Find Academic Supervisors

Meltem AksoyView profile

Associate Professor

• Sustainable Urban Development
• Social Housing Practices
• User Participation in Design
• +7 more

Meltem Aksoy is an Associate Professor at the Department of Architecture, Istanbul Technical University. Her academic work spans urbanization processes, social housing practices, and digital design technologies, with a particular focus on user participation and ecological sustainability in the built environment. Current affiliation: Department of Architecture, Istanbul Technical University Research themes: Composite cities, informal settlements, parametric design, ambient intelligence Her research explores: Intersections of digital technologies and spatial perception Methodologies for participatory social housing Computational approaches to architectural decision-making Temporal-spatial dynamics in contemporary urbanization Recent publications demonstrate trends in: AI-assisted design negotiation frameworks Fuzzy logic applications in architectural performance Critical analyses of composite urban structures Evolution of home-street relationships She has supervised 33 graduate theses spanning topics from: Architectural essay films Political space narratives Smart facade technologies Urban data representation

Morteza NamvarView profile

Senior Lecturer

• Machine Learning
• Natural Language Processing
• Large Language Models
• +7 more

Dr. Morteza Namvar is a Senior Lecturer at The University of Queensland Business School and an Affiliate of both the Centre for the Business and Economics of Health and Centre for Enterprise AI. His work bridges business contexts with advanced computational techniques, focusing on practical applications of artificial intelligence in organizational and healthcare settings. Faculty of Business, Economics and Law - School of Business Centre for the Business and Economics of Health Centre for Enterprise AI - Faculty of Engineering, Architecture and Information Technology Dr. Namvar specializes in Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLMs), with research concentrated in three primary areas: leveraging NLP and LLMs for enhanced theory building in Information Systems research, text feature engineering using advanced language models, and personalization/user experience enhancement through contextual understanding. His work systematically analyzes unstructured text data to develop robust theoretical constructs and improve machine learning model capabilities. His recent publications reveal strong trends in healthcare applications of LLMs, hate speech detection, and cryptocurrency market analysis through social media. The research consistently applies NLP techniques to solve real-world problems across healthcare, finance, and e-commerce domains, with particular emphasis on sociotechnical aspects of technology implementation. Dr. Namvar has successfully secured competitive funding including the prestigious UQ Knowledge Exchange & Translation Fund grant for developing social media monitoring tools for small businesses, and multiple grants from Medical Protection Society Limited for machine learning applications in healthcare systems. 2021-2022: Developing a context-specific social media monitoring tool to empower Australian small business (UQKx&T Fund) 2021: Enhancing Education Development: Analysing Members' Feedback Using Machine Learning Techniques (Medical Protection Society) 2021: Investigating the effective use of data and analytics in Medical Protection Systems (Medical Protection Society) He currently supervises five PhD students working on NLP and LLM applications in healthcare, data privacy regulation, and patient outcomes. His research impacts include leading machine learning projects with industry partners like Medical Protection Society and PA Hospital, where his NLP techniques have helped improve organizational strategies through text data analysis.

Michael ColaresiView profile

Professor

• International Relations
• Political Methodology
• Computational Social Science
• +3 more

Michael Colaresi is the Associate Vice Provost for Data Science and William S. Dietrich II Professor of Political Science at the University of Pittsburgh, affiliated with the Dietrich School of Arts and Sciences. He holds roles in the Center for Research Computing and Data, Computational Social Science program, and the Intelligent Systems Program at the School of Computing and Information. His research focuses on computational social science, leveraging machine learning and text analysis to study national security secrecy, political violence, and human rights dynamics. Colaresi co-directs the Responsible Data Science, Complexity, and AI for Peace (RedSCAIP) Lab, emphasizing interdisciplinary collaboration to address societal challenges. Education: PhD in Political Science from Indiana University (2002). He founded the Social Science Data Analytics initiative at Michigan State University and now leads efforts in Pitt’s Institute for Cyber Law, Policy, and Security. His work has been funded by NSF grants and foundations like Hillman and RK Mellon. Research Interests: International Relations, computational methods, conflict forecasting, and the intersection of technology with democratic governance. His 2014 book *Democracy Declassified* explores secrecy dilemmas in liberal democracies and was shortlisted for the Conflict Research Society Prize. Recent projects include the ViEWS political violence early-warning system and HR-VEAW human rights analytics platform. Awards: Co-winner of the 2017 Journal of Peace Research Best Visualization Award and 2006 Gosnell Prize for Political Methodology. He serves as co-editor of *International Interactions* and collaborates with institutions like the Peace Research Institute Oslo (PRIO) and Uppsala University’s Violence Early Warning Project. Teaching: Courses include Computational Social Science, Bayesian Statistics, and Text-as-Data methods. His pedagogical focus emphasizes bridging social science theory with modern computational tools.

Daniel Martin KatzView profile

Professor

• Legal Informatics
• Artificial Intelligence & Law
• Blockchain Technology
• +5 more

Professor Daniel Martin Katz is a Full-Time Faculty member at Chicago-Kent College of Law, Illinois Institute of Technology, and Director of the Law Lab. He holds a PhD, JD, M.P.P., and B.S., with expertise in applying computational methods to legal systems. His research focuses on Legal Informatics, AI in Law, Blockchain, and Complexity Science. Notable awards include the Fastcase 50 and ABA Journal's Legal Rebels. He co-founded LexPredict and advises legal tech startups. His work bridges law, technology, and innovation, emphasizing data-driven legal practices. Education: PhD (University of Michigan), JD (University of Michigan Law School), M.P.P. (University of Michigan), B.S. (University of Oregon). Research interests include AI ethics, legal analytics, and computational governance. He edits journals like the International Journal of Law and Information Technology and contributes to global legal tech discourse through media appearances and conferences. His articles explore AI's impact on legal assessment (e.g., GPT-4 passing the bar exam), legal complexity analysis, and NLP applications. Awards highlight his transformative role in legal innovation. Advising includes roles at LexPredict and NextLaw Labs. Labs include the Law Lab and collaborations with Stanford CodeX.

Frank MarkView profile

Professor

• Natural Language Processing
• Computational Research and Modeling
• Social Science
• +3 more

Frank Mark is a Professor in the Department of Communications at the University at Buffalo (College of Arts and Sciences), with an affiliation to the Institute for Artificial Intelligence and Data Science. He holds a PhD and specializes in computational research, natural language processing, and social science with a focus on nonverbal communication dynamics. His work integrates interdisciplinary methods to study deception detection, facial expression analysis, and multimodal data systems. Research interests prominently include automated facial action coding, intergroup emotions, and the role of nonverbal cues in deception. His publications explore topics like rapport modeling in interviews, evolutionary signals of attraction, and the development of databases like DBATES for analyzing debate speeches' audio, text, and visual features. He has contributed to peer review processes and academic journal governance, emphasizing transparency and data literacy in scholarly communication. Frank Mark's recent work emphasizes technological applications in behavioral analysis, such as laser Doppler vibrometry for facial muscle measurement, and the ethical implications of open data systems. His research bridges computational modeling with psychological insights to address real-world challenges in communication, deception, and social interaction.

Won-Jae YiView profile

Academic

• Internet of Things (IoT)
• Wireless Body Sensor Networks
• Wearable Devices for Health Monitoring
• +7 more

Won-Jae Yi is an Associate Teaching Professor of Electrical and Computer Engineering at the Illinois Institute of Technology (Illinois Tech), part of the Armour College of Engineering. He holds a B.S., M.S., and Ph.D. in Computer Engineering from Illinois Tech, completed in 2010, 2012, and 2017, respectively. In addition to his teaching role, Yi serves as the Undergraduate Laboratory and Computing Facilities Supervisor in the Department of Electrical and Computer Engineering. He has over 13 years of experience in research focusing on IoT, wireless sensor networks, wearable devices for health monitoring, FPGAs, cyber-physical systems, and edge computing. Education: B.S. in Computer Engineering (cum laude, 2010), Illinois Tech M.S. in Computer Engineering (2012), Illinois Tech Ph.D. in Computer Engineering (2017), Illinois Tech Research Interests: Yi’s work spans IoT-enabled systems, AI-driven smart devices, cybersecurity for embedded systems, and assistive technologies for visually impaired individuals. His research emphasizes practical applications such as smart home automation, wearable health monitoring, and real-time data fusion for IoT systems. He consistently integrates his expertise in hardware-software co-design, including FPGAs and SoCs, to create robust, low-power solutions. Teaching Contributions: Yi teaches undergraduate and graduate courses in Software Engineering, Internet of Things, Cybersecurity, and Artificial Intelligence. His teaching excellence was recognized with the 2023 Bauer Family Excellence in Undergraduate Teaching Award. Labs and Facilities: As Laboratory Supervisor, Yi manages computing facilities and oversees the development of experimental setups for student projects in IoT, embedded systems, and AI. His lab focuses on prototyping smart systems that bridge theoretical concepts with real-world applications.

• Semantic Computing
• Big Data Integration
• STEM Education
• +3 more

Srividya Bansal is an Associate Professor and Program Chair of the Software Engineering program at Arizona State University's School of Computing and Augmented Intelligence. She joined ASU in 2010 and previously held a visiting assistant professorship at Georgetown University. Her research focuses on semantics-based approaches for Big Data integration, knowledge graphs, and outcome-based STEM education design. She currently co-leads the NSF-funded 'SUDOKN: Supply and Demand Open Knowledge Network' project under the Proto-OKN program. Education: B.Tech in Computer Science, National Institute of Technology Warangal (1999) MS in Computer Science, Texas Tech University (2002) PhD in Computer Science, University of Texas at Dallas (2007) Industry experience: 5 years as a software engineer at SAP Labs India and Tyler Technologies Research interests include semantic computing, web service composition, and educational technology. She has over 75 publications and actively teaches courses like Semantic Web Engineering and Software Enterprise . Current NSF grants include IUSE-funded work on step-based tutoring systems for circuit analysis and Proto-OKN's open knowledge networks. Professional affiliations include IEEE Computer Society and ACM. She leads the Instructional Module Development (IMOD) System project to enhance outcome-based curriculum design in STEM fields.

Axel PolleresView profile

Professor

• Semantic Web
• Knowledge Engineering
• Data Management
• +12 more

Axel Polleres is a full professor in the area of 'Data and Knowledge Engineering' at the Vienna University of Economics and Business (WU Wien) and a faculty member at the Complexity Science Hub (CSH) in Vienna, a position he has held since January 2017. His work bridges academic research and real-world applications in semantic technologies, knowledge graphs, and data governance. His research interests lie at the intersection of semantic web technologies, knowledge engineering, and data management. Focused on querying and reasoning over ontologies, logic programming, and rule-based systems, he explores how structured data can be effectively managed, validated, and integrated—especially through standards like SPARQL and SHACL. His work extends to applications in open data, legal data, biomedical informatics, and socio-environmental challenges such as climate and public health. The most recent publications highlight a growing trend toward practical and societal applications of knowledge graphs, including climate risk assessment, crisis response, healthcare accessibility, and open data governance. These works combine technical rigor in semantic modeling with impactful real-world use cases, particularly in European and Austrian contexts. Axel Polleres has been actively involved in international standardization, notably as co-chair of the W3C SPARQL Working Group, and serves on the editorial boards of the Semantic Web Journal and Journal of Web Semantics . He has contributed to numerous European and national research projects and has published over 100 articles in top-tier journals and conferences. He advises and collaborates with researchers across disciplines and institutions, contributing to interdisciplinary teams working on complex societal challenges. His work is supported by significant research grants, though specific funding sources are not detailed in the provided text. He leads and participates in research groups focused on semantic technologies, knowledge graphs, and data-driven policy.

• Computer Vision
• Intelligent Robot Control
• 3D Computer Graphics
• +12 more

Dr Jiacheng Tan is a Senior Lecturer in the School of Computing at the University of Portsmouth , where he has been a faculty member since 2002. He is actively involved in research and PhD supervision, with a strong focus on intelligent systems and robotics. Dr Tan earned his BEng in Mechanical Engineering from Jilin University of Technology (1983), an MSc in Mechatronics from Xidian University (1989), and a PhD in Computer Graphics from De Montfort University (2001). Prior to joining Portsmouth, he served as a Visiting Researcher in robotics at the University of Salford (1996–1997) and a Research Fellow in artificial intelligence at the Open University (2000–2002). His research centers on computer vision, intelligent robot control, 3D graphics, and human-robot interaction . He investigates how robots can understand and act upon natural language commands by grounding spatial relations, recognizing objects, and reasoning about tasks in unstructured environments. His work integrates fuzzy logic, knowledge engineering, and machine learning to enable robots to learn from demonstrations and interact meaningfully with humans. The trends in his publications reveal a consistent focus on robotics and intelligent systems , evolving from early work in virtual environments and telerobotics to recent contributions in cloud-based scientific visualization and machine learning applications in astrophysics. His interdisciplinary research spans computer science, control theory, and cognitive systems. Dr Tan has not received any explicitly mentioned scientific awards in the provided text. He serves as a PhD supervisor and has contributed to multiple research projects, including the development of integrated AI frameworks for robotic manipulation. While specific grant details are not listed, his collaborations and publications suggest active involvement in funded research initiatives. He has worked with researchers across institutions on topics ranging from scientific visualization to intelligent interfaces. Dr Tan is affiliated with the Computational Intelligence Research Group at the University of Portsmouth. His lab work involves developing virtual environments, symbolic representations of 3D scenes, and natural language interfaces for robot control, supporting both academic research and practical applications in automation.

• Wireless Communications
• Telecommunications Engineering
• Video Quality Optimization
• +7 more

Berend Willem Martijn Kuipers is affiliated with Universidade Lusófona in Lisbon, Portugal, where he contributes to research in telecommunications and network systems. He holds a doctoral degree from Aalborg University of Technology, Denmark, a Master's from Delft University of Technology, Netherlands, and a Bachelor's from Rijswijk Institute of Technology. Bachelor of Science, Rijswijk Institute of Technology, The Netherlands Master of Science, Delft University of Technology, Delft, The Netherlands Doctor of Philosophy, Aalborg University of Technology, Aalborg, Denmark His research focuses on wireless communication technologies, particularly in the areas of MIMO systems, video quality optimization over IP networks, and robust communication for emergency services. His work bridges theoretical modeling and practical implementation in real-world network environments. Key interests include channel modeling, signal processing, and AI applications in networking. The publication timeline from 2002 to 2022 shows a consistent research trajectory centered on enhancing wireless network performance. Early work focused on Bluetooth and MIMO channel modeling, while later contributions address video quality, substation network delays, and the integration of artificial intelligence in communication systems. These works reflect expertise in both fundamental wireless principles and modern network challenges. No scientific awards were mentioned in the provided text. No information is available regarding student supervision, research grants, or funding sources. No specific laboratory or research team affiliations are mentioned in the provided content.

Anna BryniarskaView profile

Assistant Professor

• Knowledge Granulation
• Fuzzy Logic
• Fuzzy Descriptive Logic (Fuzzy DL)
• +4 more

Anna Bryniarska is an Assistant Professor at the Institute of Computer Science, Opole University of Technology, Poland. She holds a PhD in Automation and Robotics (2014) from the same institution, alongside BSc and MSc degrees in Computer Science (2010). Prior to her academic role since 2015, she gained practical experience in IT companies between 2010–2015. Education: BSc and MSc in Computer Science (2010), Opole University of Technology PhD in Automation and Robotics (2014), Opole University of Technology Her research focuses on knowledge granulation, fuzzy logic systems, semantic web applications, and AI-driven technical diagnostics. Recent work emphasizes machine learning applications in biological signal analysis, particularly EEG data. She has authored one book and over 30 scientific articles. No specific grants or awards are listed in the provided text. Professional networks include ORCID, ResearchGate, and SCOPUS profiles. No lab affiliations or team leadership roles are explicitly mentioned.

Reza ArghaView profile

Lecturer

• Biomedical Engineering
• Machine Learning
• Deep Learning
• +5 more

Dr. Reza Argha is a Lecturer at the Graduate School of Biomedical Engineering, UNSW, Sydney. His research focuses on AI-driven biomedical signal processing, machine learning, and digital health. He holds a PhD from UTS (2017), with earlier degrees from Shiraz University, Iran. His work includes developing algorithms for fall detection, blood pressure estimation, and ECG-based cardiovascular diagnostics. Education: BSc and MSc in Electrical Engineering, Shiraz University (Iran) PhD in Biomedical Engineering, University of Technology Sydney (2017) Research Interests: AI in healthcare, biomedical signal/image processing, telehealth systems, fall detection using mmWave radar, non-invasive blood pressure monitoring, and arrhythmia detection via ECG. Grants & Awards: ARC Research Hub for Connected Sensors for Health ($5M) ARC Industrial Transformation Research Hubs Multiple CVMM Collaborative Grants (2023-2024) Awarded 2017 UTS Chancellor’s Best PhD Thesis UTS FEIT Research Publication Awards (2014-2015) Teaching & Supervision: Teaches Biological Signal Analysis (BIOM9621) and Engineering Projects (ENGG2600). Supervises 6 current PhD students, with 1 successful completion. Seeks new students in digital health, AI clinical decision support, and radar-based fall prediction. Labs & Projects: Leads projects in mmWave radar for unobtrusive elderly monitoring, AI for blood pressure estimation, and deep learning for medical image analysis. Collaborates with industry partners on translational health tech solutions.

Jaafar AlghazoView profile

Associate Professor

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +7 more

Dr. Jaafar Alghazo is an Associate Professor in the Math, Science and Technology Department at the University of Minnesota. He currently teaches courses in Information Technology Management and Software Engineering, including ITM 3900-E90: Internship, SE 2200-001: Intro to Software Engineering, and SE 4500-001: Senior Project I. Research Focus: Machine Learning, Deep Learning, Medical Image Analysis, Cybersecurity, IoT, UAV Communications Key Publication Trends: 15 most recent works span 2025–2023, emphasizing neural network optimization, Arabic sign language recognition, flood detection, and secure medical imaging techniques

• Crowdsourcing
• IS Project Management
• Predictive Modeling
• +1 more

Abhishek Tripathi is an Associate Professor of Information Systems in the Department of Accounting and Information Systems at the School of Business, The College of New Jersey. With over ten years of teaching experience in IT-related graduate and undergraduate courses, Dr. Tripathi brings both academic expertise and professional experience from four years working in the software and telecom domain. Dr. Tripathi earned his Ph.D. from the University of Nebraska in 2016. His academic journey reflects a strong foundation in information systems theory and practice, preparing him for his current role shaping future business technology leaders. Dr. Tripathi's research focuses on several cutting-edge areas within information systems. His primary interests include Crowdsourcing, IS Project Management, Predictive Modeling, and IS Theory. His work bridges theoretical frameworks with practical applications, particularly in how organizations can leverage emerging technologies for improved decision-making and operational efficiency. His recent publications demonstrate a growing interest in the intersection of artificial intelligence with traditional information systems challenges, including applications in metaverse technologies, emotion classification, and nano-scale computing environments. Analysis of Dr. Tripathi's publication record over the past decade reveals an evolving research trajectory from foundational work in crowdsourcing and information systems theory toward more specialized applications of artificial intelligence and machine learning in business contexts. His work spans multiple domains including education technology, healthcare informatics, social media analysis, and project management. The consistent thread through his research is examining how information systems can solve complex organizational problems through innovative technological approaches. SIGITPM Membership Excellence Award 2021 Dr. Tripathi has been actively involved in service to the academic community through multiple committee roles. He has served on the Career Development Committee (2018-2020), Support of Scholarly Activities Committee (2019-2022), School of Business Curriculum Committee (2021-2022), and currently serves on the Committee on Faculty Affairs (2024-present) and Faculty Senate (2024-2025). His professional memberships include the Association of Information Systems and the New Jersey Big Data Alliance. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful research funding to support his scholarly activities. Dr. Tripathi teaches a variety of courses including Business Analytics for Decision Making, Database Management for Business, Foundations of Business Analytics, Introduction to Data and Information Science, Machine Learning, and Web Design and Development, reflecting his broad expertise across the information systems curriculum.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +3 more

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.