Find Academic Supervisors

Maciej ZasuwaView profile

Associate Professor

• Flight Simulation
• Unmanned Aerial Vehicles (UAVs)
• Artificial Intelligence in Aerospace Engineering
• +6 more

Maciej Zasuwa is an Associate Professor at Warsaw University of Technology's Faculty of Aeronautics and Power Engineering, specializing in Flight Simulation and Artificial Intelligence in Aerospace Engineering . As Vice-Dean for Education and Head of the Simulators Lab, he leads initiatives in simulator development and AI curriculum innovation. Ph.D. (with distinction) in 2004 for image processing-based navigation systems M.Sc. Eng. in 2000 His research spans UAV simulation, autonomous navigation, and AI applications in aerospace. Recent projects include: 2023 Boeing 737 MAX Simulator with anomaly detection algorithms 2022 AI-focused aerospace curriculum development Hyperloop simulator with motion platform/VR integration Academic achievements include: Dekaban Fellow at University of Michigan (2006-2007) Certified LabVIEW Associate Developer (2017) He mentors student teams in AI and simulation software development, with international collaborations spanning: Griffith University (Australia) University of Groningen (Netherlands) Lockheed Martin-sponsored OpUSS project

• Internet of Things (IoT)
• Embedded Systems
• Precision Agriculture
• +3 more

Deniz Taşkın is an Associate Professor at the Department of Computer Engineering , Trakya University , Turkey. He has served in various academic and administrative roles since 2002, including Head of Department of Information Technology (since 2024) and Deputy Head of Department (since 2016). Education: PhD in Computer Engineering (2007) Master's in Computer Engineering (2004) BSc in Computer Engineering (2002) Edirne Science High School (1998) Research Interests: His research focuses on Internet of Things (IoT) , Embedded Systems , and Precision Agriculture , with applications in sensor networks, wireless communication, and agricultural monitoring. He explores 3D Modeling techniques for optical systems and contributes to Cybersecurity in multimedia and IoT environments. Publication Trends: Deniz has published 15+ articles on IoT architectures, Bluetooth Low Energy sensor networks, 3D calibration methods, and video encryption. His work bridges Digital Circuit Design with Agricultural Technology , emphasizing energy efficiency and real-time data processing. Scientific Awards: 2017 Trakya Project Market: Electric Motor Driver for Autonomous Vehicles 2016 TÜBİTAK: Electromobile Category Runner-Up and Promotion Award 2015 TÜBİTAK: Solar-Powered Vehicle Design Award 2015 Edirne Governorship: Provincial Achievement Certificate Advising and Grants: He has supervised 5 PhD theses and 7 Master's theses , including projects on IoT in Precision Agriculture and FPGA Circuit Design . As a principal investigator, he led 8 scientific projects funded by Trakya University and KOSGEB, focusing on embedded systems, data security, and smart automation. Labs and Teams: He contributes to IoT Research and Embedded Systems Development at Trakya University, collaborating with multidisciplinary teams in agricultural technology and digital design. His work includes designing self-powered sensor nodes and FPGA-based hardware.

• Augmented Reality
• Construction Safety
• Building Information Modeling (BIM)
• +5 more

Francesco Spegni is a Researcher at the Department of Civil, Building and Environmental Engineering (DICEA) within the School of Engineering at Marche Polytechnic University (UNIVPM), Italy. His work integrates advanced computational methods with civil engineering applications, focusing on built environment management through cutting-edge technological solutions. His research spans Augmented Reality , Construction Safety , Building Information Modeling , Facility Management , and Blockchain systems. Key investigations include marker-less AR registration for infrastructure inspections, cybersecurity in construction processes, and mixed reality applications for facility operations. His interdisciplinary approach bridges computer science and civil engineering to solve practical challenges in construction safety and infrastructure management. Analysis of his 2023-2025 publications reveals a dominant trend toward seamless integration of cyber-physical systems in built environments. Core themes include marker-less augmented reality for indoor/outdoor transitions, blockchain notarization of BIM processes, and natural language processing for safety management. His work consistently targets real-world implementation challenges in unprepared environments, emphasizing accuracy, interoperability, and practical usability for construction and facility management professionals.

Rohan ChandraView profile

Research Fellow

• Artificial Intelligence
• Robotics
• Autonomous Driving
• +4 more

Rohan Chandra is a Postdoctoral Research Fellow at the University of Texas at Austin, specializing in algorithms and systems for robot navigation among humans. He is affiliated with Texas Robotics and advised by Dr. Joydeep Biswas and Dr. Peter Stone. Ph.D. and M.S. in Computer Science, University of Maryland (2018-2022) B.Tech in ECE, Delhi Technological University (2012-2016) His research focuses on enabling robots to navigate safely and efficiently in human-centric environments, with applications to autonomous driving and multi-agent systems. Key areas include game-theoretic planning, control barrier functions, and social navigation algorithms. Recent work trends include multi-agent reinforcement learning (e.g., iPLAN, SocialMAPF), control barrier functions (e.g., Neural Differentiable ICBF, LIVEPOINT), and social navigation datasets (e.g., SOCIALGYM 2.0, METEOR). Publications appear in top conferences like ICRA, IROS, CVPR, and IEEE Transactions. 2023 Microsoft Future Leader in Robotics and AI 2023 KAUST Rising Star in AI 2022 RSS Pioneer 2023 SNU PhD Award for Autonomous Navigation Best Paper/Presentation at IROS 2023 Workshop on Multi-Agent Learning He has served as Associate Editor for RA-L, organized workshops at RSS and IROS, and contributed to program committees for ICCV, ICRA, and WACV. His work involves developing systems for behavior prediction , robust trajectory planning , and human-aware navigation in unstructured environments.

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Philipp Krähenbühl serves as Associate Professor in the Department of Computer Science at the University of Texas at Austin, where he joined the faculty in 2016 as part of the university's Machine Perception Program expansion. His research bridges computer vision, machine learning, and computer graphics with significant contributions to deep learning methodologies. Academic background: Ph.D. in Computer Science, Stanford University Postdoctoral Research, UC Berkeley (2-year appointment) Dr. Krähenbühl's work centers on deep learning for visual understanding , with pioneering contributions in image segmentation , video analysis , and 3D scene reconstruction . Recent research extends into multimodal AI systems combining vision and language, autonomous driving technologies through traffic simulation, and bioinformatics applications for protein structure modeling. His methodology emphasizes efficient model design and leveraging large-scale data. Analysis of his publication trajectory reveals a strategic evolution from core computer vision problems toward interdisciplinary applications. Current work demonstrates strong convergence of vision-language models with autonomous systems, alongside unexpected cross-pollination into structural biology through protein modeling research. Award highlights: NIPS Outstanding Student Paper Award (2011) NSF CAREER Award (2016-17) Multiple Olympiad medals (International Informatics Silver 2005, Swiss Informatics/Mathematics 2004-2005) Research funding includes the NSF RI: SMALL grant for Recognizing objects in images and their properties over time (2020). While specific advisees aren't documented in source materials, he actively mentors graduate students within UT Austin's Computer Science department. His laboratory contributes to the university's Machine Perception Program through cutting-edge work in generative modeling and simulation frameworks. The research group maintains strong industry connections while pursuing fundamental advances in visual representation learning, with current projects focusing on robust autonomy systems and multimodal foundation models.

Amy PavelView profile

Assistant Professor

• Human-Computer Interaction
• Accessibility
• Artificial Intelligence
• +4 more

Amy Pavel is an Assistant Professor in the Department of Computer Science at the University of Texas at Austin. Prior to this role, she was a Postdoctoral Fellow at Carnegie Mellon University and a Research Scientist at Apple. Her research bridges Human-Computer Interaction and Accessibility, focusing on AI-driven systems for efficient and inclusive communication. Education: PhD in Computer Science from UC Berkeley (2019), advised by Björn Hartmann and Maneesh Agrawala. Teaching: Regularly teaches Human-Computer Interaction courses at UT Austin and UC Berkeley. Her work addresses accessibility challenges through systems like Rescribe (audio descriptions), CrossA11y (video accessibility), and GenAssist (image generation). Recent projects explore AI applications for low-vision learners, photosensitivity warnings in VR, and collaborative video editing. Award highlights include 2023 UIST Best Paper 2022 UIST Best Paper 2020 CHI Honorable Mention (twice) 2018 UC Berkeley EECS Outstanding Graduate Student Instructor She advises PhD students like Mina Huh and Yi-Hao Peng, as well as undergraduates and masters students. Her lab collaborates with institutions including Google, Carnegie Mellon, and UC Berkeley.

Javier Civera SanchoView profile

Associate Professor

• Computer Vision
• Robotics
• Artificial Intelligence
• +5 more

Javier Civera Sancho is an Associate Professor at the University of Zaragoza, where he serves as Deputy Director of the Institute of Research in Engineering of Aragon (I3A). He is affiliated with the School of Engineering and Architecture in the Department of Computer Science and Systems Engineering, where he leads research in the RoPeRT group (Robotics, Computer Vision and Artificial Intelligence). With a background in Industrial Engineering and a PhD in Systems and Computer Engineering, both from the University of Zaragoza, Civera has established himself as a prominent researcher in computer vision and robotics. Industrial Engineering degree (2004, University of Zaragoza) PhD in Systems and Computer Engineering (2009, University of Zaragoza) Civera's research primarily focuses on computer vision, with special emphasis on SLAM (Simultaneous Localization and Mapping), 3D reconstruction, and spatial artificial intelligence. His work aims to provide computers with human-like visual capabilities, including object recognition, 3D structure estimation, spatial context understanding, and tracking of moving objects. He has maintained a long-standing research line in scene localization and mapping, making significant contributions to visual SLAM methodologies and applications. His publication trends reveal a strong focus on advancing visual SLAM techniques, with recent work exploring neural rendering integration, semantic mapping, robust camera calibration, and applications in challenging environments like agriculture. His research bridges theoretical computer vision with practical robotics applications, showing increasing integration with deep learning approaches while maintaining strong geometric foundations. 2 research sexenios (with last granted for 2012-2017) 2 teaching quinquenios h-index: 21 (Google Scholar) 397 citations in last 5 years (Google Scholar) Civera has directed 2 doctoral theses and is currently supervising 4 PhD students. His research has been supported through various institutional frameworks at the University of Zaragoza, including his role in the I3A which provides critical research infrastructure. He is actively involved in guiding the next generation of researchers in computer vision and robotics, emphasizing both theoretical understanding and practical implementation. As a member of the RoPeRT research group, Civera collaborates with a multidisciplinary team working at the intersection of robotics, computer vision, and artificial intelligence. His work often involves developing systems that integrate multiple sensor modalities and create robust spatial understanding for autonomous agents.

Elan BarenholtzView profile

Associate Professor

• Deep Learning
• Artificial Intelligence
• Perception and Learning
• +4 more

Elan Barenholtz serves as Associate Professor in the Department of Psychology within Florida Atlantic University's Charles E. Schmidt College of Science. He holds dual leadership roles as Founder/Co-Principal Investigator of the Machine Perception and Cognitive Robotics Lab and Associate Director of the Center for the Future Mind, with additional affiliation at the Center for Complex Systems and Brain Sciences. His educational foundation includes a Ph.D. and M.A. in Cognitive Science from Rutgers University, New Brunswick, complemented by postdoctoral training at Brown University. These credentials underpin his interdisciplinary approach spanning psychology, neuroscience, and computational modeling. Dr. Barenholtz pioneers research in Deep Learning and Autoregressive Generative Theory of Cognition , challenging traditional memory models by framing cognition as dynamic generative processes akin to large language models. His lab employs embedded computational neural models —neural networks operating in physical robots—to investigate perception, language, and motor functions through behavioral experiments and neural simulations. This work bridges theoretical frameworks with real-world robotic applications. Publication trends (2014-2025) reveal consistent focus on computational cognition, evolving from foundational object recognition studies toward current AI-driven generative theories. His research integrates cognitive psychology with computer vision and robotics, increasingly emphasizing transformer-based architectures and autoregressive principles observable in systems like ChatGPT. As an active scholarly contributor, Dr. Barenholtz serves on NSF review panels and the editorial board of Frontiers in Psychology. His media presence includes TEDx talks, AI-focused interviews, and FAU faculty features examining cognition's future trajectory. He mentors students through the Machine Perception and Cognitive Robotics Lab, co-authoring publications with student researchers (marked by asterisks in publications). While specific grants aren't detailed, his NSF panel role indicates deep engagement with federal research funding mechanisms. Current projects center on validating autoregressive cognition through memory experiments and neural network simulations that replace static storage models with residual activation frameworks. Dr. Barenholtz directs the MPCRLab's research agenda while shaping the Center for the Future Mind's exploration of cognitive evolution. His YouTube channel and Substack platform extend these investigations to broader audiences, emphasizing practical AI implications for understanding human cognition.

• Machine Learning
• Autonomous Driving
• Natural Language Processing
• +4 more

Tsvetomila Mihaylova is a Postdoctoral Researcher in the Department of Computer Science at Aalto University , specializing in machine learning and human-robot interaction. Her work bridges theoretical advancements in neural networks with practical applications in autonomous systems. Fields of Interest : Latent structure learning, autonomous driving, visual-language models, and natural language processing Email : tsvetomila.mihaylova@aalto.fi Research Focus : • Latent structure modeling using discrete and undirected neural networks • Autonomous driving safety through conflict simulation and trajectory prediction • Cross-modal integration in robotic vision-language systems Publication Trends : Recent work explores 2025 in structured neural architectures for autonomous vehicles and 2024 in human-robot interaction quality evaluation, with foundational contributions to natural language processing fact-checking systems since 2019 .

Lucy Lu WangView profile

Assistant Professor

• Natural Language Processing
• Health Informatics
• Accessibility Research
• +3 more

Lucy Lu Wang is an Assistant Professor at the University of Washington Information School, where she leads the Language Accessibility Research (LARCH) Lab. She holds adjunct appointments in the Paul G. Allen School of Computer Science & Engineering, Biomedical Informatics & Medical Education, and Human Centered Design & Engineering. Additionally, she serves as a Research Scientist at the Allen Institute for AI (Ai2). Dr. Wang is an NLP and health informatics researcher focusing on designing and building language technologies to improve access to and understanding of information, particularly in high-expertise domains like science and healthcare. Her work has produced techniques and tools that enhance access to scholarly content, synthesize scientific evidence, and support better health decision-making. She specializes in creating AI systems that make complex information more accessible, with particular attention to accessibility for visually impaired users and improved health communication. Her recent publications demonstrate strong trends across natural language processing for scientific literature, accessibility research, and health informatics. The work shows consistent focus on developing practical tools that bridge the gap between advanced AI capabilities and real-world information needs, particularly in scholarly and healthcare contexts. Many projects address critical issues of accessibility, reliability, and usability of AI systems for diverse user populations. Dr. Wang actively mentors a diverse group of students across multiple departments, including PhD students in the iSchool, HCDE, and CSE. She has successfully guided numerous students who have gone on to positions at institutions like UIUC, Meta, Microsoft, and Netflix. Her research is generously supported by multiple grants and gifts from Google, the National Institutes of Health, Microsoft, and various University of Washington funding programs, focusing on AI-driven accessibility solutions, healthcare applications, and research translation. At the LARCH Lab, Dr. Wang leads a team working on designing, building, and evaluating language technologies that make information more accessible and understandable for everyone, with special emphasis on high-expertise domains such as healthcare and science communication.

• Robotics
• Computer Vision
• Machine Learning
• +4 more

Francesco Verdoja is a Research Fellow at Aalto University's Department of Electrical Engineering and Automation, specializing in robotics and computer vision. His work focuses on safe trajectory generation, localization in dynamic environments, and visual-language semantic mapping. He actively contributes to the Intelligent Robotics research group and has received multiple accolades for his research. Research Interests : Robotics, Computer Vision, Machine Learning, Human-Robot Interaction Key Contributions : Visual-language maps, Dynamic environment modeling, Safer robot navigation frameworks Scientific Achievements : 2024 IEEE/RSJ IROS Best Safety Paper finalist 2022 Best Paper Award PhD scholarship from Sisvel Technology Academic GPU Grant (NVIDIA Titan Xp) Recent publications demonstrate strong emphasis on robotic perception and autonomous navigation , with applications in UAV localization, deformable object manipulation, and people flow prediction. Collaborations include work with Prof. Ville Kyrki and other robotics experts at Aalto University.

Joan Naviyuk KaneView profile

Associate Professor

• Indigenous Poetry
• Inupiaq Culture
• Arctic Geographies
• +5 more

Joan Naviyuk Kane is an Inupiaq poet and writer with family roots from Ugiuvak (King Island) and Qawiaraq (Mary's Igloo), Alaska. She currently serves as an Associate Professor at Reed College in Oregon, where she lives with her children. Previously, she has been a faculty mentor with the low-residency MFA program at the Institute of American Indian Arts in Santa Fe, New Mexico. Her academic journey includes graduating from Harvard College as a Harvard National Scholar and earning an MFA from Columbia University's School of the Arts through a graduate Writing Fellowship. Harvard College (Harvard National Scholar) Columbia University School of the Arts (MFA, Graduate Writing Fellowship) Kane's research and creative work centers on Indigenous poetry, Inupiaq culture, Arctic geographies, and the intersections of language, land, and cultural identity. Her writing explores the profound connections between Native peoples and their lands, demonstrating how creative practices function in place-making and cultural preservation. She writes in both English and Inupiaq, creating work that exists at the threshold between cultures and contemporary poetics, often addressing themes of colonialism, displacement, motherhood, and environmental change. Her publications reveal a consistent trajectory of innovative work that blends traditional Indigenous knowledge with contemporary poetic forms. Kane's writing often features precise, evocative imagery drawn from northern landscapes while engaging with complex questions of identity, survival, and cultural continuity. Her work demonstrates a mastery of form that allows her to explore difficult historical and contemporary realities while maintaining a powerful artistic vision. Whiting Writer's Award Donald Hall Prize in Poetry American Book Award Guggenheim Fellowship in Poetry Paul Engle Prize (2023) Fulbright Specialist (2023-2026) United States Artists Fellow (2025) As an educator and mentor, Kane has influenced emerging writers through her teaching positions and residencies at institutions including the School for Advanced Research, Hermitage Artist Retreat, Lannan Foundation, Millay Arts, Harvard's Radcliffe Institute, and Madďďi Tromsø. Her work has been supported by multiple Individual Artist awards from the Rasmuson Foundation and other prestigious fellowships that recognize her contributions to American letters and Indigenous cultural expression.

• Artificial Intelligence
• Machine Learning
• Human-Computer Interaction
• +5 more

Raju Shrestha is an Associate Professor at the Department of Information Technology , Faculty of Technology, Art and Design , Oslo Metropolitan University. His work spans artificial intelligence, human-computer interaction, and cloud computing. Recent research includes: Hybrid deep learning models for plant disease detection AI-based navigation assistants for visually impaired users Cloud infrastructure optimization via GitOps and CI/CD pipelines Analysis of voice assistant efficacy in education Computer vision applications in election ballot processing His publications (2023-2025) reflect interdisciplinary applications of AI in agriculture, healthcare, education, and social media marketing automation.

Vijay KumarView profile

Professor

• Robotics
• Autonomous Systems
• Machine Learning
• +3 more

Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering and holds faculty appointments in the Departments of Mechanical Engineering and Applied Mechanics , Computer and Information Science , and Electrical and Systems Engineering at the University of Pennsylvania . Bachelor of Technology, Indian Institute of Technology Kanpur PhD, The Ohio State University (1987) Research Interests: Vijay Kumar's work spans robotics , autonomous systems , and machine learning , focusing on trajectories, control, and collaboration. Key subfields include quadrotor dynamics , semantic SLAM , multi-robot coordination , event-based vision , and LLM integration for robotic systems. His research addresses challenges in autonomous flight , resilient control , and agricultural robotics . Recent Publication Trends: Over the past two years, Kumar's research has emphasized language-specified missions , event camera applications , neural radiance fields , and time-optimal path planning for quadrotors and multi-robot teams. Notable themes include safety guardrails for LLM-powered robots , 4D metric-semantic mapping , and adaptive replanning in dynamic environments . Scientific Awards: Fellow, AAAS (2021) Fellow, ASME and IEEE ASME Mechanisms and Robotics Award (2012) IEEE Distinguished Service Award (2012) Engelberger Robotics Award (2014) IEEE Pioneer Award (2018) Elected to National Academy of Engineering (2013), American Philosophical Society (2018), and American Academy of Arts and Sciences (2022) Advising and Leadership: Kumar has mentored numerous PhD students who have won Best Student Paper Awards at major robotics conferences. He founded the General Robotics, Automation, Sensing and Perception Lab (GRASP) at Penn, which has spun off multiple robotics startups like Exyn Technologies . He has served on editorial boards of leading journals and held advisory roles in national science policy.

Mikko LauriView profile

Researcher

• Multi-agent decision-making under uncertainty
• Active perception
• Computer vision
• +5 more

Dr. Mikko Lauri is a Postdoctoral Researcher at the Department of Informatics, University of Hamburg, working in the Computer Vision Research Group. His research focuses on decision-making under uncertainty, active perception, and computer vision, with particular emphasis on multi-agent systems for cooperative tasks in robotics applications. His research interests include: Multi-agent decision-making under uncertainty Active perception and information gathering Computer vision for mobile robots Partially Observable Markov Decision Processes (POMDPs) Object pose estimation and visual object search Deep learning applications in robotics Lauri's recent work has focused on advancing theoretical frameworks for teams of agents to act cooperatively. His survey paper on POMDPs in robotics provides a comprehensive overview of decision-making under uncertainty in robotic systems. His research has practical applications in autonomous robots, including exploration, object pose estimation, and visual object search, with techniques that balance theoretical rigor with real-world implementation. Scientific contributions: Developed novel approaches for multi-agent active perception with prediction rewards Created methods for multi-sensor next-best-view planning using submodular optimization Advanced techniques for 6D object pose estimation using point clouds and deep learning Contributed to audio-visual signal processing for sound source separation Lauri collaborates extensively with researchers in the Computer Vision Research Group at the University of Hamburg and has worked with international collaborators from institutions including Aalto University (Finland) and the National University of Singapore. His work bridges theoretical foundations in decision-making under uncertainty with practical robotics applications.