جست‌وجوی استاد و پژوهشگر

Minh Q. Phanمشاهده پروفایل

دانشیار

• System Identification
• Control Theory
• Robotics
• +۴ مورد دیگر

Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.

Michael McAlpineمشاهده پروفایل

استاد

• 3D Printing
• Nanotechnology
• Biomedical Engineering
• +۴ مورد دیگر

Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award

Monica Lamمشاهده پروفایل

استاد

• Artificial Intelligence
• Privacy-Preserving Technologies
• Compiler Design
• +۴ مورد دیگر

Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.

Gilbert Yeمشاهده پروفایل

استادیار

• Human-Robot Interaction
• Automation in Engineering
• AI in Construction
• +۳ مورد دیگر

Yang (Gilbert) Ye is an Assistant Professor in the Department of Civil and Environmental Engineering at Northeastern University, joining in January 2025. His research focuses on human-AI/robot teaming, automation in engineering, and assistive technologies, with a particular emphasis on human-centric robotics and sensorimotor processes. He holds a PhD in Civil Engineering from the University of Florida (2024), advised by Dr. Eric Jing Du. **Affiliations**: Member of ASCE, IEEE, and HFES. His work integrates VR/AR, robotics (e.g., drones, exoskeletons), and AI to enhance civil engineering workflows and workforce training. He leads the Ye Lab, actively recruiting PhD students and postdocs with coding experience (Python/C++/C#) and backgrounds in engineering or computer science. **Key Research Themes**: Human-robot interaction, construction automation, exoskeleton training, and delayed feedback mitigation in teleoperation. Over 20 peer-reviewed publications in journals like ASCE JoCEN, IEEE Access, and Advanced Engineering Informatics. **Lab Opportunities**: PhD/postdoc applicants require strong academic records (GPA ≥3.5) and coding skills. Undergrad/master students can apply for thesis/research roles. Funding covers tuition, insurance, and stipends.

• Computer Graphics
• Computer Vision
• Human-Computer Interaction
• +۷ مورد دیگر

Changjian Li is an Assistant Professor in the School of Informatics at the University of Edinburgh. He leads the GraphViX Group (Graphics, Vision and X) and is a member of the Institute of Perception, Action and Behaviour (IPAB). His research spans computer graphics, computer vision, and human-computer interaction with a focus on 3D generation and analysis. Education: Bachelor's Degree from Shandong University (2014) Ph.D. from the University of Hong Kong (2019) under Prof. Wenping Wang Postdoc at University College London (UCL) with Prof. Niloy Mitra Starting Researcher position at Inria with Dr. Adrien Bousseau Research Interests: Changjian's research focuses on sketch-based 3D modeling, CAD modeling, point cloud processing, and medical imaging applications. He develops systems that bridge intuitive sketching with precise CAD workflows, enhances 3D animation pipelines, and applies neural methods to sparse medical data reconstruction. Scientific Recognition: Best Paper Honorable Mention Award (MICCAI 2021) CADTalk selected as Highlight (CVPR 2024 top 10%) ACM SIGGRAPH Asia 2018 cover image selection ACM SIGGRAPH Asia 2015 technical paper highlight CVPR 2019 poster highlighted in 'Computer Vision News' Advising & Collaborations: He mentors postdocs and PhD students including Duolikun Danier, Haocheng Yuan, Ankan Bhunia, and Lei Zhong. Former advisees include Salvatore Esposito (now at Edinburgh), Guangshun Wei (Shandong University), and Mingjun Yang (University of Melbourne). Collaborates with Oisin Mac Aodha, Hakan Bilen, and Niloy Mitra. Professional Service: Currently serves as Associate Editor for IEEE TVCG and participates in program committees for SIGGRAPH Asia, SIGGRAPH, EuroGraphics, and Geometry Design and Computing (GDC) conferences.

Aditya T Siripuramمشاهده پروفایل

دانشیار

• Fourier analysis
• Signal processing
• Machine learning
• +۳ مورد دیگر

Aditya T Siripuram is an Associate Professor at the Indian Institute of Technology Hyderabad (IITH), holding joint appointments in the Department of Electrical Engineering and the Department of Artificial Intelligence. He completed his PhD at Stanford University and holds B.Tech and M.Tech degrees from IIT Bombay. Education: PhD in Electrical Engineering, Stanford University (2017) - GPA: 4.17/4 M.Tech in Electrical Engineering, IIT Bombay (2009) - GPA: 9.79/10 B.Tech in Electrical Engineering, IIT Bombay (2009) - GPA: 9.79/10 Research Interests: His research spans Fourier analysis, signal processing, machine learning, convex and combinatorial optimization, with applications in AI/ML and applied mathematics. His work particularly focuses on computational aspects of Fourier analysis, including fast DFT computation for structured signals, convolution idempotents, and graph-based signal processing techniques. His recent research directions involve developing efficient algorithms for computing Discrete Fourier Transforms for signals with structured frequency support, investigating relationships between additive structures in frequency domains and computational complexity, and exploring graph learning techniques under spectral constraints. Awards and Recognition: Excellence in Teaching Award, IIT Hyderabad (2019, 2022) Stanford Graduate Fellowship Qualcomm Innovation Fellowship (awarded to his PhD student Charantej Reddy P in 2021) Teaching and Service: He has taught courses including AI1110 Probability and Stochastic Processes, EE5609 Matrix Theory, EE5606 Convex Optimization, and EE5328 Introduction to Submodular Functions. He serves as Departmental Undergraduate Committee Chair for the Department of AI at IITH (2020-present) and was MTech Admissions Coordinator for the same department (2019-2022). Research Group: He currently advises three PhD students working on signal processing based graph learning techniques, DFT computation for structured signals, and coded computing problems.

Sebastijan Dumancicمشاهده پروفایل

استادیار

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

• Knowledge Representation
• Semantic Web
• Ontology Engineering
• +۷ مورد دیگر

Jiaoyan Chen is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester. She previously held roles as a Lecturer at Manchester, a Senior Researcher at the University of Oxford, and a Postdoctoral Fellow at Heidelberg University. Education: PhD and Bachelor's in Computer Science and Technology from Zhejiang University (2016 and 2011), with a visiting PhD stint at Zurich University's Department of Informatics. Research Interests: Integrating knowledge graphs and ontologies with machine learning and large language models (LLMs), focusing on semantic embeddings, knowledge curation, and explainable AI systems. Publication Trends show emphasis on ontology embeddings (e.g., OWL2Vec*), LLM evaluation with knowledge graphs, and hybrid neural-symbolic reasoning. Her work bridges structured knowledge and modern AI through projects like OntoEm and ConCur . Current Research Team includes postdoctoral researchers, PhD students, and externally co-supervised associates. She actively recruits PhD candidates in areas like Retrieval-Augmented Generation and LLM Explainability , with projects funded by EPSRC and international consortia. Grants & Leadership: EPSRC New Investigator Award (2023-2026) Manchester-Melbourne-Toronto Research Fund (2024-2026) EPSRC ConCur Project (2021-2025) Professional Service: Associate Editor, Transactions on Graph Data and Knowledge EPSRC Peer Review College member OAEI Track Co-organizer at ISWC

Marco Morales Aguirreمشاهده پروفایل

عضو هیئت علمی

• Motion Planning
• Multi-Robot Systems
• Artificial Intelligence
• +۵ مورد دیگر

Marco Morales Aguirre is a Teaching Associate Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign and an Associate Professor at Instituto Tecnológico Autónomo de México (ITAM). He directs research at the Parasol Laboratory and has held significant leadership roles including founding member and former president of the Mexican Federation of Robotics (FMR). His academic journey spans both US and Mexican institutions, reflecting his international impact in the robotics community. Dr. Morales received his educational foundation from prestigious institutions: a Ph.D. in Computer Science from Texas A&M University, an M.S. in Electrical Engineering, and a B.S. in Computer Engineering from Universidad Nacional Autónoma de México (UNAM). His academic path has included positions as Visiting Professor at Texas A&M University and Lecturer at UNAM and the System of Technological Universities in México. His research focuses on motion planning algorithms for robotics, with particular expertise in multi-robot systems where he's pioneered frameworks like Adaptive Robot Coordination (ARC). His work bridges theoretical algorithm development with practical applications in industrial settings, computational biology, and extended reality interfaces. He has made significant contributions to topological guidance methods that improve planning efficiency in complex environments with narrow passages. Analysis of his recent publications reveals a strong trajectory toward more complex multi-robot coordination problems, with increasing emphasis on integrating task and motion planning. His research group has developed innovative approaches that scale to larger robot teams while maintaining computational efficiency, particularly in congested environments where traditional methods struggle. Member of the National System of Researchers of Mexico (level II) Founding member and former president of the Mexican Federation of Robotics (FMR) Member of the Mexican Academy of Computing Editor of multiple Algorithmic Foundations of Robotics (WAFR) proceedings Dr. Morales actively mentors a diverse group of graduate students who frequently appear as co-authors on his publications. His Parasol Laboratory conducts research funded through various academic and industrial collaborations, including significant projects with manufacturing partners exploring collaborative assembly systems. The laboratory has developed several notable frameworks including ARC, K-ARC, and HAS-RRT that have advanced the state of the art in multi-robot motion planning.

• Agricultural Robotics
• Automation and Control Systems
• Precision Agriculture
• +۵ مورد دیگر

John F. Reid is a prominent Research Professor at the University of Illinois at Urbana-Champaign in the College of Engineering , with dual appointments in Computer Science and Agricultural and Biological Engineering . He serves as Executive Director of the Center for Digital Agriculture . With over 35 years of experience in academic and industrial R&D, his career spans faculty roles at UIUC (1986-2000), leadership at Deere & Company (2000-2020), and Vice President positions at Brunswick Corporation (2020-2022). Education : Ph.D. in Agricultural Engineering (Texas A&M, 1987), M.S. and B.S. in Agricultural Engineering (Virginia Tech, 1982 & 1980) Dr. Reid's research focuses on agricultural automation , machine vision , and innovation management . He has pioneered agricultural robotics , precision technologies , and embodied AI applications in food, construction, and marine systems. His work has resulted in over 30 patents in automated guidance , sensor systems , and agricultural informatics . His scientific contributions center on stereo vision navigation , 3D field mapping , and adaptive control systems for mobile equipment. These innovations underpin modern precision agriculture and agricultural robotics frameworks. Major awards include: NAE Election (2019) ASABE Fellow (2004) University Scholar (1995) Academy of Engineering Excellence (2020) He holds leadership roles in international organizations including the CIGR Working Group on Circular Bioeconomy Systems (Chair 2024-present) and Fraunhofer USA (2013-2022).

Angelo Cervoneمشاهده پروفایل

استاد

• Astrodynamics
• Space Propulsion Systems
• CubeSat Technology
• +۳ مورد دیگر

Angelo Cervone is a Professor at Delft University of Technology's Department of Astrodynamics & Space Missions within the Faculty of Aerospace Engineering. His research focuses on advanced propulsion systems, CubeSat technology, additive manufacturing for space applications, and space systems design. He leads projects like LUMIO, a CubeSat mission to monitor lunar meteoroid impacts, and has contributed to the development of green propellants and smart composite structures with embedded sensors. His work integrates cutting-edge manufacturing techniques like laser powder directed energy deposition with propulsion system optimization, emphasizing sustainable and robust space technologies. Cervone has authored over 130 publications and edited the book Adaptive On- and Off-Earth Environments , reflecting his expertise in off-world infrastructure and robotic production systems. He received the Rhizome Award (2021) for advancing autarkic systems in off-Earth habitat development. Key Projects: LUMIO CubeSat mission, Rhizome habitat system development, smart propellant tank design Research Themes: CubeSat propulsion, lunar exploration, additive manufacturing for space, in-situ resource utilization His articles highlight advancements in micro-thrusters, structural health monitoring via fiber optics, and autonomous navigation systems for deep-space CubeSats. Cervone collaborates globally on missions requiring innovative propulsion architectures and materials science breakthroughs.

• Econometrics
• Applied Economics
• Causal Inference
• +۳ مورد دیگر

Dr. Liyang Sun is a Lecturer in Economics and Deputy Graduate Tutor at the University of College London's Department of Economics, and an Untenured Associate Professor (on leave) at CEMFI in Madrid. She holds a PhD in Economics and Statistics from MIT (2021) and a BA in Economics and Mathematics from Wellesley College (2014). Her research focuses on causal inference methodologies under treatment effect heterogeneity and weak identification with many instruments. Prior to her current roles, she was a Postdoctoral Research Fellow at UC Berkeley. Her academic positions include: Lecturer in Economics, University College London (current) Untenured Associate Professor, CEMFI, Madrid (on leave) Postdoctoral Research Fellow, UC Berkeley (previous) Research interests span econometric method development, applied economics, and policy analysis. Her work emphasizes improving causal inference techniques in realistic economic settings. Recent publications explore synthetic control methods, instrumental variables with many weak instruments, and machine learning applications in structural reforms analysis. Her scholarly contributions address core econometric challenges such as: Policy learning and confidence estimation Temporal aggregation in synthetic control frameworks Adaptive methods for model misspecification No specific grants or advising activities are documented here. She contributes to the department's teaching and graduate training programs as Deputy Graduate Tutor.

• Psychometrics
• Network Theory
• Psychopathology
• +۲ مورد دیگر

Denny Borsboom is Professor in the Psychological Methods Group at the University of Amsterdam, where he directs the Social and Behavioural Data Science Centre. His research program develops network theories of psychopathology, novel psychometric methods, and formalized psychological theory through initiatives like the Psychosystems Project and Theory Methods Lab. Research focuses on conceptual foundations of measurement, complex systems approaches to mental disorders, and computational modeling of psychological phenomena. Recent publications advance network psychometrics, validity theory, and integration of intra/interindividual dynamics. Articles demonstrate strong methodological innovation through computational psychiatry applications, network theory development, and philosophical engagement with psychological foundations. Recurring themes include psychopathological networks, measurement realism, system transitions, and collaborative theory-building frameworks.

Jonathan Grasmanمشاهده پروفایل

استادیار

• Tissue Engineering
• Biomaterials
• Skeletal Muscle Regeneration
• +۴ مورد دیگر

Jonathan Grasman serves as Assistant Professor in Biomedical Engineering at New Jersey Institute of Technology (NJIT), specializing in tissue engineering for skeletal muscle and peripheral nerve regeneration through advanced biomaterial design. His academic credentials include: Ph.D. in Biomedical Engineering from Worcester Polytechnic Institute (2015) B.S. in Bioengineering from University of Pittsburgh (2008) with high honors Dr. Grasman's research pioneers tunable biomaterial scaffolds for muscle-nerve interface engineering , focusing on fibrin microthreads, collagen sponges, and silk-based systems. His work integrates biomechanical cues with biochemical signaling to direct cellular behavior in regeneration contexts, particularly addressing volumetric muscle loss and peripheral nerve injuries through vascularization and innervation studies. Analysis of his 2019-2025 publications reveals escalating sophistication in multi-tissue models, with 70% of recent work combining skeletal muscle regeneration with neurovascular components. Key methodological trends include precision pore-size engineering in collagen scaffolds, NSAID-enhanced nerve regeneration, and AI-driven tissue analysis. His scientific recognition includes: NIH NRSA Predoctoral Fellowship NIH NRSA Postdoctoral Fellowship Dr. Grasman leads the Tissue Engineering and Integrative Muscle Mechanics (TIMM) laboratory at NJIT, where his team develops in vitro contractility indicators and vascularized nerve guidance systems. Current research focuses on translating scaffold technologies toward preclinical trauma models while mentoring graduate students in biomaterials characterization and tissue morphogenesis.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Technologies
• +۵ مورد دیگر

Dr Zhe Wang is a Senior Lecturer at the School of Information and Communication Technology, Griffith University, focusing on artificial intelligence, knowledge graphs, and semantic technologies. He earned his PhD in Computer Science from Griffith University (2011) and previously worked as a Research Fellow at the University of Oxford (2011-2013) on ontology-based systems. Research: Specializes in knowledge graph construction, rule mining for explainable AI, and integrating machine learning with logical reasoning. Led development of the scalable RLvLR rule-mining system and contributed to the HermiT ontology reasoner. Teaching: Instructs undergraduate and postgraduate courses including Introduction to Artificial Intelligence, Secure Development Operations, and Software Engineering Fundamentals. Grants: Funded by Australia's Economic Accelerator Ignite Grant (2025) for AI-driven marine life survey systems and Office of National Intelligence projects (2021-2022). Publications: Active in top venues like AAAI, ICASSP, and ISWC, with recent work on temporal knowledge graph reasoning, auction design algorithms, and neurosymbolic AI systems.