Find Academic Supervisors

• Social Network Analysis
• Graph Mining
• Community Detection
• +3 more

Pasquale De Meo is an Associate Professor in the Department of Information Processing Systems at the University of Messina, where he also serves as Coordinator of the Master's Degree Course in Methods and Languages of Journalism. Previously, he was a Tenured Researcher at the same university from 2011 to 2015. He received his PhD in Computer Science from the University of Calabria in 2006 and his VO Degree in Electronic Engineering (magna cum laude) from the University of Reggio Calabria in 2002. His academic journey includes prestigious fellowships such as the Marie Curie Fellowship at VU University Amsterdam (2010-2011) and roles as External Member at Birbeck Institute for Data Analytics since 2017. Dr. De Meo specializes in Social Network Analysis, Graph Mining, Community Detection, and Network Centrality. His research spans multiple domains including social media analysis, criminal network analysis (as evidenced by Salvo Catanese's PhD thesis on "The Network Structure of Mafia Syndicates"), and data science applications. He has published extensively in top-tier venues with an H-Index of 35 on Google Scholar and 29 on Scopus. His recent publications demonstrate strong interdisciplinary connections between computer science, social sciences, and practical applications, covering advanced techniques for educational technology, fraud detection, knowledge graphs, and social influence modeling. Best Italian doctoral thesis in Artificial Intelligence (AI*IA, 2006) Marie Curie Fellowship (2009-2010) Multiple best paper awards at international conferences Dr. De Meo has supervised several graduate students and serves on program committees for major conferences including ACM SIGKDD and IJCAI. His research is supported by significant grants including an ARC Discovery Project on "Dynamics and Control of Complex Social Networks" and a Microsoft Azure Research Grant for community detection in directed graphs.

Heng LiView profile

Assistant Professor

• Software Engineering
• Software Analytics
• Software Observability
• +7 more

Heng Li is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montreal, Canada. He leads the Measurement, Observation, and Optimization of Software and its Evolution (MOOSE) lab, focusing on software engineering research with emphasis on observability, log analysis, and performance engineering. His work bridges academic research with practical industry applications, drawing from his prior experience as a software engineer at Synopsys and BlackBerry. Education: Ph.D. in Computing from Queen's University, Canada M.Sc. from Fudan University, China B.Eng. from Sun Yat-sen University, China Heng Li's research spans multiple critical areas in modern software engineering. His primary focus is on software monitoring and observability , where he develops techniques to make software systems more transparent and understandable during operation. He has made significant contributions to software log mining , creating novel approaches for parsing and analyzing log data to detect anomalies and performance issues. His work in intelligent operations of software systems applies machine learning to automate various aspects of software operations. Additionally, he researches software performance engineering and mining software repositories to understand development practices and improve software quality. An analysis of Dr. Li's recent publications reveals a strong focus on practical applications of software analytics. His work consistently addresses real-world challenges in software observability, particularly log analysis and performance monitoring. A notable trend is his exploration of machine learning applications in software operations (AIOps), with increasing emphasis on efficient algorithms for log processing and anomaly detection. His research shows a progression from foundational techniques in log parsing to more sophisticated approaches for performance regression detection and privacy preservation in software logs. Many of his papers include empirical studies from industry settings, demonstrating his commitment to bridging the gap between academic research and industrial practice. Professional Service: Program Committee member for multiple top software engineering conferences including ASE, ICSE, ESEC/FSE Active participation in workshops and special tracks related to software analytics and AIOps Dr. Li leads the MOOSE (Measurement, Observation, and Optimization of Software and its Evolution) laboratory at Polytechnique Montreal. The lab focuses on developing innovative techniques for monitoring software systems, analyzing their behavior through logs and performance metrics, and optimizing their operation. Current projects in the lab include advanced log parsing algorithms, performance regression detection systems, and privacy-preserving techniques for software logs. The lab maintains strong connections with industry partners to ensure research relevance to real-world challenges.

Judith CarlisleView profile

Adjunct Professor

• Yoga Studies
• Contemplative Practices
• Social Justice
• +4 more

Judith Carlisle, PhD, serves as Adjunct Faculty at Naropa University, teaching in the MA Yoga Studies and Master of Divinity programs where she instructs the course "Power, Privilege, and Diversity." Following a 20-year career as a professor of information systems and computer science, her current academic focus bridges contemplative practices with social justice initiatives and death/dying studies. Her educational background includes: AB in English Literature from Occidental College MS in Management Information Systems from University of Arizona PhD in Management Information Systems from University of Arizona MA in Yoga Studies from Loyola Marymount University Dr. Carlisle's research demonstrates a significant evolution from technical information systems scholarship to contemplative interdisciplinary work. Early career investigations centered on the data-information-knowledge-wisdom (DIKW) hierarchy, computer ethics frameworks, and natural language processing methodologies. Current research examines how yoga traditions and contemplative practices can advance social justice movements and transform societal approaches to mortality. This dual-phase trajectory reflects deep engagement with both technological epistemology and spiritual-existential dimensions of human experience. Publication trends reveal a clear transition from information systems foundations (1990-2010) to contemporary integrations of yoga, AI, and social theory (2011-2019). Early works established theoretical contributions to knowledge management and DIKW philosophy, while recent scholarship like her 2019 DANAM Conference paper explores spatial justice through Hatha Yoga and artificial intelligence applications. This body of work consistently connects technical rigor with humanistic concerns across both career phases. No scientific awards were documented in the source materials. Information regarding graduate student mentorship, research funding, or grant activity was not provided in available records. Similarly, no laboratory affiliations, research teams, or collaborative scientific groups were referenced in the institutional profile.

Robert NehmerView profile

Professor

• Accounting Information Systems
• Auditing
• Blockchain Technology
• +8 more

Robert Nehmer is a Professor of Accounting at the School of Business Administration, Oakland University. He holds a Ph.D. in Accounting from the University of Illinois at Urbana-Champaign, where he also completed his master's and bachelor's degrees in Accountancy. Dr. Nehmer teaches graduate and undergraduate courses in auditing and accounting information systems. His research explores formal systems, internal controls, software agents, and emerging technologies in accounting. Key focus areas include blockchain applications, drone technology in auditing, cloud-based systems, continuous auditing frameworks, XBRL implementations, and financial ontologies. He collaborates with industry partners including KPMG on drone-enabled auditing research. Publications demonstrate strong emphasis on technology-driven accounting innovations, particularly blockchain systems (2020), drone auditing frameworks (2017), AI in education (2024), and semantic modeling of financial data (2022). His work consistently bridges theoretical accounting concepts with applied technological solutions. Awards & Recognition: University Service Award, Oakland University/SBA (April 2016) 10 Year Service Award, Oakland University (February 2016) He maintains active membership in the American Accounting Association, Information Systems Audit and Control Association, XBRL-US, and the Object Management Group's Finance Domain Task Force.

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +6 more

Dr. Md Shadab Mashuk is a Lecturer in the School of Science, Engineering & Environment at the University of Salford. He brings several years of industry experience as a Data Scientist and Research Software Engineer to his academic role, which enriches his teaching and fosters industry collaboration and interdisciplinary research. His academic credentials include: Currently pursuing Postgraduate Certificate in Academic Practice (PGCAP) at University of Salford (2023-present) PhD in Computer Science from University of Nottingham (2014-2019) MSc in Advanced Computing Science from University of Nottingham (2013-2014) BSc in Computer Science and Engineering from NSU, Bangladesh (2006-2010) Dr. Mashuk's research spans multiple interdisciplinary domains with a strong focus on practical AI applications. His work bridges Machine Learning, Deep Learning, Natural Language Processing, and Agent-Based Modeling with real-world challenges in healthcare, building science, and user-centered systems. Recent projects demonstrate particular emphasis on health AI applications for Functional Neurological Disorder analysis and agent-based modeling for building occupancy and energy prediction. His publication trend shows consistent output across different domains, with a notable concentration in 2024 on computer vision for building inspection, NLP for healthcare communication analysis, and agent-based modeling for energy systems. This indicates an active research trajectory with increasing focus on applying AI techniques to solve domain-specific problems. Dr. Mashuk's research contributions span several key areas: Computer vision applications for building inspection and mold detection NLP techniques for analyzing clinical communications and terminology extraction Agent-based modeling for building occupancy patterns and energy consumption prediction Indoor positioning systems for occupancy monitoring Medical training technologies using augmented/virtual reality Character recognition systems for multilingual contexts As an educator, Dr. Mashuk teaches core computing courses including Programming, Data Structures & Algorithm Analysis, and Web Development. He supervises undergraduate final year projects and is open to PhD supervision for students whose research aligns with his expertise. His industry background provides valuable practical insights that enhance both his teaching effectiveness and student supervision approach.

Sam LiuView profile

Associate Professor

• Chronic disease prevention and management
• eHealth/mHealth
• Big Data and health surveillance
• +4 more

Dr. Sam Liu is an Associate Professor at the School of Exercise Science, Physical and Health Education at the University of Victoria, Canada. As Lab Director of the Digital Health Lab (located in the McKinnon Building), he focuses on leveraging digital technologies like mobile apps, social media, and big data to enhance chronic disease prevention, physical activity promotion, and health surveillance. His work integrates behavioral science with cutting-edge digital tools to create personalized health interventions. His research spans two primary domains: (1) developing digital communication technologies for chronic disease prevention, and (2) utilizing big data to predict health behaviors and outcomes. Key projects include the Activerse app for physical activity, MoodMover for depression management, and PainCaRe for pediatric cancer pain support, all reflecting his commitment to user-centered design and real-world applicability. Keywords: Digital Health, mHealth, eHealth, Physical Activity, Chronic Disease Prevention, Big Data Analytics Subfields: Just-in-Time Adaptive Interventions, Wearable Technologies, Gamification for Health, Virtual Reality Cognitive Training, Postpartum Health Interventions, Adolescent Behavioral Analysis The Digital Health Lab actively recruits participants across diverse studies, including: Chatbots for diabetes management (exploring AI applications) Virtual reality cognitive training for older adults IBD-Move app for inflammatory bowel disease patients While Sam Liu has not received publicly listed scientific awards in this text, his lab has secured grants like the SSHRC Partnerships Engage Grant for team members, highlighting collaborative funding approaches. Current projects emphasize accessibility, with studies requiring Apple Watches, diabetes patients, and family-based interventions to improve health outcomes through technology.

• Distributed Database Design
• Query Optimization
• Evolutionary Algorithms
• +8 more

Prof. Ahmet Cosar is a faculty member in the Department of Computer Engineering at the Middle East Technical University (METU), Ankara, Turkey. He holds a PhD in Computer Science from the University of Minnesota (1996), an MS in Computer Engineering from Bilkent University (1988), and a BS in Computer Engineering from METU (1986). His research focuses on distributed database design, query optimization, evolutionary algorithms, computer networks, and cloud computing. He leads the Intelligent Data Analysis Group (IDAG) and teaches courses such as CENG 240, CMPE 275, and CENG 280. Education: PhD, Computer Science, University of Minnesota, 1996 MS, Computer Engineering, Bilkent University, 1988 BS, Computer Engineering, METU, 1986 Research Interests: Query optimization in distributed databases Evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) Cloud computing and data warehouse design Machine learning applications in cybersecurity Wireless sensor networks and data fusion Publications: Over 50 peer-reviewed articles in journals like Computers & Industrial Engineering and Neurocomputing , focusing on optimization algorithms, cloud resource allocation, and machine learning techniques. Recent work includes island-parallel metaheuristics for graph coloring and reinforcement learning for adaptive interventions. Advising & Grants: Supervised over 30 graduate students (PhD and MS) in areas like evolutionary algorithms, cloud databases, and sensor networks. Active in research grants related to distributed systems and big data. Labs/Teams: Leads the Intelligent Data Analysis Group (IDAG), collaborating on projects in optimization, machine learning, and cloud computing.

• Transportation Engineering
• Disaster Management
• Machine Learning
• +7 more

Satish Ukkusuri is the Hubert and Audrey Kleasen Professor in Civil Engineering at Purdue University's College of Engineering, with a courtesy appointment in the Department of Computer Science. His research focuses on disaster resilience, transportation networks, machine learning, and socio-physical systems. He leads the Urban Mobility Networks & Intelligent Logistics (UMNIL) lab, developing innovative solutions for urban challenges through interdisciplinary approaches. Key affiliations include the Global Engineering Program and Civil and Construction Engineering department. His work integrates agent-based modeling, graph neural networks, and data-driven methodologies to address issues like post-disaster recovery, electric vehicle adoption, and smart mobility systems. He has pioneered tools such as the Mobilkit Python toolkit for urban resilience analytics using mobility data. Research interests span transportation engineering, urban planning, and complex systems modeling. His recent publications emphasize machine learning applications in transportation networks, hurricane evacuation dynamics, and resilient infrastructure design. He actively explores interdisciplinary topics like mobility-as-a-resource frameworks and multi-agent reinforcement learning for traffic management. No formal grants or awards are explicitly listed in the provided texts, though his leadership roles and prolific publication record indicate significant academic contributions. His advising and mentorship activities are not detailed here, but his lab’s work suggests involvement in training researchers across civil engineering and computer science disciplines. Labs/Teams: Leads the UMNIL lab (https://umnilab.github.io/), focused on advancing urban mobility and intelligent logistics through data science and systems engineering.

Vasileios LamposView profile

Associate Professor

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +3 more

Vasileios (Bill) Lampos is an Associate Professor in the Department of Computer Science at University College London (UCL), affiliated with the AI Centre. His research focuses on machine learning, natural language processing (NLP), and health informatics, with applications in disease surveillance, influenza modeling, and AI-driven solutions for public health challenges. Education includes a PhD and MSc in Computer Science from the University of Bristol (2008–2012). Lampos has held roles including Principal Research Fellow (2018–2021) and Senior Research Fellow (2015–2018) at UCL. He also served as a Research Scientist at Google (2014–2015). His research integrates social media and web search data to predict health trends, such as flu prevalence and vaccine efficacy, and to model transmission dynamics of diseases like COVID-19. Notable projects include the Virus Watch cohort study, supported by Google.org, and collaborations with Public Health England. Key publications highlight innovations in time series forecasting (e.g., DeformTime ), influenza modeling using web trends, and AI applications in autism education. His work has been featured in Nature journals and mainstream media. Lampos supervises PhD students and mentors researchers. He actively seeks collaborations for projects aligning with UCL’s AI and health priorities.

Bruce SherinView profile

Professor

• Conceptual change in science
• Computational linguistics
• Programming environments for learning
• +3 more

Role: Professor of Learning Sciences at Northwestern University's School of Education and Social Policy (SESP). Affiliations: Affiliated with research labs focused on educational technology and qualitative data analysis. Creator of Tactic Text, a web-based text mining tool for qualitative researchers. Education: PhD in Science and Mathematics Education (UC Berkeley, 1996); MA in Physics (UC Berkeley, 1987); BA in Physics (Princeton University, 1985). Research Interests: Conceptual change in science education Computational linguistics and automated analysis of qualitative data Design of programming environments for learning Role of symbolic representations in physics expertise Development of Tactic Text for qualitative data analysis Publications: Over 50 peer-reviewed articles focusing on learning sciences, computational tools for education, and methodological innovations in qualitative research. Recent work explores machine learning applications in coding discourse datasets and AI-driven educational interventions. Awards: NSF CAREER Award (2001) Spencer Dissertation Year Fellowship (1994) Labs/Teams: Leads projects at SESP on educational technology design and learning analytics. Collaborates with researchers in cognitive science, computer science, and education policy.

Daisy Zhe WangView profile

Professor

• Database Systems
• Data Science
• Machine Learning
• +4 more

Dr. Daisy Zhe Wang is a Professor in the Department of Computer & Information Science & Engineering at the University of Florida, affiliated with the Herbert Wertheim College of Engineering. She directs the Data Science Research (DSR) Lab, focusing on advanced data analysis systems using machine learning and probabilistic methods. Her work spans databases, data science, and informatics, with specializations in probabilistic knowledge graphs, multimodal fusion, and healthcare analytics. Education: PhD in Computer Science from the University of California, Berkeley (2011). Awards include the Arnold and Lisa Goldberg Rising Star Professorship in Computer Science (2019-2021) and the UF Term Professorship (2018). Her lab's current projects include developing systems for tree species classification using remote sensing and improving surgical risk prediction algorithms like MySurgeryRisk. Research emphasizes bridging data science with domain-specific applications, including electronic health records (EHR) analysis, environmental remote sensing, and knowledge graph-driven decision systems. She has pioneered frameworks like RAMQA for multimodal QA and M3 for multi-hop retrieval, advancing both theoretical and applied aspects of data science. Grants and collaborations include large-scale data competitions and partnerships with healthcare institutions. The DSR Lab maintains active research in AI ethics, explainable AI, and scalable data management systems, with ongoing work on neuro-symbolic architectures and multimodal learning.

• Digital Humanities
• Chinese History
• Optical Character Recognition (OCR)
• +4 more

Dr. Donald Sturgeon is an Assistant Professor in the Department of Computer Science at Durham University. He is also a member of the Institute of Medieval and Early Modern Studies. His work focuses on applying computational methods to the study of premodern Chinese texts, including digital archiving, optical character recognition (OCR), and text reuse analysis. He leads initiatives like the Chinese Text Project, a dynamic digital library for premodern Chinese literature and philosophy. Sturgeon has been invited as a keynote speaker at major institutions including Peking University, Chinese University of Hong Kong, and Heidelberg University for his contributions to digital humanities. Research Interests His research bridges computer science and humanities through: Development of linked open data frameworks for historical records OCR solutions for premodern Chinese scripts Unsupervised text reuse identification in classical texts Crowdsourcing methodologies for scholarly knowledge creation Publications Overview His work spans technical advancements in digital humanities, including foundational papers on premodern text digitization, OCR challenges in East Asian scripts, and computational approaches to classical Chinese literature analysis. His most cited contributions address large-scale text preservation and intertextuality in early Chinese philosophical corpora. Professional Contributions Sturgeon's invited lectures (2022-2023) highlight his international recognition in digital methods for East Asian studies. He actively collaborates with institutions across Asia and Europe on projects combining machine learning with historical text analysis. His grants and advisory roles are focused on expanding open-access digital resources for premodern studies.

Tariq Alwada'nView profile

Senior Lecturer

• Cloud Computing
• Fog Computing
• Grid Computing
• +9 more

Tariq Alwada'n is a Senior Lecturer in Cyber Security at Teesside University's School of Computing, Engineering, and Digital Technologies. He serves as Course/Programme Leader for B.Sc IT and Business Technology and Cisco Academy Coordinator. With expertise in Cloud Computing, Fog Computing, IoT, and Cybersecurity, he leads research projects focusing on AI-enhanced cybersecurity training, industrial digitalization, and data-driven solutions. Education includes a PhD in Computer Science (Security for Mobile Grid Systems) from De Montfort University, an MSc in Computer and Information Networks from the University of Essex, and a BSc in Computer Engineering from Al-Balqa Applied University. Research interests span Cloud/Grid Computing, Big Data, and Security. Notable projects include CyberPathway (2025–2025) for diversity-focused cybersecurity training, Dynamic CCTV GAP (2022–2023) for business intelligence systems, and Industrial Digitalisation Technology Centre (2021–2023) supporting SMEs. He has contributed to over 40 research outputs and serves as a reviewer for journals like the International Journal of Environmental Research and Public Health. Teaching certifications include Cisco-accredited courses in Cybersecurity Essentials, IoT, and Networking. He actively mentors PhD students and seeks collaborations on Cloud Security and Distributed Systems.

James Z. WangView profile

Professor

• Bioinformatics
• Computer Networks
• Medical Imaging
• +6 more

James Z. Wang is a Professor in the School of Computing at Clemson University. He holds a B.S. and M.S. in Computer Science from the University of Science and Technology of China, and a Ph.D. in Computer Science from the University of Central Florida. He is a Senior Member of IEEE and ACM. His research focuses on bioinformatics, distributed systems, medical imaging, and data mining, with notable projects including G-SESAME for gene similarity analysis, ontology-based P2P information retrieval, and non-invasive skin cancer detection systems. He has taught numerous courses on databases, data mining, and multimedia systems since joining Clemson in 2005. Research Highlights: Developed tools for semantic similarity measurement in biomedical contexts (G-SESAME) Pioneered ontology-based approaches for P2P networks and proxy caching systems Advanced algorithms for distributed systems, self-stabilizing networks, and energy-efficient cloud computing Collaborated on medical imaging applications including melanoma detection and brain image analysis Publications reflect contributions to algorithms, distributed computing, bioinformatics, and multimedia systems, with over 100 peer-reviewed articles since 2000. His work bridges theoretical computer science with applied domains in healthcare and network infrastructure.

Joshua CatalanoView profile

Associate Professor

• U.S. History
• Early America
• Public History
• +5 more

Joshua Catalano is an Associate Professor in the Department of History at Clemson University's College of Arts and Humanities, where he also serves as the Interim Director of the Digital History PhD program and Public History Coordinator. His academic career bridges traditional historical scholarship with innovative digital methodologies. PhD, George Mason University (2019) MA, Bowling Green State University (2015) BA, Saint Vincent College (2013) Catalano specializes in U.S. History with a focus on Early America, Public History, and Digital Methods. His research examines the role of symbolism and language in settler colonialism, particularly analyzing the production, dissemination, and reinterpretation of historical memory surrounding events like the Gnadenhutten Massacre and the burning of Colonel William Crawford. He has pioneered the application of digital and computational methodologies to historical research, creating new frameworks for conducting historical scholarship in the digital age. His publications demonstrate a consistent trajectory toward integrating digital tools with historical analysis, spanning topics from treaty language analysis to media representations of historical events. Catalano's work shows particular strength in applying computational methods to memory studies and public history, with increasing focus on collaborative digital projects that bridge academic research with public engagement. Catalano has secured significant grant funding from the National Park Service for multiple projects, demonstrating his ability to translate academic research into practical applications for historical preservation and public education. As Interim Director of the Digital History PhD program, Catalano plays a key role in shaping the next generation of historians who work at the intersection of technology and historical scholarship. His grant portfolio includes major National Park Service-funded projects focused on ethnographic and historical assessments of national parks and monuments.