جست‌وجوی استاد و پژوهشگر

Ian Laneمشاهده پروفایل

دانشیار

• Natural Language Processing
• Speech Recognition
• Machine Translation
• +۵ مورد دیگر

Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Joao Pedro Leal Abalada de Matos Carvalho is a researcher at Lusofona University 's School of Communication, Architecture, Arts and Technologies , focusing on Artificial Intelligence, Machine Learning, and Unmanned Aerial Vehicle systems. He contributes to projects like ANTIDOTE (AI Attack & Defense) and REMARKABLE (Rural Environmental Monitoring). Active in AI-driven environmental monitoring and cyber-physical systems Collaborates with Horizon Europe and Marie Skłodowska-Curie Actions Key research areas: Deep Learning, Federated Learning, High-Energy Physics His work involves LoRaWAN engineering , satellite data processing , and hydroelectric forecasting models , with 55 scientific publications and 382 h-index citations. Current collaborations span Europe and Brazil , with active projects in smart healthcare , AI literacy , and radiation-hardened electronics for CERN experiments.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Chien-Sheng Wu is a prominent researcher in natural language processing and artificial intelligence, actively contributing to advancements in large language models (LLMs), dialogue systems, and information retrieval. His work focuses on enhancing factual consistency, developing frameworks for service AI agents, and exploring vision-language model limitations in arithmetic tasks. Key research areas: LLMs, knowledge grounding, dialogue summarization, and human-AI collaboration Recent publications analyze the capacity of LLM agents in CRM tasks, multihop reasoning frameworks, and content moderation tools. His collaborations span institutions like ACL, EMNLP, and NAACL. 2025 papers address workflow extraction, visual arithmetic understanding, and RAG system evaluation 2024 work includes Haystack summarization challenges and executable text-editing interfaces Scientific awards and student mentorship details are not explicitly mentioned in available data. Affiliations remain unspecified, but his contributions to NLP benchmarks and evaluation frameworks are significant.

Slavko Žitnikمشاهده پروفایل

دانشیار

• information retrieval
• information extraction
• natural language processing
• +۷ مورد دیگر

Slavko Žitnik is an Associate Professor and Vice-dean at the Faculty of Computer and Information Science, University of Ljubljana, where he is a member of the Laboratory for Data Technologies. His academic career spans multiple research projects and international collaborations focusing on data technologies and natural language processing. His primary research interests include information retrieval, information extraction, natural language processing, entity extraction, relationship extraction, coreference resolution, data merging, redundancy elimination, and ontologies. Dr. Žitnik's work often bridges theoretical computer science with practical applications in various domains including education, healthcare, and smart city ecosystems. Dr. Žitnik has led and participated in numerous significant research projects including P2-0359 on Ubiquitous Computing (2023-2027), PoVeJMo on Adaptive Natural Language Processing with Large Language Models (2023-2026), and the GOBLIN COST Action for building global networks of large-scale knowledge graphs. His recent work demonstrates a strong focus on adapting natural language processing techniques with large language models and creating practical applications of these technologies. His scientific contributions span multiple domains including: Natural Language Processing and Information Extraction techniques Knowledge graph construction and integration Applications in education, healthcare, and smart city ecosystems Development of practical tools and systems for data processing Dr. Žitnik has established international collaborations with institutions including Harvard University's Department of Biomedical Informatics (where he conducted a research visit from July to October 2022), the University of South Florida, and various European partners through COST Actions and other collaborative frameworks.

• Developmental Psychology
• Cognitive Development
• Child Language Acquisition
• +۳ مورد دیگر

Bahia Guellai is a Professor and Teacher-researcher at Université Toulouse - Jean Jaurès, affiliated with the Cognition, Languages, Language, Ergonomics (CLLE) research unit. Her academic work spans developmental psychology with a focus on early childhood cognition, language acquisition, and social development. Her research interests encompass Developmental Psychology , particularly examining cognitive development in infants and young children. She investigates how infants process social cues, develop language skills, and respond to environmental factors including technological exposure. Her work on screen exposure effects has contributed significantly to understanding digital media's impact on early cognitive development. She also explores cross-species learning mechanisms through studies on vocal learning in birds, demonstrating interdisciplinary approaches to understanding developmental processes. Analysis of her recent publications reveals consistent focus on early cognitive development, with particular attention to social cognition, language acquisition, and the influence of environmental factors. Her research employs diverse methodologies including experimental studies with infants, literature reviews, and cross-species comparisons. The trajectory shows increasing attention to contemporary issues like digital technology's impact on child development alongside foundational work on infant perception and social cognition. Professor Guellai has made substantial contributions through numerous publications including the textbook Psychologie du développement (2018, 2024), multiple journal articles in Frontiers in Psychology, and book chapters addressing developmental psychology topics. Her collaborative work with researchers like Rana Esseily demonstrates strong academic partnerships. Her research activities involve work with the Laboratoire Éthologie Cognition Développement (LECD) at Université Paris Nanterre, indicating cross-institutional collaboration. Her studies often employ innovative methodologies to investigate fundamental questions about early cognitive development, social perception, and language acquisition in infants and young children.

Aude Olivaمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Generative Models
• +۵ مورد دیگر

Aude Oliva serves as MIT director of the MIT-IBM Watson AI Lab and director of strategic industry engagement at the MIT Schwarzman College of Computing. As a Senior Research Scientist at MIT CSAIL, she leads the Computational Perception and Cognition group, driving interdisciplinary research at the intersection of human intelligence and artificial systems. Her roles position her at the forefront of translating academic AI research into real-world applications through major industry partnerships. Dr. Oliva earned her MS and PhD in cognitive science from Institut National Polytechnique de Grenoble, France, establishing her foundation in human perception and computational modeling. Her research integrates computer vision, deep learning, and cognitive neuroscience to understand visual information processing in both biological and artificial systems. She develops computational models that mimic human visual recognition while creating AI systems capable of compositional reasoning and efficient video understanding. Current work emphasizes neuroscience-inspired architectures, resource-efficient deep learning, and multimodal representation learning, with applications spanning healthcare, robotics, and human-computer interaction. Her cross-disciplinary approach uniquely bridges theoretical neuroscience with practical AI development. Analysis of recent publications reveals a clear trajectory toward tighter integration of neuroscience and AI, particularly through brain imaging datasets like BOLD Moments. Her group consistently advances efficient deep learning techniques (Trans-LoRA, VA-RED²) while exploring fundamental questions in visual cognition through projects like the Algonauts Challenge. The work demonstrates increasing industry relevance with strong representation in NeurIPS and Nature Communications. Her major recognitions include: NSF Career Award in computational neuroscience Guggenheim fellowship in computer science Vannevar Bush Faculty Fellowship in cognitive neuroscience As director of the $240M MIT-IBM Watson AI Lab, Dr. Oliva oversees substantial research funding while advising graduate students through MIT's EECS department. Her lab benefits from unique industry-academic synergy, with students gaining access to IBM resources and real-world deployment challenges. The collaborative environment fosters innovation in efficient AI systems with tangible societal impact. The Computational Perception and Cognition group operates as a dynamic hub where computer scientists, neuroscientists, and cognitive scientists collaborate on fundamental questions of intelligence. Current projects focus on making AI systems more human-like in visual reasoning while ensuring computational efficiency for real-world deployment, leveraging the unique resources of the MIT-IBM partnership.

Chuang Ganمشاهده پروفایل

استادیار

• Computer Vision
• Graph Deep Learning
• Multimodal Learning
• +۵ مورد دیگر

Chuang Gan is a distinguished researcher holding dual positions as a Principal Research Staff Member at the MIT-IBM Watson AI Lab and an Assistant Professor at the University of Massachusetts Amherst. His work bridges academic research and industrial applications in artificial intelligence, with particular focus on advancing the frontiers of computer vision and multimodal learning systems. Dr. Gan's research interests span multiple interconnected domains within artificial intelligence. He specializes in video understanding, with deep expertise in representation learning, neural-symbolic visual reasoning, audio-visual scene analysis, and embodied intelligence. His work frequently integrates graph deep learning techniques with neuro-symbolic approaches to create more interpretable and robust AI systems. The recurring themes across his research portfolio include developing models that can understand physical dynamics from visual inputs, creating systems capable of embodied reasoning, and building bridges between symbolic and neural approaches to artificial intelligence. His publications reveal a strong trend toward increasingly sophisticated multimodal systems that integrate visual, auditory, and linguistic information. Over time, his work has evolved from basic video understanding tasks to complex embodied reasoning systems capable of physical simulation, 3D scene understanding, and multi-agent collaboration. A notable pattern is the progression from analyzing static scenes to understanding dynamic physical interactions and embodied agent behaviors in increasingly complex environments. Microsoft Fellowship Baidu Fellowship Dr. Gan's research has received significant recognition from major technology companies through prestigious fellowships and has been widely covered by leading media outlets including CNN, BBC, The New York Times, WIRED, Forbes, and MIT Tech Review. His work at the MIT-IBM Watson AI Lab provides him with access to substantial resources for cutting-edge AI research, while his academic position enables him to train the next generation of AI researchers. His collaborations with prominent researchers like Antonio Torralba demonstrate his integration within the top echelons of the computer vision and AI research community. At the MIT-IBM Watson AI Lab, Dr. Gan leads research initiatives focused on advancing video understanding and embodied intelligence. His work contributes to the lab's mission of developing AI systems that can perceive, reason about, and interact with the physical world in more human-like ways. His research group likely focuses on developing novel architectures for multimodal learning, creating benchmarks for physical reasoning, and building systems that can transfer knowledge between simulation and real-world environments.

Leslie Kaelblingمشاهده پروفایل

استاد

• Robotics
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Leslie Kaelbling serves as the Panasonic Professor in MIT's Department of Electrical Engineering and Computer Science (EECS) and Director of Research for the MIT Quest for Intelligence, while also holding an investigator position at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her academic background includes a BA in philosophy and PhD in computer science, both earned at Stanford University. Professor Kaelbling's research centers on building intelligent robots capable of operating in uncertain environments through advanced learning, state estimation, and planning techniques. Her work bridges theoretical AI foundations with practical robotics applications, focusing on creating systems that adapt to complex real-world scenarios through probabilistic reasoning and decision-making under uncertainty. Analysis of her recent publications reveals a strong trajectory toward integrating foundation models with robotic planning systems, particularly leveraging vision-language models for constraint inference and diffusion models for task-motion planning. Her research increasingly emphasizes uncertainty quantification, neuro-symbolic representations, and sample-efficient learning methods for long-horizon manipulation tasks. Key scientific recognitions include: Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) Founder and inaugural Editor-in-Chief of the Journal of Machine Learning Research As Director of Research for the MIT Quest for Intelligence, she leads strategic initiatives advancing human and machine intelligence research, fostering cross-laboratory collaborations between CSAIL, neuroscience, and cognitive science groups to develop next-generation AI systems. Her leadership extends to shaping research directions in embodied intelligence and foundational learning algorithms.

• Digital Signal Processing
• Image Processing
• Computer Vision
• +۲ مورد دیگر

Dimitrios Kosmopoulos serves as Professor in the Computer Engineering and Informatics Department at the University of Patras, Greece, with extensive experience across multiple academic institutions including National Technical University of Athens (NTUA), Rutgers University, and University of Texas at Arlington. His research bridges theoretical computer science with practical applications in accessibility, agriculture, and cultural heritage preservation. Education: B.Eng. in Electrical and Computer Engineering, National Technical University of Athens (1997) PhD in Electrical and Computer Engineering, National Technical University of Athens (2002) Professor Kosmopoulos' research integrates computer vision, machine learning, and signal processing to solve real-world problems. His primary focus areas include sign language recognition systems for museum accessibility, precision agriculture applications for crop monitoring and disease detection, and digital restoration of ancient scripts like Mycenaean Linear B. His methodological innovations frequently involve geometric analysis, time-series modeling, and multimodal data fusion techniques that advance both theoretical frameworks and practical implementations. Analysis of his recent publications (2023-2025) reveals three dominant research thrusts: accessibility technologies for deaf communities (particularly museum navigation systems), agricultural automation using computer vision (olive grading, tomato disease detection), and computational archaeology (Linear B tablet restoration). His work consistently employs cutting-edge approaches including geometric knowledge distillation, coupled learning architectures, and 3D motion analysis, demonstrating strong interdisciplinary connections between computer science, agriculture, and humanities. No scientific awards were documented in the provided source materials. While specific advising details and grant information were not explicitly stated, his leadership in projects like HealthSign (sign language healthcare systems) and MuseLearn (museum accessibility platforms) indicates substantial research funding and collaborative supervision activities spanning computer vision, robotics, and assistive technology domains. Professor Kosmopoulos operates within the Division of Hardware and Computer Architecture at the University of Patras, collaborating with the Computer Technology and Architecture Laboratory, VLSI Microelectronics Laboratory, Signals and Telecommunications Laboratory, and Computer Communications Networks Laboratory. His current research integrates these facilities to develop systems like the SignGuide project for museum tours and frameworks for early pest detection in greenhouse crops, emphasizing practical implementations of machine learning in constrained environments.

• Medical Image Analysis
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Jolita Bernatavičienė serves as a Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies within the Image and Signal Analysis Group. With a Doctorate in Technological Sciences (Informatics), she has established herself as a leading researcher in medical image analysis and artificial intelligence applications in healthcare. Her extensive research portfolio spans over 15 years of continuous contributions to the field. Dr. Bernatavičienė's research interests primarily focus on medical image analysis, particularly in ophthalmology and oncology applications. Her work integrates advanced machine learning techniques with medical diagnostics, specializing in eye fundus image analysis for glaucoma detection and prostate MRI analysis for cancer identification. She has made significant contributions to deep learning architectures, signal processing methodologies, and data analysis frameworks applicable to biomedical challenges. Her publication record demonstrates strong trends in applying cutting-edge AI techniques to solve concrete medical problems, with a noticeable shift toward more sophisticated deep learning architectures in recent years. The research spans multiple medical domains including ophthalmology, cardiology, oncology, and renewable energy systems monitoring, reflecting her interdisciplinary approach to data science applications. Leader of International Conference 'Data Analysis Methods for Software Systems (DAMSS)' 2015-2024 Member of IEEE Computer Society section (since 2022) Member of the Council of the Lithuanian Computer Association, Artificial Intelligence Section Member of the Lithuanian Operations Research Society Expert at the Science, Innovation and Technology Agency (MITA) (2020-2022) Dr. Bernatavičienė actively supervises doctoral and master's students, with current doctoral student Roman Surkant working on prostate MRI analysis. She leads multiple significant research projects including 'Developing Talents in Artificial Intelligence to Solve Disruptive Environmental Problems' and serves as scientific leader for the Research Council of Lithuania funded project on cardiac MRI texture analysis. Her work has been supported by various national and international funding mechanisms including COST activities, EuroHPC programs, and Lithuanian national research grants. She is principal investigator for the long-term project developing a database of depersonalized fundus images (2018-2030) and has led numerous projects related to medical image analysis, AI applications in healthcare, and data science methodologies. Her research group maintains strong international collaborations through COST actions and other European research networks.

• Systems Neuroscience
• Motor Learning and Control
• Animal Behavior
• +۷ مورد دیگر

Prof. Mackenzie Weygandt Mathis is Tenure-Track Assistant Professor and Bertarelli Foundation Chair of Integrative Neuroscience at EPFL’s Brain Mind Institute . Leading the Mathis Lab since 2020, she unites machine learning and systems neuroscience to decipher how brains learn and control movement. Education : PhD in Neuroscience, Harvard University (2017, advisor Naoshige Uchida) Post-doctoral training, University of Tübingen with Matthias Bethge (2017) Rowland Fellow, Harvard University (2017-2020) Research Focus : the lab develops open-source AI tools ( DeepLabCut , CEBRA , AmadeusGPT ) and combines them with large-scale neural recordings in behaving mice to uncover the neural basis of adaptive motor control. Core themes include sensorimotor learning, proprioception, and brain-inspired algorithms for robotics. Publications Trend : recent work spans robust machine-learning methods (ICLR, AISTATS 2025), foundational models for pose estimation ( SuperAnimal , 2024), and integrative studies linking neural population dynamics to muscle-level control ( Nature 2023, Cell 2024). Scientific Awards : Swiss Science Prize Latsis 2024 Robert Bing Prize 2024 Eric Kandel Young Neuroscientist Prize 2023 FENS EJN Young Investigator Prize 2022 Vallee Scholar, ELLIS Scholar, NSF Graduate Fellow Advising & Funding : she mentors 4 current PhD students and several postdocs, supported by SNSF Starting Grant (1.5 M CHF), CZI, Novartis, Radala Foundation, and Kavli Foundation grants. Labs & Teams : the Mathis Lab is located at Campus Biotech , Geneva, and actively collaborates with the Alexander Mathis group, Allen Institute, and international consortiums on open-source neuroscience tools.

George D Konidarisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۲ مورد دیگر

George D Konidaris serves as Associate Professor of Computer Science at Brown University, where his research bridges artificial intelligence, machine learning, and robotics with emphasis on autonomous decision-making systems. His work focuses on developing algorithms that enable robots and AI agents to learn hierarchical structures, discover reusable skills, and operate effectively in complex environments. Education: 2010: PhD, University of Massachusetts, Amherst 2003: MS, University of Edinburgh 2001: BS, University of the Witwatersrand 2000: BS, University of the Witwatersrand His research spans reinforcement learning , robotic motion planning , and hierarchical abstraction , with significant contributions to skill discovery, temporal abstraction, and model-based methods. Current work integrates visuo-haptic perception for manipulation tasks and explores language-guided robotics using large language models. His approach emphasizes creating systems that learn compact world representations for efficient long-horizon planning in partially observable environments. Analysis of his 2025 publications reveals strong trends in model-based reinforcement learning with focus on memory mechanisms, uncertainty quantification, and hierarchical skill composition. Key themes include temporal abstraction for planning efficiency, visuo-haptic fusion for robotic manipulation, and language grounding for task specification. His work increasingly connects cognitive science concepts like theory of mind with AI capabilities. Teaching responsibilities include CSCI 1410 (Artificial Intelligence) and CSCI 2951X (Reintegrating AI), where he bridges theoretical foundations with practical robotics applications.

Amy Ahnمشاهده پروفایل

پژوهشگر

• Autism Spectrum Disorder Diagnostics
• Developmental Psychology
• Behavioral Analysis Technology
• +۳ مورد دیگر

Amy Ahn serves as Associate Research Director in Harvard University's Department of Psychology, leveraging technology and multidisciplinary approaches to advance objective behavioral measurement for early risk identification and diagnostic acceleration. Her educational background includes: B.S. in Human Development from Cornell University Ph.D. in Developmental Psychology from the University of Miami Her research integrates computer vision, machine learning, and clinical psychology to develop objective metrics for autism spectrum disorder (ASD) diagnostics, focusing on social communication behaviors, facial expressions, and vocalizations in children. She pioneers automated tools like infant facial action recognition (AFAR) and AAC usage pattern analysis, while examining parent-infant interactions through paradigms like Face-to-Face/Still-Face. Her work bridges technological innovation with clinical application to improve early intervention pathways. Analysis of her 15 most recent publications reveals consistent advancement in objective ASD assessment tools, with increasing emphasis on machine learning applications (e.g., stacked autoencoders) and pandemic-related service disruptions. Her research trajectory shows progression from foundational infant behavior studies toward scalable diagnostic technologies and real-world implementation challenges. No scientific awards were mentioned in the provided text. The source material contains no information about student advising or grant funding. Dr. Ahn directs research operations at Harvard's Nock Lab, which specializes in technology-enhanced psychological assessment and intervention development for developmental disorders, with particular focus on objective behavioral metrics and early detection systems.

Alexander Eckerمشاهده پروفایل

استاد

• Machine Learning
• Computational Neuroscience
• Visual Perception
• +۳ مورد دیگر

Prof. Dr. Alexander Ecker is Professor of Data Science at the Institute of Computer Science, University of Göttingen, and concurrently holds the prestigious Max Planck Fellow position at the Max Planck Institute for Dynamics and Self-Organization. Since 2020 he also serves on the Executive Board of the Campus Institute Data Science in Göttingen. He leads the Neural Data Science research group, comprising 14 PhD students and 2 postdoctoral researchers, focusing on the interface of machine learning and computational neuroscience. His educational background includes a Dr. rer. nat. in Neuroscience (2014) from the Graduate School of Neural and Behavioral Sciences/IMPRS, University of Tübingen, followed by post-doctoral and group-leader positions at the University of Tübingen and the Max Planck Institute for Biological Cybernetics. Research Interests Machine Learning & Deep Learning: developing novel algorithms for representation learning and generative modeling. Computational Neuroscience: large-scale data-driven modeling of visual cortical circuits. Visual Perception: bridging biological vision and computer vision via biologically inspired architectures. His work has produced a steady stream of influential publications (2019-2025) in leading journals such as Nature Communications , Nature , Nature Methods , PLOS Computational Biology , ICLR , NeurIPS , and CVPR . The publications trend toward integrating high-resolution neural recordings with state-of-the-art machine-learning models to uncover principles of sensory processing, neuron-type classification, and behavior. Scientific Awards & Honors Max Planck Fellow, Max Planck Institute for Dynamics and Self-Organization (ongoing) Executive Board Member, Campus Institute Data Science, Göttingen (since 2020) Teaching, Advising & Grants Regularly teaches advanced courses: “Deep Learning for Image Synthesis”, “Current Topics in Deep Learning”, and “Graph Machine Learning”. Supervises 14 current PhD students and 2 postdocs within the Neural Data Science Group. Offers numerous Bachelor’s and Master’s thesis projects, with topics ranging from neuronal morphology clustering to primate vocalization analysis. Leads or co-leads large collaborative consortia with labs in Göttingen, Tübingen, Baylor College of Medicine, and other institutions across the US and Germany. Labs & Teams The Neural Data Science Group operates at the Institute of Computer Science, University of Göttingen, and is tightly integrated with the Max Planck Institute for Dynamics and Self-Organization. The group maintains active collaborations with over a dozen partner laboratories, including groups led by Fabian Sinz, Andreas Tolias, Thomas Euler, Tim Gollisch, and Viola Priesemann, fostering an interdisciplinary environment that spans computer science, physics, biology, and psychology.

Jivko Sinapovمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Developmental Robotics
• Human-Robot Interaction
• +۴ مورد دیگر

Jivko Sinapov is an Associate Professor with dual appointments in the Department of Computer Science and Department of Mechanical Engineering at Tufts University's School of Engineering. He also serves as a CEEO Fellow at the Center for Engineering Education Outreach. His research focuses on enabling physical robots to operate and learn in human-inhabited environments through developmental approaches. Education: PhD in Computer Science and Human-Computer Interaction, Iowa State University (2013) BSc in Computer Science and Mathematics, University of Rochester (2005) Professor Sinapov's research centers on Artificial Intelligence, Developmental Robotics, Computational Perception, and Human-Robot Interaction . His work addresses fundamental questions about implementing intelligence in physical robots, with emphasis on enabling extended operation in human environments. His laboratory develops methods for behavioral object exploration, multi-modal perception, and knowledge transfer between robots, with applications ranging from educational robotics to space exploration. Current research directions include neurosymbolic approaches for handling novelty in open worlds, multimodal object property learning, and augmented reality interfaces for improved human-robot collaboration. Scientific Awards and Recognition: Winner of the Verizon 100K 5G EdTech Challenge (Spring 2019) for AR-based robotics education NSF CAREER Award: "Learning and Sharing Transferable Grounded Object Knowledge for Collaborative Robots" (2023) CEEO Fellow at the Center for Engineering Education Outreach Professor Sinapov actively mentors graduate students in the Multimodal Learning, Interaction, and Perception (MLIP) Lab, currently advising five PhD students across Computer Science and Mechanical Engineering departments. His research has been supported by significant grants including his NSF CAREER award. He has co-organized prominent symposia including the AAAI Spring Symposium on "Interactive Multi-Sensory Perception for Embodied Agents" (2017) and the AAAI Fall Symposium on "AI for Human-Robot Interaction" (2019). He directs the Multimodal Learning, Interaction, and Perception (MLIP) Lab , which develops cognitive robotics systems capable of learning through environmental interaction. The lab's research spans robot learning, computational perception, and human-robot interaction, with applications in education, space technology, and collaborative robotics systems operating in complex human environments.