جست‌وجوی استاد و پژوهشگر

Chancellor Johnstoneمشاهده پروفایل

استادیار مدعو

• Operations Research
• Statistics
• Machine Learning
• +۳ مورد دیگر

Chancellor Johnstone serves as an Adjunct Assistant Professor at the Air Force Institute of Technology (AFIT), specializing in interdisciplinary research bridging military applications with advanced analytics. His work integrates operations research, statistical modeling, and machine learning to solve complex defense-related challenges. His academic credentials include: PhD in Statistics, Iowa State University, 2020 MS in Operations Research, Air Force Institute of Technology, 2015 BS in Operations Research, United States Air Force Academy, 2013 Dr. Johnstone's research focuses on robust optimization for decision-making under uncertainty, anomaly detection in complex systems, and federated learning frameworks for distributed data environments. His methodology emphasizes conformal prediction for uncertainty quantification and physiology-based classification in human-machine systems, with direct applications to Air Force operations including pilot training, mission planning, and personnel selection. Analysis of his 2021-2024 publications reveals three dominant research trajectories: (1) military logistics optimization incorporating cyber-physical constraints; (2) physiological signal processing using graph neural networks for multi-modal data; and (3) responsible AI development addressing bias in high-stakes military personnel decisions. His work consistently demonstrates translation of theoretical advances—particularly in conformal prediction and federated learning—into operational Air Force contexts.

Samuel A. Bryanمشاهده پروفایل

پژوهشگر ارشد

• Spectroelectrochemical Sensors
• Nuclear Waste Analysis
• Real-time Process Monitoring
• +۳ مورد دیگر

Samuel A. Bryan serves as a Lab Fellow and Chemist at Pacific Northwest National Laboratory (PNNL), where he pioneers spectroelectrochemical sensor development for measuring chemical species in highly complex nuclear systems. His innovations have resolved critical Department of Energy safety issues, particularly regarding ferrocyanide concentration determination in nuclear waste and hydrogen flammability in Hanford waste tanks. Dr. Bryan earned his B.S. in Chemistry from Boise State University (1979), followed by M.S. and Ph.D. degrees in Inorganic Chemistry from Washington State University (1983, 1985). His educational background established the foundation for his expertise in complex chemical systems analysis. His research focuses on real-time spectroscopic monitoring methodologies for nuclear applications. Key contributions include developing the first-ever luminescence detection from technetium complexes, creating sensors for nuclear waste analysis, and establishing predictive models for hydrogen gas generation that continue to inform Hanford Waste Treatment Plant safety designs 25 years later. His work bridges fundamental chemistry with practical nuclear engineering solutions. Analysis of his recent publications reveals strong emphasis on multi-modal spectroscopy (Raman, UV-Visible, NIR) combined with chemometric analysis for nuclear applications. His research spans from fundamental sensor development to practical implementation in nuclear fuel recycling, waste treatment, and safeguards verification. Fellow of the American Chemical Society Chair of Richland Section of the ACS (1998 and 2004) Fitzner-Eberhardt Award for Outstanding Contributions to Science and Engineering Education PNNL Laboratory Director's award (2005) ACS ChemLuminary Award for Outstanding Performance by Richland Section (2004) Dr. Bryan's technical leadership extends to mentoring junior scientists and contributing to national initiatives in nuclear safeguards. His current research focuses on microfluidic sensor systems, multi-modal spectroscopy approaches, and advanced data analysis techniques for nuclear applications, continuing to address critical challenges in nuclear waste management and national security.

• Computer Vision
• Robotics
• 3D Reconstruction
• +۴ مورد دیگر

El Mustapha Mouaddib is a Professor in the Perception and Robotics department at Universite de Picardie Jules Verne, affiliated with Laboratory Heudiasyc (UMR CNRS 7253). His research bridges advanced robotics with cultural heritage preservation, focusing on developing novel computer vision techniques for complex documentation challenges. His primary research interests include omnidirectional vision systems , hyperspectral imaging , and 3D reconstruction methodologies , with significant emphasis on applications for cultural heritage documentation. Mouaddib's work particularly addresses challenges in temporal illumination compensation , laser scanning registration , and multi-scale digitization of historical structures, as evidenced by his extensive Notre-Dame de Paris cathedral research. Analysis of his 15 most recent publications reveals a consistent trajectory toward heritage robotics - developing specialized computer vision algorithms for cultural preservation. His work demonstrates increasing sophistication in integrating multi-modal sensor data (TLS, hyperspectral, RGB-D) solving illumination challenges in historical documentation developing adaptive robotic systems for complex environments Notably, his Notre-Dame research forms a cohesive body of work examining structural changes through advanced 3D analysis. Mouaddib actively participates in major interdisciplinary projects including SAMURAI , ASSIDUITAS , SCANBOT , ADAPT , and SUMUM , which focus on heritage digitization and robotic exploration. His collaborative approach is evident through extensive co-authorship with institutions like CNRS and international partners in Japan and Italy. His laboratory work centers on the E-Cathedrale initiative, creating comprehensive digital twins of Gothic cathedrals through multi-temporal and multi-scale documentation. This involves developing specialized hardware (like the HDROmni camera system) alongside novel algorithms for processing challenging heritage environments.

• Transport Economics
• Traffic Engineering
• Geoinformatics
• +۳ مورد دیگر

Roxani Gkavra M.Sc. is a researcher at the Institute of Transport Studies within the University of Natural Resources and Life Sciences, Vienna (BOKU). Her work focuses on transport economics , traffic engineering , and geoinformatics applications in mobility research. She actively contributes to projects addressing equitable and accessible transportation systems. Location: Peter-Jordan-Straße 82, 1190 Vienna Contact: roxani.gkavra@boku.ac.at Research Interests: Roxani Gkavra specializes in analyzing shared micromobility adoption , mobility hub effectiveness , and rural demand-responsive transport systems . Her work evaluates how technological and policy interventions impact urban mobility patterns and transport equity . Key Projects: She contributes to initiatives like Wiener Linien's Hüpfer pilot and the SmartHubs series, examining mobility hub impacts across Europe. Funders include the Austrian Research Promotion Agency (FFG) and National Companies . Project durations span 2021-2026. Scientific Engagement: Gkavra serves as reviewer for journals including Sustainable Futures and European Transport Research Review . She is a member of the Central European Excellence in Transportation Research Association (CEETRA) and frequently presents at international conferences like the International Association of Travel Behavior Research . Advisory Role: While not explicitly mentoring students, she collaborates extensively on transport behavior research with senior academics like Yusak Susilo and Oliver Roider. Her work informs policy recommendations for equitable mobility solutions.

Julie Haasمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Electrophysiology
• +۳ مورد دیگر

Julie Haas is a Professor at Lehigh University investigating neural attention mechanisms through electrical synapses in the thalamic reticular nucleus (TRN). Her research integrates electrophysiology, optogenetics, and computational modeling to decode how inhibitory circuits filter sensory information, with implications for attention disorders. Her educational journey includes a B.A. in Music and Mathematics from Indiana University, a Ph.D. in Biomedical Engineering from Boston University, and postdoctoral training at Harvard University and UC San Diego, supplemented by Computational Neuroscience studies at the Marine Biological Laboratory. Research focuses on electrical synapse plasticity as the core mechanism for attentional selection. The Haas lab examines how dopamine, GABA receptors, and amygdala inputs modulate TRN circuitry using in vitro brain slices and optogenetic tools. Key discoveries include activity-dependent long-term potentiation at electrical synapses and their role in sensory gating, bridging molecular dynamics to circuit-level functions through innovative computational models. Publication analysis reveals a decade-long trajectory from foundational electrical synapse characterization (2012-2015) to neuromodulatory mechanisms (2016-2021) and recent dopamine receptor investigations (2022-2024). This evolution demonstrates increasing complexity in understanding how electrical synapses integrate neuromodulatory signals for attentional control. Funding from NIH, NSF, Whitehall Foundation, and Brain and Behavior Foundation supports her lab's work. She teaches advanced courses including Synapses, Plasticity and Learning (Bios 385/415) and Neurophysiology Laboratory (Bios 278), training next-generation neuroscientists in cutting-edge techniques. The Haas lab operates within Lehigh's neuroscience facilities with specialized electrophysiology rigs, optogenetic systems, and computational resources for multi-scale analysis of thalamic circuitry, currently exploring electrical synapse dysfunction in neurodevelopmental disorders.

Shibiao Wanمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Computational Biology
• +۱۴ مورد دیگر

Dr. Shibiao Wan serves as Assistant Professor in the Department of Genetics, Cell Biology and Anatomy at University of Nebraska Medical Center (UNMC), with a courtesy appointment in Biostatistics. He is Co-Director for the Bioinformatics and Systems Biology (BISB) PhD Program and Assistant Director for the Bioinformatics and Systems Biology Core. With over 14 years of experience in machine learning and bioinformatics, Dr. Wan leads an active research program developing computational methods for biomedical data analysis. Dr. Wan's research spans computational biology and biomedical informatics with focus on single-cell analysis, multi-omics integration, spatial transcriptomics, and cancer research. His laboratory develops AI and machine learning approaches to analyze genomics, transcriptomics, epigenetics, proteomics, metabolomics, and medical imaging data. Key contributions include methods for protein subcellular localization prediction, cancer subtyping, and multi-omics integration for precision medicine applications. His recent publications show a strong trend toward multi-modal data integration for disease diagnosis and subtyping, particularly in cancer (medulloblastoma, leukemia, lung cancer) and neurodegenerative disorders (Alzheimer's disease). His laboratory has developed numerous bioinformatics tools including SHARP for single-cell RNA-seq analysis, RaMBat for medulloblastoma classification, RanBALL for leukemia subtyping, and WIMOAD for Alzheimer's diagnosis. Dr. Wan has received significant recognition including the Springer Nature Editor of Distinction Award (2025), UNMC New Investigator Award (2024), FIRST Award from Nebraska EPSCoR (2023), and the Outstanding Young Alumni Award from HK PolyU (2022). He was named among the top 1% reviewers globally by Clarivate in both 'Cross-Field' and 'Biology and Biochemistry' categories (2019). As Co-Director of the BISB PhD Program, Dr. Wan actively mentors graduate students in bioinformatics and computational biology. His laboratory comprises a multidisciplinary team working at the intersection of computer science, statistics, and biomedical research. Dr. Wan serves as Editor-in-Chief for Current Proteomics and holds editorial positions with numerous high-impact journals including Briefings in Functional Genomics, BMC Bioinformatics, and Frontiers journals. The Wan Lab at UNMC focuses on machine learning and bioinformatics (MLAB), developing computational methods to unravel molecular biological systems using heterogeneous biomedical data. The lab collaborates extensively with scientists in cancer biology, metabolism, immunology, pathology, and developmental biology to translate computational findings into biological insights and potential clinical applications.

David Loمشاهده پروفایل

استاد

• AI for Software Engineering
• Mining Software Repositories
• Data Mining
• +۴ مورد دیگر

David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. He has held significant leadership roles including General Chair of MSR'22 and ASE'16, and Program Committee Co-Chair for ASE'20, FSE'24, and ICSE'25. Lo has championed the field of AI for Software Engineering (AI4SE) since the mid-2000s, demonstrating how data mining, machine learning, information retrieval, natural language processing, and search-based algorithms can transform software engineering data into actionable insights and automation. His research spans Mining Software Repositories (MSR), large language models for code, software testing, smart contract analysis, and developer tooling. His recent publications reveal a strong focus on the intersection of large language models and software engineering, with particular attention to code generation, evaluation, documentation, and the practical implications of AI tools for developers. His work increasingly addresses economic efficiency, privacy concerns, and human factors in AI-assisted development. Two Test-of-Time awards Eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards ACM Fellow IEEE Fellow ASE Fellow National Research Foundation Investigator (Senior Fellow) Lo has supervised numerous students and collaborated extensively across the software engineering community. His work on Mining Software Repositories has led to practical tools and insights that have shaped the field. He regularly contributes to major conferences and has served in leadership roles across ASE, ICSE, and FSE communities. As founding Director of the Center for Research in Intelligent Software Engineering (RISE) at SMU, Lo leads a research group focused on advancing AI techniques for software engineering problems, with emphasis on practical applications that address real developer pain points.

F. Alijaniمشاهده پروفایل

پژوهشگر

• Nonlinear Dynamics
• Nanomechanical Resonators
• Graphene Engineering
• +۴ مورد دیگر

F. Alijani is a researcher at TU Delft in the Dynamics of Micro and Nano Systems department. Their work focuses on nonlinear dynamics, nanomechanical resonators, and graphene-based sensor technology. Department: Dynamics of Micro and Nano Systems Research interests include: Nonlinear dynamics of 2D materials Graphene engineering for bio-sensing Atomic force microscopy (AFM) applications Optimization of nanomechanical systems Structural and aeroelastic modeling Recent publications highlight advancements in topology optimization, bacterial nanomotion detection, and AFM techniques. Collaborations include institutions like TU Delft and TU Delft - 4TU.ResearchData for datasets. No scientific awards are explicitly mentioned in the provided data. Labs & teams: Dynamics of Micro and Nano Systems group at TU Delft, working with Prof. P.G. Steeneken and Prof. A.M. Aragón.

• Computer Vision
• Metric Learning
• Image Generation
• +۳ مورد دیگر

Dr. Xinyu Zhang is a Research Fellow at the Australian Institute for Machine Learning (AIML), University of Adelaide's Faculty of Sciences, Engineering and Technology. Her research bridges computer vision and machine learning, focusing on image/video generation, self-supervised learning, and multimodal retrieval for human-centric AI applications. Zhang's current investigations include: Causal representation learning and multimodal integration Bayesian deep learning frameworks Video generation with temporal consistency Lightweight detection transformers Unsupervised person re-identification Analysis of recent publications reveals strong emphases on generative modeling innovations (especially video synthesis), efficient transformer architectures for real-time applications, and self-supervised representation learning. Her work frequently addresses the alignment between latent representations and human perception across vision-language tasks. Dr. Zhang co-supervises graduate students on projects involving multi-agent 3D scene generation and knowledge transfer in low-supervision learning. She serves as conference reviewer for premier venues including CVPR, ICCV, and NeurIPS, contributing to the advancement of computer vision research.

• Virtual Reality (VR)
• Human-Computer Interaction
• Haptic Feedback Systems
• +۷ مورد دیگر

Roope Raisamo is a Professor in the Department of Computing Sciences at Tampere University, part of the Faculty of Information Technology and Communication Sciences. His research focuses on advanced human-computer interaction, virtual/augmented reality systems, haptic feedback technologies, and their applications in medical, automotive, and assistive technology domains. He leads multidisciplinary projects involving collaboration with industry partners like car manufacturers and healthcare institutions. Key areas of expertise include: (1) Design of immersive XR environments with multimodal feedback integration, (2) Haptic actuator development using smart materials like magnetorheological fluids, (3) Ergonomic interfaces for autonomous vehicles, and (4) Accessibility solutions for seniors and disabled populations. His work bridges theoretical HCI research with practical applications seen in surgical training systems, automotive UIs, and wearable communication aids. Raisamo's research outputs from 2023-2025 show strong focus on: - Sensory augmentation in food perception through XR (2025) - Medical VR applications for surgical planning and tumor visualization (2024-2025) - Haptic-mediating technologies for automotive and industrial interfaces (2023-2025) He has pioneered the use of embedded haptic waveguides in steering wheel interfaces and developed novel textile-based AAC systems for non-verbal communication. Current projects include exploring the medical metaverse for collaborative surgical planning and creating AI-enhanced research tools like TAUCHI-GPT.

Lisa Leeمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Reinforcement Learning
• Continual Learning
• +۵ مورد دیگر

Lisa Lee is a Research Scientist at Google DeepMind, focusing on creating AI agents that emulate biological learning and adaptability. She previously taught at Princeton University and received TA awards for Deep Reinforcement Learning and Probabilistic Graphical Models. Education: PhD in Machine Learning from Carnegie Mellon University (advised by Ruslan Salakhutdinov and Eric Xing); A.B. in Mathematics from Princeton University (advised by Sanjeev Arora). Her research centers on AI embodiment, intrinsic motivation, and hierarchical planning. She explores how evolutionary-inspired inductive biases and memory mechanisms can enable agents to generalize across physical and conceptual domains, as demonstrated in her work on robotic agility benchmarks and multimodal transformers. Notable scientific contributions include the Barkour quadruped robot benchmark, Gemini multimodal models, and theoretical work on causal language models. She co-organized key AI workshops at NeurIPS and ICML, and her awards include Princeton's TA of the Year for technical courses. Leadership: ICML Workflow Chair (2019), NeurIPS workshop co-organizer (2019, 2021), peer reviewer for top AI conferences.

Prateek Bansalمشاهده پروفایل

استادیار

• Transportation Engineering
• Data Science
• Artificial Intelligence
• +۶ مورد دیگر

Dr. Prateek Bansal is a Presidential Young Assistant Professor at the National University of Singapore (NUS), leading the Behavioural Cognitive Science (BeCoS) lab. His research focuses on developing AI-driven methodologies to analyze mobility behavior and urban systems. He holds a PhD from Cornell University and has held fellowships at Imperial College London and visiting roles at multiple institutions. His expertise spans transportation engineering, econometrics, and causal inference. Dr. Bansal is an elected board member of the International Association of Travel Behaviour Research and serves on editorial boards of top journals like Transportation Research Part B . He has received prestigious awards including the Presidential Young Professorship (2021) and Leverhulme Trust Fellowship (2020). His lab investigates individual-level decision-making models and system-level urban planning frameworks. Education: PhD, Transportation Engineering (Cornell University, 2016-2019); MS, Transportation Engineering (UT Austin, 2013-2015); BTech, Civil Engineering (IIT Delhi, 2008-2013). Research interests include neurophysiological data modeling, activity-based urban systems, and causal inference for infrastructure planning. Notable contributions include studies on electric vehicle adoption, ride-sourcing demand estimation, and ethical decisions in autonomous systems. His work integrates machine learning with traditional transportation models to address contemporary challenges like urban congestion and sustainable mobility. Professional activities include organizing conferences, delivering keynotes (e.g., 2023 Summer School of Behavior Modeling), and advising on policy issues such as carsharing and road safety. The BeCoS lab collaborates globally, leveraging interdisciplinary approaches to advance transportation science.

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

Ke Yan is an Associate Professor in the Department of Computer Science at the National University of Singapore's College of Design and Engineering. With extensive research output from 2021-2026, their work spans computer vision, artificial intelligence, and multimodal learning systems. National University of Singapore (Primary Affiliation) Collaborations with University of Electronic Science and Technology of China Research ties with Tencent Youtu Lab in Shanghai Research focuses on advancing computer vision techniques, particularly in medical image analysis, vision-language integration, and fault diagnosis systems. Their work bridges theoretical AI development with practical applications in healthcare, industrial systems, and environmental monitoring. Notable contributions include novel approaches to multimodal learning, medical image segmentation with sparse annotations, and improving reliability of large vision-language models. Recent publication trends (2024-2026) demonstrate increasing focus on multimodal systems, with significant contributions to vision-language model reliability, medical AI applications, and efficient transfer learning techniques. Their work frequently addresses critical challenges like hallucination mitigation in large models and sparse data scenarios in medical imaging. As an advisor, they mentor multiple researchers including Junlong Du, Shouhong Ding, and Zhiwen Lin, with whom they frequently collaborate on cutting-edge computer vision projects. Their research group maintains strong industry connections, particularly with Tencent's AI research division. The research is conducted within NUS's computer vision and AI research ecosystem, collaborating with multiple laboratories focused on multimodal intelligence and practical AI deployment in real-world systems.

Fritz Buschمشاهده پروفایل

استاد

• Traffic Engineering
• Urban Transportation Systems
• Environmental Engineering
• +۴ مورد دیگر

Prof. Dr.-Ing. Fritz Busch holds the Professorship of Traffic Engineering and Control at the Department of Civil, Geo and Environmental Engineering at Technische Universität München (TUM). His research focuses on urban transportation systems, traffic dynamics, and sustainable mobility solutions. He leads projects such as RASCH (RAdSCHnellwege) and RadOnTime, exploring efficient bicycle infrastructure and traffic management strategies. His work integrates advanced simulation techniques, data-driven methodologies, and cooperative vehicle-infrastructure systems to enhance urban mobility resilience and reduce environmental impacts. Educational Background: While specific academic qualifications are not detailed here, his title of Professor Dr.-Ing. indicates a doctoral degree in engineering and significant academic contributions. His professorship at TUM underscores expertise in traffic engineering and control systems. Research Interests: Busch’s research spans macroscopic traffic dynamics, bicycle and pedestrian traffic management, emissions reduction through electric vehicle integration, and the application of smart technologies (e.g., V2X communication) for traffic optimization. His projects often involve interdisciplinary collaboration, combining engineering, data science, and urban planning to address complex mobility challenges. Awards and Recognition: While no specific awards are listed, his prolific publication record and involvement in high-profile TUM initiatives reflect his scholarly impact and leadership in transportation engineering. Advising and Grants: Though specific grants are not mentioned, his active participation in projects like UR: BAN (Urban Road Network) and numerous collaborations suggest extensive grant-funded research. Advising activities are inferred through his role as a professor and project leader but are not explicitly detailed here. Labs and Teams: His work leverages the TUM-VT bicycle simulator and SUMO (Simulation of Urban MObility) tools for traffic simulation studies. He collaborates with interdisciplinary teams on projects such as eco-sensitive traffic management and automated vehicle integration.

O. Catsمشاهده پروفایل

استاد

• Transportation Engineering
• Public Transportation Systems
• Operations Research
• +۴ مورد دیگر

O. Cats is a Professor of Passenger Transport Systems and Head of the Department of Transport & Planning at Delft University of Technology (TU Delft). They also serve as a Guest Professor at KTH Royal Institute of Technology. Their research focuses on multi-modal passenger transport networks, combining simulation, operations research, behavioral sciences, and complex network theory to address challenges in public transport, shared mobility, and long-distance travel dynamics. Key interests include network robustness, service operations, and passenger demand modeling. Dr. Cats leads the Smart Public Transport Lab at TU Delft, collaborating closely with transport authorities and operators. They hold editorial roles at journals like the Journal of Public Transportation and have received prestigious grants, including the ERC Starting Grant for the CriticalMaaS project. Awards include recognition for work on crowding valuation in urban transport systems. Recent publications emphasize optimization of electric vehicle infrastructure, sustainability in travel behavior, and dynamics of ridesourcing markets. Their work bridges theoretical research with practical applications, aiming to enhance transport efficiency and sustainability globally.