Find Academic Supervisors

• Computational Neuroscience
• Machine Learning
• Cognitive Neuroscience
• +3 more

Dr. Katja Seeliger serves as a Research Fellow at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, affiliated with the Methods and Development Group for Neural Data Science and Statistical Computing and the Research Group Vision and Computational Cognition. Her research integrates computational neuroscience, machine learning, and cognitive science to decode neural representations of visual stimuli. She specializes in developing statistical models for fMRI data analysis, with emphasis on object recognition, naturalistic stimulus reconstruction, and high-level visual cortex mapping using deep neural networks. Her methodological innovations bridge neuroimaging with artificial intelligence to model brain information processing. Analysis of her 2022-2025 publications reveals a cohesive research trajectory centered on large-scale neuroimaging datasets (notably CNeuroMod-THINGS) and attention-based neural decoding architectures. Recurring themes include gaze-informed encoding models, cross-species visual processing comparisons, and interpretable dimensionality reduction for neural representations, demonstrating consistent focus on advancing computational frameworks for visual neuroscience. Dr. Seeliger actively contributes to the Neural Data Science and Statistical Computing group, where she develops open-source tools for neuroimaging analysis and collaborates on methodological advancements in brain-optimized convolutional neural networks.

Taotao CaiView profile

Lecturer

• Graph Data Processing
• Social Network Analytics
• Data Mining
• +6 more

Dr. Taotao Cai serves as an Honorary Lecturer and Postdoctoral Research Fellow in the School of Computing at Macquarie University, bringing expertise in graph analytics and machine learning. Previously, they held an Associate Research Fellow position at Deakin University's School of Information Technology. Their academic foundation includes a Ph.D. from Deakin University and a Master's in Computer Science from Shenzhen University, China. Education: Ph.D. in Computer Science, Deakin University, Australia Master of Computer Science, Shenzhen University, P.R. China Research spans graph data processing, social network analytics, and data mining with significant contributions to causal graph neural networks, influence maximization, and privacy-preserving machine learning. Recent work demonstrates strong interdisciplinary applications in remote sensing, healthcare decision systems, and battery management, while maintaining core focus on dynamic network analysis. The research portfolio reveals increasing sophistication in causal reasoning within graph structures and practical implementations for real-world systems. Publication trends from 2023-2025 show concentrated activity in graph neural networks (40%), privacy/security (25%), and remote sensing applications (20%), with emerging work in causal classification and medical AI. This evolution reflects both theoretical advancement and practical problem-solving across diverse domains. No formal scientific awards are documented in available sources. While no formal student advising is recorded, collaborative patterns indicate active mentorship within research teams. Grant activity remains unspecified, but extensive cross-institutional collaborations (particularly with Deakin University and international partners) suggest participation in multiple funded projects. Current trajectories point toward causal graph neural networks for explainable AI and privacy-enhanced federated learning systems as primary research frontiers. Active participation in international research networks is evident through co-authorship spanning Australia, China, and global institutions, with particular strength in graph algorithm development and social network analysis communities.

• Autonomous Driving
• Multi-Object Tracking
• Sensor Calibration
• +3 more

Leah Strand is a Ph.D. candidate and research assistant at the Technical University of Munich (TUM) in the Department of Robotics, Artificial Intelligence and Real-time Systems under Prof. Knoll. She joined TUM in March 2020 and contributes to projects like Providentia++ , which focuses on creating real-time digital twins of traffic on the Autobahn A9. Education: M.Sc. in Mechatronics and Information Technology, B.Sc. in Engineering Science (both from TUM) Research interests include: Autonomous Driving Multi-Object Tracking Sensor Calibration and Fusion Her recent publications emphasize image enhancement , multi-camera calibration , and traffic surveillance datasets . She actively participates in teaching and project advising, with expertise in real-time systems and infrastructure-based perception for autonomous vehicles. Contact: leah.strand@tum.de

Milad ToutounchianView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Data Science
• +3 more

Milad Toutounchian serves as an Assistant Teaching Professor within the Department of Information Science at Drexel University's College of Computing & Informatics (CCI). Prior to joining Drexel, he gained industry experience as a data scientist, lead data science instructor at Make School, and mentor at Springboard's Data Science Career Track. His professional background bridges academic theory with practical industry applications. Dr. Toutounchian's educational background includes: PhD in Electrical Engineering from Simon Fraser University BSc in Telecommunication from Sharif University of Technology His primary research interests lie in applied machine learning and deep learning, with a focus on real-world problem solving. Recent work explores AI-driven medical diagnostics, particularly in breast cancer detection using multi-scale and multi-view transformer frameworks. Earlier research addressed signal processing challenges in wireless communications, including beamforming for MIMO systems and cognitive radio networks. This evolution highlights a transition from communications engineering to data science applications in healthcare. Analysis of his publication history reveals a clear shift in research focus. From 2010 to 2016, his work centered on wireless communications and signal processing, contributing to MIMO systems and beamforming techniques. Since 2023, he has pivoted toward healthcare applications of AI, developing deep learning frameworks for human-centric breast cancer diagnostics. This trajectory demonstrates adaptability across technical domains while maintaining core expertise in machine learning methodologies. No scientific awards or major honors were documented in the available information. While his biography notes teaching responsibilities and industry mentoring experience, specific details about graduate student advising or research grants were not provided in the source material. No information regarding laboratory affiliations, research teams, or collaborative groups was mentioned.

• Experimental Psychology
• Psychophysics
• Visual Perception
• +4 more

Chris Paffen is an Associate Professor in the Department of Psychology at Utrecht University's Faculty of Social and Behavioural Sciences. His position combines teaching, research, and management duties within the Experimental Psychology program. He is also associated with the Helmholtz Institute, focusing on experimental approaches to visual perception and cognition. Dr. Paffen received his PhD in 2004 and has since established himself as a prominent researcher in visual perception. His research spans multiple areas of experimental psychology with particular emphasis on visual perception, time perception, consciousness, and psychophysics. His recent work has expanded into human-centered artificial intelligence, examining how cognitive principles can inform AI development. Paffen's extensive publication record demonstrates consistent research productivity, with over 155 research outputs spanning two decades. His work primarily investigates visual working memory, binocular rivalry, attention mechanisms, and the relationship between visual perception and consciousness. Recent publications show increasing focus on memory dynamics, temporal processing, and the neural mechanisms underlying visual awareness. As an educator, Paffen teaches across multiple programs including Applied Cognitive Psychology, Artificial Intelligence, Psychology, and Neuroscience and Cognition at undergraduate, graduate, and post-master levels. His teaching portfolio includes courses such as Cognition and Behavior and Sensation and Perception, reflecting his research expertise. His laboratory work appears to focus on experimental paradigms related to visual perception, with particular attention to binocular rivalry, continuous flash suppression, and visual working memory paradigms. His research often involves collaborations with colleagues at Utrecht University and international institutions, as evidenced by his co-authored publications.

Amit AshokView profile

Professor

• Inverse Problems
• Bayesian Inference
• Information Theory
• +8 more

Amit Ashok is a Professor of Optical Sciences at The University of Arizona's Wyant College of Optical Sciences and an Assistant Professor in the Department of Electrical and Computer Engineering. His research focuses on computational sensing and quantum-inspired imaging. Education: Ph.D. (2008), University of Arizona; M.S. (2001), University of Cape Town; B.Sc. (1998), University of Swaziland Professional Affiliations: Senior Member of The Optical Society (OSA), Member of IEEE, SPIE Senior Member His research leverages physical optics, machine learning, and information theory to design optical imagers and sensors. Key areas include inverse problems, compressive imaging, and quantum-limited resolution. The Intelligent Imaging and Sensing Lab (I2SL), led by Professor Ashok, develops computational sensing frameworks using reconfigurable optics and parallel computing architectures for applications in high-dimensional imaging and X-ray anomaly detection. Scientific Awards: SPIE Best Paper Award (2010), University of Cape Town Scholarships (1999, 2000), University of Swaziland Dean's, Vice-Chancellor, and Sino-Swazi awards (1995-1998)

Alain PaganiView profile

Researcher

• Computer Vision
• Artificial Intelligence
• Image Understanding
• +6 more

Alain Pagani is a Principal Researcher and Deputy Director of the Augmented Vision department at the German Research Center for Artificial Intelligence (DFKI) . He leads a team of 10 PhD students and researchers, focusing on Augmented Reality, Computer Vision, and Machine Learning. Research Interests include 3D Computer Vision, Image-based Scene Reconstruction, Object Detection and Tracking, and Extended Reality (AR/VR) applications. His work spans both industrial and academic projects, such as the EU initiatives dAIEDGE , CORTEX² , and FLUENTLY . Scientific Awards include a Google Research Award, Best Paper Award (INI-GraphicsNet 2007 Honorable Mentions), and Best Paper Award (INI-GraphicsNet 2008 Honorable Mentions). Projects like dAIEDGE (AI at the Edge) and CORTEX² (Collaborative XR) highlight his leadership in distributed AI and real-time extended reality systems. He has also coordinated EU projects such as LARA (Outdoor AR using Galileo) and Eyes of Things (Low-power Computer Vision).

Sufyan Ali MemonView profile

Professor

• Target Tracking
• Unmanned Aerial Vehicles
• Data Association
• +6 more

Sufyan Ali Memon serves as a Professor in the Department of Defense Systems Engineering at Sejong University, Republic of Korea. His academic journey includes Assistant Professor positions at Isra University (2016-2017), UNIST as Postdoc Research Associate (2017-2018), Indus University (2018-2019), and Mehran University of Engineering & Technology (2019-2021) before joining Sejong University in 2021. His research focuses on Tracking, Estimation, Guidance, Navigation, and Control systems, with particular expertise in multi-target tracking in cluttered environments, UAV trajectory estimation, and smoothing algorithms. His fingerprint analysis reveals strong activity in Target Tracking (97%) , Multi-Target Tracking (83%) , Unmanned Aerial Vehicles (37%) , and State Estimation (27%) . Analysis of his 42 research outputs (2015-2025) shows consistent growth in publication output, with 8 papers in 2025 alone. His work spans journals like Expert Systems with Applications , IEEE Access , and Sensors , demonstrating interdisciplinary applications from defense systems to social media analysis and pipeline inspection robotics. The publications reveal a clear evolution from fundamental tracking algorithms toward practical implementations in UAVs, industrial systems, and security applications. h-index 12 330 Citations Web of Science ResearcherID: AAD-9239-2019 His professional background includes significant contributions to smoothing algorithms for target tracking in cluttered environments, with major achievements including dynamic trajectory estimation for UAVs and multi-UAV tracking systems. Current research appears focused on integrating machine learning with traditional tracking systems and expanding applications to industrial inspection and security domains.

• Space Physics
• Atmospheric Science
• Geophysics
• +6 more

Torsten Aslaksen is a Senior Lecturer at the Department of Physics and Technology, UiT The Arctic University of Norway. With expertise spanning space physics, atmospheric science, and plasma dynamics, he has contributed extensively to auroral research, meteoroid analysis, and polar mesospheric phenomena. His work includes both empirical studies and educational initiatives in distance learning. Department: Physics and Technology Location: Tromsø Research Interests Aslaksen's research focuses on auroral phenomena, space plasmas, and atmospheric interactions of extraterrestrial objects. He also contributes to e-learning infrastructure in Arctic education. His scientific work bridges observational data from rockets/radars with theoretical models of dusty plasmas and substorm dynamics. Selected Projects Notable projects include multi-instrument fireball tracking (2022), discovery of anti-black auroras (2021), and foundational research on charged dust in planetary rings (2001). He led initiatives for Northern Lights tourism analysis and Venus transit public outreach (2012). Teaching & Outreach Active in educational technology, he established thematic networks for e-learning (2008) and developed observational systems like TACO (Telescope for All-sky, Cloudy, and Online) for student engagement in astronomical research.

• Artificial Intelligence in Construction
• Building Information Modeling
• Graph Neural Networks
• +3 more

Dr. Zijian Wang is an independent postdoctoral researcher at the TUM Georg Nemetschek Institute (TUM GNI), Technical University of Munich, advancing construction digitalization through AI and graph-based approaches to Building Information Modeling (BIM). His educational background includes a PhD from Technion – Israel Institute of Technology (Marie Curie Early-Stage Researcher in EU Horizon 2020 Cloud BIM project), a master's in Computer and Machine Vision from Cranfield University, a master's in Civil Engineering from Central South University, and a bachelor's in Civil Engineering from Chongqing Jiaotong University. He has also conducted research at the University of Cambridge and industry work at Trimble Finland. Dr. Wang's research centers on Building Information Graphs (BIGs), transforming design data into graph structures to enable machine learning applications. His work addresses critical gaps in BIM interoperability through semantic enrichment using graph neural networks, with applications in construction safety monitoring, multidisciplinary change propagation, and automated consistency maintenance. Specific projects include PPE detection via deep learning, tunnel temperature data mining, and graph-based object classification. His publication trajectory (2021-2025) demonstrates increasing sophistication in graph-based BIM solutions, evolving from foundational room classification to complex cross-domain interoperability frameworks and generative AI applications for construction planning constraints. He has received significant recognition: Thorpe Medal, European Council on Computing in Construction (EC3) (2023) Eastman Best Ph.D. Paper, Int Joint Conference CIB W78 - LDAC (2021) Dr. Wang supervises theses in TUM's Software Lab and contributes to EU-funded research initiatives. His current BIGs research aims to establish graph-based foundations for generative design tools, fostering more collaborative and intelligent construction workflows through explicit relationship modeling and multi-modal data embedding.

Reham Mohamed AburasView profile

Assistant Professor

• mobile computing
• security and privacy
• virtual reality security
• +4 more

Reham Mohamed Aburas is an Assistant Professor in the Department of Computer Science and Engineering at the American University of Sharjah (AUS) in Sharjah, UAE. Her academic career spans research and teaching in mobile computing security, with a focus on advancing secure and privacy-preserving technologies for modern devices across multiple platforms including smartphones and emerging virtual reality systems. Dr. Aburas earned her Ph.D. in Computer Science from Purdue University, where she worked with Professor Z. Berkay Celik at the PurSec Lab. Prior to that, she completed both her B.Sc. and M.Sc. degrees in Computer and Systems Engineering from Alexandria University in Egypt, establishing her technical foundation in systems engineering. Her primary research centers on the advancement of security, privacy, and usability of mobile computing technologies. In today's world, where modern devices are ubiquitous and technology is deeply integrated into various aspects of daily life, Dr. Aburas focuses on developing advanced systems that enrich user experiences while preserving security and privacy. Her work spans multiple domains including: Mobile security and privacy vulnerabilities Virtual and augmented reality security Biometric authentication systems User-centered security design Location privacy in mixed reality environments Arabic natural language processing Dr. Aburas's publication record demonstrates a strong trajectory from foundational work in mobile computing and indoor localization to cutting-edge research in virtual reality and mixed reality security. Her recent work (2023-2025) has focused on emerging threats in WebXR and VR environments, including UI attacks, speech extraction from sensors, and semantic location inference. She has published in top security venues including USENIX Security Symposium and NDSS, indicating the high impact and relevance of her research in the security community. Her earlier work (2014-2020) established expertise in mobile security, biometric authentication, and Arabic language processing through the Al-Bayan project. As an educator, Dr. Aburas has served as a course instructor at AUS, teaching subjects including COE 59412: Usable Security and Privacy and CMP 340: Design and Analysis of Algorithms. She has also served as a Teaching Assistant at Purdue University for courses including Great Issues in Computer Science, Data Mining, and Data Engineering in Python. At Alexandria University, she taught Probability Theory, Statistics, Data Mining, and Data Structures, demonstrating expertise across both theoretical computer science and practical security applications. Her research appears to be conducted in collaboration with the PurSec Lab at Purdue University, where she completed her Ph.D. Her work shows strong interdisciplinary connections between computer science, human-computer interaction, and security engineering, with particular attention to the practical usability aspects of security systems.

• Bioinspired Robotics
• Morphological Computation
• Soft Robotics
• +7 more

Sara Abad Guaman is a Lecturer in Robotics, Control and Autonomous Systems at the Department of Mechanical Engineering , University College London , where she joined in 2022. Her research focuses on bioinspired robotics and morphological computation to enhance robotic adaptability for global challenges in healthcare, sustainability, and biodiversity. PhD in Robotics (Imperial College London) MSc in Artificial Intelligence (University of Southampton) BEng in Electronics and Control (Escuela Politécnica Nacional, Ecuador) Research Interests include soft robotics , haptics , and medical robotics . Her work bridges design engineering , bioinspiration , and control systems to create adaptable robotic systems. Key Awards : Policy Engagement and Impact Fellowship (2021) UCL IHE Impact Fellow (2020) SENESCYT Scholarship for MSc/PhD studies UNIVERSIDADES DE EXCELLENCIA award (2014-2019) Teaching includes Module Leadership for MECH0010 Instrumentation and Control, mentoring undergraduate students, and supervising MSc/PhD projects. She has also guest-lectured at UCL and served as Teaching Assistant at Imperial and King's College London.

Ulrik Pagh Schultz LundquistView profile

Professor

• Robotics
• Unmanned Aerial Vehicles
• Programming Languages
• +6 more

Ulrik Pagh Schultz Lundquist is a Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), where he serves as Head of both the SDU UAS Center and SDU Climate Cluster. With over 123 research outputs spanning robotics, drone technology, and programming languages, Lundquist leads significant initiatives including WildDrone (2023-2026) and Terra Salva (2024-2026), and has established himself as a leading expert in unmanned aerial systems research with extensive field applications in wildlife conservation. Lundquist's research focuses on the intersection of robotics and environmental science, with particular expertise in drone swarms for ecological monitoring. His work bridges theoretical computer science with practical conservation challenges, developing programming languages and formal models specifically for modular robotics systems. Key research thrusts include minimizing wildlife disturbance through optimized drone operations, multi-perspective data collection techniques, and scalable BVLOS (Beyond Visual Line of Sight) systems for conservation areas. His fingerprint analysis reveals strong contributions to domain-specific languages (67%), unmanned aerial vehicles (28%), and object-oriented programming (28%). Recent publications demonstrate a clear trajectory toward practical conservation applications, with increasing emphasis on ethical drone deployment, wildlife disturbance minimization, and operational safety in complex environments. Lundquist's work shows growing integration of swarm intelligence techniques with traditional conservation practices, particularly in African ecosystems as evidenced by field trials at Kenya's Ol Pejeta Conservancy. Lundquist actively leads multiple research projects including WildDrone (focusing on multi-perspective animal monitoring), Terra Salva (terahertz data transmission via drones), and a High Altitude Balloon platform for vegetation monitoring. His professional leadership includes chairing the ACM International Conference on Generative Programming Steering Committee (2019-2022) and significant roles in COST Action IC1405 (2015-2019). As a highly visible expert, he has contributed to 107 media appearances, frequently advising on drone policy including Denmark's regulatory approaches to drone safety and wildlife protection. As Head of the SDU UAS Center, Lundquist directs a multidisciplinary research team specializing in conservation drone technology. The center maintains strong international collaborations, particularly with African conservation organizations, focusing on developing drone swarm methodologies that balance data quality with minimal ecological disturbance. Current team efforts prioritize field-deployable systems that can operate effectively in remote natural habitats while providing conservationists with high-quality multi-perspective monitoring capabilities.

• Developmental Psychology
• Autism Spectrum Disorder
• Infant Development
• +6 more

Terje Falck-Ytter is a Professor in the Department of Psychology at Uppsala University, specializing in Developmental Psychology. With a PhD awarded in 2010 following clinical training in psychology, his research focuses on characterizing and understanding the development of infants later diagnosed with neurodevelopmental conditions, particularly autism spectrum disorder (ASD). His primary research interests include developmental psychology, autism spectrum disorder, infant development, twin studies, genetics and environmental influences, eye tracking research, and neurodevelopmental conditions. His work investigates how genetic and environmental factors contribute to various developmentally informative measures early in life. Falck-Ytter's extensive publication record demonstrates significant contributions to understanding social attention, sensory processing, and early biomarkers for autism. His research utilizes advanced methodologies including eye tracking, twin studies, and genetic analyses to uncover developmental pathways and early indicators of neurodevelopmental conditions. His work has practical implications for early detection and intervention strategies for children at risk. Infants' looking preferences for social versus non-social objects reflect genetic variation (2024) Social attention: Developmental foundations and relevance for autism spectrum disorder (2023) The sensory-first account of autism (2023) Joint attention in infancy and the emergence of autism (2019) Enhanced pupillary light reflex in infancy is associated with autism diagnosis in toddlerhood (2018) Falck-Ytter leads the Babytwins Study Sweden (BATSS), a multi-method infant twin study examining genetic and environmental factors influencing infant brain and behavioral development. His research has been widely cited and has appeared in high-impact journals including Nature Human Behaviour, Nature Communications, and Biological Psychiatry. His work bridges clinical practice and research, with implications for early identification and intervention strategies for children with neurodevelopmental conditions. He has mentored numerous researchers and collaborated extensively with international teams studying early autism markers across European populations.

Torrey WagnerView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Analytics
• +7 more

Dr. Torrey Wagner serves as an Assistant Professor of Data Analytics at the Air Force Institute of Technology (AFIT), where he supports the Data Analytics graduate certificate program. He develops and teaches core courses including DASC 522 Machine Learning, SATI 532 Data Visualization & Communication, and DASC 511 Object-Oriented Programming using Python. His instructional focus centers on applying data analytics techniques to solve military problems through the CRISP-DM process framework. Dr. Wagner earned his PhD in Electrical Engineering with an Electro-optics focus from AFIT in 2010, an MS in Aerospace Systems Engineering from Loughborough University (UK) in 2004, and a BS in Electrical Engineering from the University of Minnesota in 2000. His educational background bridges electrical engineering, aerospace systems, and advanced data analytics. His research concentrates on applying predictive and generative artificial intelligence techniques to enhance Department of Defense organizational efficiency and performance. Dr. Wagner's scholarly work spans machine learning applications for defense systems, energy optimization for military operations, quantum computing advancements, and natural language processing solutions for defense contexts. He has developed specialized methodologies for military problem-solving through data analytics, with emphasis on practical implementation in operational environments. Dr. Wagner's publication record reveals a strategic evolution toward cutting-edge AI applications. Recent work demonstrates significant focus on large language models, retrieval-augmented generation systems, and quantum computing implementations. His research portfolio includes machine learning applications for aircraft engine cost prediction, satellite imagery analysis, seismic event classification, and energy system optimization for austere military environments. This work consistently bridges theoretical AI advances with tangible defense applications. As an educator and mentor, Dr. Wagner has guided dozens of students through their first publications and presentations. His students frequently co-author peer-reviewed journal articles and conference papers, primarily at the master's level with several doctoral candidates. He has contributed to AI education within military contexts through his chapter "Augmenting a Curriculum Review with Generative AI" in the AFIT Generative AI Teaching Guidebook and other educational resources. Dr. Wagner's research supports multiple AFIT initiatives focused on data analytics for defense applications. His work involves extensive collaboration with Department of Defense organizations to implement AI technologies in practical military settings, particularly in energy systems optimization, aircraft operations analytics, and decision support tools for commanders. Current projects include secure offline large language model implementations, quantum communication network resilience, and advanced data visualization techniques for military decision-making.