Find Academic Supervisors

Hao WangView profile

Researcher

• Data Management
• Machine Learning
• Recommender Systems
• +7 more

Hao Wang is an Assistant Researcher in the Department of Computer Science and Technology at Nanjing University, China. He is affiliated with the Reasoning & Learning Group and conducts research at the intersection of data management and machine learning. Bachelor of Mathematics, Nanjing University (2005) Master of Computer Science, Nanjing University (2008) Ph.D. in Computer Science, The University of Hong Kong (2014) His research interests span Data Management and Machine Learning , with recent focus on rank-aware query processing, recommender systems (especially location-based), reinforcement learning, and transfer learning. His work often integrates user behavior modeling, efficient indexing, and scalable algorithms for large-scale data. Hao Wang's publication record shows a consistent trend in solving practical problems in data-intensive AI systems. His recent articles focus on personalized location recommendation , crowdsourced data labeling , distributed learning for imbalanced data , and reinforcement learning transfer . These works appear in top venues such as VLDB, SIGMOD, ICDM, AAAI, and journals like TKDE and GeoInformatica, indicating strong technical depth and interdisciplinary impact. Hao Wang has not been explicitly mentioned as receiving scientific awards in the provided text. He has collaborated extensively with researchers such as Yang Gao, Nikos Mamoulis, and David Cheung. While no formal advisees are listed, his involvement in supervising PhD work (as co-supervisor) and publishing with students suggests an active mentoring role. His research has been supported through academic collaborations and institutional affiliations, though specific grant details are not provided. Hao Wang is a member of the Reasoning & Learning Group at Nanjing University, where he contributes to advancing intelligent systems that combine logical reasoning with statistical learning. The group focuses on foundational and applied aspects of AI, particularly in data mining, knowledge discovery, and autonomous decision-making.

Mustafa ÖzgerView profile

Assistant Professor

• Wireless Communications
• Network Architecture
• Sensing and Communication Systems
• +5 more

Mustafa Özger is a Tenure Track Assistant Professor at the Department of Electronic Systems within the Technical Faculty of IT and Design at Aalborg University, Denmark. His research focuses on advanced communication systems, including 6G networks, non-terrestrial networks (NTN), unmanned aerial vehicles (UAVs), cognitive radio systems, and ultra-reliable low-latency communication (URLLC). He has contributed significantly to edge computing and networking, with a particular emphasis on integrating sensing capabilities with communication systems. His work also addresses challenges in network architecture design, handover management, and deep reinforcement learning applications for improving connectivity and reliability. Key research interests include airspace and network integration for future communication systems, signal classification using neural networks, and low-latency solutions for multi-connectivity scenarios. He has published extensively, with over 73 peer-reviewed articles since 2013, covering topics from cognitive radio networks to satellite constellations. His research often explores the intersection of machine learning and communication systems, with practical applications in IoT, vehicular networks, and underwater acoustic communications. Özger’s recent work highlights his leadership in emerging 6G technologies, particularly in adaptive network architectures that combine terrestrial and non-terrestrial elements. He has also pioneered methods for minimizing handover delays in NTN environments using Open RAN functional splits and developed denoising techniques for localization systems using autoencoder algorithms.

• Business Analytics
• Data Mining
• Applied Machine Learning
• +7 more

Mahinda Mailagaha Kumbure is an Assistant Professor (Tenure Track) at LUT Business School, Lappeenranta-Lahti University of Technology (LUT University), Finland, where he is part of the Business Analytics research team. He holds a Doctor of Science in Economics and Business Administration (2022), a Master’s in Computational Engineering (2018), and a Bachelor’s in Mathematics and Statistics (2014) from the University of Ruhuna. He has been employed at LUT University since 2020, progressing from Junior Researcher to Postdoctoral Researcher, and now Assistant Professor. Doctor of Science in Economics and Business Administration, LUT University (2018–2022) M.Sc. in Computational Engineering, LUT University (2016–2018) B.Sc. in Mathematics and Statistics, University of Ruhuna (2009–2014) His research centers on data mining, applied machine learning, fuzzy systems, and fuzzy qualitative comparative analysis (fsQCA) , with applications in business, management, and financial forecasting. He develops and enhances fuzzy classification and regression models, particularly focusing on fuzzy k-nearest neighbor (k-NN) variants using aggregation operators like OWA, Bonferroni mean, and power means. His work also explores causal cognitive maps for modeling managerial cognition and organizational learning, and hybrid feature selection techniques for financial prediction. His recent publications (2019–2025) show a strong focus on improving fuzzy classification algorithms , applying fsQCA in decision support , and using machine learning for stock market forecasting . His work spans journals in artificial intelligence, soft computing, expert systems, and business research, indicating interdisciplinary impact. He frequently employs granular computing, Minkowski distance, and ensemble methods to enhance model robustness and interpretability. He has served as a peer reviewer for top journals including Expert Systems with Applications , Information Sciences , Engineering Applications of AI , and Renewable Energy , demonstrating active engagement in the research community. Mahinda advises no students listed in the provided data and has not been mentioned in relation to specific grants, awards, or leadership of a lab or research team. However, his collaborative work with researchers such as Pasi Luukka, Anssi Tarkiainen, Jan Stoklasa, and Ari Jantunen suggests strong team integration within the Business Analytics group at LUT.

Perihan SAVAŞView profile

Professor

• Online and Mobile Education
• Distance/Online Education Support Systems
• Blended Learning
• +15 more

Prof. Dr. Perihan SAVAŞ is a Professor at the Department of Foreign Language Education, Faculty of Education, Middle East Technical University (METU). She holds a Ph.D. from the University of Florida (2006) in Curriculum and Instruction with TESOL, a M.A. and B.A. from METU in English Language Teaching and Foreign Language Education, and a diploma in Trainer Training from Bilkent University (2000). Education: Ph.D. 2006: University of Florida, Curriculum and Instruction (TESOL) M.A. 1998: METU, English Language Teaching B.A. 1996: METU, Foreign Language Education Diploma 2000: Bilkent University, Trainer Training Her research focuses on integrating technology into foreign language education, online/mobile education in teacher training, blended learning, curriculum design, qualitative research methods, and situational leadership in educational institutions. She has contributed extensively to areas such as EFL material design, alternative assessment techniques, and in-service teacher training. No specific scientific awards or notable grants are explicitly mentioned in the provided texts. However, her research output includes 59 publications (as per METU's Research Information System). She is affiliated with the Faculty of Education at METU and maintains an office in EF-B 01. Her contact information includes email perihans@metu.edu.tr and phone +90 312 210 4079.

• Fuzzy Logic
• Computational Intelligence
• Multiple-Criteria Decision Making (MCDM)
• +7 more

Pasi Luukka is a Full Professor (tenured) at the LUT Business School, Lappeenranta University of Technology (LUT University), Finland, where he is affiliated with the Department of Business Studies. His research bridges computational intelligence, fuzzy systems, and business decision-making, with a strong emphasis on practical applications in finance, innovation, and industrial performance. His research interests include: Fuzzy Logic and Soft Computing Multiple-Criteria Decision Making (MCDM) Machine Learning and Feature Selection Real Option Valuation under Uncertainty Financial Forecasting and Trading Strategies Innovation and Knowledge Management His recent publications (2022–2025) show a strong trend in enhancing fuzzy classification methods (e.g., fuzzy k-NN), developing aggregation operators (OWA, Bonferroni), and applying fuzzy logic to real-world problems such as stock market prediction, R&D investment analysis, and water-energy nexus modeling. His work frequently appears in top-tier journals like Expert Systems with Applications , Information Sciences , and Fuzzy Sets and Systems . He has received recognition through a substantial body of peer-reviewed publications and active collaboration with leading researchers in fuzzy systems and decision science. Pasi Luukka advises several graduate students, including Mahinda Mailagaha Kumbure and Christoph Lohrmann, and has contributed to numerous research projects involving real options, innovation evaluation, and intelligent systems. He has served as a peer reviewer for journals such as Decisions in Economics and Finance , Group Decision and Negotiation , and Soft Computing . He is a key member of a research group focused on intelligent decision support systems, fuzzy logic applications, and computational methods in business and finance. The team actively publishes and collaborates on interdisciplinary projects involving energy, finance, and industrial systems.

Jan StoklasaView profile

Professor

• Fuzzy Logic
• Multi-Criteria Decision Making
• Linguistic Modeling
• +7 more

Jan Stoklasa is a Full Professor in the Department of Business Studies at LUT Business School, LUT University, Lappeenranta, Finland. He also holds the position of Assistant Professor in the Department of Applied Economics at Palacký University, Olomouc, Czech Republic, since 2015. His research lies at the intersection of fuzzy logic, decision sciences, and behavioral economics. His primary research interests include Fuzzy Logic , Multi-Criteria Decision Making (MCDM) , Linguistic Modeling , Uncertainty in Evaluation , and Behavioral Operations Research . He investigates how human cognition, emotions, and risk attitudes influence expert and consumer decisions, particularly under uncertainty. His work integrates computational intelligence with practical applications in energy systems, supply chain management, and business analytics. Recent publications (2023–2025) demonstrate a strong trend in developing advanced fuzzy-based decision support methods, including fuzzy TOPSIS with OWA operators, possibilistic pay-off methods for real options, and q-rung orthopair fuzzy models for crisis management. He also explores residential demand response behavior using agent-based modeling and causal maps for knowledge development assessment. Scientific Awards and Recognition: While specific awards are not listed in the text, his extensive publication record in top-tier journals such as Information Sciences , Fuzzy Sets and Systems , Applied Energy , and Expert Systems with Applications indicates significant recognition in his field. He actively serves as a peer reviewer for journals including Fuzzy Sets and Systems , Applied Soft Computing , and Journal of Business Research . Advising and Grants: Although no formal list of students is provided, his collaborative publications with multiple co-authors suggest active supervision and mentoring. He is likely involved in research grants related to energy systems, decision support, and cognitive modeling, given the applied nature of his work. His collaborations with researchers like Pasi Luukka and Mikael Collan indicate participation in funded projects. Labs and Research Teams: Jan Stoklasa is associated with research groups focusing on computational intelligence, decision support, and energy systems at LUT University. His work on fuzzy systems and cognitive maps suggests involvement with interdisciplinary teams bridging computer science, management, and behavioral sciences.

Kai WangView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Optimization
• +5 more

Kai Wang is an Assistant Professor at Georgia Tech's School of Computational Science and Engineering. He holds a Ph.D. in Computer Science from Harvard University, advised by Professor Milind Tambe. His research bridges AI, machine learning, optimization, and multi-agent systems to address societal challenges in healthcare, public health, and environmental sustainability. Key projects include maternal and child health support systems, wildlife conservation via game theory models, and industrial environmental monitoring. Education: Ph.D. in Computer Science, Harvard University (Advisor: Prof. Milind Tambe) Research Interests focus on Optimization, Reinforcement Learning, Multi-Agent Systems, Algorithmic Game Theory , and their applications in real-world impact scenarios. His work emphasizes practical deployments with NGOs and healthcare institutions to ensure tangible societal benefits. Major Recognition: Siebel Scholars Schmidt Science AI2050 Fellowship NSF CISE Core Medium Grant Children's Healthcare of Atlanta PTC Support Advising & Grants: Active in mentoring students and leading projects funded by NSF and industry collaborations. Collaborates closely with hospitals and NGOs to deploy AI solutions in social impact domains. Labs/Teams: Leverages interdisciplinary teams to integrate theoretical AI advancements with field-deployable systems, prioritizing real-world efficacy over purely academic outcomes.

Fiona ScottView profile

Senior Lecturer

• Digital Literacies
• Children's Digital Practices
• Digital Play
• +3 more

Dr. Fiona Scott is a Senior Lecturer in Digital Literacies at the University of Sheffield's School of Education, specializing in children's digital lives within family and community contexts. Her research examines digital technology engagements, social class influences, and digital play-wellbeing relationships through collaborations with UNICEF Innocenti, LEGO Foundation, and CBeebies. As Editor of the Journal of Early Childhood Literacy, she bridges academic research with public discourse through BBC media appearances and The Conversation articles. Research Focus Scott's work centers on digital literacies in early childhood, emphasizing ecological perspectives on children's technology use across diverse socioeconomic contexts. Her PhD with CBeebies analyzed preschoolers' home media engagements, evolving into current leadership of the multinational 'Responsible Innovation in Technology for Children' project investigating digital play and subjective well-being. She pioneers more-than-human research methodologies while examining digital inequalities, pandemic impacts on family media practices, and cross-cultural technology access. Scholarly Contributions Her recent publications (2020-2025) reveal converging themes: digital play's dual role in child development and well-being, socioeconomic mediators of technology access, and innovative research methods for studying children in disrupted environments. Work with LEGO Foundation and UNICEF highlights policy-relevant insights into ethical digital design for children, while pandemic-era studies document evolving family media practices and parental mediation strategies across global contexts. Academic Leadership Principal Investigator: Responsible Innovation in Technology for Children (LEGO Foundation/UNICEF) Co-Developer: MA Digital Literacies, Culture and Education curriculum Editorial Leadership: Journal of Early Childhood Literacy; Media Education Research Journal Teaching and Supervision Scott teaches across BA Education, Culture and Childhood; MA Education; MA Early Childhood Education (Distance); and MA Digital Literacies programs. Her doctoral supervision spans children's YouTube use, digital technologies in Chinese early childhood settings, and teacher digital literacies. She actively recruits PhD/EdD candidates researching digital lives of children aged 0-8, with focus areas including family mediation, practitioner attitudes, and diversity in digital practices across geographic and socioeconomic contexts.

Omer BobrowskiView profile

Professor

• Topological Data Analysis
• Stochastic Topology
• Applied Topology
• +4 more

Omer Bobrowski is a Professor in Mathematical Data Science at Queen Mary University of London, affiliated with the School of Mathematical Sciences. His research focuses on stochastic topology, topological data analysis (TDA), and their applications in signal processing and natural language processing. He explores theoretical aspects like phase transitions in stochastic topology and noise distribution in TDA tools, while developing statistical methods for practical applications. His work has been supported by grants from the EPSRC (£396,018, 2024–2027) and the Leverhulme Trust (£330,778, 2024–2027). Collaborators include Primoz Skraba and others in the Centre for Probability, Statistics, and Data Science. Research Interests include: Random Topology, Applied Topology, Stochastic Geometry, and Probability Theory. His lab includes Research Staff Dr. Shu Kanazawa, Dr. Uzu Lim, and Dr. Duncan Parker. Bobrowski’s publications span foundational TDA theory and applied methodologies across diverse datasets.

Elina E. KetonenView profile

Lecturer

• Educational Psychology
• Academic Emotions
• Student Motivation
• +3 more

Elina E. Ketonen is a University Lecturer in University Pedagogy and Post-Doctoral Researcher in Educational Psychology at the University of Helsinki's Department of Education. She specializes in advanced statistical methods, including multilevel modeling and intraindividual analysis, applied to cross-sectional, longitudinal, and psychophysiological datasets. Her research focuses on academic emotions, student motivation, learning environments, and well-being. Key interests include how learning environments can foster engagement, and the role of emotions in academic achievement. She completed her PhD in Educational Psychology at the University of Helsinki (2018) and conducted postdoctoral research at the University of Oxford (2019). Recent work emphasizes dynamic interactions between motivation, emotions, and academic performance. Her articles explore topics like media multitasking impacts, classroom dynamics, and doctoral supervision wellbeing. She has led projects funded by the Academy of Finland and contributed to international collaborations on early childhood leadership and crisis management in education. Elina has received awards including the Doctoral Dissertation Award (2019) and the Distinguished Reviewer designation (2023). She supervises doctoral candidates in the Doctoral Program in School, Education, Society and Culture. Her research has been applied in teacher training programs and policy development initiatives.

• Online Learning
• Instructional Design
• Educational Technology
• +3 more

Noelle Wall Sweany is a Clinical Professor of Learning Design & Technology in the Department of Educational Psychology at Texas A&M University's College of Education and Human Development. She specializes in online learning strategies, instructional design, and self-regulation in digital environments. Dr. Sweany holds a Ph.D. in Educational Psychology from the University of Texas at Austin and a B.A. in Computer Science from the University of North Texas. Her research emphasizes evidence-based practices for online education, including course design methodologies and student engagement techniques. She leads the online M.Ed. program in Learning Design & Technology and has been recognized with awards such as the 2019 AFS College-Level Teaching Award and the 2017 Exemplary Distance Educator designation. Dr. Sweany serves on multiple committees, including the CEHD Assessment Committee and TAMU's Teaching and Transformational Learning Technologies Committee. Her leadership roles include Associate Department Head positions in Graduate and Academic Affairs (2020–2024) and chairing the APT Promotion & Review Committee. Her publications span topics like virtual interdisciplinary partnerships in education, machine learning applications for identifying at-risk learners, and hypermedia navigation strategies. She teaches courses on online course design, emerging technologies, and mobile learning design.

Natalia Pinzón JiménezView profile

Researcher

• Agroecology
• Climate Change Resilience
• Socio-ecological Systems
• +7 more

Natalia Pinzón Jiménez, Ph.D., is a Postdoctoral Scholar at the University of California, Davis, in the Department of Environmental Science and Policy within the College of Agricultural and Environmental Sciences. She is affiliated with the Galt Lab and the Food Systems and Sustainability Lab, and is a key contributor to the Subseasonal-to-Seasonal (S2S) Climate Research Project, a multi-institutional collaboration involving UCs and NASA. She is the co-founder of Farmer Campus, a virtual learning network for farmers, and actively consults on agroecology research, evaluation, and education. Education: A.S. in Biology and Chemistry, Raritan Valley Community College B.S. in Conservation and Natural Resources, University of California, Berkeley M.A. and Ph.D. in Geography, University of California, Davis Natalia's research centers on agroecology as a transformative strategy for equitable food systems under climate change. Her work bridges socio-ecological research, climate resilience, agricultural education, and participatory methodologies. She investigates how farmers can lead in wildfire resilience, climate adaptation, and sustainable food systems. Her expertise includes developing innovative research tools such as participatory data analysis frameworks, bibliometric methods for transdisciplinary research, and AI-based fraud detection for online surveys. She emphasizes distance education, grant writing, and impact evaluation, particularly for underserved farming communities. Her recent publications from 2023–2025 reveal a strong thematic focus on climate resilience in agriculture, farmer-led adaptation, digital equity in agricultural technology, and the integration of social sciences into agroecology. These works reflect her commitment to co-producing knowledge with farmers, enhancing accessibility of climate tools, and building socio-ecological resilience through education and action-research. Scientific Awards: AFRI Predoctoral Fellowship (2021–2024) Switzer Environmental Fellows, Robert & Patricia Switzer Foundation (2021) Outstanding Project Award: USDA Western ERME (2023) Natalia has extensive experience in advising and grant-supported research. She has evaluated three BFRDP programs and ten USDA-funded initiatives, contributed to a four-year national research-extension program, and advised a research program for Latinx farmers in the Western U.S. She is developing a UC-wide online course, 'Climate Change and Agriculture,' funded by the UC Office of the President’s Innovative Learning Technology Initiative (UCOP-ILTI), in collaboration with Professor Mark Cooper. She also co-developed the 'Farming Through Wildfire Season' program, which includes a guidebook and online course. Her leadership in educational programming and farmer engagement demonstrates a strong commitment to applied, community-centered research. She is actively involved in several key organizations: serving as Financial Liaison for the Latin American Scientific Society for Agroecology (SOCLA), and on the boards of the Organic Seed Alliance (OSA) and the California Farmer Justice Collaborative. These roles underscore her dedication to sustainable agriculture, equity, and farmer empowerment.

Haichong ZhangView profile

Associate Professor

• Medical Robotics
• Biomedical Imaging
• Medical Imaging Systems
• +4 more

Haichong Zhang is an Associate Professor of Robotics Engineering at Worcester Polytechnic Institute (WPI), affiliated with the Department of Computer Science. He holds a PhD in Computer Science from Johns Hopkins University (2017), an MS in Computer Science from Johns Hopkins (2015), and prior degrees in Human Health Sciences from Kyoto University. His research focuses on medical robotics, biomedical imaging, and intervention systems, with an emphasis on integrating ultrasound and photoacoustic imaging with robotic platforms to enhance diagnostic and therapeutic accuracy. Key projects include co-robotic imaging systems to reduce user dependency, PA-based functional imaging for surgical guidance, and algorithmic advancements for computer-assisted interventions. Dr. Zhang leads the Medical FUSION Lab, which collaborates across robotics, acoustics, optics, and biology. His work has been supported by grants such as the NIH Director's Early Independence Award and DoD CDMRP Early Investigator Award. He has developed systems for applications like kidney transplant screening, prostate cancer detection, and robotic ultrasound guidance for pandemic response. Recent innovations include miniaturized imaging catheters and reflector-integrated ultrasound systems for clinical translation. Education PhD Computer Science, Johns Hopkins University, 2017 MS Computer Science, Johns Hopkins University, 2015 MS Human Health Sciences, Kyoto University, 2013 BS Human Health Sciences, Kyoto University, 2011 Research Interests His interdisciplinary research bridges robotics and biomedical engineering, addressing challenges in image-guided interventions, real-time surgical navigation, and novel imaging modalities. Specific areas include robotic-assisted ultrasound systems, spectroscopic photoacoustic imaging for tumor detection, and mathematical frameworks for clinical accuracy improvement. Grants & Awards NIH Director's Early Independence Award (2022) CDMRP Early Investigator Research Award (2018) WPI Seed Grants (2023-2024) Labs & Teams Medical FUSION Lab focuses on developing robotic imaging systems with expertise in robotics, medical imaging, and clinical translation. Collaborations include multidisciplinary teams in acoustics, optics, and biology for cutting-edge medical device innovation.

Daniele SalvatiView profile

Associate Professor

• Audio Signal Processing
• Acoustic Signal Processing
• Computer Audition
• +13 more

Daniele Salvati is an Associate Professor in the Department of Mathematics, Computer Science and Physics at the University of Udine, Italy. He is actively involved in research, teaching, and editorial roles in leading signal processing journals. Associate Professor, University of Udine Ph.D. in Acoustic Source Localization, University of Udine (2012) Senior Member, IEEE Associate Editor, IEEE/ACM Transactions on Audio, Speech, and Language Processing Handling Editor, Signal Processing His research focuses on audio and acoustic signal processing , particularly in the areas of source localization, speaker identification, and computer audition. He integrates traditional signal processing with modern machine learning and deep learning techniques to develop robust systems for real-world environments. His recent publications demonstrate a strong trend toward end-to-end deep learning models using raw waveforms, beamforming optimization (especially diagonal unloading), and applications on mobile and drone-based platforms . His work spans both journal and conference venues, with a focus on IEEE and ISCA publications. Scientific recognition includes: National Scientific Qualification as Full Professor (IINF-05/A) Senior Member of IEEE Editorial board roles in top journals He mentors doctoral students as part of the Ph.D. program in Computer Science and Artificial Intelligence and collaborates widely with researchers such as Carlo Drioli and Gian Luca Foresti. His research is supported by publications in high-impact journals and participation in international conferences. He leads work in the G.L. Foresti's Lab, focusing on intelligent systems and signal processing. His work includes developing microphone array-based interfaces for music and human-computer interaction, acoustic scene analysis, and automated sensor configuration. Future directions likely involve further integration of AI with physical sensor systems, especially in autonomous and mobile contexts.

Murat SARANView profile

Assistant Professor

• Human-Computer Interaction
• Gamification
• Intelligent Learning Systems
• +5 more

Dr. Murat SARAN is an Assistant Professor in the Department of Computer Engineering at Çankaya University, Ankara, Turkey. He serves as Director of the Distance Education Application and Research Center (DEARC), focusing on innovative educational technologies. His academic background includes a PhD (2009) and MSc (2003) in Computer and Educational Technologies from Middle East Technical University (1999-2009). Education: Bachelor's: Middle East Technical University, Computer and Educational Technologies (1999) Master's: Middle East Technical University, Computer and Educational Technologies (2003) PhD: Middle East Technical University, Computer and Educational Technologies (2009) Research Interests: Dr. SARAN specializes in Human-Computer Interaction, Gamification, and Mobile Learning. His work spans algorithmic thinking, context-aware systems, virtual reality integration in education, and data augmentation techniques for machine learning. He has contributed to projects addressing digital literacy in primary education and emotion recognition via computer vision. Key Projects: ICOINS (2018–2020): Developed Industry 4.0 training tools for SMEs (€203,858) Mesh-Based Mobile Education Platform (2012–2014): Managed a TL 373,128 project for SME/educational institutions HESAPRO (2011–2013): Analyzed health-safety-productivity links in workplaces (€103,000) Grants & Labs: His research has been supported by TÜBİTAK and EU grants. He leads the DEARC lab, focusing on distance education innovation and mobile learning systems.