Find Academic Supervisors

• Educational psychology
• Developmental Disorders
• Anger Management
• +4 more

Professor Keiko Honda is a distinguished faculty member at Waseda University's Faculty of Education and Integrated Arts and Sciences, School of Education. With a Doctorate in Education from Teachers College, Columbia University, she has established herself as a leading expert in educational psychology, particularly focusing on developmental disorders, anger management, crisis intervention, and school counseling. Her educational background includes: Graduate School, Division of Psychology, Counseling Psychology at Teachers College, Columbia University (1994) Faculty of Liberal Arts, Social Study at International Christian University Professor Honda's research focuses on understanding and addressing the challenges faced by children with developmental disorders, particularly in emotional regulation and social interaction. Her work has led to the development of specialized intervention programs including anger management techniques for children with borderline intelligence and social skills training for children with special needs. She has pioneered approaches that combine clinical psychology with educational practices to create effective support systems in school environments. Her research has identified key developmental stages in children's needs and self-control, resulting in categorized intervention strategies. Her work has been particularly influential in developing practical tools for educators and counselors working with children who have difficulties in social interaction and emotional regulation, including VRICS (Violence Risk Check Sheet) and specialized anger management programs implemented in schools and correctional facilities. Professor Honda is actively involved in professional organizations including: Japan Psychological Association Japan School Psychologist Association Japanese Association of Criminal Psychology Japanese Association of Correction Education Japanese Association of Educational Psychology Japanese Association of Learning Disorder She supervises graduate students across multiple departments including the Graduate School of Education and the Graduate School of Letters, Arts and Sciences, teaching diverse courses related to school psychology, educational psychology, and brain-based learning in inclusive classrooms. Her current research projects focus on developing comprehensive systems for inclusive education and prosocial problem-solving strategies for children with developmental disabilities.

• Information Visualization
• Data Visualization
• Human-Computer Interaction
• +4 more

Dr. Stuart Marshall serves as Associate Dean of Academic Development in the Faculty of Science and Engineering at Victoria University of Wellington - Te Herenga Waka, where he also holds the academic rank of Senior Lecturer. Previously, he served as Head of School for the School of Engineering and Computer Science from 2015-2021 after joining the institution as a Lecturer in 2003. His academic journey includes completing his BSc, MSc, and PhD in Computer Science at Victoria University of Wellington, with his doctoral research focusing on software reuse. Marshall's research interests center on information and data visualization, particularly exploring how visualization can function as collaborative tools in team environments rather than single-user applications. He also investigates explainable AI and mobile user interface design with emphasis on promoting healthy device usage patterns. His work bridges theoretical computer science with practical applications in education and environmental analysis. Analysis of his publication record reveals a consistent focus on visualization techniques, with recent work shifting toward immersive analytics and virtual reality applications for complex data analysis, particularly in ecosystem services. Earlier publications concentrated on mobile learning applications grounded in transactional distance theory, software visualization for large codebases, and graph layout algorithms. Marshall has supervised six PhD students and eight Masters students to completion, with current doctoral supervision spanning immersive environments for reading assistance, medical diagnosis interfaces, and cardiac procedure simulation. His grant portfolio includes multiple ACM ISS 2022 projects with industry partners including Sensor Holdings Limited, Niantic, Autodesk, and Northeastern University, along with an Australian Research Council grant for digital games preservation. Teaching responsibilities encompass foundational programming, safety-critical systems, human-computer interaction, and data visualization across undergraduate and graduate courses. He teaches subjects including SWEN326 (Safety-Critical Systems), CGRA151 (Introduction to Computer Graphics and Games), and DATA301 (Data Science in Practice), with teaching assignments scheduled through 2026.

• AI Agents
• Multi-Agent AI
• Artificial Intelligence
• +14 more

Saptarashmi Bandyopadhyay is a Tenure-Track Assistant Professor of Computer Science at the City College of New York and the Graduate Center at the City University of New York (CUNY). Her research focuses on Artificial Intelligence Agents and Autonomous Decision Making, with special emphasis on Multi-Agent Reinforcement Learning, Multi-Agent Imitation Learning, and related paradigms. She has established significant collaborations with leading institutions including Google DeepMind, Carnegie Mellon University, Oxford University, and MIT. Dr. Bandyopadhyay received her PhD from the University of Maryland, College Park, where she was advised by Professor John Dickerson and Professor Tom Goldstein. Prior to that, she graduated from Penn State in 2020 with a thesis on Multimodal Computer Vision in Medical Domain advised by Prof. William Evan Higgins. Her research expertise spans multiple domains of AI including Multi-Agent Systems, Reinforcement Learning, Imitation Learning, and Multimodal Perception. She specializes in developing AI agents for applications in climate conservation, economic systems, and AI safety. Her work integrates techniques from computer vision, natural language processing, and robotics to create more robust and explainable AI systems that can operate effectively in complex, real-world scenarios. Current work includes improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Analysis of Dr. Bandyopadhyay's publication record reveals a clear progression from foundational work in medical imaging and natural language processing toward increasingly sophisticated multi-agent AI systems. Her recent publications demonstrate a strong focus on Multi-Agent Reinforcement Learning frameworks like JAXMARL, with applications spanning from supply chain orchestration to climate conservation. The interdisciplinary nature of her work is evident in publications spanning computer vision, NLP, and multi-agent systems conferences including AAAI, NeurIPS, AAMAS, EMNLP, and ACL. DoGood Fellow (2022) UMD Dean's Summer Fellow (2021) Dr. Bandyopadhyay has been actively involved in securing research funding from major agencies including NSF, NIH, DoD, and ARL. She served as the lead PhD student RA in a DoD project for Multi-Agent Explainable AI to improve AI trustworthiness. Her service to the academic community includes membership on program committees for major conferences including IJCAI 2024, KDD 2024, ACL 2024, and AAMAS 2023-2024. She has also created the AI Agents Seminar Series at UMD in 2022 with over 1,000 participants from six continents. Currently, Dr. Bandyopadhyay leads research on improving explainable AI, developing Multimodal LLM/VLM/Robotic Agents, and creating libraries to speed up Multi-Agent evolutionary training with JAXMARL. Her lab collaborates prominently with researchers from Google DeepMind, Carnegie Mellon University, Oxford University, University of Sheffield, Waymo, Meta AI, and MIT, with special focus on Dr. Jakob Foerster's and Dr. Robert Loftin's groups.

Alison Clune BergView profile

Associate Professor

• Community Nutrition Education
• Implementation Science
• Chronic Disease Prevention
• +6 more

Dr. Alison Clune Berg is an Associate Professor and Extension Nutrition and Health Specialist at the University of Georgia's College of Family and Consumer Sciences. She holds a PhD, MS, and BS in Foods and Nutrition from UGA, graduating in 2015, 2009, and 2007 respectively. Her research evaluates community nutrition education programs to prevent chronic diseases like obesity, cardiovascular disease, diabetes, and cancer, with a focus on implementing evidence-based interventions through the Cooperative Extension model. Education: PhD, MS, BS in Foods and Nutrition from University of Georgia Current Role: Associate Professor and Extension Nutrition and Health Specialist Dr. Berg specializes in chronic disease prevention through community nutrition education, implementation science, and translational research. Her work includes the National Diabetes Prevention Program (DPP) and Cancer Prevention initiatives. She bridges academic research with real-world health promotion, particularly in rural Georgia. Recent projects analyze dietary behavior change, program feasibility, and health equity in underserved populations. Her recent publications (2023-2025) span implementation science, diabetes prevention, rural community health, and adolescent nutrition education. Awards include the 2024 Chris Todd Outstanding Outreach Award, multiple 2024 Georgia Extension Association first-place awards for curriculum development, and the 2023 D.W. Brooks Excellence in Extension. She has authored or co-authored 20+ peer-reviewed articles and extension publications, including peer-reviewed guides on nutrition labels, diabetes management, and cancer prevention. Scientific honors include: 2024 Chris Todd Outstanding Outreach Award 2024 Georgia Extension Association Team Awards 2023 D.W. Brooks Excellence in Extension 2022 NEAFCS National Educational Awards 2019 Educational Curriculum Package Awards Dr. Berg collaborates with county extension agents, leads state-wide nutrition programs like Cooking for a Lifetime of Cancer Prevention, and mentors graduate students. She supervises research through courses like NUTR 7000/9000 and guest lectures on nutrition-disease relationships.

Jinghui ChengView profile

Associate Professor

• Human-Computer Interaction
• Software Engineering
• User-Centered Design
• +3 more

Jinghui Cheng is an Associate Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal . He holds a PhD in Computer Science from DePaul University (2017), preceded by an MSE and BSE from Xi’an Jiaotong University, China. His research uniquely bridges Human-Computer Interaction (HCI) with Software Engineering , focusing on technologies that support domain experts with specialized information needs. Awards : Canada Research Chair Tier 2 in User Experience Design of Data-Driven Systems His recent work examines playful AI interactions (e.g., ChatGPT), designer-developer collaboration , and privacy motivation in UI/UX. He actively supervises PhD and Master’s students in the HCD Lab , with over 20 completed theses. Collaborations span institutions like the École Polytechnique and partnerships with researchers such as Jin L. C. Guo and Bram Adams . Cheng’s 15 most recent publications (2023-2025) reflect trends in AI-assisted design , usability in open-source communities , and ethical technology development . His grants include the AUDACE grant (2020) co-led with Dr. Gabrielle Pagé, focusing on healthcare applications.

Fu YunView profile

Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +1 more

Yun Fu is a Distinguished Professor at Northeastern University, affiliated with the College of Engineering and Khoury College of Computer Science. He holds tenure in Electrical and Computer Engineering (ECE). His roles include Professor, Senior Vice President at Shiseido Americas, founder of Giaran (acquired by Shiseido), and co-founder of TVision Insights. He earned his Ph.D. from the University of Illinois at Urbana-Champaign. His research focuses on artificial intelligence, computer vision, machine learning, and data mining. Key achievements include over 500 publications, 50+ patents, and prestigious awards like IEEE Fellow, OSA Fellow, and AAIA Fellow. He leads the SMILE Lab, exploring AI applications in vision, robotics, and healthcare. Notable entrepreneurship includes AI-driven ventures in cosmetics and media analytics. Research interests emphasize AI-driven solutions for computer vision challenges, including anomaly detection, trajectory prediction, and multimodal learning. His work bridges academia and industry, with impactful contributions to both fields.

Konrad KowalczykView profile

Associate Professor

• Speech and Audio Signal Processing
• Machine Learning
• Deep Neural Networks
• +6 more

Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.

• AI for Sound
• Machine Learning
• Signal Processing
• +4 more

Mark Plumbley is a Professor of Signal Processing at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering at the University of Surrey. He holds an EPSRC Fellowship in 'AI for Sound' and has led major research initiatives, including the DCASE challenges. His work focuses on AI-driven analysis of acoustic scenes and events, with contributions to machine learning, audio source separation, and sparse representations. Previously, he was Director of the Centre for Digital Music at Queen Mary University of London and Head of the School of Computer Science at Surrey. Education: PhD in Neural Networks (1991). Academic roles include Professorships at King’s College London (1991–2002) and Queen Mary University of London (2002–2014). Research spans audio event detection, sound scene classification, and generative AI for audio synthesis. He leads projects like the EPSRC-funded 'Making Sense of Sounds' and 'Musical Audio Repurposing using Source Separation', and co-edited the Springer book on Computational Analysis of Sound Scenes and Events. Research Interests: AI for Sound: Machine learning applied to real-world audio analysis. Acoustic Scene and Event Recognition: Developing models for sound classification and localization. Generative Audio Models: Text-to-audio systems and diffusion models for sound synthesis. Healthcare Applications: Audio-based diagnostics and bioacoustic signal processing. Grants and Awards: EPSRC Fellowships, EU-funded networks (SpaRTaN, MacSeNet), and Fellowships from IET and IEEE. Notable awards include the IEEE Young Author Best Paper Award (co-authored with students) and leadership in the DCASE community. Labs and Collaborations: CVSSP at Surrey, collaborations with BBC R&D, and interdisciplinary projects on urban soundscapes and noise pollution (UK Acoustics Network Plus).

• Artificial Intelligence
• Neural Networks
• Computational Neuroscience
• +4 more

Summary Pawel Andrzej Herman is an Associate Professor at the Division of Computational Science and Technology within the School of Computer Science and Communication (CSC) at KTH Royal Institute of Technology. His research focuses on computational neuroscience, brain-inspired AI, and machine learning applications in healthcare and cognitive science. He teaches multiple courses including Artificial Neural Networks and Deep Architectures , supervises degree projects across computer engineering and electrical engineering disciplines, and actively contributes to interdisciplinary research initiatives. His work bridges theoretical neuroscience with practical AI solutions, emphasizing synaptic plasticity models, neuromorphic computing, and medical diagnostic systems. Key areas include olfactory perception modeling, working memory mechanisms, and FPGA-accelerated neural networks. He collaborates internationally on projects such as AI-driven medical imaging and cognitive neuroscience studies. Dr. Herman’s research has been published in high-impact journals and conferences, with recent contributions to understanding neural mechanisms of odor naming deficits, beta/alpha oscillations in working memory, and spiking neural network architectures. His technical leadership spans HPC frameworks like StreamBrain and interdisciplinary tools for scientific data storage (NoaSci).

Junlin YuView profile

Researcher

• Educational Psychology
• School Burnout
• Achievement Goals
• +5 more

Junlin Yu is an Academy Postdoctoral Researcher at the Department of Education, University of Helsinki (Faculty of Educational Sciences). His research focuses on educational psychology, particularly student motivation, school burnout, achievement goals, and gender role influences. Yu has supervised doctoral theses at multiple universities including University College Dublin and Vrije Universiteit Amsterdam. He has received significant funding including Academy Research Fellowships and Strategic Research Council grants. Research Interests Student motivation and mindset profiles Socioemotional well-being in educational contexts Gender differences in academic performance and engagement Climate change education and youth development Key Projects "ClimComp": Climate change competency education (2021-2026) Resilient School Systems (2021-2024) Growth mindset cultivation in classrooms (2023-2028) Yu's recent work explores interconnected trajectories of achievement goals and well-being, as well as the impact of teacher beliefs on student mindset development. His studies frequently appear in top journals like Learning and Instruction and British Journal of Educational Psychology.

• Nano-CMOS modeling
• Physical design
• Nanotechnology
• +6 more

Yu Cao is an Adjunct Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). He holds a Ph.D. in Electrical Engineering from the University of California, Berkeley (2002), an M.A. in Biophysics (1999), and a B.S. in Physics from Peking University (1996). His research focuses on nanoscale technology modeling, variability and reliability in electronics, and hardware design for on-chip learning. Key interests include predictive technology modeling, post-silicon integration, and energy-efficient semiconductor devices. Dr. Cao has led multiple grants funded by NSF, Samsung, Semiconductor Research Corporation, and academic institutions. His work emphasizes bridging technology and design automation gaps in nanometer-scale integration. Courses taught include VLSI design, digital systems, and graduate research supervision. He has authored two books and numerous articles on nano-CMOS modeling and physical design. His recent research advances include stable, efficient organic light-emitting diodes (OLEDs) and electrochromic devices leveraging phosphorescent molecular aggregates. These innovations address challenges in color stability, energy efficiency, and device lifetime. Notable grants include NSF-funded projects on hardware and algorithms for on-chip learning (2015–2016) and low-power bio-signal processing (2015–2016). His contributions span semiconductor reliability, nanoelectronic device applications, and predictive circuit simulation techniques.

Per LynggaardView profile

Professor

• Integrated Circuit Design
• Wireless Sensor Networks (WSN)
• Machine Learning
• +8 more

Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.

Ling DingView profile

Associate Professor

• Biochemistry
• Chemistry
• Molecular Biology
• +7 more

Ling Ding is an Associate Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark, and a key member of the DTU Microbes Initiative and Center for Microbial Secondary Metabolites. Their research focuses on microbial ecology, secondary metabolite biosynthesis, and antifungal compound discovery. Recent work involves genome mining in Streptomyces species, NMR spectroscopy, and studying microbial interactions such as actinobacterial and Pseudomonas fluorescens systems. Projects explore bioactive phosphorus-containing molecules, in situ detection of metabolites, and data-driven biosynthesis mechanisms. Their research contributes to UN Sustainable Development Goals (SDGs) related to health and environmental sustainability, with publications spanning microbial chemistry, drug discovery, and synthetic biology. Supervised PhD projects include omics-driven antifungal metabolite discovery and microbial interaction studies. Key publication trends include Streptomyces-derived bioactive compounds (e.g., lydicamycins, alligamycin), azoxy/polyketide metabolites, and computational approaches for fungal interaction classification. Methodologies emphasize NMR, genome mining, and deep learning models. Advising: PhD students: M. Haahr, A. Kostoglou, D. J. Otto, A. Svetlova, A. Kaltenyte Projects: Active roles in omics-driven antifungal metabolite discovery, phosphorus-containing molecule mining, and secondary metabolite biosynthesis mechanisms.

Engin ErzinView profile

Professor

• Artificial Intelligence
• Multimedia Systems
• Computer Vision
• +6 more

Engin Erzin is a Professor at Koç University's College of Engineering, leading the KUIS AI Lab and Multimedia, Vision and Graphics Lab . His research focuses on AI-driven human-centric systems, affective computing, and multimodal interaction analysis. He has contributed extensively to robotics, speech processing, and human-robot interaction through over 70 peer-reviewed publications since 2008. Research interests include: Affective computing and emotion recognition from speech/gestures Human-robot interaction and socially engaging agents Speech-driven animation and gesture synthesis Multimodal data fusion for interaction analysis Deep learning applications in robotics and biomedical engineering Recent work emphasizes: Developing adaptive pHRI controllers for manufacturing tasks Creating engagement measurement frameworks for human-machine interfaces Advancing Turkish speech recognition through self-supervised learning Designing multimodal databases for interaction studies Labs: KUIS AI Lab : Focuses on AI applications in robotics and human-computer interaction Multimedia Lab : Specializes in vision, graphics, and audiovisual analysis

Frederik QuestierView profile

Researcher

• Educational Technology
• Blended Learning
• Digital Media Literacy
• +3 more

Frederik Questier is a Researcher in Educational Science at Vrije Universiteit Brussel (VUB), located at Pleinlaan 2 in Brussels, Belgium. He holds a PhD in clustering and feature selection methods, demonstrated through his supervision of doctoral theses such as 'Contributions to Clustering and Feature Selection Methods for Clustering' (2005). His work focuses on educational technology, blended learning models, and international ICT initiatives in education systems. Research Interests: Questier explores the intersection of technology and education, including digital media literacy, mobile-assisted language learning (MALL), open-source software implementation, and public health education. His recent work on face masks during the COVID-19 pandemic highlights interdisciplinary collaboration between education and healthcare sectors. Projects & Grants: Active in both fundamental and applied research, he leads projects like the FOD27 e-health initiative (2016–2019) and MarMOOC (2016–2020), which developed hybrid learning systems in Moroccan universities. He also oversees Ghana's ICT education project (2013–2015) and manages international collaborations through VLIR-UOS in Ethiopia. Awards & Recognition: While no explicit awards are listed, his extensive publication record (113+ outputs) and h-index of 17 reflect peer recognition. His work on digital media literacy (2024) and MALL (2019) have garnered significant citations. Labs & Teams: Collaborates with institutions like Routledge (peer-review committee), Vlaams Forum voor Onderwijsonderzoek, and partners in Morocco, Ghana, and Ethiopia through projects addressing ICT integration in education systems.