جست‌وجوی استاد و پژوهشگر

Fabrizio Marozzoمشاهده پروفایل

دانشیار

• Big Data Analysis
• Social Media Analysis
• High Performance Computing
• +۶ مورد دیگر

Fabrizio Marozzo is an Associate Professor in Computer Engineering at the University of Calabria, Italy, since 2019. He is also co-founder of DtoK Lab S.r.l., an academic spin-off established in 2014. His research focuses on big data analysis, social media analysis, high-performance computing, cloud and edge computing, and machine learning. He is an IEEE Senior member and serves as associate editor for multiple journals including IEEE Transactions on Big Data and SN Computer Science. In 2022, he obtained the Italian National Academic Qualification as Full Professor in Computer Engineering. Marozzo has authored over 100 scientific papers and co-authored several books, including Data Analysis in the Cloud (Elsevier, 2015) and Programming Big Data Applications (World Scientific, 2024). His research interests span edge-cloud continuum systems, distributed data analysis frameworks, and AI-driven solutions for social media and healthcare applications. He actively contributes to interdisciplinary projects, such as surgical documentation enhancement using multimodal AI and disaster monitoring through LLMs. Marozzo's work emphasizes scalable tools and frameworks to address challenges in distributed computing environments. Education: PhD in Computer Engineering (not explicitly detailed) Affiliations: University of Calabria, DtoK Lab S.r.l. His articles reflect a strong focus on edge-cloud integration, efficient machine learning techniques, and practical AI applications. Key themes include optimizing distributed systems, reducing latency, and leveraging LLMs for real-world problems in healthcare, smart cities, and agriculture. Scientific Awards: 2024 Top Scholar in Big Data (ScholarGPS) Abilitazione Scientifica Nazionale as Full Professor (2022) Marozzo supervises research projects and advises students, contributing to academic-industry collaborations through DtoK Lab. His work bridges theoretical advancements with practical implementations in distributed systems and AI.

David Koonceمشاهده پروفایل

استاد

• Data Integration
• Data Mining
• Data Visualization
• +۴ مورد دیگر

David Koonce is Dean of the Graduate College and Professor of Industrial and Systems Engineering at Ohio University's Russ College of Engineering and Technology. He holds a Ph.D. (1993), M.S. (1990), and B.S. (1988) in Industrial Engineering from Louisiana State University. His research focuses on data integration, mining/visualization, cost estimation, and manufacturing systems. Recent work emphasizes ontology-driven smart manufacturing, STEM education equity, and RFID technology adoption. He has authored over 50 peer-reviewed articles and conference papers, with notable contributions to Industry 4.0 integration frameworks and gender equity studies in community college STEM faculty roles. As academic leader, he oversees Ohio University's graduate programs and maintains active research collaborations in manufacturing informatics and educational policy. Education: Ph.D. in Engineering Science, Louisiana State University (1993) M.S. in Industrial Engineering, Louisiana State University (1990) B.S. in Industrial Engineering, Louisiana State University (1988) Research Themes: Manufacturing ontology systems STEM workforce diversity Cost estimation methodologies RFID implementation challenges Student retention analytics Recent Scholarship Trends: Transition from traditional scheduling optimization (2000s) to ontology-based smart manufacturing (2010s-present), with parallel focus on institutional gender equity in STEM education since 2010. His work bridges technical systems engineering with educational policy, exemplified by seminal studies on female STEM faculty experiences at two-year institutions. Current research explores Industry 4.0 interoperability through ontology frameworks and transfer student success in engineering pathways.

Ashraf Aboulnagaمشاهده پروفایل

استاد

• Database Systems
• Cloud Computing
• Big Data
• +۳ مورد دیگر

Ashraf Aboulnaga is a Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA). He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison and has held prior roles as Chief Scientist at the Qatar Computing Research Institute, Associate Professor at the University of Waterloo, and Research Staff Member at IBM Almaden Research Center. His research focuses on database systems, cloud computing, and graph analytics, with recent emphasis on cloud-native database architectures and scalable graph processing. Education: Ph.D. (2002), M.S. (1999) from University of Wisconsin-Madison; B.S./M.S. (1993/1996) from Alexandria University. Affiliations: ACM Distinguished Scientist (2015), IEEE Senior Member (2009). Research interests include cloud database systems, distributed transaction processing, and knowledge graph integration. He has authored over 100 publications in top venues like SIGMOD, VLDB, and ICDE, and received Best Paper Awards at SoCC 2015 and VLDB 2011. Grants: Current funding includes NSF travel support for ICDE 2024 and a $1.4M NSF MRI grant for DiscourseLab (2025). Past grants include DOE support for soil microbiome data integration. Service: Associate Editor for VLDB Journal, PVLDB, and Distributed and Parallel Databases. Served as Program Committee member for SIGMOD, ICDE, and EDBT. Industry collaborations include advising on cloud DBMS architectures and distributed systems.

• Intelligent Transportation Systems (ITS)
• Traffic Signal Systems
• Smart Traffic Sensors
• +۳ مورد دیگر

Taylor Li is an Associate Professor in the Department of Civil Engineering at The University of Texas at Arlington (UTA). He specializes in Intelligent Transportation Systems (ITS), Traffic Signal Systems, and Smart Traffic Sensors. His work focuses on improving traffic management through advanced technologies like LiDAR, connected vehicles, and data-driven optimization. Education PhD in Civil Engineering, Virginia Polytechnic Institute and State University (2009) MS in Systems Engineering, Beijing Jiaotong University (2002) BS in Mechanical Engineering and Control, Beijing Jiaotong University (1999) Research Interests Dr. Li's research emphasizes traffic signal optimization, sensor-based safety systems, and the integration of connected and automated vehicles (CAVs) into infrastructure. His work addresses challenges in urban traffic efficiency, pedestrian safety, and resilient transportation networks. Recent projects include developing dynamic signal timing algorithms using real-time data and LiDAR-based tracking systems to reduce red-light running incidents. Grants & Awards Recipient of multiple grants from USDOT, TxDOT, and NSF totaling over $3 million Finalist for TRB Network Modeling Committee Best Paper Award (2016) Best Paper Awards from ITS America and the International Road Federation (2009) Professional Service Associate Editor, ASCE Journal of Urban Planning and Development Chair, TRB Traffic Signal Systems Committee (ACP25) Member, Transportation Research Board Simulation Committees (ACP80) Labs & Teams Leads the Smart Transportation Systems Lab at UTA, collaborating with industry partners like PTV AG and TxDOT on real-world deployment of innovative traffic management solutions.

David Pisingerمشاهده پروفایل

استاد

• Operations Research
• Management Science
• Network Design
• +۳ مورد دیگر

David Pisinger is a Professor at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). His research focuses on Operations Research, Management Science, and Optimization, with significant contributions to maritime transportation, offshore wind farm design, and stochastic optimization. He leads the DECIDE project (ERC-2022-ADG), exploring machine learning for decision-making under uncertainty. His research interests include network design, vehicle routing, energy systems modeling, and combinatorial optimization. Notable recent work addresses challenges in multi-parallel-aisle warehouses, tramp ship routing, and immunization strategies for epidemic outbreaks. Research Trends in Articles: Recent publications emphasize maritime logistics, renewable energy optimization (offshore wind farms), and stochastic programming applications. His work bridges theoretical optimization and real-world problems in transportation, energy, and telecommunication networks. Awards: ERC-2022-ADG Grant (2023) Finalist - EURO Excellence in Practice Award (2021, 2018) Best Student Paper Award (ICORES 2017) Advising & Grants: Supervises PhD students in stochastic optimization, energy systems, and maritime logistics. PI of projects like DECIDE (2024–2029), SUSTAINABULK (2022–2025), and others. Teams/Labs: DECIDE Project Team (machine learning for decision-making under uncertainty). Collaborations on offshore wind farm optimization, energy system modeling, and maritime logistics.

Tom Hayesمشاهده پروفایل

دانشیار

• Theoretical computer science
• Machine learning
• Markov chains
• +۷ مورد دیگر

Tom Hayes is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, School of Engineering and Applied Sciences. His research focuses on theoretical computer science and machine learning, with particular emphasis on convergence rates for Markov chains, sampling algorithms, physics of algorithms, and distributed algorithms for radio-enabled sensor networks. Dr. Hayes holds a PhD in Computer Science from the University of Chicago. His academic journey has led him to specialize in probabilistic methods and theoretical foundations of computing. His research interests span theoretical computer science with applications to machine learning. He investigates fundamental questions about how algorithms behave, particularly focusing on Markov chains, random combinatorial structures, and distributed systems. His work bridges theoretical computer science with practical applications in networked systems and optimization problems. His research has significant implications for understanding algorithmic behavior in complex systems. Analysis of his recent publications reveals a consistent focus on theoretical aspects of distributed computing, graph algorithms, and probabilistic methods. His work shows increasing attention to energy efficiency in wireless networks and optimal mixing properties of Markov chains. The research demonstrates strong connections between theoretical computer science and practical applications in network design and analysis. Dr. Hayes actively contributes to the academic community through publications at major theoretical computer science conferences. His work appears regularly in proceedings of conferences like APPROX/RANDOM, reflecting his standing in the theoretical computer science community. He serves as an educator in the Computer Science department, teaching foundational courses including CSE 191 (Intro Discrete Structures) and CSE 331 (Algorithms and Complexity). His teaching emphasizes theoretical foundations while connecting concepts to practical applications in computer science.

• Cloud Computing
• Serverless Computing
• Distributed Systems
• +۵ مورد دیگر

Ass.-Prof. Dr. Sashko Ristov is an Assistant Professor at the Department of Computer Science, University of Innsbruck. His research focuses on serverless computing, distributed systems, and workflow orchestration, with applications in cloud computing, healthcare informatics, and high-performance computing. He leads research on federated serverless infrastructures, resilient function choreographies, and digital twins in construction engineering. His work addresses challenges in cross-cloud resource management, workflow scheduling, and anomaly detection in microservices. Recent contributions include frameworks like StoreLess for federated storage orchestration and CODE for cross-platform serverless deployment. Research trends in his publications emphasize bi-objective optimization for batch workflows, GPU-accelerated document processing, and disaster-resilient cloud systems. He actively participates in workflows community summits to advance scientific workflow standards and interoperability. Dr. Ristov has collaborated on projects like the Montage workflow analysis and ECG monitoring systems using serverless architectures. His work bridges theoretical computing models with practical implementations in healthcare and civil engineering domains.

Raju Rangaswamiمشاهده پروفایل

استاد

• Operating Systems
• Storage Systems
• Virtualization
• +۷ مورد دیگر

Raju Rangaswami is an Eminent Scholar Chaired Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU), where he directs the Systems Research Laboratory. His academic roles include serving as Associate Professor (2009-2019), Assistant Professor (2004-2009), and a visiting role at IIT Bombay (2015). He holds a Ph.D. in Computer Science from the University of California, Santa Barbara, an M.S. from the same institution, and a B.S. in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur. His research focuses on operating systems , storage systems , virtualization , real-time systems , and computer security , with applications in cloud computing, distributed systems, and mobile computing. Key contributions include work on caching strategies, persistent memory, and energy-efficient storage architectures. Rangaswami has received prestigious awards such as the NSF CAREER Award (2008-2013), DOE ECPI Award (2006-2010), and multiple NetApp Faculty Fellowships. He actively contributes to the field through roles like Program Chair for the USENIX File and Storage Technologies Conference, editorial work for the Springer Journal of Distributed and Parallel Databases , and NSF panel service. His research spans over 50 publications in top venues like FAST, USENIX ATC, and IEEE conferences, focusing on topics like cache replacement policies, storage tiering, and secure data management. Current projects explore AI-driven caching systems and energy-efficient hybrid memory architectures.

Ramgopal Mettuمشاهده پروفایل

دانشیار

• Algorithms
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Ramgopal Mettu is Associate Professor and Graduate Studies Director in Computer Science at Tulane University. His research spans algorithms, computational biology, and robotics, with applications in protein structure prediction and additive manufacturing. Mettu earned a PhD from University of Texas at Austin (2002) and has held positions at Dartmouth and UMass Amherst. His group develops efficient algorithms for structural biology and 3D printing optimization, with publications in top conferences including ICRA and IEEE BIBM. NSF CAREER Award (2007) IEEE CSB Best Paper Award (2003) ICRA Best Paper Award (2016)

Meysam Rabieeمشاهده پروفایل

استادیار

• Healthcare Analytics
• Sustainable Supply Chain
• Multi-Criteria Decision Making
• +۲ مورد دیگر

Meysam Rabiee is an Assistant Professor of Business Analytics at the University of Colorado Denver Business School. He earned his PhD in Operations & Business Analytics from the University of Oregon. His office is located in BUS 4004. Professor Rabiee's research develops decision support systems applying analytics to social good challenges. Primary domains include healthcare operations optimization, sustainable supply chain management, and multi-criteria group decision frameworks. Methodological expertise spans mathematical modeling, evolutionary algorithms, and machine learning applications. Recent publications demonstrate expanding focus on explainable AI approaches and circular economy frameworks. Healthcare applications progress from ambulance logistics to occupational risk management systems, while supply chain work increasingly integrates sustainability and resilience considerations. Robin & Roger Best Teaching Award (2021, 2022) Robin & Roger Best Research Award (2022) Laboratory affiliations weren't specified, but research involves collaborative frameworks for group decision-making in supply chain and healthcare contexts.

• Business Analytics
• Optimization
• Mathematical Programming
• +۳ مورد دیگر

Alkiviadis Vazacopoulos is a Teaching Professor at the School of Business, Stevens Institute of Technology (since August 2016). He holds a PhD in Industrial Administration from Carnegie Mellon University (1994), an MS in Operations Research from Carnegie Mellon (1990), an MS in Management Information Systems from Florida International University (1988), and a BA in Business Administration from the University of Piraeus (1986). PhD (1994): Carnegie Mellon University (Industrial Administration) MS (1990): Carnegie Mellon University (Operations Research) MS (1988): Florida International University (Management Information Systems) BA (1986): University of Piraeus (Business Administration) His research focuses on Business Analytics, Optimization, and Machine Learning applications. He pioneered Parallel Mixed Integer Programming techniques for Large-Scale Optimization in Banking and Retail, and co-authored four major papers with Dr. Egon Balas. His dissertation on 'Guided Local Search for Jobshop Scheduling' has over 500 citations. He actively consults Fortune 500 firms in finance, supply chain, retail optimization, and decision analytics. Awards & Recognition: Research Award from Silberman College (1999-2000) Visiting Scientist at FAW Institute (1997) William Larimer Mellon Fellowship (1988-1991) Advising & Entrepreneurship: Founder & CEO of Industrial Algorithms (2012–present) Consulted global companies like Bank of America, Procter & Gamble, and Department of Energy He leads academic initiatives at Stevens while maintaining industry ties through his ventures and advisory roles.

Jian Shiمشاهده پروفایل

دانشیار

• Power Systems
• Renewable Energy
• Artificial Intelligence
• +۵ مورد دیگر

Jian Shi is an Associate Professor jointly appointed in the Department of Engineering Technology and the Department of Electrical and Computer Engineering at the University of Houston, within the Cullen College of Engineering. He holds a Ph.D. in Electrical and Computer Engineering from Mississippi State University (2014). His research focuses on sustainable energy systems, smart grids, maritime decarbonization, and AI-driven power system optimization. Shi leads the SOAR Lab, advancing innovations in energy storage, grid resilience, and transportation electrification. Education: Ph.D. in Electrical and Computer Engineering, Mississippi State University, 2014 Research Interests: Power Systems & Energy Storage Renewable Integration & Decarbonization Artificial Intelligence in Grid Management Maritime & Port Sustainability Resilient Infrastructure Design His work bridges theoretical advancements with practical applications, including hybrid energy storage systems, fault-tolerant architectures, and game-theoretic models for green shipping. Recent projects address urban sustainability through multimodal accessibility analysis and CO₂ pipeline optimization. Lab & Leadership: Director of the SOAR Lab, fostering interdisciplinary research in energy systems and smart technologies.

Jean-Pierre Corriveauمشاهده پروفایل

دانشیار

• Object-Oriented Testing
• Text Understanding
• Behavior-Driven Development
• +۳ مورد دیگر

Jean-Pierre Corriveau is an Associate Professor and Graduate Director at the School of Computer Science, Carleton University. He holds a Ph.D. in Natural Language Processing from the University of Toronto (1991), an M.C.S. from the University of Ottawa (1984), and a B.Sc. from the University of Ottawa. His research focuses on Object-Oriented Testing (scenario testing, test data generation) and Text Understanding (contextualized and generative comprehension), with recent emphasis on behavior-driven development and acceptance testing of systems with complex scenarios. Dr. Corriveau has extensive industry experience, including roles at Nortel where he contributed to the TELOS project, leading to the ObjecTime startup and subsequent IBM Rational Software Architect tools. His work bridges academia and industry, addressing challenges in software engineering, cybersecurity, and natural language processing. Notable contributions include frameworks for MapReduce optimization, secure localization in wireless sensor networks, and privacy-preserving data anonymization techniques. His publications span topics like AI detection in text, federated learning for NER, intrusion detection systems, and resilient network algorithms. He has advised on graduate studies as Director and contributed to teaching strategies for millennial students through innovative virtual learning environments in platforms like Second Life. His research trends reflect a blend of theoretical advancements in computational linguistics and practical software engineering solutions, with a recurring focus on system reliability, distributed computing, and privacy-preserving technologies. Grants and collaborations are implied through his tool development and industry partnerships, though specific grant details aren’t explicitly listed. Labs/teams associated with his work include the ObjecTime initiative and Carleton’s distributed systems research groups, though no dedicated lab names are mentioned. Future directions emphasize scalable testing methodologies and ethical AI integration in text analysis.

Ran Jiمشاهده پروفایل

استادیار

• Stochastic Optimization
• Distributionally Robust Optimization
• Uncertainty Quantification
• +۶ مورد دیگر

Ran Ji is an Assistant Professor in the Department of Systems Engineering and Operations Research at George Mason University, affiliated with the College of Engineering and Computing. His research focuses on stochastic optimization, distributionally robust optimization, uncertainty quantification, and data-driven decision-making across domains like financial risk modeling, supply chain analytics, and disaster management. He teaches in the Data Analytics Engineering Program, emphasizing hands-on skills with state-of-the-art tools. Education includes a PhD in Decision Sciences from George Washington University (GWU), an MS in Systems Engineering from GWU, and a BE in Hydraulic and Hydropower Engineering from China Agricultural University. Research interests span optimization under uncertainty, disaster relief logistics, machine learning applications in engineering systems, and equity-focused resource allocation. His work integrates advanced mathematical programming with real-world challenges in finance, supply chains, and environmental systems. Recent studies include optimizing hurricane shelter locations using predictive frameworks, enhancing equity in disaster relief through multi-objective optimization, and developing distributionally robust models for humanitarian networks. He also explores machine learning-driven approaches for disaster preparedness assessments and smart municipal waste management systems. No scientific awards are explicitly listed in the provided materials. Advising and grants details are not specified here, but his research spans collaborative efforts in interdisciplinary applications of optimization and analytics. His work contributes to labs and teams focused on systems engineering resilience, data-driven decision-making, and applied operations research solutions for societal challenges.

Xuemin Tuمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +۳ مورد دیگر

Xuemin Tu is a Professor in the Department of Mathematics at the University of Kansas, serving as the Scheduling Officer. His research focuses on scientific computing, numerical analysis, and applied mathematics, emphasizing domain decomposition methods, BDDC algorithms, and data assimilation. He is affiliated with the College of Liberal Arts & Sciences and actively contributes to interdisciplinary computational science. Research interests include iterative methods for partial differential equations, numerical solutions of fluid dynamics problems, and the development of efficient algorithms for large-scale simulations. His work bridges theoretical analysis and practical applications in engineering and geophysics. Key trends in his recent articles (2018–2025) highlight advancements in BDDC algorithms for fluid and porous media systems, implicit sampling techniques for data assimilation, and machine learning applications in image recognition. He also explores stochastic models for parameter estimation and uncertainty quantification. No scientific awards or grants are explicitly listed in the provided text. Advising roles or student mentorship details are not included, though his research areas suggest contributions to graduate training in computational mathematics. Labs or collaborative teams are not detailed here, but his affiliations imply involvement with computational science initiatives at the University of Kansas.