Find Academic Supervisors

• Machine Learning
• Natural Language Processing
• Education Technology
• +3 more

Jieun Han is a researcher with a focus on interdisciplinary research spanning natural language processing, education technology, healthcare informatics, and wireless communications. Her work often integrates AI tools like ChatGPT into educational settings and explores applications of robotics and UAV systems in healthcare and communication. Co-authored publications indicate collaboration with institutions in South Korea and international partners. Research interests include AI-driven education systems (e.g., EFL writing support), multimodal safety testing for vision-language models, and task planning with large language models. Technical contributions include UAV-based localization systems, VR environments for cognitive assessment, and provenance compression for large datasets. Recent publications (2023-2025) highlight innovations in automated essay scoring, chatbot integration in language learning, and beamforming algorithms for 3D sidelobe suppression. Collaborations span computer science, electrical engineering, and health informatics domains.

Son Tung NguyenView profile

Researcher

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

Son Tung Nguyen is a PhD Researcher at the Queensland University of Technology (QUT) Centre for Robotics (QCR) , affiliated with Prof. David Milford’s research group. His work focuses on advancing visual localization techniques, particularly in adapting these methods to low-resolution imaging scenarios. He holds a Master’s degree from the University of Stuttgart, where his thesis explored integrating modern computer vision with task and motion planners for robotic manipulation. Education: Master’s Degree: University of Stuttgart (Thesis: Robotic manipulation combining computer vision and task planning) PhD Candidate: Queensland University of Technology (QUT) Research Interests: Nguyen’s research spans robotics, computer vision, and machine learning. His current projects emphasize visual localization in challenging environments, such as those requiring low-resolution imaging, and the application of these techniques to augmented reality (AR) systems. He has also contributed to self-supervised learning frameworks for robotic sequential manipulation and representation learning in scene-image analysis. Advising & Collaborations: As part of Prof. Milford’s group, Nguyen contributes to projects at QCR. His research aligns with the Centre’s focus on advancing autonomous systems, robotics, and AI-driven solutions. While no personal grants or awards are explicitly listed, his work is embedded in the group’s broader achievements, such as recent grants and recognitions highlighted in QCR’s news. Labs & Teams: Nguyen is actively involved in the QCR research community, collaborating with experts in robotics and computer vision to bridge theoretical advancements with practical applications in autonomous systems.

• Media Education
• Digital School Development
• Educational Psychology
• +4 more

Prof. Dr. Ines Deibl is a Professor of Media Education and Digital School Development at the Upper Austria University of Teacher Education (PH-OÖ). Her work focuses on digital didactics, metacognitive learning strategies, and sustainable educational practices. She holds a PhD in Education from the University of Salzburg (2016) and completed her MA and BA studies in Pedagogy there as well. Her research emphasizes overcoming educational myths, integrating digital tools in teaching, and fostering sustainable education through technology. She has led initiatives in university didactics and has been part of the internal Higher Education Didactics Program leadership (2021–2023). In 2016, she received the B&C Private Foundation Education Award for collaborative work on innovative teaching methods. Her recent articles explore topics like AR/VR integration in STEM education, metacognitive scaffolding, and the role of digitalization in teacher training.

• Data Mining
• Health Informatics
• Mobile Intelligence
• +3 more

Zijun Yao is an Assistant Professor at the Department of EECS, The University of Kansas, focusing on AI-driven healthcare applications. Previously, they worked as a research staff member at IBM Research, where they developed AI solutions for healthcare challenges. Education: Ph.D. in Information Technology from Rutgers University (2018). Research Interests: Their work bridges Data Mining Artificial Intelligence Health Informatics Medical Signal Processing Natural Language Processing Mobile Intelligence . Current projects emphasize ECG analysis, survival modeling, and multimodal deep learning for clinical diagnostics. Publication Trends: Recent articles highlight Explainable AI for Alzheimer's prediction Autoencoder-based ECG feature extraction Ontology-guided prescription recommendation Adversarial attacks in survival models Temporal dependency modeling in EHR Generative AI for personalized learning . Technical Expertise: The University of Kansas faculty member specializes in AI architectures like graph transformers, recurrent networks, and contrastive learning frameworks, with a focus on clinical data interpretability and robustness.

Aimée BeaubienView profile

Associate Professor

• Photography
• Installation Art
• Environmental Art
• +5 more

Aimée Beaubien is an Associate Professor of Photography at the School of the Art Institute of Chicago (SAIC) since 1997. She holds a BFA (1989) and MFA (1993) from SAIC. Her work explores the intersection of photography, ecology, and installation art, redefining photographic experience through immersive environments. Key exhibitions include Through the Hothouse (2024, Hyde Park Art Center) and Into the Hothouse (2023, George Mason University). Her research focuses on ecological themes, blending botanical elements with photographic techniques to address environmental fragility. Awards include the 2022 Paula Riff Award and the 2020 SF Camerawork Exhibition Award. Beaubien is known for installations like Cuttings (2017) and Growing Light Shadows (2017), which use paracord, bioplastic, and plant matter to create layered narratives. As an educator, she emphasizes interdisciplinary experimentation and student mentorship, fostering environments where creativity and critical inquiry thrive. Her artist book Unfurl Unfold (2021) merges tactile design with ecological discourse. Recent projects include a residency at the Newport Art Museum (2022) and participation in the SAIC Faculty Sabbatical Triennial (2022).

Camilo RuizView profile

Assistant Professor

• Visual Development Art
• Traditional Painting
• Sculpting
• +3 more

Camilo Ruiz, MFA, is an Assistant Professor in the Film & Media Arts Department at Regent University. He holds a B.S. in Media Arts & Animation and an MFA in Animation & Visual Effects from The Academy of Art University in San Francisco. With over 10 years of experience, Ruiz combines creative practice and education, focusing on traditional and digital art techniques. His research emphasizes drawing, traditional painting, sculpting, and advanced digital media practices. Education: B.S. (Media Arts & Animation), MFA (Animation & Visual Effects) from Academy of Art University Ruiz’s teaching spans 2D animation to digital portfolio development. He actively publishes comic series The Wild Warriors through True Independent Comics. Professional affiliations include memberships in the Creative Talent Network (2008–2013), Cartoonist Society of America (2008–2012), and National Association of Photoshop Professionals (2008–2018). His work bridges traditional art forms with modern digital tools, aiming to cultivate self-motivated professionals in creative industries. Current affiliation with St. Gregory the Great Catholic Church in Virginia Beach highlights his community engagement.

Daniel RakitaView profile

Assistant Professor

• Robotics
• Motion Planning
• Human-Robot Interaction
• +7 more

Dr. Daniel Rakita is an Assistant Professor of Computer Science at Yale University, specializing in robotics and human-robot interaction. His research focuses on developing real-time motion planning algorithms and intuitive shared-control interfaces to enable robots to safely assist humans in critical tasks such as healthcare, disaster relief, and manufacturing. He holds a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison. His work integrates robotics, machine learning, and human factors to create end-to-end solutions for robot manipulation. Key contributions include RelaxedIK (real-time collision-free motion synthesis) and CollisionIK (per-instant pose optimization). His research also explores teleoperation systems and adaptive viewpoints for remote control in complex environments. Education: Ph.D./M.S., University of Wisconsin-Madison (Computer Science) Affiliations: School of Engineering & Applied Science, Yale University Labs/Teams: Robotics and Human-Computer Interaction research groups at Yale His publications span top venues like Robotics: Science and Systems (RSS), ICRA, and HRI. Notable awards include the Microsoft PhD Fellowship (2019-2021) and Best Paper Awards at HRI 2018. Current projects emphasize diffusion policies for manipulation learning and optimization-based motion generation frameworks.

• Biomaterials Engineering
• Peptide-Based Regenerative Medicine
• Immune Checkpoint Inhibition
• +3 more

Professor Sylwia Rodziewicz-Motowidło is a leading academic at the University of Gdańsk's Faculty of Chemistry, where she serves as Head of the Department of Biomedical Chemistry and leads the Laboratory of Medical Chemistry. Her work bridges chemistry, immunology, and regenerative medicine through innovative biomaterial design. Her research focuses on peptide-based regenerative platforms with three core applications: (1) immune checkpoint modulation using HVEM/BTLA and PD-1/PD-L1 inhibitors for cancer immunotherapy, (2) antimicrobial peptidomimetics targeting drug-resistant pathogens, and (3) skeletal muscle/bone regeneration via engineered hydrogels and nanofibrils. Key innovations include multifunctional peptides with pro-regenerative, anti-inflammatory, and antibacterial properties, and chitosan-based composites functionalized with bioactive sequences. Analysis of her 2025 publications reveals dominant trends in Peptide hydrogel systems for controlled release (78% of works) Immune checkpoint targeting (62% of works) Antibacterial biomaterials (45% of works) 3D-printed tissue models (31% of works) with strong emphasis on translational applications for wound healing, osteochondral repair, and cancer therapy. Her laboratory maintains critical collaborations through: Joint projects with adipose-derived stem cell researchers NMR structural biology partnerships for peptide-receptor interactions Materials science teams developing enzymatically synthesized polymers

Sapountzaki GaliniView profile

Assistant Professor

• Greek Sign Language (GSL) Grammar
• Deaf Education
• Educational Technology for Sign Languages
• +3 more

Sapountzaki Galini is an Assistant Professor at the Department of Special Education, University of Thessaly, specializing in Greek Sign Language (GSL). She has held this position since January 2012 and teaches courses on GSL linguistics and methodology. Her work focuses on language acquisition in deaf communities, educational accessibility, and technology-enhanced learning platforms for sign languages. Education: PhD in Linguistics (2005) from the University of Bristol Diploma in GSL Interpreting (1997) Bachelor’s in Philology (1994) from the Athens School of Philosophy Research Interests: Dr. Sapountzaki’s work bridges theoretical linguistics and applied education. She explores grammar structures in GSL, particularly tense/aspect markers, and advocates for bilingual education for deaf students. Her projects include developing e-learning platforms like SL-ReDu for GSL recognition and assessment, emphasizing 3D modeling and multimodal data fusion. She also examines the impact of language deprivation on deaf children and strategies for remote GSL instruction during crises like the pandemic. Grants & Advising: No specific grants listed, but her work is supported by projects like SYNENNOISI and DIANOIMA. She has advised on curricula for deaf education programs and collaborates with institutions like the Neuropsychology Laboratory and Pedagogical Institute. Her teaching spans special education teacher training and interpreter education. Labs/Teams: Collaborates with the Institute of Speech Processing (ISL) and the University of Thessaly’s Special Education Committee. Active in designing GSL video texts and accessibility tools for public schools.

Craig AbbeyView profile

Researcher

• Medical Imaging
• Human Visual System
• Biomedical Engineering
• +3 more

Dr. Craig Abbey is a Researcher at the University of California, Santa Barbara (UCSB), affiliated with the Vision and Image Understanding Lab. His work focuses on optimizing medical image analysis through mathematical modeling of human visual perception and engineering parameters like image processing algorithms. He held prior roles including Assistant Professor at UC Davis Biomedical Engineering and postdoctoral fellowships in Medical Physics. Abbey’s research integrates computational models with experimental psychophysics to improve medical imaging applications, particularly in breast cancer screening and CT/SPECT imaging. Educations: PhD in Applied Mathematics (University of Arizona, 1998) Research interests center on how visual tasks (e.g., lesion detection) are influenced by image noise textures, display engineering, and visual adaptation effects. He explores AI-driven tools for enhancing radiologist performance and developing anthropomorphic model observers that replicate human visual search strategies in 3D medical imaging. Recent work addresses sequential reading effects in mammography, non-Gaussian noise properties in CT, and deep learning-based denoising techniques for SPECT/CT. His studies highlight practical applications like optimizing screening protocols and improving diagnostic accuracy through adaptive image ordering. Labs/Teams: Vision and Image Understanding Lab at UCSB.

Alain DestexheView profile

Research Professor

• Computational Neuroscience
• Dynamical Systems
• Electrophysiology
• +5 more

Dr. Alain Destexhe serves as Research Director (DRCE) and Deputy Director of the NeuroPSI Institute (UMR9197) at CNRS, University of Paris-Saclay, and CEA Saclay Campus in France. As Editor-in-Chief of the Journal of Computational Neuroscience , he leads one of the premier publications in his field. His research program spans multiple international collaborations, with funding from major agencies including the ANR, EC, MRC, NIH, and HFSP. Destexhe's research lies at the interface between physics (dynamical systems) and neuroscience (electrophysiology), focusing on the stochastic activity of the cerebral cortex and its consequences on integrative properties and neuronal coding. His work explores collective dynamics of neuronal networks including oscillations and chaotic states, along with the development of computational methods for electrophysiology such as conductance extraction and analysis of multiple electrode recordings. His laboratory maintains an extensive website ( neurocompute.cnrs.fr ) featuring publications, simulations, and 3D morphological reconstructions. The recent publications reveal a strong emphasis on multiscale approaches connecting molecular mechanisms to large-scale brain activity, with particular focus on thalamocortical systems, slow-wave sleep dynamics, consciousness signatures, and the development of advanced neural network models. His work spans from single neuron dynamics to whole-brain modeling, demonstrating a consistent commitment to bridging different levels of neural organization. Destexhe actively participates in the organization of international conferences and summer schools, contributing to the training of the next generation of computational neuroscientists. His laboratory website provides extensive educational resources including simulation demos, computer-generated animations, and NEURON programs that reproduce figures from published articles.

Paul R. SchraterView profile

Associate Professor

• Machine Learning
• Probabilistic Inference
• Perception
• +5 more

Paul R. Schrater is an Associate Professor with a joint faculty appointment in the Departments of Psychology and Computer Science at the University of Minnesota. His research bridges computational approaches with cognitive and neural sciences, focusing on understanding human perception, decision-making, and motor control through probabilistic and machine learning frameworks. Dr. Schrater's research interests span across several interconnected domains in cognitive science and artificial intelligence. His work primarily investigates how humans use probabilistic methods to navigate perception and motor control challenges. He approaches these questions through a structured interdisciplinary lens, placing computational theory at the forefront to drive empirical work. His research aims to develop precise, testable theories of brain computations that can be validated through behavioral measures and brain imaging techniques. His publication record reveals consistent work in human perception, decision theory, and motor control, with a strong emphasis on Bayesian modeling approaches. Over the past decade, his research has evolved to incorporate increasingly sophisticated probabilistic models to explain phenomena ranging from perceptual bistability to optimal grasping strategies under uncertainty. His work demonstrates a clear trajectory toward integrating multiple sensory modalities and cognitive processes within unified computational frameworks. Dr. Schrater directs the Computational Perception and Action Laboratory, located in Elliott Hall at the University of Minnesota. The lab is equipped with a Visuo-Haptic workbench for testing visuo-motor behavior with virtual objects, and an adjustable reach laboratory for studying reaching behavior with actual objects. His teaching includes graduate courses such as "Mathematical Models of Human Behavior" (PSY 5018H) and "Artificial Intelligence II" (CSCI 5512), reflecting his interdisciplinary approach that bridges psychology and computer science.

Romeil Singh SandhuView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Network Science
• +4 more

Romeil Singh Sandhu serves as Assistant Professor in Biomedical Informatics at Stony Brook University with adjunct appointments in Computer Science and Applied Mathematics & Statistics, directing the Laboratory for Imaging, Networks, and Control (LINC) from the Health Sciences Center. His academic credentials include: B.S. from Georgia Institute of Technology (2006) M.S. from Georgia Institute of Technology (2009) Ph.D. from Georgia Institute of Technology (2010) Dr. Sandhu's research integrates geometry, statistics, and control theory to advance computer vision (3D reconstruction, satellite pose estimation), network science (hypergraph dynamics, Ricci curvature), and systems biology (protein interaction networks, cellular robustness). His methodological innovations span level-set methods, variational techniques, and curvature-based network analysis with applications in medical imaging and aerospace systems. Analysis of his 2019-2023 publications reveals three dominant trajectories: (1) geometric network analysis using Ricci curvature to quantify biological network fragility; (2) distributed reinforcement learning with communication-efficient multi-agent actor-critic frameworks; and (3) medical/satellite image reconstruction via radar-based variational methods and active surfaces. These threads consistently leverage differential geometry to solve inverse problems in complex systems. The Laboratory for Imaging, Networks, and Control (LINC) develops computational frameworks bridging theoretical mathematics with healthcare and aerospace applications, particularly focusing on shape analysis, network dynamics, and control systems for medical diagnostics and satellite imaging.

Fabrice LamarcheView profile

Associate Professor

• Virtual Humans
• Crowd Simulation
• Motion Planning
• +5 more

Fabrice Lamarche serves as an Associate Professor at Université de Rennes 1 within the ESIR School of Engineering, while maintaining dual affiliation with the MimeTIC research team at IRISA / INRIA Rennes. His academic career spans uninterrupted service since 2004, evolving from Assistant Professor roles at IFSIC (2003-2009) to current positions at ESIR. As co-founder of Golaem (2009), he bridges academic research with commercial application in crowd simulation technology. His institutional journey includes sequential membership in SIAMES (2004-2006), Bunraku (2007-2011), and MimeTIC (2011-present) research teams at INRIA. Lamarche earned his PhD in Computer Sciences from Université de Rennes 1 in 2003 with thesis work on virtual human autonomy. His educational foundation includes a Master of Computer Sciences specializing in Computer Graphics and AI (1999-2000), a Master of Engineering from INSA de Rennes (1997-2000), and a Technical degree from IUT de Limoges (1995-1997). His research program centers on virtual human behavior modeling with emphasis on decision-making systems, path planning under environmental constraints, and crowd simulation architectures. Key innovations include TopoPlan for human-scale navigation and frameworks integrating high-level task scheduling with low-level motion planning. This work addresses fundamental challenges in creating autonomous virtual characters capable of navigating complex 3D environments while exhibiting realistic behaviors, with applications spanning virtual reality, gaming, and simulation-based training systems. Publication analysis reveals consistent output from 2001-2014, evolving from foundational behavioral animation (2001-2004) to sophisticated crowd simulation systems (2013-2014). A notable trajectory shows increasing integration of cognitive modeling with motion planning, alongside exploration of Brain-Computer Interfaces for virtual navigation. Recent work demonstrates particular strength in semantic decomposition of urban environments and time-space constrained task scheduling. His scientific contributions have earned significant recognition: Rennes city medal (2009) for research excellence Second prize at Deutsch Telekom Awards (FMX 2008) for TopoPlan/MKM integration As an active researcher and educator, Lamarche advises students through Université de Rennes 1 while leveraging INRIA resources and Golaem industry partnerships. His publication record indicates sustained grant funding, particularly through INRIA channels, with collaborative projects extending to neuroscience applications via Brain-Computer Interface research. The MimeTIC team affiliation provides infrastructure for multimodal interaction research in complex virtual environments. Lamarche's laboratory work through MimeTIC focuses on developing practical implementations of virtual human autonomy systems. His research group maintains strong industry connections via Golaem, which commercializes crowd simulation technology. Current efforts emphasize semantic understanding of virtual urban spaces and robust path planning under dynamic constraints, building on foundational work in topological navigation and behavioral decision systems.

Richard KempterView profile

Professor

• Computational Neuroscience
• Memory Systems
• Hippocampal Circuitry
• +5 more

Richard Kempter is a Professor at Charité - Universitätsmedizin Berlin's Institute of Neuroscience, where he leads research in computational neuroscience with a focus on hippocampal circuitry and memory systems. His work bridges experimental neuroscience with theoretical modeling, examining how neural networks support spatial navigation, memory formation, and consolidation processes. His research interests center on the computational principles underlying hippocampal function, particularly in memory consolidation, spatial navigation, and neural coding. Kempter investigates how hippocampal circuits generate sharp wave-ripple events, how grid cells form spatial representations, and how memory traces transform during systems consolidation. His work combines computational modeling with experimental data analysis to develop testable theories about neural mechanisms. Analyzing Kempter's recent publications reveals a strong focus on hippocampal CA3 circuitry, with particular attention to sharp wave-ripple complexes and their role in memory consolidation. His work demonstrates how specific connectivity patterns between pyramidal neuron subtypes enable memory replay, while his computational models explain how grid-like representations emerge in entorhinal cortex. The research spans multiple scales from single-cell properties to network dynamics, with applications to both rodent and human memory systems. Kempter's scientific contributions have appeared in top-tier journals including Nature , Neuron , and PNAS , reflecting the significance of his work in understanding fundamental neural mechanisms. His publications demonstrate consistent innovation in developing computational frameworks that explain experimental observations while generating new testable predictions about memory systems. His research team collaborates extensively across institutions, working with experimental neuroscientists to bridge theoretical models with empirical data. Current projects examine how neural synchrony creates functional filters during rest states, how population sparseness affects memory capacity, and how subtype-specific connectivity enables sequential activation during memory replay events.