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Alireza PakgoharView profile

Senior Lecturer

• Supply Chain Management
• Project Management
• Logistics
• +2 more

Dr. Alireza Pakgohar is a Senior Lecturer in Business Operations and Systems at Sheffield Business School, Sheffield Hallam University, affiliated with the Department of Finance, Accounting and Business Systems. He has been a faculty member since 1996 with over 22 years of teaching experience in Industrial Engineering and Management. His industry background includes roles as Logistics Manager at Legrond and Azin Khodro Co., and Project Control Manager at Roshd Sanat Company. Education: PhD in Project Management & Supply Chain Management (University of Exeter, 2014) Advanced Certificate in Higher Education (University of Exeter, 2013) Industrial Engineering degrees from Azad University (1995, 1992) Research Focus: Dr. Pakgohar specializes in Supply Chain Management, Project Management, and Business Operations, with emphasis on quantitative modeling, optimization techniques, and real-world applications in manufacturing and service sectors. His work integrates industrial experience with academic research in operations optimization. Publication Trends: His scholarly output focuses on supply chain optimization, project portfolio management, and operational decision-making, employing methods like game theory, agent-based modeling, and stochastic optimization. Recent works explore applications in Islamic finance, healthcare systems, and lean service operations. Professional Affiliations: Chartered Institute of Logistics and Transport (CILT) Higher Education Academy (HEA) Postgraduate Supervision: Currently supervises PhD students including K. Alnuaimi (Digital Health Adoption) and D. Hawcroft (Big Data Marketing Strategies).

Scott BroderickView profile

Associate Professor

• Materials informatics
• Electronic structure calculations
• Atom probe tomography
• +7 more

Dr. Scott Broderick is an Associate Professor in the Department of Materials Design and Innovation at the University at Buffalo (State University of New York). His research focuses on materials informatics, electronic structure calculations, atom probe tomography, and combinatorial materials science. He holds a PhD in Materials Science and Engineering from Iowa State University (2009), with a thesis on statistical learning for alloy design, and a BS in Materials Science and Engineering from North Carolina State University (2005). His work bridges computational and experimental approaches to accelerate materials discovery, particularly in high-entropy alloys, nanomedicines, and energy storage systems. Dr. Broderick’s research interests emphasize data-driven methods for materials design, including machine learning applications for predicting material properties, optimizing battery architectures, and developing antimicrobial nanomedicines. His interdisciplinary work spans quantum materials, ferroelectric energy systems, and vaccines against animal pathogens like BRSV and white spot syndrome virus in shrimp. His publications highlight innovations in atom probe tomography analysis, materials informatics frameworks, and computational modeling of material behavior under extreme conditions. Notable contributions include frameworks for high-temperature alloy design, data-driven solar cell optimization, and nanovaccine development. His work aligns with the broader goal of integrating materials science and information science to address global challenges in energy, health, and sustainability. Dr. Broderick is affiliated with the School of Engineering and Applied Sciences and collaborates with institutions like the University of Tokyo (visiting student experience in 2006). His lab focuses on advancing materials informatics tools and their application to real-world industrial and biomedical problems.

Vladimir GrupcevView profile

Associate Professor

• Computer Science
• Mathematics
• Data Analysis
• +1 more

Vladimir Grupcev is Associate Professor of Instruction in Mathematics & Statistics at USF's College of Arts and Sciences. Holding a PhD in Computer Science from USF, his research bridges mathematics, algorithms, and big data analysis. He develops computational methods for molecular simulation and spatial data processing. He teaches courses across multiple institutions including Intermediate Algebra, Business Calculus, and Analysis of Algorithms. His technical expertise spans programming languages (C/C++), mathematical software (Maple/Mathematica), and educational platforms (MyMathLab, Blackboard).

Pedro DelicadoView profile

Professor

• Statistics
• Data Science
• Big Data
• +7 more

Pedro Delicado is a Full Professor at the Department of Statistics and Operations Research, Universitat Politècnica de Catalunya (UPC), based in Barcelona. He holds a degree in Mathematics and a doctorate in Economics. His research focuses on advanced statistical methods, data science, functional data analysis, and machine learning applications. He leads the ADBD research group (Analysis of Complex Data for Business Decisions) and collaborates with the Institut de Matemàtiques de la UPC. His current project 'Advanced Statistics and Data Science 2: New data, new models, new challenges' (PID2023-148158OB-I00) addresses modern data challenges, including Big Data, wearable devices, EEG analysis, graph data, and nonlinear dimensionality reduction. He supervises PhD students and has expertise in predictive modeling, explainable AI, and interdisciplinary applications in health, environmental science, and education. Delicado has published extensively in top journals like Machine Learning, Test, and Environmental and Ecological Statistics. He teaches advanced statistics and data science courses, leveraging virtual platforms. His work bridges statistical theory with practical applications, emphasizing interpretability and real-world impact.

Manos PapagelisView profile

Associate Professor

• Data Mining
• Graph Mining
• Machine Learning
• +3 more

Dr. Manos Papagelis is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He serves as the Graduate Program Director for the MSc and MScAI programs. His research focuses on data science and machine learning, particularly in data mining, graph mining, big data analytics, mobility analytics, and knowledge discovery. Education: PhD in Computer Science (University of Toronto), MSc and BSc in Computer Science (University of Crete, Greece). Prior to York, he held postdoctoral and research roles at UC Berkeley, Yahoo! Labs Barcelona, and FORTH, Greece. Research emphasizes trajectory analysis, emotion recognition, and scalable systems. Recent work includes TrajLearn (2025) and Disease Outbreak Detection (2025), highlighting contributions to mobility and health informatics. He has filed three U.S. patents and designed systems like Confious (conference management) and Green2.0 (socio-technical building analysis). Honors include the Lassonde Educator of the Year (2021) and IEEE MDM Best Paper Awards (2020, 2018). His advising spans interdisciplinary teams in AI and data science, with active involvement in grants related to mobility analytics and healthcare technologies. Labs/Teams: Director of the Data Mining Lab, collaborating on projects like trajectory prediction and emotion-aware systems.

Tianyi LiView profile

Assistant Professor

• Digital Twin
• Graph Neural Networks
• Energy Engineering
• +8 more

Tianyi Li is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on data engineering, spatiotemporal systems, and digital twins, with applications in energy, transportation, and vehicular networks. Key areas include trajectory data management, graph neural networks, compression techniques, and federated learning. He has contributed to over 37 publications, including works on digital twin frameworks for autonomous driving and smart grids, trajectory prediction systems, and efficient time series compression. His research integrates machine learning, distributed systems, and cybersecurity to address challenges in data-intensive environments. Key Projects: Digital twin-enabled voltage control, federated vehicular networks, trajectory simplification frameworks. Labs: Involved with the Daisy Center, focusing on data-centric systems and scalable solutions. Technical Expertise: Blockchain systems, energy systems optimization, and real-time data processing.

Carola WenkView profile

Professor

• Algorithms
• Computational Geometry
• Shape Matching
• +5 more

Carola Wenk is a Professor of Computer Science and Chair of the Department of Computer Science at Tulane University, within the School of Science and Engineering. She also holds an adjunct appointment in the Department of Mathematics. Her research focuses on algorithms, computational geometry, shape matching, and topological data analysis , with applications in geospatial analysis, biomedical informatics, and transportation systems. Wenk earned her Ph.D. in Computer Science from Freie Universität Berlin (2002) and a Diplom in Mathematics (1998), both with distinction. She has held academic positions at the University of Texas at San Antonio (UTSA) and the University of Arizona. Her work includes pioneering contributions to Fréchet distance algorithms , map construction from GPS trajectories , and computational methods for prostate cancer diagnosis . Her research has been funded by NSF, NIH, DARPA, IARPA, and ARPA-H, totaling over $40M in grants. Key Projects : MAGIC-SCAN: Machine Learning Assisted Gigantic Image Cancer Margin Scanner (ARPA-H, $22.9M) METROPOL: Modeling and Encoding TRajectories Of Patterns Of Life (IARPA, $953K) PathCAM: Real-time pathology slide imaging (NIH, $1.69M) Wenk has received prestigious awards including the NSF CAREER Award (2007), Mortar Board Excellence in Teaching (2014), and multiple Best Paper Awards at SIGSPATIAL, IEEE Vis, and computational geometry conferences. She leads interdisciplinary initiatives, such as the Computational Systems Biology Core at Tulane. Her lab focuses on topological data analysis , geospatial algorithms , and medical imaging , collaborating with biomedical engineers and clinicians to translate computational methods into clinical applications.

Saptarshi DebroyView profile

Associate Professor

• Computer Networking
• Network Security
• Big Data Networking
• +9 more

Saptarshi Debroy is an Associate Professor of Computer Science at Hunter College and a member of the Doctoral Faculties at The Graduate Center, CUNY. He leads the Distributed Systems and Security (DiSSect) Lab and co-leads the CoSSMO Lab, focusing on resilient distributed systems, edge computing, and cyber security. His research addresses challenges in disaster response systems, federated cloud workflows, and moving target defense mechanisms. He holds a PhD in Computer Engineering from the University of Central Florida and has held roles including Post-Doctoral Fellow at the University of Missouri. Education: PhD (Computer Engineering, UCF, 2014), MTech (Distributed & Mobile Computing, Jadavpur University, 2008), BTech (Information Technology, West Bengal University of Technology, 2006) Research Interests His work spans distributed systems, edge/AI-driven networks, cyber security, and resource optimization in disaster scenarios. Key areas include fog computing frameworks for real-time video processing (EFFECT), secure federated workflows (ADON), and MTD-based cloud defense strategies. He emphasizes practical applications like disaster response and healthcare data security. Grants & Awards Recipient of NSF CAREER Award (2020), NSF grants totaling over $1.1M, and Google Research Award. Notable projects include a trusted framework for health incident response (NSF), volunteer edge-cloud resource allocation (NSF), and DDoS mitigation strategies (NSF). Teaching & Service Teaches graduate courses on distributed systems, cloud computing, and networks at CUNY. Serves on NSF review panels and as chair for IEEE/ACM conferences. Leads initiatives like the Cyber Security Summer School for Women to promote diversity in tech. Labs & Collaborations Directs the DiSSect Lab, which develops edge-native systems for disaster response and secure AI. Collaborates with FDNY, perfSONAR, and NSF-funded testbeds like PAWR. Partnerships include projects with bioinformatics and high-performance computing communities (CyVerse, XSEDE).

Annunziata D'AversaView profile

Research Fellow

• Time Series Forecasting
• Nowcasting
• Big Data Analytics
• +1 more

Annunziata D'Aversa is a Research Fellow and PhD student in the Department of Computer Science at the University of Bari Aldo Moro, specializing in energy sector forecasting models. Her work focuses on developing incremental update methodologies for time-sensitive data streams in electricity networks and renewable energy systems. She earned her Master's degree in Data Science from the University of Bari Aldo Moro in July 2021 with full marks and honors, completing a thesis on spatial autocorrelation modeling for energy consumer forecasting in electricity networks. Her research interests include Time Series Forecasting, Nowcasting, Big Data Analytics, and Neural Networks, with emphasis on spatial-temporal pattern recognition for energy applications. She develops adaptive models that process real-time data streams for grid management and wind power optimization. Her 2022-2024 publications demonstrate consistent innovation in spatio-temporal modeling, applying model trees and linear architectures to multi-step forecasting challenges across smart grids and wind power plants. Key contributions include spatially-aware nowcasting techniques and autocorrelation leverage for consumption prediction.

Alex QuistbergView profile

Academic

• Road Traffic Safety
• Built Environment
• Global Health
• +6 more

Alex Quistberg is an Associate Research Professor at Drexel University’s Urban Health Collaborative and Department of Environmental & Occupational Health. He holds a courtesy appointment as Adjunct Professor at Universidad de los Andes’ School of Medicine in Bogotá, Colombia. His work focuses on road traffic safety, urban health, and the built environment, leveraging advanced methods like GIS, computer vision, and deep learning. Education: PhD in Epidemiology (University of Washington), MPH in Epidemiology (University of Washington), BA in Health & Societies (University of Pennsylvania). Research Interests: Road traffic injury prevention, built environment analysis, global health, spatial epidemiology, AI applications in public health, and urban policy. Current projects include the Fogarty-funded BEPIDL study and the Lacuna Fund-supported ESCALA study, both collaborating with Latin American institutions. He co-leads the SALURBAL project, examining urban health across Latin America. Key Contributions: Over 30 peer-reviewed articles on topics like pedestrian safety, policy impact evaluations, and climate change adaptation in urban settings. His work emphasizes data-driven solutions to improve active mobility and reduce health disparities. Funding: Grants from NIH Fogarty, Wellcome Trust, and the Lacuna Fund. Collaborations include Universidad de los Andes, Universidad del Norte, and IHME’s Global Burden of Disease Study. Professional Roles: Statistical Editor for Injury Prevention since 2016, member of Drexel’s Standing Committee on Artificial Intelligence (SCAI).

Periklis AndritsosView profile

Associate Professor

• Big Data analytics
• Cluster analysis
• Data mining
• +3 more

Periklis Andritsos is an Associate Professor at the Faculty of Information (iSchool) of the University of Toronto. He holds a PhD in Computer Science from the University of Toronto and a BSc in Electrical and Computer Engineering from the National Technical University of Athens. Prior to his return to Toronto, he held academic positions at the University of Lausanne (Switzerland), University of Trento (Italy), Free University of Bozen-Bolzano (Italy), and was a Visiting Professor at the Technical University of Berlin (Germany). His research focuses on large-scale data analysis, structure discovery, and applying information-theoretic and probabilistic techniques to identify redundancies and errors in evolving datasets. He developed the LIMBO algorithm for categorical data clustering, widely used in academia and industry. Professor Andritsos has received several awards, including the Techcrunch 50 Award (2009) as co-founder of Thoora.com and Best Paper Awards at ICPM (2021), ADBIS (2019), and DSS (2017). He teaches courses such as INF1343H (Data Modeling and Database Design) and INF2190H (Introduction to Data Analytics). His current advisee is Mirai Gendi. He is affiliated with the Data Sciences Institute at the University of Toronto and has led grants from MITACS, SSHRC, and the Data Sciences Institute. His work spans customer journey mapping, process mining, and distributed data systems. Andritsos has also contributed to industry collaborations, including co-founding Odaia Intelligence (2018–present). His media engagements include interviews on data privacy, AI ethics, and technology trends in outlets like CBC, The Toronto Star, and Voice Magazine. His research bridges theoretical foundations with practical applications in data management and analytics.

Jillian AurisanoView profile

Assistant Professor

• Data Visualization
• Visual Analytics
• Human-Computer Interaction
• +5 more

Jillian Aurisano, PhD, is an Assistant Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. Her research focuses on data visualization, human-computer interaction, and collaborative technologies, with a particular emphasis on large display environments and immersive analytics. She leads projects involving multi-modal interaction design, virtual reality, and conversational interfaces for data exploration. Key research interests include: Large-scale visualization systems (e.g., SAGE2 platform) Design of intuitive interaction techniques for big displays Integration of speech/gesture with visual analytics Collaborative work environments for data-driven tasks Her work has explored applications in biology (e.g., genome visualization, animal movement tracking) and ecological systems. Recent research trends emphasize improving user experiences through intelligent conversational assistants and addressing challenges in evaluating large-display systems. Notable projects include BactoGeNIE (comparative genome visualization) and ARTICULATE2 (conversational interface for data exploration). She collaborates with interdisciplinary teams across computer science, biology, and environmental science domains.

Amanda L. WebbView profile

Assistant Professor

• Building Energy Efficiency
• Sustainable Buildings
• Building Decarbonization
• +5 more

Dr. Amanda L. Webb is an Assistant Professor at the University of Cincinnati's Department of Civil and Architectural Engineering and Construction Management. She holds a Ph.D. from Penn State, an M.Arch. from MIT, and a B.A. from Yale University. Her research focuses on improving the energy efficiency of existing and historic buildings through data-driven methods, emphasizing equity and resilience. She has contributed to ASHRAE guidelines for historic building energy performance and leads federal and private grants totaling over $4M. Education: Ph.D. in Architectural Engineering, Pennsylvania State University (2017) SMArchS in Architecture Studies (Building Technology), MIT (2012) B.A. in Architecture & Philosophy, Yale University (2006) Research Interests: Sustainable building retrofits, energy equity, decarbonization strategies, historic preservation, and energy policy. Her work bridges technical innovation with social equity, particularly in low-income housing and urban resilience. Awards: ASHRAE New Investigator Award (2020), Federal grants from NSF and DOE for decarbonization initiatives, and leadership in ASHRAE committees. Grants & Advising: Principal Investigator (PI) on 7 grants including NSF CAREER Award ($414K) and DOE-funded decarbonization projects. Advises UC's ASHRAE Student Branch and collaborates with interdisciplinary teams on community-engaged projects. Labs/Initiatives: Leads the NSF-funded REALIZE-2050 REU Site for zero-emission research and develops energy modeling platforms for equitable retrofit planning.

Vassil VassilevView profile

Professor

• Artificial Intelligence
• Cyber Security
• Machine Learning
• +6 more

Prof. Vassil Vassilev is a Professor of AI and Intelligent Systems and Head of the Cyber Security Research Centre at London Metropolitan University. His work spans artificial intelligence, cyber security, and data integration with a focus on applications in urban systems, malware analysis, and cyber-physical systems. He leads initiatives in secure transaction risk assessment (CyDRA) and smart city technologies. Research interests include reinforcement learning for cyber incident response, AI policy validation frameworks, and adaptive security systems. He explores hybrid AI models, IoT-based environmental monitoring, and semantic frameworks for vulnerability detection. His work bridges theoretical AI advancements with practical implementations in cyber defense, urban data spaces, and financial security. Grants and funding support his projects on cyber security analytics, threat intelligence, and low-cost data platforms. He collaborates on initiatives like the first urban data space in Bulgaria and innovative authentication techniques using audio steganography. His leadership at the Cyber Security Research Centre drives applied research in both academic and enterprise contexts.

Nicholas NagleView profile

Professor

• Spatial Statistics
• Spatial Data Science
• Demography
• +4 more

Nicholas Nagle is a Professor and Program Director of the NSF Human Networks and Data Science Program at the University of Tennessee's Department of Geography. He specializes in spatial statistics, spatial data science, and demography. His research focuses on improving population survey methodologies, particularly with the U.S. Census Bureau and USDA, and integrates multi-source data like surveys and imagery. Currently on leave until August 2026, he advises on the redesign of the 2020 Decennial Census through the National Academy of Sciences. His work spans climate change demography, forest inventory estimation, and health disparities analysis. Education: Ph.D., University of California, Santa Barbara. Teaching focuses on quantitative methods and population geography. Research interests emphasize spatial data fusion, uncertainty visualization, and policy applications. Recent projects include UrbanPop (spatial microsimulation), RegRake (forest data mapping), and near-real-time pandemic monitoring. His interdisciplinary approach bridges demography, environmental science, and public health. Grants include NSF-funded work on Human Networks and Data Science. Collaborations involve federal agencies and international development projects in Mali and Florida.