جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Machine Learning
• Robotics
• +۶ مورد دیگر

Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation

Azad J Naeemiمشاهده پروفایل

استاد

• Nanotechnology
• Nanoelectronics
• Spintronics
• +۵ مورد دیگر

Azad J Naeemi is a Professor holding the Dean's Professorship in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He serves as Editor-in-Chief of the IEEE Journal on Exploratory Computational Devices and Circuits and Associate Director for Computation of the NSF-supported National Nanotechnology Coordinated Infrastructure (NNCI). His educational background includes a B.S. in Electrical Engineering from Sharif University (1994) and M.S./Ph.D. in Electrical and Computer Engineering from Georgia Tech (2001/2003). Prior to academia, he worked as a design engineer in Tehran (1994-1999) and as a research engineer at Georgia Tech's Microelectronics Research Center (2004-2008). Professor Naeemi's research spans nanotechnology with focus on emerging nanoelectronic devices, spintronics, ferroelectric devices, and design technology co-optimization for CMOS/beyond-CMOS technologies. His work bridges materials, devices, circuits, and systems, particularly investigating integrated circuits based on nanoscale devices and interconnects. Educational research includes experiential learning environments for engineering education. Recent publications (2024-2025) demonstrate strong emphasis on spin-orbit torque MRAM, ternary content addressable memories, ferroelectric/antiferroelectric devices, and plasmonic circuits. Key trends include energy-efficient hardware accelerators, neuromorphic computing applications, and compact modeling for advanced technology nodes. His scientific honors include: IEEE Solid-State Circuits Society James Meindl Innovators Award (2022) IEEE Electron Devices Society Paul Rappaport Award (2008) NSF CAREER Award (2013) SRC Inventor Recognition Award (2010) Multiple Georgia Tech teaching awards Professor Naeemi leads research supported by NSF (including NNCI infrastructure) and SRC. His editorial role with IEEE JXCDC positions him at the forefront of exploratory computational devices. He previously served as General Co-Chair for the IEEE International Interconnect Technology Conference (2013). His work connects with Georgia Tech's Microelectronics Research Center and national nanotechnology initiatives through the NNCI network, focusing on computational infrastructure for nanoscale device characterization and design.

• Data-Driven Organizations
• Artificial Intelligence
• Digital Transformation
• +۳ مورد دیگر

Javier Zamora is a Professor at IESE Business School , part of the University of Navarra. He holds a Ph.D. in Electrical Engineering from Columbia University and an M.Sc. in Telecommunications Engineering from the Polytechnic University of Catalonia. Education Ph.D., Electrical Engineering, Columbia University M.Sc., Telecommunications Engineering, Polytechnic University of Catalonia PDG, IESE Business School Research Interests : Zamora specializes in digital transformation, focusing on data-driven organizations and artificial intelligence. His work explores how new technologies reshape business models, organizational structures, and leadership strategies. Publications span topics from AI implementation in finance and sports management to cloud computing in crisis scenarios. His research emphasizes practical frameworks like the "Digital Density" concept and tools for assessing digital maturity. Professional Roles : He co-founded Inqbarna (AI-powered mobile solutions) and served as CEO of eNeo Labs (digital home products). Currently, he directs executive programs at IESE, including Digital Transformation: Senior Management Program and Navigating IT Architecture .

• Security and Privacy
• Internet Measurement
• Data Mining
• +۴ مورد دیگر

Gang Wang is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science. He also holds affiliate roles in the Department of Electrical and Computer Engineering, the Informatics Program (School of Information Sciences), and the Coordinated Science Laboratory (CSL). He joined UIUC in 2019, previously serving as an Assistant Professor at Virginia Tech. Education: Ph.D. in Computer Science from UC Santa Barbara (2016), advised by Ben Y. Zhao and Heather Zheng; B.E. from Tsinghua University (2010). Awards include the NSF CAREER Award (2018), Amazon Research Award (2021), and multiple best paper awards in top conferences like USENIX Security and ACM CCS. Research focuses on Security and Privacy, Internet Measurement, and Data Mining. Key areas include email spoofing vulnerabilities, adversarial machine learning, and AI-driven security tools. Recent projects involve explainable AI for phishing detection, benchmark contamination in LLMs, and mitigating deepfake disinformation. Publications span top venues like USENIX Security, IEEE S&P, ACM CCS, and IMC. He collaborates with industry partners and researchers in HCI and AI. Current roles include Associate Director of the Capital One Illinois Center for Generative AI Safety (ASKS) and leadership in NSF-funded initiatives like the ACTION AI Institute.

Andrew McCallumمشاهده پروفایل

استاد

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

Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.

Zeynep Temelمشاهده پروفایل

استادیار

• Bio-Inspired Robotics
• Compliant Mechanisms
• Robotic Manipulation
• +۳ مورد دیگر

Zeynep Temel is an Assistant Professor at Carnegie Mellon University's College of Engineering, jointly appointed in the Biomedical Engineering and Robotics Institute. She leads the Zoom Lab, focusing on bio-inspired compliant mechanisms for robotic systems. Current research emphasizes adaptable robots for complex environments through mechanical intelligence and embedded control . Key application areas include surgical robotics , search-and-rescue , and micromanipulation . Her work spans bio-inspired design, compliant robotics, and human-centered applications. Recent publications highlight advancements in: Swarm robotics for collaborative exploration Soft actuators using bioplastics and gelatin Dexterous manipulation via delta robot frameworks The Zoom Lab trains students in robotic fabrication and biological modeling, with members transitioning to roles in academia and industry.

• Photosynthesis
• Global Change Biology
• Protected Cropping
• +۴ مورد دیگر

Professor Oula Ghannoum is a renowned plant scientist and academic leader at the Hawkesbury Institute for the Environment (HIE), Western Sydney University. She serves as Director of the ARC Training Centre for Smart and Sustainable Horticulture and holds leadership roles, including Biological Sciences Discipline Lead and Associate Editor at Functional Plant Biology . Her research focuses on photosynthesis, global change biology, and protected cropping, aiming to enhance food security and climate resilience through crop improvement and sustainable agricultural practices. Education: BSc (Honours) in Plant Biochemistry, University of NSW (1993) PhD in Plant Physiology, Western Sydney University (1998) Research Interests: Professor Ghannoum’s work addresses global challenges like food security and climate change by exploring plant responses to environmental stress. Her lab uses advanced technologies like smart glasshouses and hyperspectral imaging to optimize crop yield and quality. Key areas include sugar signaling pathways in C3/C4 plants, water use efficiency, and heat tolerance in cereal crops. Grants & Funding: She has secured over $25M in research funding, leading projects on C4 photosynthesis, automated crop monitoring, and protected cropping systems. Notable collaborations include the ARC Centre of Excellence for Translational Photosynthesis and Future Food Systems CRC. Awards: 2024 Vice-Chancellor’s Excellence in Research Award 2023 Education and Outreach Award (Australian Society of Plant Scientists) 2001 ARC Postdoctoral Fellowship Labs & Teams: Her team develops innovative frameworks for sustainable horticulture, combining biology with AI and smart technologies. Ongoing work includes imaging-based crop monitoring and phenotyping for climate-resilient crops.

Rosemary Monahanمشاهده پروفایل

استاد

• Software Verification
• Dependable Software Systems
• Formal Methods
• +۳ مورد دیگر

Rosemary Monahan is a Professor in the Department of Computer Science at Maynooth University and an affiliate of the Hamilton Institute. She holds BSc and MSc degrees from University College Dublin and a PhD from Dublin City University. As Maynooth University's institutional lead for ADAPT (SFI Research Centre for AI-Driven Digital Content Technology), she focuses on advancing software dependability through formal methods and AI integration. Her research interests include safety-critical systems, dependable software, formal verification, and computational thinking education. She co-founded the VerifyThis competition series and leads projects such as MAIVV (Modular AI Verification and Visualisation) funded by SFI, and VALU3S (Verification and Validation of Automated Systems) funded by Horizon 2020. She has secured over €2.5M in EU funding for the Erasmus Mundus programs in dependable software systems. Monahan’s educational contributions include pioneering computational thinking resources (CoCoA and InSPECT projects) and teaching modules on software verification and rigorous software processes. She supervises PhD students in data science and advanced networks and collaborates with institutions like INRIA, Microsoft Research, and Amazon Web Services. Her professional roles include editorships in journals like Science of Computer Programming and leadership in conferences like iFM and FMICS. She actively promotes gender equality in computing through initiatives like INGENIC and TechMate toolkits.

Ioannis Athanasiadisمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Agricultural Modeling
• +۷ مورد دیگر

Ioannis Athanasiadis is a Full Professor and Chair of Artificial Intelligence at Wageningen University & Research (The Netherlands). He leads the Artificial Intelligence (AIN) group, focusing on advancing AI methods for global challenges in agriculture, ecology, and sustainability. Previously, he was faculty at the Dalle Molle Institute for Artificial Intelligence (IDSIA, Switzerland) and the Democritus University of Thrace (Greece). He holds a PhD (2005, cum laude) in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research integrates machine learning, knowledge engineering, and environmental modeling to address food security, climate adaptation, and ecosystem services. He leads initiatives like AgML (AgMIP's machine learning benchmarking effort) and coordinates European grants such as LTER-LIFE and CYBELE . Prof. Athanasiadis has supervised over 40 PhD/postdoc researchers and serves as Editor of Environmental Modelling and Software . He collaborates internationally on projects involving AI for crop modeling, digital twins, and sustainable agriculture. His team develops frameworks like Crop2ML and PyCrop2ML to enhance interoperability between process-based models and machine learning systems.

Toni Kotnikمشاهده پروفایل

دانشیار

• Computational Design
• Architectural Engineering
• Structural Design
• +۴ مورد دیگر

Toni Kotnik is an Associate Professor in the Department of Architecture at Aalto University, Finland. He holds dual expertise in mathematics and architecture, with a Ph.D. from the University of Zurich and a second degree in architecture. His roles include principal of d’HKL , a Zurich-based experimental architecture firm, and adjunct lecturer at institutions like Harvard University and the Bartlett School of Architecture. His research focuses on integrating science, engineering, and computation into architectural design, emphasizing structural innovation and sustainability. Education: Ph.D. in Mathematics (Dr. sc. Nat.), University of Zurich, 1999 Second degree in Architecture Research Interests: Computational design and generative systems Structural aesthetics and neuroaesthetics Sustainable urban and architectural solutions Parametric modeling and material systems Key Contributions: Advances in graphic statics and form-finding Design strategies for high-density urban environments Integration of AI and generative tools in architectural practice Recent Work Trends: Recent articles emphasize computational methods for industrial layout optimization, AI’s role in architecture, and embodied perception in structural design. His work bridges theoretical research with practical applications, such as the Singapore Hawker Centre redesign and experimental structures like hypar-combined shells. Awards: Aalto University Doctoral Thesis Awards (2022) Best Presentation Award (2019) Oscari-Vilamo Award (2023) Advising & Grants: Supervised 4 theses, including recipients of awards like the Metex Award shortlist. Active in international academic networks, serving on conference committees (e.g., International Association for Shell and Spatial Structures) and editorial roles (e.g., Automation in Construction journal). Labs & Teams: Leads the d’HKL studio, collaborating globally on projects like the AA/ETH Pavilion and the Hypercube for Gardens by the Bay. Research focuses on experimental design and material systems.

Muhammad Rashedمشاهده پروفایل

استادیار

• Electronic Design Automation (EDA)
• In-Memory Computing
• Artificial Intelligence Acceleration
• +۵ مورد دیگر

Dr. Muhammad Rashed is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, within the College of Engineering. He holds a Ph.D. in Computer Engineering from the University of Central Florida (2024) and a B.S. in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology (2015). Ph.D. : Computer Engineering, University of Central Florida, 2024 B.S. : Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, 2015 His research focuses on electronic design automation (EDA), in-memory computing, AI acceleration, and sustainable computing. He explores novel computing paradigms to overcome the limitations of traditional architectures, particularly in data-intensive applications such as AI and scientific computing. His work emphasizes hardware-software co-design and leveraging emerging non-volatile memories for energy-efficient processing. The 15 most recent publications highlight a consistent focus on in-memory computing, particularly in path-based and flow-based architectures, logic synthesis, and AI acceleration. Key themes include optimization, fault tolerance, verification, and the use of advanced data structures like sentential decision diagrams. His work is published in top-tier venues such as DAC, ICCAD, ASP-DAC, and IEEE/ACM journals. Scientific Awards: UTA CARES Grant for OER Creation Research Experiences for Undergraduates (REU) Grant Alireza Seyedi Doctoral Research Innovation Endowed Scholarship David T. & Jane M. Donaldson Memorial Scholarship IEEE/ACM William J. McCalla ICCAD Best Paper Award Nomination Best Research Video Award, Design Automation Conference (DAC) Dr. Rashed advises several graduate and undergraduate students in the NextGen Computing Lab and is involved in research grants including the UTA CARES Grant and REU funding. He actively contributes to academic service through roles such as conference TPC member, journal reviewer (e.g., IEEE TCAD, ACM TODAES), and committee participation in the department and college. His lab, the NextGen Computing Lab, is dedicated to building scalable, energy-efficient computing systems for next-generation AI and scientific workloads, aligning with national initiatives in advanced computing.

Saad Khanمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Dr. Saad Khan is a Senior Lecturer in Cyber Security at the Department of Computer Science, School of Computing and Engineering, University of Huddersfield, United Kingdom. He is an active researcher and educator, supervising multiple PhD students and contributing to government-funded cybersecurity projects with Innovate UK, DCMS, and DASA. He is also a Fellow of the Higher Education Academy and serves on program committees for major conferences. His research focuses on intelligent systems for cyber security and digital forensics. Key areas include Security Information and Event Management (SIEM), access control, authentication, vulnerability assessment, anomaly detection, and image forensics. He aims to develop automated software tools that enhance digital infrastructure resilience against modern cyber threats. The recent publications reflect a strong trend in applying machine learning and AI to cybersecurity challenges, particularly in IoT security, zero-day attack detection, and human-centric security awareness. His work bridges technical innovation with practical implementation in real-world environments. Scientific Awards: Fellow of the Higher Education Academy Dr. Khan actively supervises PhD students and contributes to research grants through collaborations with UK government agencies. He has led work in three major funded projects and regularly reviews for top-tier journals and conferences. He is a member of the Centre for Cybersecurity at the University of Huddersfield, where he collaborates on interdisciplinary research initiatives focused on secure digital transformation and intelligent defense systems.

Adrian Linacreمشاهده پروفایل

استاد

• Forensic Science
• Forensic DNA Analysis
• Wildlife Forensic Science
• +۱۲ مورد دیگر

Adrian Linacre is a Professor and Chair in Forensic DNA Technology at Flinders University, within the College of Science and Engineering, Department of Biological Sciences. He is a leading figure in forensic science, with a focus on DNA analysis, wildlife forensics, and crime scene investigation. BSc in Biological Sciences (Hons), University of Edinburgh, 1984 DPhil in Molecular Genetics, University of Sussex, 1988 His research centers on getting more from less at crime scenes , particularly through developing highly sensitive DNA typing methods and studying the transfer and persistence of biological materials. He also pioneers the use of non-human DNA in forensic investigations, notably in wildlife forensic science , aiding in species identification and combating illegal wildlife trade. His recent publications reflect a strong trend in trace DNA analysis , body fluid identification , and DNA transfer dynamics , with applications in drug cases, sexual assault investigations, and environmental DNA degradation. His work increasingly integrates molecular techniques with real-world forensic challenges. Notable scientific awards include: Medal of the Order of Australia (OAM), 2020 Inspirational Scientist of the Year, Royal Society of Edinburgh, 2005 Fellow of the Royal Society for the Encouragement of Arts and Commerce (FRSA) Finalist, South Australian Science Excellence and Innovation Awards (2023, 2024) He has successfully supervised several students, including Piyamas Kanokwongnuwut and Alicia Haines, many of whom have won international recognition. He has secured significant research funding and contributed to national and international forensic policy, including a key review for the UK Home Office on low-template DNA. His professional leadership includes presidencies of the ANZFSS and ISFG, and vice presidency of the IAFS. Linacre is actively involved in editorial roles, serving as Associate Editor for Forensic Science International: Genetics and on the boards of Forensic Science, Medicine and Pathology and the Australian Journal of Forensic Science . He is a sought-after expert witness and media commentator in forensic science.

• Medical Image Understanding
• Radiology Report Generation
• Deep Learning
• +۳ مورد دیگر

Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.

Merry Maniمشاهده پروفایل

دانشیار

• Biomedical Imaging
• Signal Processing
• Image Reconstruction
• +۴ مورد دیگر

Dr. Merry Mani is an Associate Professor in Radiology and Imaging Sciences and Biomedical Engineering, specializing in biomedical imaging and signal processing. Her work focuses on advancing MRI-based imaging technologies to study neurological disorders such as Alzheimer's, Autism, and Epilepsy. She holds a Ph.D. in Electrical and Computer Engineering from the University of Rochester (2014) and completed a postdoctoral fellowship at the University of Iowa School of Medicine (2018). Her research combines biophysical modeling with machine learning to explore brain microstructures. Key achievements include the NNARSAD Young Investigator Grant and NIH-funded projects like 'Fast Multi-dimensional Diffusion MRI with Sparse Sampling'. Her lab develops cutting-edge reconstruction methods like qModeL and MUSSELS, prioritizing high spatio-temporal resolution imaging. Major contributions span diffusion MRI acquisition, model-based deep learning, and clinical applications in neurodegenerative diseases. Notable grants include NIH R01EB031169 for Alzheimer’s neurodegeneration studies and projects on rTMS for depression. Her work bridges imaging innovation with clinical impact, aiming to improve diagnosis and treatment through advanced imaging biomarkers.