Find Academic Supervisors

Maribeth PriceView profile

Professor

• Geographic Information Systems
• Remote Sensing
• Spatial Analysis
• +3 more

Maribeth Price serves as Dean of Graduate Education and Professor at South Dakota Mines, with faculty tenure since 1995. Previously chaired Geology and Geological Engineering (2006-2011). Holds M.S./Ph.D. from Kharkov State University (Ukraine) and D.Sc. from Donetsk Physics & Technology Institute. Research expertise spans geospatial technologies , planetary remote sensing, and spatial database design. Authored instructional texts on GIS applications and taught professional workshops for over 1,300 participants. Planetary science contributions include Magellan Mission analysis of Venusian tectonics. Curates Maps at Museum of Geology and promotes online pedagogy as Quality Matters certified reviewer. Interdisciplinary collaborations include hydrogeology, forestry, carbon sequestration, and lightning pattern analysis.

Mark StampView profile

Professor

• Information Security
• Machine Learning
• Malware Analysis
• +5 more

Mark Stamp is a Professor in the Department of Computer Science at San Jose State University (SJSU), part of the College of Engineering. His research focuses on information security, machine learning, malware analysis, and cryptography, with a strong emphasis on applying these fields to cybersecurity challenges. He has authored multiple textbooks, including Information Security: Principles and Practice and Introduction to Machine Learning with Applications in Information Security . Education: PhD in Mathematics (Texas Tech University, 1992), MS in Mathematics (Texas Tech, 1988), BS in Computer Science (Morningside College, 1983). Research interests include malware detection using machine learning, cryptographic algorithms, and adversarial attack analysis. His work spans theoretical research and practical applications, such as developing tools for malware clustering, steganography analysis, and secure communication protocols. Recent projects include AI-driven precision agriculture and detecting AI-generated content. Teaching includes courses on information security (CS 166), machine learning (CS 171), and cryptography. He advises numerous graduate and undergraduate students, many of whom contribute to his research in cybersecurity and AI. Publications span over 200 papers in journals like Journal of Computer Virology and Hacking Techniques and IEEE Transactions . He has also contributed to conferences and edited several Springer volumes on AI and cybersecurity. His work often bridges academic research with real-world security challenges.

Victoria ChiuView profile

Associate Professor

• Accounting Bibliometrics
• Fraud Detection
• Data Analytics
• +4 more

Dr. Victoria Chiu is an Associate Professor of Accounting at the School of Business, SUNY Oswego, specializing in accounting bibliometrics, fraud detection, and data analytics. She joined SUNY Oswego in 2016 after serving as an Assistant Professor at SUNY New Paltz and a doctoral fellow at the AAA/Deloitte Foundation/J.M. Cook Consortium. Dr. Chiu holds a Ph.D. in Accounting from Rutgers University (2013) and a BS from National Changhua University of Education (2008). Her research focuses on applying bibliometric methods to analyze accounting subfields, with a particular emphasis on fraud detection, continuous auditing, and emerging technologies in accounting education and practice. Dr. Chiu has published over 25 peer-reviewed articles in journals like International Journal of Accounting Information Systems, Accounting & Finance, and Journal of Forensic Accounting Research. She has received two Best Faculty Paper Awards from the AAA Strategic and Emerging Technologies Workshop (2017, 2018) and has served on editorial boards for major journals, including the International Journal of Accounting Information Systems and Journal of Emerging Technologies in Accounting. Her teaching spans undergraduate and graduate courses in accounting, including Principles of Accounting and Cost Accounting. She has been recognized for her teaching excellence through multiple Professor Recognition Awards (student-nominated) and actively contributes to curriculum development integrating emerging technologies like process mining and machine learning. Dr. Chiu’s research also emphasizes pedagogical innovation, such as hands-on learning approaches for transaction cycles and adapting AACSB’s Standard A5 into accounting curricula. Professional service includes roles on the AAA Research and Publications Committee (2023–2024) and the Innovation in Accounting Education Awards Committee (2019–2020). Her work bridges academic research and practical applications, particularly in leveraging technology to enhance auditing, fraud detection, and educational outcomes.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +5 more

Dr. Masoud Makrehchi is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, part of the Faculty of Engineering and Applied Science. His research focuses on Natural Language Processing, Artificial Intelligence, Machine Learning, and Social Computing, with a strong emphasis on applications in network science and moral AI. He holds a PhD from the University of Waterloo (2007), along with earlier degrees from Shiraz University and Iran University of Science and Technology. Prior to academia, he worked as a Senior Research Scientist at Thomson Reuters (2008–2012) and completed a postdoctoral fellowship at the University of Waterloo (2007–2008). His research expertise includes text mining, social network analysis, and network science, addressing challenges such as signed social network analysis, document classification, and crime trend prediction using social media data. He has received several awards, including the NSERC Postgraduate Scholarship and the SBP Challenge Award (2012). His work spans over 50 peer-reviewed publications in journals like Expert Systems with Applications, Social Network Analysis and Mining, and Web Intelligence, with contributions to conferences such as GECCO and IEEE/ACM. Dr. Makrehchi’s publications highlight innovations in feature selection, text segmentation, and AI-driven frameworks for requirements elicitation. His recent work explores coherence graphs for text segmentation, generative AI in education, and bias detection in medical datasets. He has also contributed to patent applications related to sentiment analysis and text extraction systems.

Zhonghua ZhengView profile

Assistant Professor

• Data Science
• Environmental Engineering
• Atmospheric Science
• +6 more

Dr. Zhonghua Zheng is an Assistant Professor (tenured) in Data Science & Environmental Analytics at the Department of Earth and Environmental Sciences, The University of Manchester. He leads the Manchester Environmental Data Analytics Lab (MEDAL) and serves on editorial boards of journals like Atmospheric Chemistry and Physics . His research focuses on data-driven approaches to urban climate modeling, aerosol science, and environmental systems analysis. Education : PhD in Environmental Engineering (UIUC, 2020) MCS in Computer Science (UIUC, 2022) MS in Agricultural & Biological Engineering (UIUC, 2016) BEng in Biosystems Engineering (Zhejiang University, 2015) Research Interests : Dr. Zheng integrates data science (AI, ML) with Earth system modeling to address challenges in urban climate adaptation, air quality, and agricultural-environmental nexus. Key themes include: Urban heat stress projections using machine learning Aerosol mixing state characterization via foundation models Climate adaptation strategies for net-zero cities Agricultural emissions modeling in Southeast Asia Recent Article Trends : His 2025 publications emphasize machine learning applications in urban climate and aerosol science, including pollen fragmentation dynamics and black carbon mixing effects. Earlier work (2024) focuses on model improvements for urban surface albedo and gas-phase chemistry. Awards : 2025: Aerosol Science Career Development Grant 2024: Best Student Poster Award (AOGS) 2023: City Brain Open Research Youth Scholar Advising & Grants : Supervises 10+ PhD students (e.g., Yuan Sun, Fei Jiang) and has secured grants including a Royal Society AI + Air Quality award. Leads interdisciplinary projects with NERC, The Alan Turing Institute, and Yunqi Academy. Labs & Teams : Runs MEDAL lab collaborating with global institutions (MIT, NCAR, Columbia). Active in initiatives like UK Atmospheric Chemistry Conference and Open Data Computing Workshop.

Arthur ter HofstedeView profile

Professor

• Business Process Automation
• Process Mining
• Data Quality
• +3 more

Arthur ter Hofstede is a Professor in the Faculty of Science and Head of the School of Information Systems at Queensland University of Technology (QUT). He previously held a Professorship at Eindhoven University of Technology (2010–2018) and has been a Visiting Professor at Tsinghua University (China), Sapienza University of Rome (Italy), and Sun Yat-sen University (since 2015). His research focuses on Business Process Automation (BPM), Process Mining, and Data Quality, with particular emphasis on the YAWL workflow system. He has supervised numerous PhD students in areas like process-data governance and gamified data cleaning. His work bridges academia and industry, addressing challenges in healthcare, insurance, and logistics. Key contributions include frameworks like ProcessProfiler3D for performance visualization and OrgMining 2.0 for organizational model mining. Awards include prestigious visiting scholar appointments and leadership roles in international collaborations. Education: Ph.D. and M.Sc. from Katholieke Universiteit Nijmegen (Netherlands). Research projects include Australian Competitive Grants on topics like cost-aware BPM and liquid process model collections. Notable publications explore event log imperfection patterns, privacy-preserving process mining, and robotic process automation integration. Grants: DP150103356 (Liquid Process Model Collections), DP120101624 (Cost-aware BPM) Labs/Teams: Centre for Data Science, YAWL Development Team International Collaborations: Sapienza University, Tsinghua University, Sun Yat-sen University

Jon CarrView profile

Professor

• Entrepreneurship
• Family Business
• Organizational Behavior
• +4 more

Jon Carr is the Jenkins Distinguished Professor of Entrepreneurship at North Carolina State University's Poole College of Management, within the Department of Management, Innovation & Entrepreneurship. He previously taught at Texas Christian University and the University of Southern Mississippi. His research focuses on entrepreneurship, family business dynamics, and organizational behavior, with notable work on 'Familiness' in family businesses. He has secured millions in grants from NASA, the U.S. Department of Labor, and collaborated with Lockheed-Martin's Earth Observation Division. Carr actively serves as Associate Editor for Family Business Review , and has contributed to journals like the Academy of Management Journal and Journal of Applied Psychology . Education: Ph.D. in Business from Mississippi State University (2001). Research interests include entrepreneurship, business model innovation, family business governance, and strategic planning. He advises on venture capital, technology transfer, and leadership development. Key contributions include studies on hybrid entrepreneurship, serial entrepreneurship drivers, and the impact of workaholism/sensation-seeking on entrepreneurial decisions. His work emphasizes practical applications like the NC State Entrepreneurship Clinic's 'fail-safe' approach for students. Grants and collaborations highlight his focus on innovation and scalability. He has been recognized with awards for teaching, research, and service, though specific award names are not detailed in the text. Carr's professional roles extend to mentoring entrepreneurs, consulting for for-profit/nonprofit organizations, and speaking engagements. His work integrates academic rigor with real-world entrepreneurship challenges, emphasizing global exposure and ethical practices through initiatives like the Business Sustainability Collaborative (BSC).

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His primary research focuses on Artificial Intelligence, Machine Learning, and their applications in healthcare, finance, and biomedical informatics. He leads interdisciplinary projects in clinical monitoring, anomaly detection, and predictive modeling of electronic health records (EHRs). Education: PhD in Electrical Engineering and Computer Science (MIT, 1997), M.Sc. in Electrical Engineering (Slovak Technical University, 1988) Affiliations: Department of Computer Science, University of Pittsburgh; School of Computing and Information His research integrates AI with probability theory, statistics, and operations research. Key areas include planning under uncertainty, machine learning for time-series data, and AI-driven solutions for clinical decision support. Current projects involve real-time patient monitoring, hierarchical models for EHR analysis, and control of biological processes. He has advised over 20 PhD students and holds grants from NIH, DARPA, and other agencies. Notable contributions include outlier detection in clinical actions, temporal predictive patterns, and state-space models for healthcare data. His work emphasizes translating AI advancements into practical clinical tools for improved patient care.

• Data Mining
• Text Mining
• Intelligent Systems
• +3 more

Jiawei Han is the Michael Aiken Chair Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He specializes in data mining, text mining, and intelligent systems, leading the Data Mining Research Group (DMG) and the Data and Information Systems Research Laboratory (DAIS). Education: Ph.D. (1985) in Computer Science from the University of Wisconsin-Madison. Research Interests: Focuses on advancing data mining methodologies, including heterogeneous information networks, social media analysis, and knowledge discovery in cyberphysical systems. His work bridges theoretical foundations with applications in genomics, bioinformatics, and geospatial understanding. Projects & Grants: NSF AI Institute for Molecular Discovery (Molecule Maker Lab) NSF I-GUIDE (Geospatial Understanding via Integrative Discovery) DARPA INCAS/KAIROS (Event Schema Reasoning) Teaching: CS412: Data Warehousing & Mining CS512: Data Mining Principles CS598JH: Text Mining with LLMs (Fall 2024) Labs & Teams: Directs DMG and DAIS labs, fostering collaborative research in multidimensional data analysis and structure-rich networks.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +5 more

Ahmed AbuRa'ed is a Researcher at the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra (UPF), Barcelona. He is affiliated with the TALN research group and the Large-Scale Text Understanding Systems Lab. His work focuses on advancing knowledge in scientific text summarization, information extraction, and machine learning. Education: PhD in Computer Science (2020), UPF, Barcelona, Spain M.Sc. in Computer Science (2015), University of Trento, Italy B.Sc. in Computer Information Systems (2007), An-Najah University, Nablus, Palestine Research Interests: Natural Language Processing (NLP), Machine Learning/Deep Learning, Semantic Web, Information Extraction, Data Mining, and Scientific Document Summarization. His projects include developing systems for automatic generation of state-of-the-art reports, scientific text summarization, and cross-document relation discovery. Publications Focus: His 15 most recent articles (2016–2021) emphasize advancements in scientific literature analysis, including citation detection, text simplification, and cross-document summarization. Notable works involve systems like LaSTUS/TALN for scientific text processing and OlloBot for Arabic health dialogue agents. Labs & Teams: Active member of the TALN research group and the Large-Scale Text Understanding Systems Lab at UPF's DTIC department. Open to collaborations in NLP, Machine Learning, and related fields via email or Skype.

Moe Thandar WynnView profile

Professor

• Process Mining
• Data Quality
• Business Process Management
• +3 more

Professor Moe Thandar Wynn is a Co-Director of QUT's Centre for Data Science and holds a Professorship in the School of Information Systems at Queensland University of Technology (QUT). She leads the Process Science Academic Program and serves as the Academic Lead of Research for the School of Information Systems. Her expertise spans Process Mining, Data Quality, and Robotic Process Automation (RPA). Prof Wynn has attracted over AUD 6 million in research funding and holds an h-index of 41 with 8700+ citations. She is a member of the Australian Research Council College of Experts (2023–2025) and has received prestigious awards including the QLD Women in Technology Excellence Award (2024). Her research focuses on formal foundations of process modeling, verification, and automation. She has contributed to international conferences as a co-chair and program committee member, and co-edited special issues on RPA and process dynamics. Prof Wynn collaborates with industries like healthcare, insurance, and agriculture to optimize business processes through data-driven insights. Current research includes privacy-preserving process mining and quality-driven event log enhancement. Education: PhD (QUT, 2006), M. Information Technology (Research, QUT) Key Projects: Hospital Capacity Optimization, Liquid Process Model Collections, Risk-Aware BPM Supervision: Over 10 completed PhD/MSc students in process mining and analytics Awards: Multiple QUT Excellence Awards, ARC College Membership Her lab focuses on advancing process intelligence and RPA, with ongoing efforts in data quality frameworks and process mining standards (e.g., IEEE XES). She actively participates in industry partnerships, such as the CRC Food Agility project, to bridge research and real-world applications.

Richard BoyceView profile

Associate Professor

• Clinical Decision Support Systems
• Pharmacovigilance
• Drug Interaction Analysis
• +3 more

Richard Boyce is an Associate Professor in the Department of Biomedical Informatics at the University of Pittsburgh's School of Computing and Information. His work focuses on clinical decision support systems (CDSS), pharmacovigilance, and distributed knowledge bases. He leads research in drug interaction analysis, particularly involving natural products like cannabis, and develops machine learning tools for healthcare applications. His address is 530 DBMI Building, Pittsburgh, PA 15260, with contact email rdb20@pitt.edu. Boyce’s research integrates clinical and pharmacological data to improve patient safety through advanced analytics. Recent projects include predicting Alzheimer’s disease using EHR data, falls prediction in nursing homes, and optimizing CDSS modules for resource-limited settings. His team also develops open-source knowledge graphs for life sciences, enhancing data interoperability. Education: Details not explicitly listed in the provided text. Labs/Teams: Affiliated with the Department of Biomedical Informatics and School of Computing and Information at Pitt. Publications highlight his contributions to pharmacovigilance signal detection, machine learning in healthcare, and ontology engineering. Notable works include studies on cannabis-drug interactions and CDSS design for clinical practice.

• Digital Humanities
• Literary Studies
• Cultural Analytics
• +3 more

Dr. James O'Sullivan is a Senior Lecturer in Digital Humanities at University College Cork (UCC), affiliated with the School of English & Digital Humanities and the Department of Digital Humanities. He previously held roles at the University of Sheffield and Pennsylvania State University, and has adjunct and visiting positions at several institutions. His academic roles include serving on UCC's Research & Innovation Committee and the inaugural board of the Future Humanities Institute, where he leads the Digital Cultures, New Media, & Cultural Analytics research cluster. Dr. O'Sullivan's research focuses on digital literature, cultural analytics, and the intersection of technology with literary studies. He has edited major works like The Bloomsbury Handbook to the Digital Humanities and Technology in Irish Literature & Culture , and authored Towards a Digital Poetics . His work explores topics such as electronic literature, digital editing, and the societal impact of AI. He leads high-profile research projects including CASCADE (Horizon Europe MSCA Doctoral Network) and C21 Editions (IRC/AHRC-funded). His peer-reviewed publications span journals like Digital Scholarship in the Humanities and Poetics , addressing topics from authorship attribution to computational stylistics. Dr. O'Sullivan contributes to public policy through roles like Teaching & Learning Policy Advisor for AI at Ireland's Higher Education Authority. He chairs Sample-Studios, Cork's largest artist-led group, and regularly publishes op-eds in outlets like The Guardian and The Irish Times .

James BradburyView profile

Lecturer

• Machine Learning
• Sound Art
• Computer Music
• +3 more

James Bradbury is a Music Lecturer at the Conservatorium of Music, University of Western Australia. He holds a PhD in Music Technology (2021) from the same institution. His research focuses on integrating machine learning into music creation and performance, with expertise in sound art, experimental art, and software development for creative applications. Previously, he served as a Post-Doctoral Research Fellow in Creative Coding at the University of Huddersfield (2021–2022). Education: Doctor of Philosophy in Music Technology, University of Western Australia (2021) Research Interests : Bradbury explores machine learning's role in shaping contemporary music through real-time interactive systems, sound art installations, and experimental music performance. His work emphasizes computational tools for creative processes, including the Fluid Corpus Manipulation (FluCoMa) platform, which enables novel compositional workflows. He investigates how algorithmic approaches can expand artistic expression while maintaining human creativity at the core. Research Trends : Recent works highlight interdisciplinary collaborations between music and computer science, particularly in developing open-source tools like FluCoMa. His performances and publications demonstrate a focus on non-narrative musical forms and real-time algorithmic generation. The 2025 Audible Edge performance exemplifies this through live machine learning-driven sample selection. Awards and Grants : No specific awards or grants mentioned in the provided texts. His contributions are primarily reflected in creative outputs and academic publications. Labs and Teams : Active within the Conservatorium of Music, Bradbury collaborates with institutions like the University of Huddersfield and participates in festivals such as Audible Edge. His work is closely tied to the FluCoMa research collective, emphasizing open-source software development for artistic innovation.

Cezary KaliszykView profile

Professor

• Theoretical Computer Science
• Automated Reasoning
• Formal Methods
• +13 more

Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne. His research focuses on automated reasoning, formal methods, and learning for reasoning, particularly in the context of interactive theorem proving and formalized mathematics. He leads the ERC project 'FormalWeb3' and has been involved in several other significant research initiatives. Theoretical Computer Science Automated Reasoning Formal Methods Interactive Theorem Proving Machine Learning for Theorem Proving Formalized Mathematics Proof Guidance Learning for Reasoning His research explores the integration of machine learning techniques with formal reasoning systems to enhance automation in theorem proving. This includes developing systems like CoqHammer and Tactician, advancing premise selection, proof guidance, and learning-based proof search strategies. His work bridges logical foundations with practical AI-driven tools for formal verification. The recent publications demonstrate a consistent focus on advancing automated and interactive theorem proving through learning techniques, formalization of mathematical concepts (like surreal numbers), and improving reasoning systems (e.g., Prover9, tableaux methods). There is a strong emphasis on practical system development, formalization projects, and learning-based enhancements to reasoning. ERC project 'FormalWeb3' - Principal Investigator Cost Action EuroProofNet - WG5 Leader until 2024 FWF project P26201 - developing HOL(y)Hammer Other projects: JSPS P10044, NWO MathWiki, SURF WebDed, ProofWeb He has advised several PhD students to completion, including Michael Färber, Thibault Gauthier, Yutaka Nagashima, Stanisław Purgał, and Liao Zhang, and is currently supervising Daniel Ranalter and Neil Vyas. He has not received any explicitly mentioned scientific awards in the provided text. His work involves leadership in collaborative systems such as ProofWeb and HOL Import, and participation in major formalization efforts including the Mizar library integration with Isabelle. He is actively involved in the development of tool ecosystems for formal mathematics and automated reasoning.