جست‌وجوی استاد و پژوهشگر

Jason Heinمشاهده پروفایل

دانشیار

• Mechanistic Organic Chemistry
• Automated Reaction Analysis
• Self-Driving Laboratories
• +۷ مورد دیگر

Jason Hein is an Associate Professor in the Department of Chemistry at the University of British Columbia's Faculty of Science. His research focuses on the development of automated reaction analysis technology and self-driving laboratories that integrate robotics with synthetic organic chemistry. Dr. Hein leads the Hein Lab, which pioneers innovative solutions for mechanistic organic chemistry, catalytic reaction mechanisms, and chemical manufacturing processes. His research interests center on creating modular robotic tools and integrated analytical hardware for automated reaction profiling, with applications in pharmaceutical manufacturing, battery materials processing, and sustainable chemistry. The lab's work combines advanced robotics, artificial intelligence, and process analytical technology to develop self-optimizing chemical systems that accelerate discovery and improve manufacturing efficiency. Analysis of Hein's recent publications reveals a strong focus on AI-driven laboratory automation, with particular emphasis on crystallization optimization for battery materials, computer vision for process monitoring, and interoperable software systems for self-driving laboratories. His work bridges fundamental mechanistic understanding with practical industrial applications, particularly in lithium extraction from waste brines and pharmaceutical process development. NSERC Postdoctoral Fellowship Dr. Hein's research program includes significant grant funding supporting the development of self-driving laboratory technologies and their application to challenging chemical problems. His lab actively collaborates with industry partners in pharmaceuticals and clean energy sectors to translate fundamental insights into deployable technologies. Current projects focus on battery-grade lithium carbonate production, continuous manufacturing processes, and AI-optimized chemical synthesis. The Hein Lab operates as a multidisciplinary research environment combining expertise in organic chemistry, robotics engineering, computer science, and data analytics to create the next generation of autonomous chemical discovery systems.

Yan Chenمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Computer Science Education
• Real-Time Data Analysis
• +۳ مورد دیگر

Yan Chen is an Assistant Professor at the Virginia Tech College of Engineering , where he leads the PRIME Lab (Programming with Intelligent Machines & Environments) . His work focuses on creating interactive Human-AI systems to enhance real-time data analysis and programming education, particularly addressing barriers in collaborative learning environments. University of Toronto (Postdoctoral Fellow) University of Michigan (Ph.D., Information Science) University of Colorado, Boulder (BS/MS in Applied Math & Electrical & Computer Engineering) His research bridges Human-Computer Interaction (HCI) and Computer Science Education , with a focus on real-time data analysis , AI-driven programming assistance , and scalable learning tools . He employs LLMs and human-centered design to simplify complex computational processes, enabling data workers to detect critical patterns efficiently. Recent publications highlight trends in generative AI for education , proactive AI programming support , and collaborative analytics . Key themes include real-time classroom insights , intergenerational smartphone learning , and automated feedback systems . Scientific recognition includes: 🏆 Best Paper at L@S 2024 🏅 Best Paper Honorable Mention at CHI 2023 🏅 Best Paper Honorable Mention at UIST 2022 🏆 Best Short Paper at VL/HCC 2020 He mentors a team of PhD and MS students in projects spanning AI-assisted education, web automation, and collaborative coding tools, with active recruitment for future research directions.

Maria Sharminaمشاهده پروفایل

استاد

• Energy Systems
• Circular Economy
• Climate and Energy Policies
• +۳ مورد دیگر

Maria Sharmina is a Professor in Energy and Sustainability at The University of Manchester’s School of Engineering, affiliated with the Tyndall Centre for Climate Change Research and Policy@Manchester. She holds roles including Co-Director of Policy@Manchester, UKRI Digital Security & Resilience Theme advisor, and Sustainable Robotics Committee member at BSI. Her work focuses on low-carbon business models, circular economy, and climate policy, with leadership in projects like the £6M UKRI BuildZero programme. Education: PhD in the Tyndall Centre, background in economics and statistics. She advises governments (e.g., UK Government Office for Science, BEIS) and has served on University Senate and Departmental Research Leadership roles. Research Interests: Energy systems transformation, food-energy-water nexus, emission scenarios, and policy design. She emphasizes sustainable innovation, net-zero housing, and plastics circularity. Key projects include analyzing hydrogen’s role in decarbonization, SME compliance with plastics policies, and financing smart local energy systems. Awards: 2024 Social Responsibility Medal and Making A Difference Award for contributions to social/environmental impact via entrepreneurship. Her work bridges academia and policy, with outputs including 79 research publications and 16 projects. Grants/Consultancy: Principal Investigator on BuildZero (UKRI £6M), funded studies on community energy finance, and consultancy with Oxfam, Manchester City Council, and the Co-operative Group. Labs/Teams: Lead researcher at Tyndall Manchester, collaborates across disciplines (e.g., Digital Futures Institute) to address sustainability challenges through systemic approaches.

Vincent Sitzmannمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Vincent Sitzmann is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Scene Representation Group and is part of the Visual Computing research community at CSAIL. His work focuses on advancing artificial intelligence's ability to perceive and interact with the physical world, particularly through neural fields, 3D scene representations, and robotics. His research bridges computer vision, machine learning, and robotics, aiming to create systems that emulate human perception and decision-making. He holds a dual role in the PI Core/Dual program at MIT and contributes to interdisciplinary efforts in AI & ML, Graphics & Vision, and Robotics. His recent projects include developing generative models for 3D avatars, robust camera pose estimation, and learning-based control for soft robots. He collaborates widely within MIT’s engineering ecosystem and has led initiatives such as the Collaborative Research grant on compositional implicit representations for 3D scene understanding (2022). His lab, the Scene Representation Group, emphasizes scalable 3D reconstruction, material estimation, and embodied AI. Notable technologies include Flowmap for camera calibration and Dittogym for soft robotics control. While no awards are explicitly listed, his work has been featured in top conferences like SIGGRAPH and IEEE Robotics.

Mehrdad Arashpourمشاهده پروفایل

دانشیار

• Automation and Robotics
• Computer Vision
• Deep Learning
• +۷ مورد دیگر

Summary Associate Professor Mehrdad Arashpour is an internationally recognized researcher and educator in construction and civil infrastructure, focusing on automation and information technologies. He leads the ASCII Lab at Monash University's Department of Civil and Environmental Engineering. His academic roles include Head of Construction Engineering and membership in the CIB's Working Commission on Off-site Construction (W121) and Infrastructure Task Group (TG91). Education: Ph.D., RMIT University, Australia M.Sc., Grenoble University, France B.Sc., IU University, Iran Research Interests: Digital twins, computer vision, robotics, BIM integration, sustainable construction, and automation in construction processes. His work contributes to UN Sustainable Development Goals, particularly in sustainable cities and communities. Grants & Awards: Over $6M in grants from ARC, Austroads, and industry partnerships. Recognitions include Editor's Choice Paper (ASCE, 2019) and Outstanding Reviewer (Elsevier, 2016). Teaching: Courses like Risk Management in Engineering Projects and Infrastructure Research Project. Advises on PhD topics in computer vision, robotics, and BIM. Labs & Collaborations: ASCII Lab focuses on smart, sustainable solutions for construction. Collaborates with global researchers and organizations like SPARC Hub and Building 4.0 CRC.

Bruce A. Maxwellمشاهده پروفایل

استاد آموزشی

• Computer Vision
• Robotics
• Computer Graphics
• +۳ مورد دیگر

Bruce A. Maxwell is a Teaching Professor and Assistant Director of Computing Programs at Northeastern University’s Seattle Campus, following roles as Chair of the Computer Science (CS) Department at Colby College (2013–2020) and leadership in establishing the Khoury College MS CS Align Program at the Roux Institute (2020–2022). His academic journey includes affiliations with Northeastern’s Seattle Campus and ongoing collaboration with Colby CS as a research scientist. He specializes in Computer Vision, Robotics, Computer Graphics, Game Design, and Data Analysis, with notable contributions to concussion management research through the Maine Concussion Management Initiative (MCMI), focusing on sports-related injury analysis and symptom monitoring. His research spans over two decades, with significant work in human-robot interaction, autonomous systems, and educational technology. Notable projects include developing tools for real-time shadow removal in autonomous driving contexts and analyzing cognitive outcomes in student-athletes post-concussion. Maxwell has authored over 50 peer-reviewed publications, emphasizing interdisciplinary approaches bridging computer science, sports medicine, and educational policy. Teaching innovations include integrating thematic elements (e.g., Lord of the Rings) into CS1 coursework and advocating for writing in computer science curricula. He maintains active roles in academic service, including SIGCSE conference contributions and panel discussions on gender equity in tech education. Education: Ph.D. in Robotics from Carnegie Mellon University (1996), M.Phil. in Engineering from Cambridge University (1993). Awards: Recognized for pedagogical contributions but no named awards listed in provided materials. Labs/Teams: Collaborates with the Maine Concussion Management Initiative and Khoury College’s Align Program team.

Wenchao Liمشاهده پروفایل

استادیار

• Dependable Computing
• Cyber-Physical Systems
• Electronic Design Automation
• +۴ مورد دیگر

Wenchao Li is an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University, directing the Dependable Computing Laboratory. He holds a B.S., M.S., and Ph.D. in Electrical Engineering and Computer Sciences, along with a B.A. in Economics from UC Berkeley. His research focuses on dependable computing, applying formal verification, machine learning, and control theory to cyber-physical systems, electronic design automation, and AI safety. Key research interests include neural network verification, safe reinforcement learning, autonomous systems security, and resilient control strategies for connected vehicles. His work emphasizes provable safety guarantees and defense against adversarial attacks in critical infrastructure systems. Notable awards include the ACM Outstanding Ph.D. Dissertation Award and the Leon O. Chua Award. His lab investigates topics such as neural network repair, secure multi-robot coordination, and formal methods for autonomous systems. He advises students like Jiameng Fan and collaborates on projects funded by grants in AI safety and cyber-physical systems. Labs/Teams: Dependable Computing Laboratory Grants: Focus on formal verification, AI safety, and autonomous systems resilience

Angelica Limمشاهده پروفایل

استادیار

• Human Robot Interaction
• Affective Computing
• Multimodal Perception and Learning
• +۶ مورد دیگر

Angelica Lim is an Assistant Professor of Professional Practice and Rajan Family Scholar in the School of Computing Science at Simon Fraser University. Her research focuses on Human Robot Interaction, Affective Computing, and Multimodal Perception with applications in healthcare and developmental robotics. She holds a PhD in Informatics from Kyoto University (2014), an M.Sc. from Kyoto University (2012), and a B.Sc. in Computing Science from SFU (2008). Her work bridges robotics and human-centered AI through projects like the ROSIE Lab, exploring emotion-aware systems, socially assistive robots, and VR programs for aging populations. Key contributions include benchmarking emotional speech recognition (BERSting), developing embodied emotion models for robots, and co-designing healthcare technologies with patient partners. Recent publications emphasize ethical AI, multimodal perception systems, and human-robot collaboration in dynamic environments. Teaching includes courses on software engineering, artificial intelligence, and introductory computer science. Her research has been applied in dementia care through VR programs, robotic companionship for older adults, and emotion-aware human-robot communication systems. Current initiatives focus on inclusive HRI design and sim2real methodologies for underrepresented data in affective computing.

Matti Tedreمشاهده پروفایل

استاد

• Computer Science Education
• ICT4D
• Social Studies of Computer Science
• +۵ مورد دیگر

Matti Tedre is a Professor at the School of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland. His research focuses on computer science education, ICT4D, social studies of computer science, and the history and philosophy of computer science. He leads the Technologies for Learning and Development research group and is involved in the Generation AI project (2022–2028), exploring AI education for security mindset development. His work bridges theory and practice, emphasizing educational technology, AI literacy, and participatory design. Recent projects include developing low-cost AI kits for novice learners and co-designing ML-driven apps with children. He collaborates globally on topics like K-12 computing education, data agency, and ethical AI integration in classrooms. Key contributions include studies on scaffolding in ML education, children’s understanding of algorithmic biases, and the role of generative AI in creative learning. His research also addresses challenges in Tanzanian ICT adoption, including financial management systems for informal groups and timetabling software for higher education institutions. Tedre’s interdisciplinary approach spans computer science, education, and sociology, with a focus on democratizing AI access and fostering critical digital literacy among youth and educators.

Tim Oatesمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۷ مورد دیگر

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

Chris Monicoمشاهده پروفایل

دانشیار

• Cryptology
• Computational Algebra
• Number Theory
• +۶ مورد دیگر

Chris Monico is an Associate Professor in the Department of Mathematics & Statistics at Texas Tech University . He has been a faculty member there since 2003, following post-doctoral research at the University of Notre Dame. Education B.S. in Mathematics – Monmouth University M.S. in Mathematics – University of Notre Dame Ph.D. in Mathematics – University of Notre Dame Research Focus Monico’s scholarship centers on the intersection of cryptology , computational algebra , and number theory . A significant recent thrust has been the application of machine-learning techniques to mathematical finance , evidenced by work on random-forest models for option pricing and high-frequency trading risk metrics. Parallel lines of inquiry include post-quantum cryptographic schemes built on tropical algebra and semigroup actions, as well as classical problems in Ramsey theory and combinatorial semigroups . Publication Trends Between 2015 and 2025 Monico has published prolifically, with a clear shift around 2020 toward mathematical finance and machine-learning applications , alongside continued output in algebraic cryptanalysis and combinatorics . His 2024–2025 articles emphasize data-driven models in trading, whereas 2020–2021 works concentrate on cryptanalyses of tropical and group-based key-exchange systems. Earlier contributions focus on computational number theory and semigroup-based cryptography. Contact Information Email: c.monico@ttu.edu Phone: 806-834-4144 Office: Department of Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409-1042 Advising & Grants No specific doctoral or master’s students, funded grants, or named awards are detailed in the provided text. Laboratory or Research Group The text does not mention any dedicated laboratory or research group.

Lyndia Wuمشاهده پروفایل

استادیار

• Biomechanical engineering
• Biomedical instrumentation
• Biomedical signal processing
• +۱۱ مورد دیگر

Lyndia Wu is an Assistant Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science, where she holds the prestigious Canada Research Chair in Wearable Brain Injury Sensing. She leads the SimPL (Sensing in Biomechanical Processes Lab) and maintains an active research program focused on biomechanics and medical device development. Her educational background includes: B.A.Sc. from the University of Toronto M.S. from Stanford University Ph.D. from Stanford University Postdoctoral Fellowship from Stanford University Dr. Wu's research program centers on developing novel sensing and data analytics technologies to study human biomechanics in health and disease states. Her primary research areas encompass brain injury or concussion biomechanics using advanced sensing, modeling, and machine learning approaches, as well as the development of innovative sensors and algorithms for studying sleep disorders like obstructive sleep apnea. She specializes in wearable sensors for brain health monitoring, traumatic brain injury mechanisms, and AI applications in healthcare settings. Analysis of her recent publications reveals a strong focus on sports-related head impacts (particularly in soccer), EEG monitoring following impacts, and sleep monitoring after concussions. Her work demonstrates interdisciplinary collaboration across biomechanical engineering, neuroscience, and clinical medicine, with publications spanning biomechanics, neurotrauma, biomedical instrumentation, and signal processing domains. Dr. Wu has received significant recognition for her work, including: Scholar Award from the Michael Smith Foundation for Health Research (2019) Junior Faculty Teaching Award from UBC Mechanical Engineering (2022) She actively supervises graduate students in Mechanical Engineering programs (MASc and PhD) and collaborates extensively across disciplines. Dr. Wu is affiliated with multiple research centers including the Institute for Computing, Information and Cognitive Systems (ICICS), Origins of Balance Deficits and Falls, and SmarT Innovations for Technology Connected Health (STITCH), reflecting her interdisciplinary approach to solving complex biomedical challenges. As director of the SimPL lab, she leads a research team developing cutting-edge sensing solutions for biomechanical processes with particular emphasis on brain injury prevention, monitoring, and recovery assessment through innovative engineering approaches.

Patrick Linمشاهده پروفایل

استاد

• Technology Ethics
• Artificial Intelligence Ethics
• Robotics Ethics
• +۱۰ مورد دیگر

Patrick Lin is a Professor in the Philosophy Department at California Polytechnic State University (Cal Poly), where he serves as Director of the Ethics + Emerging Sciences Group, a non-partisan organization established at Cal Poly in 2007 to focus on the risk, ethical, and social impact of emerging sciences and technologies. He is frequently quoted in national publications on topics including ethics of autonomous vehicles, artificial intelligence, robotics, outer space, Arctic frontiers, military and policing applications, virtual and augmented reality, and smart cities. Lin received his Ph.D. and M.A. from the University of California, Santa Barbara, and his B.A. from the University of California, Berkeley. His academic appointments include Affiliate Scholar at Stanford Law School's Center for Internet and Society, Fulbright Specialist at the University of Iceland's Centre for Arctic Policy Studies (2018), and Visiting Senior Research Fellow at the Centre for Applied Philosophy and Public Ethics in Australia (2010-2016). Lin's research spans technology ethics broadly, with specific expertise in AI ethics, robotics ethics, autonomous vehicle ethics, space ethics, cybersecurity ethics, and military ethics. His work bridges philosophical theory with practical application, examining how emerging technologies challenge traditional ethical frameworks. His research demonstrates consistent themes across different technological domains: examining risk assessment methodologies, developing ethical frameworks for emerging technologies, analyzing social and political implications of technological adoption, and providing practical guidance for developers, policymakers, and users. His publication record shows a progression from early work on nanotechnology ethics to current focus areas including space cybersecurity, AI kitchens, and ethical frameworks for autonomous systems. The articles reflect his interdisciplinary approach, combining insights from philosophy, law, engineering, and policy studies to address complex ethical challenges in emerging technologies. Cal Poly/Academic Senate, Distinguished Scholarship Award (2017) American Philosophical Association's Public Philosophy Op-Ed Award (2015) Cal Poly/College of Liberal Arts, Outstanding Scholarship Award (2009) Lin has secured significant grant funding from organizations including the National Science Foundation, US Department of Defense, and Canadian Institute for Advanced Research for research on military AI risk assessment, AI kitchens and robot cooks, outer space cybersecurity, and autonomous vehicles. He has advised numerous students through his teaching and research activities at Cal Poly, where he teaches courses including Philosophy of Technology, Ethics of Science and Technology, and Introduction to Philosophy. Lin directs the Ethics + Emerging Sciences Group at Cal Poly, which serves as a hub for interdisciplinary research on technology ethics. He also participates in several other research initiatives including his role as Research Director for the Consortium for Emerging Technologies, Military Operations, and National Security (CETMONS) and as a member of the Emerging Technologies of National Security and Intelligence initiative at the University of Notre Dame.

Nikolas Braschمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• SLAM
• 3D Scene Understanding
• +۱ مورد دیگر

Nikolas Brasch is a PhD candidate and researcher at the Chair of Computer Aided Medical Procedures (CAMP) within the Department of Informatics at the Technical University of Munich. He is affiliated with the research group led by Prof. Nassir Navab and maintains his office at Boltzmannstr. 3, 85748 Garching b. München, Room 5613.03.039. Brasch's research centers on 3D computer vision with emphasis on SLAM (Simultaneous Localization and Mapping) and 3D scene understanding. His work bridges computer vision with medical applications, particularly in medical augmented reality and surgical robotics. He has developed innovative approaches for improving depth estimation in challenging environments and creating robust SLAM systems that maintain accuracy in dynamic settings. His publication record shows a consistent trajectory in advancing 3D vision techniques with practical applications in medical contexts. His research demonstrates particular strength in sensor fusion techniques and semantic understanding of 3D environments. Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments (2021) Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments (2020) Semantic Monocular SLAM for Highly Dynamic Environments (2018) As an educator, Brasch serves as a teaching assistant for the Praktikum on 3D Computer Vision course at TUM. He mentors students on various thesis projects related to 3D vision, human pose estimation, and semantic reconstructions, typically collaborating with Prof. Federico Tombari. His teaching activities reflect his research expertise and commitment to training the next generation of computer vision researchers.

• Artificial Intelligence
• Robotics
• Deep Learning
• +۵ مورد دیگر

Ozgur S. Oguz is an Assistant Professor at Bilkent University , Faculty of Computer Engineering, and the lead of the Learning for Intelligent Robotic Agents (LiRA) Lab . His research focuses on enhancing autonomous agents' capabilities in learning, reasoning, and planning, particularly for robotics applications. Education : PhD in Computer Science from TU Munich , studies at University of British Columbia (UBC) and Koç University , postdoctoral work at University of Stuttgart and Max Planck Institute for Intelligent Systems . His research explores algorithms for autonomous decision-making, with emphasis on deep learning , reinforcement learning , and robotics . Recent work includes diffusion-based reinforcement learning , hindsight experience prioritization , and hybrid manipulation planning , often addressing challenges in sequential task execution and tactile-based control. Key trends in his publications revolve around robotic manipulation , motion planning , and human-robot interaction . He has contributed to conferences like NeurIPS , ICRA , IROS , and journals such as IEEE TRO and Scientific Reports .