engineering Department within The Iby and Aladar Fleischman Faculty of Engineering. She has served as department chair during two separate terms (2004-2006 and 2019-2021) and previously held faculty positions at the Wharton School of the University of Pennsylvania and as a visiting faculty member at Northwestern University's Department of Industrial Engineering and Management Sciences. From 2015-2017, she served as president of the Operations Research Society of Israel (ORSIS). Her research spans multiple critical domains in operations research and industrial engineering, with particular focus on Online Transportation Problems, Machine Learning combined with Optimization, Humanitarian Logistics, Vehicle Sharing Systems, Supply Chain Management, and Inventory Management. Her work demonstrates a consistent evolution from foundational theoretical work in inventory and lot-sizing problems toward increasingly applied research addressing contemporary challenges in transportation, humanitarian logistics, and shared mobility systems. Notably, her recent publications show a strong emphasis on fairness considerations in resource allocation and optimization problems. Prof. Tzur's publication record reveals a clear trajectory from foundational work in inventory theory and lot-sizing problems toward increasingly applied research addressing real-world challenges. Her work on bike-sharing systems represents one of her most impactful research streams, with multiple publications analyzing different aspects of vehicle sharing systems. More recently, she has made significant contributions to humanitarian logistics, with several papers addressing critical challenges in disaster response and resource allocation, earning her a Best Paper Award in 2018. Best Paper Award for 'Designing Humanitarian Supply Chains by Incorporating Actual Post-Disaster Decisions' in European Journal of Operational Research (2018) Prof. Tzur has successfully advised numerous doctoral students, with seven PhD students listed in her record including Mor Kaspi, Iris Forma, Reut Noham, Ohad Eisenhandler, Adi Sarid, Gal Neria, and Gabriel Deza. Her collaborative approach is evident in many of her publications, particularly with colleagues like Tal Raviv, with whom she has jointly supervised several students and co-authored multiple papers. While specific grant information isn't detailed in the provided text, her sustained research output across multiple domains suggests successful funding acquisition throughout her career. Though specific lab information isn't explicitly provided in the text, Prof. Tzur's research appears to be conducted through collaborative efforts within the Industrial Engineering Department at Tel Aviv University, with strong connections to the Operations Research community in Israel and internationally. Her recent work on humanitarian logistics and transportation systems suggests involvement in research groups focused on applied optimization for societal benefit.

Emre Sürenمشاهده پروفایل

پژوهشگر

• LLM Security
• IoT Hacking
• Cyber Threat Intelligence
• +۶ مورد دیگر

Dr. Emre Süren serves as a Researcher at KTH Royal Institute of Technology within the Division of Network and Systems Engineering and concurrently leads Cybercampus Sweden's Royal Hacking Lab as its Head. His dual roles position him at the forefront of national cybersecurity research with extensive collaborations across EU Horizon projects, Swedish government entities, and international academic institutions. His research spans critical domains including LLM security, IoT hacking, cyber threat intelligence, and digital forensics, with particular emphasis on AI-driven vulnerability research. Current work focuses on developing frameworks like CORTEX for AI-enhanced threat intelligence and Applied VORTEX for AI-based vulnerability discovery, supported by substantial grants from Vinnova, Swedish Research Council, and EU Horizon programs. Dr. Süren's publication portfolio demonstrates consistent innovation in security research, with recent articles exploring procedural generation for power systems security, penetration testing methodologies for connected households, and practical IoT threat research frameworks. His work increasingly integrates AI/ML techniques to address evolving cybersecurity challenges, particularly in zero-day exploit detection and IoT security. National Academic Infrastructure for Supercomputing grants (NAISS) for LLM-based threat intelligence EU Horizon funding for DFIRent (AI-assisted digital forensics) Swedish Research Council funding for Applied VORTEX (5.7M SEK) Digital Futures funding for CORTEX (3M SEK) Vinnova Cybercampus grant (17.5M SEK total) As an active educator, he supervises numerous PhD and Master's students on projects ranging from mobile application security to AI-powered honeypot architectures. His technical committee service includes NordSec, DFIR Review, and IEEE Security & Privacy, while his practical contributions include open-source tools like Memotopsy (memory forensics) and PatrIoT (IoT vulnerability research methodology).

• Transport Engineering
• Logistics
• Port Operations
• +۴ مورد دیگر

M.B. Duinkerken serves as a Professor in the Department of Transport Engineering and Logistics within the Faculty of Civil Engineering and Geosciences at Delft University of Technology. With an active research profile evidenced by 61 research outputs including articles, reports, and conference contributions, Duinkerken maintains significant external collaborations across international port and transport networks. Research interests focus on Transport Engineering and Logistics with specialized expertise in port operations, freight systems, and sustainable infrastructure. Key areas include: Port safety and risk management for emerging technologies like ammonia bunkering Renewable energy infrastructure logistics, particularly offshore wind farm installation Intelligent transportation systems for freight and high-speed rail networks Digital transformation in container terminal operations and supply chain transparency Recent publications reveal a strong trend toward sustainability-focused transport solutions, with 60% of 2022-2025 outputs addressing decarbonization challenges in ports, freight, and rail systems. The research consistently bridges engineering design with operational implementation, often utilizing living lab methodologies and data-to-value frameworks. Duinkerken's collaborative network spans European rail authorities, port operators, and maritime technology developers, with editorial experience including DUP Satellite (2004). The research portfolio demonstrates consistent funding support through involvement in major infrastructure projects and dataset development, particularly for European high-speed rail optimization and port safety frameworks.

Remco Dijkmanمشاهده پروفایل

استاد

• Business Process Management
• Data-driven Optimization
• Quantitative Business Process Analysis
• +۴ مورد دیگر

Remco Dijkman is a Full Professor in Information Systems and chair of the Information Systems group at Eindhoven University of Technology (TU/e) within the Industrial Engineering and Innovation Sciences school. He leads research in Business Process Management with a focus on data-driven optimization of business processes. His work bridges theoretical advancements with practical applications in transportation and high-tech supply chains. Dr. Dijkman received his PhD and Master's degrees in computer science from the University of Twente. His academic journey includes visiting positions at New York University, Hasso Plattner Institute, IBM Zurich Research Lab, Humboldt-University Berlin, and Queensland University of Technology. PhD in Computer Science - University of Twente Master's in Computer Science - University of Twente Professor Dijkman's research focuses on the detection, diagnosis and prediction of optimal execution scenarios, mathematical models for quantitative analysis of business processes, and resource assignment optimization. His work integrates advanced techniques including queueing models, stochastic programming, and deep reinforcement learning to solve complex business process challenges. He has particular expertise in applying these methods to transportation logistics and high-tech supply chain planning, where he investigates how data-driven predictions can improve transport order assignment and supply chain planning. His recent publications demonstrate a strong trend toward integrating artificial intelligence techniques with traditional operations research methods. The research shows increasing sophistication in handling uncertainty in business processes, with a growing emphasis on real-time decision making and optimization. His work spans theoretical advancements in process modeling and practical applications in logistics and supply chain management, reflecting his commitment to bridging academic research with industry needs. Professor Dijkman serves on the editorial board of Information Systems journal and has published over 100 papers in prestigious venues including Information Systems, Computers in Industry, and Transactions on Software Engineering and Methodology. He currently leads or participates in several major research projects including DynaPlex, CERTIF-AI, SLEM, and FENIX. Additionally, he serves as research director for high-tech supply chain at the European Supply Chain Forum, a networking organization with over 50 multinational companies. His adjunct professorship at Queensland University of Technology demonstrates his international academic engagement. Professor Dijkman's research group focuses on developing advanced methods for business process analysis and optimization, with particular emphasis on handling uncertainty and making real-time decisions in complex business environments. The team works closely with industry partners to ensure practical relevance of their research, particularly in transportation and high-tech manufacturing sectors.

• Network Function Virtualization (NFV)
• 5G/6G Network Slicing
• Quality of Service (QoS)
• +۷ مورد دیگر

Stanislav Lange is an Associate Professor at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU). His research focuses on network function virtualization, 5G/6G network slicing, quality of service (QoS), quality of experience (QoE), and machine learning applications for network management. His recent publications address decentralized autoscaling, 5G New Radio scheduling impacts, and survivability of network slices during outages. Key trends in his work include network intelligence, in-network computing, and deterministic performance guarantees in virtualized environments. Lange contributes to teaching courses in network programming, cybersecurity, and computer networks for instrumentation. His collaborations span IEEE conferences and journals, addressing challenges in network virtualization, resource optimization, and next-generation communication systems.

Fraser A. Dalyمشاهده پروفایل

دانشیار

• Applied Probability
• Random Graphs and Networks
• Markov Processes
• +۴ مورد دیگر

Fraser A. Daly serves as Associate Professor in the Department of Actuarial Mathematics & Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh. His academic appointment includes leadership roles as undergraduate admissions tutor and instructor for statistical modeling and programming courses. From 2022-2024, he directed the Scottish Mathematical Sciences Training Centre (SMSTC), a consortium serving nine Scottish universities, and previously held positions as MSc Financial Mathematics programme director and Royal Statistical Society Applied Probability section vice chair. Research interests center on applied probability with emphasis on distributional approximation techniques. His work develops theoretical frameworks for approximating complex probability distributions using tractable models like Gaussian or Poisson distributions, with explicit error bounds. Key application areas include random graphs and networks , Markov processes , and actuarial models . The research fingerprint reveals strong focus on approximations (100%), randomness (53%), distribution theory (51%), and Poisson approximation (41%), with significant contributions to Gaussian distribution analysis and graph theory. Analysis of his 15 most recent publications (2021-2025) shows consistent focus on Stein's method applications, distributional approximations across diverse contexts (asymmetric Laplace, geometric-type, negative binomial), and network theory applications. The work demonstrates interdisciplinary reach spanning probability theory, statistical inference, epidemiology modeling, and actuarial science, with increasing emphasis on random graph asymptotics and optimal control in stochastic systems. Professional service includes editorial contributions such as the 2019 Stochastic Networks special issue introduction, reflecting leadership in the probability community. His role in directing SMSTC from 2022-2024 demonstrates commitment to graduate mathematical education across Scotland. Actively supervising PhD students, Daly welcomes projects in random graphs, Markov processes, and actuarial models. His research program shows sustained productivity with 21 total publications including multiple high-impact articles in journals like Scandinavian Actuarial Journal and Journal of Statistical Physics , with growing citation impact (29 Scopus citations for his 2016 Conway-Maxwell-Poisson work).

• Computer Science
• Hardware Design
• Cryptography
• +۷ مورد دیگر

Dr. Yasir Ali Shah is a Lecturer in Computer Science at Ulster University's School of Computing, Engineering and the Built Environment , based at the Derry~Londonderry campus. His research focuses on hardware design, cryptography, and secure communication systems. Specialization: Post-Quantum Cryptography, Elliptic Curve Cryptography (ECC), FPGA/ASIC implementations Key Contributions: Optimization of cryptographic algorithms for IoT security, high-throughput hardware architectures Recent publications highlight advancements in post-quantum cryptography, including NTT/INTT optimizations, ECC hardware accelerators, and lightweight cipher implementations. His work spans both theoretical and applied domains, with a 2024 study on CPR efficacy indicating interdisciplinary collaborations. Selected trends in research output: 2024: 5 major papers on post-quantum crypto, IoT security, and medical applications 2023: Error-resistant NTT architectures and license plate recognition algorithms 2022: Optical interconnect designs and antiparasitic drug evaluations

• Computational Thinking
• STEM Education
• Artificial Intelligence Literacy
• +۳ مورد دیگر

Vladimir Dolgopolov is a Senior Researcher at the Educational Systems Group of Vilnius University. His work focuses on computational thinking, STEM education, and curriculum design, with a particular emphasis on integrating digital technologies into teaching practices. Research Interests: Computational Thinking, STEM/STEAM Education, Artificial Intelligence Literacy, Curriculum Transformation Key Collaborations: Valentina Dagienė, Tatjana Jevsikova, Eglė Jasutė Recent publications highlight his contributions to Industry 5.0 education, AI literacy frameworks, and model-centered approaches to scientific programming. His work often addresses the pedagogical challenges of digital transformation and the VUCA (volatility, uncertainty, complexity, ambiguity) paradigm in higher education. He has explored semiotics perspectives in computational thinking and co-developed methodological guidelines for software learning objects in scientific programming education. Notable projects include the DECART initiative for agile curricula and studies on novice software engineering students' temperament styles. His research spans case studies on Android game-based programming motivation, inverted classroom models, and queueing system simulations for teaching scientific computing.

• Stochastic Programming
• Mathematical Modeling
• Financial Mathematics
• +۷ مورد دیگر

Prof. habil. dr. Leonidas Sakalauskas is an Affiliated Scientist at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University. His extensive research career spans mathematical modeling, stochastic programming, and statistical analysis with significant contributions across multiple disciplines. He maintains an active research profile with recent publications extending into 2025. Professor Sakalauskas' research focuses on stochastic programming, mathematical modeling, financial mathematics, statistical analysis, optimization, and queueing theory. His work demonstrates a consistent pattern of interdisciplinary research, bridging theoretical mathematics with practical applications in finance, social sciences, and computer science. He has developed innovative approaches to agent-based modeling, fractal analysis, and Monte Carlo methods, contributing significantly to both theoretical frameworks and practical implementations. His recent publications reveal a continuing evolution of research interests, with increasing emphasis on data science applications, computational social science, and advanced statistical modeling techniques. The articles demonstrate sophisticated methodological approaches to complex problems across diverse domains including financial modeling, social cohesion analysis, and network performance optimization. Throughout his career, Professor Sakalauskas has maintained a strong publication record in high-impact journals and conference proceedings, reflecting his ongoing engagement with the international research community. His work shows consistent methodological rigor combined with practical relevance across multiple application domains. Professor Sakalauskas has collaborated extensively with researchers across Lithuania and internationally, demonstrating strong teamwork and interdisciplinary engagement. His research has practical applications in financial modeling, social science analysis, and computational systems optimization, contributing to both theoretical advances and real-world implementations.

Olivier Gallayمشاهده پروفایل

مدرس

• Applied Probabilities
• Queueing Theory
• Production Networks
• +۴ مورد دیگر

Olivier Gallay is a Lecturer and Invited Guest at École Polytechnique Fédérale de Lausanne (EPFL), with cross-disciplinary affiliations across Microengineering (STI), Humanities and Social Sciences (CDH), and Management of Technology (CDM). His work integrates Applied Probabilities, Queueing Theory, and Information Theory to solve complex problems in production networks and sustainable logistics. Teaching roles in Corporate Sustainability, Social Innovation, and Supply Chain Dynamics Research spans drought resilience, sweet potato supply chains, and hybrid truck-drone delivery systems Key methodologies: stochastic modeling, agent-based simulations, and combinatorial optimization Recent publications analyze nonlinear economic equilibria through van der Waals modeling, decentralized logistics platforms, and weariness dynamics in service networks.

Soojeong Leeمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Biomedical Processing
• +۴ مورد دیگر

Soojeong Lee is an Assistant Professor in the Department of Computer Science and Engineering at Sejong University, focusing on Deep Learning, Machine Learning, and Uncertainty Estimation for biomedical processing. She completed her Ph.D. (2008), M.Sc. (2000), and B.Sc. (1997) in Computer Engineering at Kwangwoon University and Korea National Open University. Research Interests: Biometric signal processing, wearable health devices, AI-driven medical diagnostics Collaborations: University of Ottawa, Carleton University, Federal University of Uberlandia (Brazil) Her recent projects include AI vision-based defect detection , PPG signal-based blood pressure algorithms , and uncertainty reduction in bio-signal measurement . Selected for 15 most recent publications spanning biomedical AI , respiratory rate estimation , and blood pressure monitoring . Scientific Awards: Acoustical Society of Korea Outstanding Presentation (2006, 2008) Bronze Award in Vocational Multimedia Contest (2002) Professional Engineer License (2003) Supervised Ph.D. and Master's students in speech enhancement and biomedical signal processing. Grants include National Research Foundation of Korea projects on deep learning-based bio-signal processing (2016-2019) and blood pressure measurement algorithms (Samsung Electronics, 2018-2019).

Christopher DeFlitchمشاهده پروفایل

استاد

• Emergency Medicine
• Clinical Decision Support
• Diabetes Management
• +۳ مورد دیگر

Christopher DeFlitch is a Professor in the Department of Emergency Medicine at Pennsylvania State University, specializing in emergency care optimization, clinical informatics, and healthcare systems transformation. His work integrates operations research with clinical practice to address critical challenges in emergency department workflows. Research interests encompass: Emergency Department Patient Flow and Triage Optimization Clinical Decision Support for Glycemic and Diabetes Management Healthcare Organization Transformation and Systems Engineering Epidemiology and Public Health Informatics for Disease Spread Modeling Opioid Prescribing Practices and Prescription Drug Monitoring Analysis of his 2017-2022 publications reveals a consistent focus on data-driven solutions for emergency medicine, particularly in diabetes care within acute settings and pandemic response modeling. His work demonstrates strong interdisciplinary collaboration between clinical medicine, computer science, and public health. Dr. DeFlitch has secured significant National Science Foundation funding as Co-Principal Investigator for five major projects, including the RAPID grant for coronavirus spread modeling and the Center for Healthcare Organization Transformation. These grants support research in human mobility patterns, infection dynamics, and healthcare system optimization during public health crises.

• Network Traffic Modeling
• Performance Evaluation
• Queueing Theory
• +۳ مورد دیگر

Kentaro Hoshi is an Assistant Professor at the Global Education Center, focusing on information science and programming education. He has taught courses such as Fundamentals of Information Science , Basic AI Programming , and Introduction to Python/Java/C++ Programming across multiple quarters in 2025. His research interests include network traffic modeling, performance evaluation of web systems, and applying queueing theory to analyze non-Poisson traffic processes. He has worked on advanced traffic models with general arrival and service time distributions, including Round-Robin processing systems, validated through simulation. The Scopus API data (as of 2025) indicates 6 publications, 8 citations, and an h-index of 2. Key projects involved modeling timeout traffic in web networks and evaluating system performance under generalized distributions and feedback mechanisms.

Richard J. Boucherieمشاهده پروفایل

استاد

• Operations Research
• Probability
• Healthcare Logistics
• +۵ مورد دیگر

Richard J. Boucherie is a Full Professor at the University of Twente, affiliated with the Digital Society Institute, TechMed Centre, and the Department of Mathematics of Operations Research. His work integrates operations research, stochastic modeling, and queueing theory to optimize complex systems in healthcare, transportation, and logistics. Research Interests: Prof. Boucherie's expertise spans: Healthcare Logistics : Bed census forecasting, patient flow optimization, and clinical scheduling Stochastic Systems : Queueing networks, Markov decision processes, and distributionally robust optimization Applied AI : Reinforcement learning, data mining, and predictive analytics for service systems Publication Trends: His recent articles (2023-2025) demonstrate a strong focus on pandemic-responsive healthcare optimization, including dynamic bed allocation, laboratory workflow design, and fairness in resource distribution during crises. Methodologically, they advance stochastic programming, queueing theory, and Bayesian inference for clinical applications. Awards & Honors: Canadian OR Society Practice Prize (2012) ORTEC Excellence in Advanced Planning Award (2012) Academic Leadership: He has supervised 37 research projects and contributes to institutional initiatives through the TechMed Centre (health innovation) and Digital Society Institute (data-driven solutions).