جستجوی استاد راهنمای دانشگاهی

Antony M. Joseمشاهده پروفایل

استاد

• Molecular Biology
• Genetics
• RNA Regulation
• +۴ مورد دیگر

Antony M. Jose is a Professor in the Department of Cell Biology and Molecular Genetics at the University of Maryland, College Park. He leads the Jose Lab, which focuses on understanding regulatory mechanisms in living systems, particularly through RNA-mediated gene regulation in Caenorhabditis elegans . His research explores the origins and limits of regulatory architectures, combining conceptual modeling with experimental approaches. Jose holds a Ph.D. from Yale University (2005) and completed postdoctoral training at Harvard University (2011). He has received prestigious awards, including the 2019 Graduate Faculty Mentor of the Year and the 2016 National Academy of Sciences Kavli Fellow. His work spans molecular to population-level regulation, with recent studies addressing intergenerational RNA transport, heritable epigenetic changes, and RNA interference mechanisms. The lab has been continuously funded by NIH (NIGMS) and NSF, enabling interdisciplinary projects that integrate computational modeling and experimental biology. Jose teaches graduate courses in quantitative modeling and systems biology, and his lab collaborates on projects such as drug toxicity studies and transgenerational inheritance mechanisms. Ongoing research emphasizes systems-level understanding of regulatory dynamics and their evolutionary implications.

• Machine Learning
• Computer Vision
• Structural Health Monitoring
• +۱ مورد دیگر

Senjian An is a Senior Lecturer in the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University, Faculty of Science and Engineering. His research focuses on machine learning, computer vision, and structural health monitoring, with applications in 3D displacement measurement and anomaly detection. Key interests include physics-guided neural networks, unsupervised learning for time-series data, and vision-based structural analysis. His recent publications emphasize robustness in damage detection, synthetic data generation, and transformer-based models for industrial monitoring. An's work integrates deep learning with engineering challenges, advancing non-contact measurement techniques for civil infrastructure. No students or awards are noted. Collaborative projects involve vibration analysis and maritime surveillance, reflecting a strong applied focus in AI and computer vision.

Sorin Draghiciمشاهده پروفایل

استاد

• Computational Biology
• Data Integration
• Disease Subtyping
• +۱ مورد دیگر

Sorin Draghici is a Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research specializes in computational biology, integrating multi-omics data to discover disease subtypes and repurpose drugs. He develops bioinformatics tools like ROntoTools and CPA for pathway analysis and single-cell data interpretation. Research Focus: Machine learning applications in oncology and infectious diseases, including COVID-19 proteomics and ovarian cancer recurrence prediction. Recent innovations include AI-driven data interrogation (LmRaC) and upstream regulator prediction (PURE). Publications emphasize scalable algorithms for big data challenges.

Heidi Cheungمشاهده پروفایل

مدرس

• Public Health
• Epidemiology
• Healthcare Education
• +۲ مورد دیگر

Heidi Cheung, PhD, is Director of Placement Learning at Sheffield Hallam University. She oversees placement learning quality and strategy across the Faculty, focusing on robust, equitable, and safe placement management informed by research. Her work integrates curricular interpretation with best practice in inter-professional education. Teaching focuses on public health, epidemiology, and quantitative data analysis. Research expertise includes clinical placement engagement in primary care, community, and PIVOs (private/independent/voluntary organizations), supported by HEE North West funding. She led the Placement Pathway Project, published in the British Journal of Nursing and as a faculty report. Professional activities include Partner Governor at a NHS Foundation Trust and Trustee for a healthcare charity. Her work bridges academic research with practical healthcare workforce development strategies.

Panos Trahaniasمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Humanoid Robots
• +۱ مورد دیگر

Panos Trahanias is Professor and Vice Chair in the Department of Computer Science at the University of Crete, and heads the Computational Vision and Robotics Laboratory (CVRL) at Foundation for Research and Technology - Hellas (FORTH). His research bridges computational vision, robotics, and embedded systems, with specific focus on humanoid robot navigation, real-time SLAM implementations, and adaptive control systems. Recent investigations include developing climbing quadruped robots, robust grip-lifting mechanisms, and probabilistic contact estimation methods for dynamically challenging terrains. Medical applications feature prominently through neural network approaches for ventilator waveform analysis in critical care settings. Hardware innovation is demonstrated through FPGA-accelerated visual SLAM architectures and reconfigurable embedded systems designed for resource-constrained robotic platforms. His work consistently advances the integration of probabilistic methods, deep learning, and adaptive control in autonomous systems.

• Data Mining
• Machine Learning
• Biomedical Informatics
• +۴ مورد دیگر

Michael Steinbach is a Researcher in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, working in the research group of Prof. Vipin Kumar. He holds B.S. in Mathematics, M.S. in Statistics, and M.S./Ph.D. in Computer Science from the University of Minnesota. His research focuses on data mining, machine learning, biomedical informatics, and statistics, with applications in environmental science, healthcare, and engineering. He co-authored the widely used textbook Introduction to Data Mining , translated internationally. Previously, he held software engineering roles at Silicon Biology, Racotek, and NCR. Research interests emphasize integrating scientific knowledge with machine learning for real-world systems, such as climate modeling, flood forecasting, and healthcare analytics. His work spans causal inference, physics-guided AI, and interpretable predictive models. Collaborations include environmental monitoring, wildfire management, and clinical decision support systems in healthcare. Publications highlight innovations in knowledge-guided learning, multi-scale modeling, and causal discovery. His contributions bridge theory and practice, advancing AI applications in complex systems. The lab engages in interdisciplinary projects, combining computational methods with domain expertise to address societal challenges.

Tapan Mukerjiمشاهده پروفایل

استاد پژوهش

• Rock Physics
• Wave Propagation Physics
• Spatial Data Science
• +۷ مورد دیگر

Tapan Mukerji is a Professor (Research) at Stanford University with joint appointments in the Department of Energy Science & Engineering, the Department of Earth & Planetary Sciences, and the Department of Geophysics within the School of Earth Sciences. He co-directs the Stanford Center for Earth Resources Forecasting (SCERF), the Basin Processes and Subsurface Modeling (BPSM) consortium, and the Stanford Rocks and Geomaterials Project (SRGP), and previously co-directed the Stanford Rock Physics and Borehole Geophysics Project (SRB). His educational background includes: Ph.D. in Geophysics from Stanford University (1995) M.Sc.(Tech) in Geophysics from Banaras Hindu University, India (1989) B.Sc. in Physics from Banaras Hindu University, India (1986) Tapan Mukerji's research focuses on integrating rock physics, wave propagation physics, spatial data science, and machine learning to address challenges in remote sensing of subsurface systems, stochastic geomodeling, uncertainty quantification, and value of information analysis in Earth sciences. His work uses theoretical, computational, and statistical methods to discover fundamental relations between geophysical data and rock properties, quantify uncertainty in subsurface models, and address decision making under uncertainty. He is particularly interested in forging links between geosciences, engineering, and decision sciences, believing these interdisciplinary connections are critical for the future of energy resources research. His research has broad applications in hydrocarbon exploration, geothermal energy, carbon sequestration, and critical mineral exploration. His recent publications demonstrate a strong trend toward integrating advanced machine learning techniques with traditional geophysical methods. There's increasing focus on physics-informed neural networks, generative models for geological facies simulation, and uncertainty quantification in subsurface characterization. His work bridges the gap between theoretical rock physics and practical applications in energy resource development, with particular emphasis on making robust decisions under uncertainty. Professor Mukerji has received numerous scientific awards and recognitions: Karcher Award for Outstanding Young Geophysicist, Society of Exploration Geophysicists (2000) ENI Award 2014: New frontiers of Hydrocarbons - upstream, ENI - Italy (2014) Best paper, honorable mention, Society of Exploration Geophysicists (2020) Best paper, International Association of Mathematical Geosciences (2010) Multiple best paper awards from various geophysical societies Invited keynote speaker at numerous international conferences Haider Fellowship and Green Fellowship from Stanford University Professor Mukerji actively advises and mentors graduate students, serving as Doctoral Dissertation Advisor for Jaehong Chung and Jiayuan Huang, Doctoral Dissertation Reader for several students, and Postdoctoral Faculty Sponsor for Qi Hu and Suihong Song. His research has been supported by multiple industrial consortia including the Stanford Rock Physics and Borehole Geophysics Project (SRB), Stanford Center for Earth Resources Forecasting (SCERF), Basin Processes and Subsurface Modeling (BPSM), Stanford Rocks and Geomaterials Project (SRGP), and Smart Fields Consortium (SFC). He has also received funding from the Department of Energy and various fellowship programs throughout his career. Professor Mukerji co-directs several major research groups at Stanford including the Stanford Center for Earth Resources Forecasting (SCERF), the Basin Processes and Subsurface Modeling (BPSM) consortium, and the Stanford Rocks and Geomaterials Project (SRGP). These groups bring together faculty, researchers, and industry partners to tackle complex problems in subsurface characterization, reservoir modeling, and energy resource development. His labs focus on developing computational methods for integrating geophysical data with rock physics models, creating advanced uncertainty quantification frameworks, and building decision support tools for subsurface resource management.

• Ecology
• Zoology
• Evolutionary Biology
• +۷ مورد دیگر

Dr. Julia Schroeder is a Senior Lecturer in the Department of Life Sciences (Silwood Park) at Imperial College London, affiliated with the Faculty of Natural Sciences and centers including the Georgina Mace Centre for the Living Planet and the Grantham Institute. Her research focuses on behavioral ecology, evolutionary biology, and seabird ecology, with particular interests in traits influencing individual variation in behavior, aging mechanisms, and the genetic basis of social networks. She leads the Schroeder Lab, emphasizing data-driven approaches to study avian behavior and conservation challenges. Recent work includes developing automated methods for behavioral analysis (e.g., YOLO-Behaviour framework) and investigating telomere dynamics in wild populations. Her affiliations span interdisciplinary initiatives at Silwood Park Campus and the Ecosystems and Environment group. She actively invites PhD applicants interested in seabird ecology, citizen science, or genomics of life-history traits. Research methodologies include meta-analyses, machine learning, and long-term field studies, often addressing topics like social behavior, fitness correlates, and human-wildlife interactions. Her work bridges theoretical and applied ecology, with contributions to understanding biodiversity conservation and evolutionary processes. Publications highlight trends in behavioral flexibility, telomere length heritability, and social network robustness. She collaborates widely, integrating computational tools into ecological research while addressing challenges in wildlife management and invasive species perception.

Guanglin Zhouمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Artificial Intelligence
• Domain Generalization
• +۴ مورد دیگر

Dr. Guanglin Zhou is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, part of the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. He holds a Doctor of Philosophy in Electrical Engineering and Computer Science from the University of New South Wales. His research focuses on machine learning, causal inference, and domain adaptation, with applications in healthcare, recommendation systems, and computer vision. Key research interests include developing counterfactual learning frameworks, enhancing model robustness through causal methods, and advancing domain generalization techniques. His work bridges causal theory with deep learning, particularly in generating clinically realistic data and improving foundation models' adaptability to distribution shifts. Dr. Zhou's publications span topics like contrastive visual prompting for domain adaptation, cycle-balanced representation learning for causal inference, and deep reinforcement learning in recommender systems. He is available for supervision and has contributed to interdisciplinary projects combining knowledge graphs with spatio-temporal analysis for urban congestion prediction.

Tom Melhamمشاهده پروفایل

استاد

• Formal Verification
• Hardware Verification
• Theorem Proving
• +۲ مورد دیگر

Tom Melham is Professor of Computer Science at the University of Oxford, Tutorial Fellow at Balliol College, and Fellow of the Royal Society of Edinburgh. His research develops formal methods for hardware and software verification, with recent focus on security applications and neural network verification. Research interests include automated theorem proving, symbolic trajectory evaluation, hardware/software co-verification, and security assurance methods. Recent work applies formal verification to processor security, vulnerability management, and deep learning systems. Publications demonstrate foundational contributions to hardware verification methodologies, with increasing focus on security engineering and machine learning verification in recent years. Research integrates theoretical advances with practical industrial applications. Melham directs projects on processor security verification, automated exploit generation, and hybrid approaches to memory safety. He teaches computer science and law courses while advising graduate students in formal methods research.

• Robotics
• Mechatronics
• Motion Control
• +۵ مورد دیگر

Roberto Oboe is an Associate Professor at the University of Padova's Department of Management Engineering. His research focuses on advanced motion control systems, mechatronics, and robotics, with notable contributions to remote handling systems for nuclear facilities, admittance control methodologies, and sensor technology integration in industrial and aerospace applications. He has led projects involving SPES (Selective Production of Exotic Species) facility automation, haptic feedback systems, and UAV localization. His technical work spans multiple disciplines including robotic control, nuclear engineering safety protocols, and sensor-based anomaly detection. Key projects include the development of intelligent remote handling systems for radioactive materials and the optimization of bilateral teleoperation systems. He actively contributes to IEEE editorial boards and serves as a Ph.D. examiner. Research highlights include: Admittance control frameworks for high-stiffness robotic interactions Pioneering work on MEMS-based mirror control systems Development of semi-quantitative risk assessment methodologies Advancements in indoor UAV navigation systems Publications emphasize practical applications with over 50 peer-reviewed articles since 2016, covering topics from neuronal avalanche analysis to industrial servopositioner improvements.

Mark Johnsonمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۵ مورد دیگر

Mark Johnson is a Professor at Macquarie University's School of Computing, affiliated with the Centre for Language Sciences (CLaS). His research focuses on computational linguistics, natural language processing, and machine learning, with contributions to parsing algorithms, child language acquisition models, and generative AI systems. He leads projects on generative models of life stories, logistics optimization algorithms, and visual dialogue systems using deep neural networks. Research interests include natural language understanding, data augmentation techniques, and model robustness. Notable projects involve developing frameworks for synthetic training data, OOD detection, and integrating named entity recognition with context tags. His work emphasizes practical applications in dialog systems, document analysis, and language model optimization. Collaborations span cross-linguistic studies and AI-driven visualization systems. Key achievements include over 200 research outputs, including influential articles on child language acquisition models and OOD detection. He oversees projects funded by MQRC and industry partnerships, focusing on advancing NLP and AI methodologies. His lab, CLaS, fosters interdisciplinary research in language sciences and computational methods.

• Computational Geometry
• Geometric Algorithmics
• GIScience
• +۵ مورد دیگر

Prof. Marc van Kreveld is a Professor and Head of the Department of Computer Science at Utrecht University since February 2023. His research focuses on computational geometry, geometric algorithmics, and their applications in GIScience, cartography, and puzzle design. He leads the Geometric Computing group and has made significant contributions to algorithms for trajectory data, geometric similarity, and spatial data analysis. Education: PhD in Computer Science from Utrecht University (1992). Research Interests: Computational geometry, GIScience, graph drawing, LIDAR point cloud reconstruction, and geometric puzzle analysis. His work bridges theoretical foundations and real-world applications, such as route data analysis and environmental modeling. Publications: Over 290 peer-reviewed papers, including foundational work on Fréchet distance algorithms, geometric spanners, and trajectory grouping. Recent contributions include advancements in collision detection for modular robots and mobility data science. Awards & Editorial Roles: Editor-in-Chief of Computing in Geometry and Topology , and editorial board member of Journal of Computational Geometry . Active in program committees for conferences like GIScience and SoCG. Teaching: Specializes in computational geometry and its applications, with a focus on algorithm design and geometric computing. Labs & Teams: Leads the Geometric Computing group at Utrecht University, collaborating on projects like braided river network modeling and automated puzzle generation.

Zhang Tianweiمشاهده پروفایل

دانشیار

• Generative AI Safety
• Deep learning security
• Robotics and autonomous driving security
• +۲ مورد دیگر

Associate Professor at Nanyang Technological University's College of Computing and Data Science, serving as Deputy Director of the Cyber Security Research Centre @ NTU (CYSREN) and Associate Director of the NTU Centre Computational Technologies for Finance (CCTF). His research focuses on building trustworthy, efficient, and intelligent systems with emphasis on security and privacy across AI, robotics, and cloud infrastructures. Educational background: Bachelor of Physics, Peking University, 2011 Ph.D. in Electrical Engineering, Princeton University, 2017 Research spans five core domains: Generative AI Safety (vulnerability identification, safety testing, misuse detection), Deep learning security (adversarial examples, backdoor attacks, privacy protection), Robotics security (perception system attacks, safety testing), Machine learning optimization (workload scheduling, acceleration), and Computer architecture security (side-channel defenses, cloud security). His work bridges theoretical security with practical system implementations across diverse applications. Recent publications (2024-2026) reveal intense focus on securing generative AI systems, particularly text-to-image models and large language models, with significant contributions to red teaming methodologies, backdoor attack mitigation, and multimodal security. Emerging trends show expanding research into autonomous vehicle security and privacy-preserving machine learning with cryptographic techniques. Key awards include: Distinguished Artifact Award (CCS 2024) Stamatis Vassiliadis Best Paper Award Nominee (FPL 2024) Outstanding Paper Award (ACL 2024) Distinguished Artifact Award (Usenix Security 2024) Actively supervises PhD students and research staff while leading multiple high-impact grants: Ongoing: NRF CREATE Quantum Security (2025-2029), Continental NTU Corp Lab Automotive HPC (2025-2028), CRPO EV Charging Security (2025-2027) Completed: MoE AcRF Tier2 IP Protection (2022-2025), NTU S-Lab Efficient GPU Scheduler (2020-2025) Leads research within CYSREN and TAICeN (Trustworthy AI Centre NTU), directing interdisciplinary teams that investigate security threats across AI deployment stacks while developing practical defenses for real-world systems.

• Explainable AI
• Interpretable Machine Learning
• Deep Learning
• +۱۳ مورد دیگر

Wojciech Samek is a Professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and Head of the AI Department at Fraunhofer Heinrich Hertz Institute (HHI), Germany. He holds a joint appointment, bridging academic research and industrial application in artificial intelligence. He is a Fellow at BIFOLD and ELLIS Unit Berlin, and a Principal Investigator in several DFG projects including DeSBi and BIOQIC. PhD (Dr. rer. nat.) with distinction, Technical University of Berlin, 2014 Studies in Computer Science, Humboldt University, Heriot-Watt University, University of Edinburgh His research centers on Explainable AI (XAI) , Trustworthy Deep Learning , and Efficient AI . He pioneered Layer-wise Relevance Propagation (LRP), a foundational method for interpreting deep neural networks. His work spans model interpretation, robustness against adversarial attacks, neural network compression, federated learning, and applications in healthcare, communications, and multimedia. The recent articles highlight a strong trend toward extending explainability beyond deep models , with research on concept-level explanations, unsupervised learning interpretability, and XAI-driven model improvement. There is a consistent focus on robustness, privacy, and efficiency in distributed learning settings, particularly for federated and edge AI. Applications in medical AI and neuroscience are prominent, emphasizing safety and interpretability in high-stakes domains. Best Paper Award, Pattern Recognition (2020) Digital Signal Processing Best Paper Prize (2022) Fellow, BIFOLD - Berlin Institute for the Foundation of Learning and Data Member, Germany's Platform for Artificial Intelligence Senior Editor, IEEE TNNLS; Associate Editor, Pattern Recognition, Digital Signal Processing, PLoS ONE Area Chair, NeurIPS, ICML, NAACL Program Chair, IEEE MLSP 2023 Contributor to ISO/IEC MPEG-17 NNC standard Prof. Samek advises a large group of PhD students and leads a vibrant research team at Fraunhofer HHI and TU Berlin. His group has secured significant funding through DFG, BIFOLD, and industrial collaborations. He has co-authored over 200 peer-reviewed papers, many of which are ESI Hot or Highly Cited. He is deeply involved in organizing workshops and tutorials on XAI, federated learning, and neural compression at top venues like NeurIPS, ICML, CVPR, and IEEE conferences. His lab develops open-source tools such as the LRP Toolbox , Keras Explanation Toolbox , DeepCABAC , and Quantus , promoting reproducibility and adoption of interpretable and efficient AI methods. The team is actively working on next-generation AI that is not only accurate but also transparent, robust, and trustworthy.