Find Academic Supervisors

Iti ChaturvediView profile

Lecturer

• Signal Processing
• Artificial Intelligence
• Social Media Analysis
• +5 more

Iti Chaturvedi is a Lecturer in the Department of Information Technology at James Cook University (JCU). She holds a Ph.D. in Computer Engineering from Nanyang Technological University, Singapore. Her research focuses on signal processing and AI applications in social media, including emotion recognition, speech analysis, and sentiment analysis. She has been recognized as a Top 2% Most Cited Researcher globally (2022) and received the JCU CSE Early Career Researcher Award (2020). She teaches courses such as Machine Learning and Data Science, Programming III, and Design Thinking I. Current research projects include sentiment prediction from social media (since 2020). She serves as an Associate Editor for the Expert Systems journal (2023) and has been an ARC Assessor (2020). Key contributions include work on speech emotion recognition, constrained manifold learning for videos, and multimodal emotion recognition systems. Her research outputs span journals like Expert Systems , Signal Processing , and conferences including IJCNN and AAAI.

Danielle SulikowskiView profile

Senior Lecturer

• Comparative Cognition
• Cognition
• Behavioral Ecology
• +6 more

Danielle Sulikowski is a Senior Lecturer and Associate Head of School in the School of Psychology at Charles Sturt University (Bathurst Campus). She holds a PhD from Macquarie University (2010), a BSc(Hons) from Macquarie University (2005), and a BSc from the University of New England (2003). Her research focuses on comparative cognition, integrating psychology, behavioral ecology, and evolutionary principles to study spatial cognition, attractiveness, visual attention, and lateralization in humans and animals like noisy miners. Her teaching responsibilities include PSY201 (Research Methods and Statistics in Psychology) and PSY307 (Cognition), delivered both internally and via distance learning. Key research interests span cognition, learning, perception, biopsychology, and evolutionary psychology, with projects involving human subjects and avian species. She maintains a research laboratory website for her comparative cognition work. Recent publications explore topics ranging from dark personalities in online behavior to facial symmetry studies and evolutionary mate strategies. Her work frequently bridges traditional psychology with ecological and evolutionary frameworks, emphasizing adaptive cognitive mechanisms in foraging and social behavior. No scientific awards are explicitly mentioned, though her extensive publication record reflects significant academic contribution. Teaching and advisory roles include no listed students, but her research engages with broader societal issues like domestic violence policy implications and digital media effects.

Jacob WhitehillView profile

Associate Professor

• Applied Machine Learning
• Speech and Speaker Recognition
• Educational Data Mining
• +3 more

Jacob Whitehill is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), affiliated with the Learning Science & Technologies (LST) program. His research focuses on applying machine learning to education and human-computer interaction, including speech recognition, emotion analysis, and automated classroom observation. He leads the NSF-funded project Developing New Scientific Instruments for Classroom Observation and collaborates on the AI Institute for Student-AI Teaming (iSAT) . His research interests span Applied Machine Learning (e.g., multi-modal systems, speaker diarization), AI for Education (e.g., automated instructional evaluation, child speech recognition), and Emotion Recognition (e.g., affective computing in classrooms). Recent work emphasizes classroom observation tools and improving student-teacher interaction analysis through video and audio data. Notable projects include: NSF-funded classroom observation tools AI Institute for Student-AI Teaming (iSAT) Schmidt Futures-funded Hybrid Human-Agent Tutoring for math education His team includes PhD students Xinlu He (Data Science), Jiani Wang (Computer Science), and Yiwen Guan (Computer Science), along with visiting scholar Cecilia Tivir. He advises students on topics like speaker recognition, educational data mining, and computer vision in classroom settings. Grants and collaborations include NSF, Schmidt Futures, and industry partnerships. His lab develops tools for automated feedback on teaching practices, leveraging LLMs and multimodal data. For more details, contact jrwhitehill@wpi.edu .

Jean PonceView profile

Professor

• Computer Vision
• Machine Learning
• Robotics
• +3 more

Jean Ponce is a Professor of Computer Science at Ecole Normale Superieure (ENS) in Paris and a Part-Time Global Distinguished Professor at New York University's Courant Institute of Mathematical Sciences and Center for Data Science (CDS). He previously served as Director of the ENS Computer Science Department (2011-2017) and held positions at Inria (2017-2022), University of Illinois at Urbana-Champaign (1998-2006), MIT, Stanford, and Inria (1982-1985). Academic Leadership: Scientific Director of PRAIRIE Interdisciplinary AI Research Institute in Paris Startup Involvement: Co-founder and CEO of Enhance Lab (2022) Editorial Roles: Senior Editor-in-Chief of International Journal of Computer Vision (2019-2022) Conference Leadership: Chair of IEEE CVPR (1997,2000), ECCV (2008), and upcoming ICCV (2023) Research Focus: Computer vision, machine learning, robotics, and AI with applications in exoplanet imaging, 3D reconstruction, and image quality assessment. His work bridges statistical learning and deep learning approaches. Awards: IEEE Fellow (2003) ELLIS Fellow (2019) ERC Advanced Grant (2011) IEEE CVPR Longuet-Higgins Prizes (2016,2020) ICML Test-of-Time Award (2019) Patents & Publications: Co-author of influential textbook Computer Vision: A Modern Approach (translated into Chinese, Japanese, Russian). Holds two US patents and one pending French patent. Google Scholar h-index of 78 with over 55,000 citations.

Gady AgamView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +8 more

Dr. Gady Agam is an Associate Professor in the Department of Computer Science at Illinois Institute of Technology (Illinois Tech), affiliated with the College of Computing. His primary research focuses on Computer Vision, Machine Learning, and Artificial Intelligence, with applications in medical imaging, remote sensing, and security systems. He leads the Visual Computing Lab, which explores topics such as deep learning, geometric modeling, and computational methods for data analysis. Dr. Agam teaches courses including CS584 (Machine Learning), CS512 (Computer Vision), and CS577 (Deep Learning). His research has resulted in over 100 peer-reviewed publications and collaborations with organizations like SPIE. He has held leadership roles in conferences such as Document Recognition and Retrieval and has contributed to industry-relevant projects like the MuscleX software. His academic contributions include advancements in image registration, feature detection, and automated medical diagnostics. He actively supervises graduate students in his lab and maintains partnerships with academic and industrial entities to drive innovation in visual computing technologies.

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Prof. Margret Keuper is a Professor in the Department of Computer Vision and Machine Learning at the Max Planck Institute for Informatics. Her research focuses on advancing machine learning and computer vision techniques, with an emphasis on model fairness, adversarial robustness, and multimodal interactions. She leads interdisciplinary projects exploring topics such as dataset analysis, generative models, and climate action through visual narrative analysis. Research Interests: Her work bridges theoretical foundations and practical applications in domains like adversarial training, image classification robustness, and robotics perception. She explores how vision-language models can be steered to align with human biases and develops methods for data-efficient learning and interpretability. Recent Contributions: Recent work includes FAIR-TAT (model fairness via adversarial training), VSTAR (video synthesis), and TikZero (zero-shot graphics program generation). Her publications in top venues like CVPR, ICCV, and ICLR highlight contributions to both methodological innovation and real-world impact. Collaborations: Works closely with researchers across Max Planck and academic partners, focusing on projects such as sensor layout optimization, climate discourse analysis via social media imagery, and domain-aware foundation model fine-tuning.

Jingchao NiView profile

Assistant Professor

• Machine Learning
• Time Series Analysis
• Cross-Modal Learning
• +9 more

Jingchao Ni is an Assistant Professor in the Department of Computer Science at the University of Houston. He previously worked as a researcher at NEC Labs America (2018-2022) and AWS AI Labs (2022-2024). He earned his Ph.D. in Computer Science from The Pennsylvania State University's College of Information Sciences and Technology in 2018 under Prof. Xiang Zhang. Research Interests: Machine Learning, Time Series Analysis (Cross-Modal/Multimodal Integration, LLM Reasoning), Graph Learning, Anomaly Detection, Generative Models, and applications in Healthcare (personalized systems, Cyber-Physical Systems, AIOps). His recent publications focus on multimodal time series analysis, vision models for temporal data, and interpretable graph neural networks, with deployments in AWS cloud systems. He has advised students on projects involving LLM agents, causal discovery, and robust forecasting. Awards include a AAAI 2019 Most Influential Paper (PaperDigest) and an ICLR 2022 Spotlight Presentation. He leads the Data-Driven Intelligence (D2I) Group and has contributed to tutorials at KDD 2025 and IJCAI 2025.

Md Saifuddin KhalidView profile

Associate Professor

• Artificial Intelligence
• Learning Analytics
• Digital Training Technologies
• +3 more

Md Saifuddin Khalid is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on digital training technologies, learning analytics, and human-computer interaction in educational contexts. He leads and collaborates on projects exploring AI applications in education, adaptive learning systems, and the impact of digital tools on teaching and learning outcomes. His work contributes to UN Sustainable Development Goals related to quality education and industry innovation. Khalid actively supervises PhD students and has published extensively on topics such as generative AI for literature reviews, user satisfaction in AI chatbots, and usability of digital training technologies in industries like pharmaceuticals. Key Projects: MIXAP-EU: Developing mixed reality tools for teachers in Europe. HEGenAI: Integrating generative AI into higher education assessments. Methods for evaluating digital training technologies in pharmaceutical production. Editorial Roles: Peer reviews for journals like International Journal of Human-Computer Interaction and Computers & Education . Khalid’s research emphasizes practical applications of technology in education and industry, with a focus on improving user experiences and outcomes through systematic evaluations and design frameworks.

Jane WangView profile

Professor

• Biological Physics
• Theoretical Condensed-Matter Physics
• Computational Fluid Dynamics
• +4 more

Affiliations and Roles Professor Wang holds dual appointments as Professor of Physics and Mechanical and Aerospace Engineering at Cornell University. She is affiliated with the Sibley School of Mechanical and Aerospace Engineering and the College of Arts and Sciences. Education B.S. in Physics, Fudan University, Shanghai, China (1989) Ph.D. in Physics, University of Chicago (1996) NSF-NATO Postdoctoral Fellow, Theoretical Physics, Oxford University (1997) Visiting Member, Courant Institute of Mathematical Sciences, NYU (1997-1999) Research Her research focuses on the physics of living organisms, particularly insect flight dynamics , biophysics , and computational modeling . Key projects include: Dragonfly righting reflex mechanisms Neuro-mechanical control in fruit flies Unsteady aerodynamics and fluid-structure interactions Awards and Honors Simons Fellowship in Theoretical Physics (2020) Radcliffe Fellowship (2007) Cornell Provost's Award for Distinguished Scholarship (2005) David and Lucile Packard Fellowship (2002) Labs and Collaborations Her work integrates experimental and computational approaches, often conducted in collaboration with institutions like the Janelia Research Campus (HHMI) and the Joint Texas Experimental Tokamak (J-TEXT).

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

Damir IsovicView profile

Associate Professor

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +3 more

Damir Isovic is an Associate Professor and Vice-Chancellor for Internationalization at Mälardalen University's Academy of Innovation, Design and Technology. Previously, he served as Dean of the School of Innovation, Design and Engineering. His roles include leadership in academic administration and participation in national boards. He holds a PhD and has extensive international teaching experience. Research focuses on real-time systems, embedded systems design, and scheduling algorithms. Notable contributions include seminal work in real-time scheduling recognized by the IEEE Technical Community on Real-Time Systems. He has organized major conferences and delivered keynotes globally. His publications emphasize hybrid scheduling approaches, real-time operating systems (RTOS), media processing in resource-constrained systems, and MPEG standards. Recent work integrates memetic algorithms with fuzzy controllers and explores multi-core scheduling fairness. His research bridges theoretical scheduling models with practical embedded system implementations. No scientific awards explicitly listed in the text. Advising activities include supervising PhD students, though specific names are not provided. Lab affiliations include the Division of Networked and Embedded Systems, where he develops frameworks like GENESIS for embedded system engineering. His work emphasizes cross-disciplinary collaboration and industry partnerships in education and technology development.

• Artificial Intelligence
• Extended Reality
• Computer Science
• +3 more

Pasquale Cascarano is a fixed-term Assistant Professor at the University of Bologna , affiliated with the Department of the Arts . His research bridges computer science with creative industries (cinema, art, fashion) and biomedical applications, focusing on Artificial Intelligence and Extended Reality paradigms. He collaborates with national and international institutions on interdisciplinary projects. Institutional Affiliation: University of Bologna Academic Role: Assistant Professor (fixed-term) Research Interests center on integrating AI and XR into creative sectors and healthcare, with emphasis on: Generative AI for immersive environments Medical imaging and diagnostics Digital heritage preservation XR-based educational tools 3D visualization techniques Creative technology applications Recent Publications highlight trends in: Medical XR for surgical training AI-driven image restoration LLM integration with AR/VR Privacy in collaborative AR Fashion and cultural heritage digitization Deep learning for video enhancement

• Artificial Intelligence
• Medical Imaging
• Deep Learning
• +3 more

Caner Özer is a Researcher affiliated with Istanbul Technical University's Department of Artificial Intelligence and Data Engineering and the University of Twente's MIA group. He holds a PhD in Computer Engineering from Istanbul Technical University (2020), an MSc in Telecommunication Engineering (2017-2020), and a BSc in Electronics and Communications Engineering (2013-2017). His research focuses on medical imaging AI, explainable artificial intelligence (XAI), deep learning applications in healthcare, and computer vision techniques for artifact detection in medical imaging. He has conducted visiting research at the University of Twente (2024) and serves on academic committees at Istanbul Technical University. Research interests include developing explainable models for mammogram analysis, enhancing medical image quality assessment via transformers and neural networks, and addressing challenges in cardiovascular MRI segmentation through motion artifact detection. His work bridges deep learning theory with practical clinical applications, emphasizing transparency and accuracy in AI-driven medical diagnostics. Notable contributions include cross-domain artifact correction for cardiac MRI, joint CNN-RNN models for intracranial hemorrhage detection, and XAI methods for chest X-ray analysis. His research has been published in top-tier venues with a focus on medical imaging and deep learning advancements.

• Sound Design
• Audiovisual Art
• Multisensory Experience
• +7 more

Associate Professor Darrin Verhagen is an award-winning sound designer and composer at RMIT University's School of Design, where he teaches in the Sound Design specialization within the Digital Media Program and directs the Audiokinetic Experiments (AkE) Lab. His academic work bridges creative practice with scientific inquiry into multisensory perception. Verhagen's research centers on the neurobiology of aesthetic experience, particularly exploring how sound interacts with vision, movement, and vibration. His work in the AkE Lab utilizes motion simulators, 4D cinema seating, and light to create installations that investigate the relationship between hearing, vision, movement and vibration. His academic background includes postgraduate research on musical extremes - lowercase sound for his Masters and Noise for his Doctorate. His creative output spans sound design for contemporary dance, theatre, installation, film, television and computer games. Verhagen has released over 20 albums internationally and performs audiovisual live shows globally. He was founder and curator of Dorobo records, which showcased Australian sound art for 15 years. Industry collaborations include work with Melbourne Theatre Company, Malthouse, Chunky Move, Australian Dance Theatre, and others. Notable awards include multiple Green Room Awards for Sound Design, Australian Academy of Cinema & Television Arts award finalist status, APRA Art Music awards finalist for experimental music, RMIT Research Award for technical innovation, and RMIT Teaching Award for excellence in teaching practice. Verhagen's supervision projects reveal a trajectory toward increasingly sophisticated explorations of sound in relation to space, perception, and human experience, with recent work focusing on transdisciplinary design, spatial sound applications, distributed audio systems, and vocal ecology. His work consistently bridges artistic practice with technological innovation and scientific inquiry into perceptual phenomena.

Melissa TullyView profile

Professor

• News Literacy
• Misinformation
• Global Media Studies
• +3 more

Melissa Tully is a Professor and CLAS Collegiate Scholar at the University of Iowa, serving as Director of the School of Journalism and Mass Communication. She holds the Easton Professorship of Research and is a Senior Research Fellow at the Public Policy Center. Her research focuses on news literacy, misinformation, and global media with emphasis on African contexts. She teaches courses on social/digital media and leads the Global Media Studies Working Group at the Obermann Center. Tully earned her Ph.D. from the University of Wisconsin-Madison in 2011. Recent grants include projects on media literacy impact measurement and combating misinformation in Kenya/Senegal. She has advised numerous grants totaling over $2M, including roles as Principal Investigator for studies on WhatsApp misinformation and news literacy frameworks. Her work spans Sub-Saharan Africa and the U.S., addressing topics like mobile money adoption, cyberbullying, and sponsored content ethics. Key research themes include media literacy behaviors, misinformation countermeasures, and digital inequalities. Over 60 peer-reviewed articles highlight her contributions to Science Communication, Journalism Theory, and African media studies. Collaborations with international organizations like Innovations for Poverty Action reflect her commitment to applied research.