جست‌وجوی استاد و پژوهشگر

Giorgio Ascoliمشاهده پروفایل

استاد

• Computational Neuroanatomy
• Neuroinformatics
• Neuronal Morphology
• +۵ مورد دیگر

Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.

Zhibo Pangمشاهده پروفایل

استاد مدعو

• Embodied Intelligence
• Artificial Intelligence/ML
• Robotics
• +۷ مورد دیگر

Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.

• Brain-Computer Interfaces
• Machine Learning
• Neural Signal Processing
• +۵ مورد دیگر

Professor Guan Cuntai serves as Deputy Dean of the College of Computing & Data Science and holds the President's Chair in Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He also maintains a courtesy appointment as Professor at the Lee Kong Chian School of Medicine. His leadership extends to multiple research institutes including serving as Director of the Artificial Intelligence Research Institute (AI.R) and Co-Director of both S-Lab for Advanced Intelligence (2020-2025) and the Rehabilitation Research Institute of Singapore (2015-2018 & 2024-2025). Professor Guan's research focuses on Brain-Computer Interfaces (BCI), Machine Learning, Neural Signal & Image Processing, and Artificial Intelligence. His work at the Centre for Brain-Computing Research (CBCR) investigates deep learning algorithms for motor activity decoding, cognitive mechanism understanding, and explainable AI for brain decoding modeling. His research spans applications in motor decoding, attention and emotion detection, silent speech decoding, and olfactory response analysis from brain signals. His significant contributions to the field have been recognized through numerous awards: Annual BCI Research Award (First Prize) King Salman Award for Disability Research Nanyang Research Award IES Prestigious Engineering Achievement Award Achiever of the Year (Research) Award Finalist of the President Technology Award Professor Guan has delivered over 100 keynote speeches and invited talks globally, including at the opening ceremony of the 7th International BCI Meeting and IEEE International Conference on Rehabilitation Robotics. His extensive publication record includes 420 refereed journal and conference papers, and he has secured significant research funding resulting in 26 granted patents and patent applications, with 17 technologies licensed to six companies across Singapore and USA. He leads the Centre for Brain-Computing Research (CBCR) which focuses on fundamental and applied BCI research, and previously established the Brain-Computer Interface research program and founded the Department of Neural & Biomedical Technology at the Institute for Infocomm Research, A*STAR before joining NTU in 2016.

Wan Shouمشاهده پروفایل

استادیار

• Multiscale manufacturing
• Laser-based manufacturing and materials processing
• Novel manufacturing processes
• +۱۲ مورد دیگر

Wan Shou is an Assistant Professor in the Department of Mechanical Engineering at the University of Arkansas. His research focuses on multiscale manufacturing, advanced materials, and functional devices, with applications in wearables, robotics, and sustainable technologies. Ph.D., Mechanical Engineering, Missouri University of Science and Technology M.S., Mechanical Engineering, University of Louisiana at Lafayette B.E., Textile Engineering, Tianjin Polytechnic University, China Dr. Shou’s research spans laser-based manufacturing , nanomanufacturing , machine learning-assisted processes , and bioresorbable electronics . He explores 3D printing of polymer and metal composites, energy materials , and functional textiles for wearable sensors and environmental applications. Recent publications highlight his work in additive manufacturing , computational design of composites, and self-powered sensing systems . His team integrates machine learning with materials discovery to optimize performance. Editor’s pick of Science Magazine US Patent 11,752,700: Data-driven material formulation US Patent 11,993,850: Laser-assisted nanoparticle printing Dr. Shou’s patents and publications reflect a commitment to innovative manufacturing and environmentally conscious design . His work bridges materials science , robotics , and smart systems , advancing energy and water technologies.

• Market Microstructure Theory
• Digital Finance
• FinTech
• +۳ مورد دیگر

Prof. Dr. Peter Gomber is a Professor of e-Finance at the Faculty of Economics and Business Administration , Goethe University Frankfurt, since 2004. He co-chairs the Data Science Institute (efl) and holds adjunct professorships at the University of Bamberg (2004), Mannheim (2009), and Luxembourg (2018). His roles include board memberships at the Frankfurt Stock Exchange, Clearstream Banking AG, and advisory positions for European regulatory bodies. Education : Diplom-Kaufmann in Economics, University of Gießen (1999), PhD in Business Informatics. Research Interests focus on market microstructure , FinTech , electronic trading , and regulatory impacts on financial markets. His work explores algorithmic trading , liquidity dynamics , and AI-driven compliance . Publication Trends (15 most recent) emphasize digital finance , market fragmentation , regulatory analysis , and AI applications in trading and compliance. Key subfields include blockchain , high-frequency trading , and news-driven liquidity shocks . Scientific Awards : Reuters Innovation Award (2000) Hochschulpreis des Deutschen Aktieninstituts (1999) IBM SUR Grant (2007) Best Paper Awards (multiple conferences) Best Information Systems Publications Award (2020) Advising & Grants : Teaches in executive programs (Goethe Business School, Amsterdam Institute of Finance). Secured grants from public/private institutions, including a U.S. patent for market model innovation. Labs & Teams : Leads the e-Finance professorship and contributes to the Data Science Institute (efl) , fostering industry-academia collaborations with Deutsche Börse, Capveriant, and others.

Mahdi S. Hosseiniمشاهده پروفایل

استادیار

• Deep Learning
• Computer Vision
• Computational Pathology
• +۳ مورد دیگر

Mahdi S. Hosseini is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and a faculty member of the Applied AI Institute. He holds a PhD from the University of Toronto (2016) and completed a postdoctoral fellowship at UofT, supported by MITACS-Elevate and NSERC fellowships. His research focuses on advancing deep learning and computer vision for computational pathology and healthcare technologies, aiming to develop AI tools for clinical diagnosis. He currently supervises graduate students and has published over 30 papers and two patents. Education: PhD in Electrical and Computer Engineering from the University of Toronto (2016), postdoctoral training at UofT collaborating with Huron Digital Pathology Inc. (Waterloo, Ontario). Research interests include deep learning, computer vision, computational pathology, medical imaging, and AI ethics (P4AI project). His work emphasizes developing explainable AI systems for clinical pathology, biomarker discovery, and efficient learning algorithms. Professional service includes serving as Area Chair for NeurIPS 2023, CVPR 2023-2024, and ECCV 2024. He reviews grants for CIHR, NSERC, and serves on program committees for key conferences (ICCV, CVPR, NeurIPS). Teaching includes courses on applied AI, machine learning, and deep learning for computational pathology at both graduate and undergraduate levels. Awards: MITACS-Elevate Fellowship (postdoc), NSERC Research Funding (2016-2017). His work has led to patents in diagnostic systems and has collaborated with hospitals and pathologists to advance clinical applications. Labs/Teams: Active collaborations with the Applied AI Institute at Concordia, Huron Digital Pathology, and healthcare institutions. Research emphasizes interdisciplinary approaches between computer science and clinical medicine.

David Blaauwمشاهده پروفایل

استاد

• Ultra-Low Power Circuits
• IoT and Sensor Networks
• RF Communication
• +۷ مورد دیگر

David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)

Simon Razniewskiمشاهده پروفایل

استاد

• Knowledge-based Artificial Intelligence
• Natural Language Processing
• Data Science
• +۳ مورد دیگر

Simon Razniewski is a Professor of Knowledge-based Artificial Intelligence at TU Dresden and ScaDS.AI, focusing on integrating language models and knowledge bases. He previously held roles at Bosch Center for AI (2023–2024), Max Planck Institute for Informatics (2017–2021), and Free University of Bozen-Bolzano (2014–2017). His research spans knowledge extraction, computational logic, and data science applications. He holds a PhD (2014) and Diplom (MSc, 2010) from Free University of Bozen-Bolzano and TU Dresden, respectively. Research interests include large language models (LLMs), knowledge graph construction, and uncertainty quantification in natural language processing. His work bridges AI theory and practice, with publications at top venues like ACL and EMNLP. He teaches courses on LLMs and knowledge-aware AI, and has advised numerous collaborative projects across academia and industry. Prominent contributions include frameworks like GPTKB for LLM knowledge materialization and QUITE for Bayesian reasoning in NLP. His prior roles at Siemens IT and Globalfoundries inform his applied research focus. The International Center for Computational Logic (ICCL) at TU Dresden is his primary research hub, emphasizing interdisciplinary computational logic and AI advancements.

Laurent Daudetمشاهده پروفایل

استاد

• Machine Learning
• Optics
• Signal Processing
• +۳ مورد دیگر

Laurent Daudet is a Professor of Physics at Université Paris Cité (on leave) and CTO & co-founder of LightOn, a startup developing optical computing technologies. His research spans signal processing, wave physics, and machine learning, with a focus on scalable AI solutions. He holds a PhD in Applied Mathematics from Marseille University and is a graduate of École Normale Supérieure in Paris. Research Interests: Laurent’s work bridges academia and industry, addressing challenges in massive-scale AI, optical computing, and hardware optimization. He leads cross-disciplinary R&D projects at LightOn, advancing technologies like the Optical Processing Unit (OPU) for low-power, parallel computing. Awards: Fellow of the Institut Universitaire de France Grants & Advising: Over 200 scientific publications and patents; collaborates globally with researchers and engineers. Former academic roles include Visiting Senior Lecturer at Queen Mary University of London and Visiting Professor at the National Institute for Informatics (Tokyo). Labs & Teams: Leads LightOn’s R&D initiatives, integrating optics, ML, electronics, and software engineering to tackle AI scalability challenges.

Benoît Sagotمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Computational Linguistics
• Machine Translation
• +۷ مورد دیگر

Benoît Sagot is a Senior Researcher in Natural Language Processing and Computational Linguistics at Inria , currently holding the 2023-2024 Informatics and Digital Sciences Annual Chair at Collège de France. He directs the ALMAnaCH research team and contributes to the PRAIRIE Institute for AI research. Research Focus: His work spans neural language models, machine translation, text simplification, multimodal NLP, and lexical resource development for French and low-resource languages. He explores computational morphology, etymology, and historical linguistics, with applications in opinion mining and computational oenology. Recent Articles emphasize language model interpretability, cross-lingual transfer, and multimodal integration (speech, image). Tools & Resources: He has developed morphological lexicons (Le fff, Alexina), corpora (OSCAR, CAMEMBERT), and parsing pipelines (SxPipe). Projects: Involved in initiatives like ANR BASNUM (Furetière's dictionary digitization) and 3IA PRAIRIE (AI research). His career combines foundational work in syntactic analysis with evolving deep learning approaches.

Deepa Kundurمشاهده پروفایل

استاد

• Cybersecurity
• Signal Processing
• Complex Dynamical Networks
• +۴ مورد دیگر

Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .

Simon Crouchمشاهده پروفایل

پژوهشگر ارشد

• Statistical Modeling
• Epidemiology
• Cancer Research
• +۶ مورد دیگر

Simon Crouch is a Senior Research Fellow in Biostatistics at the University of York's Health Sciences department. With a strong mathematical background from Cambridge and Warwick, he leads the analytics team within the Epidemiology and Cancer Statistics Group and works closely with the Haematological Malignancy Research Network (HMRN) and Cardiovascular Health team. His work focuses on statistical modeling of complex epidemiological data related to hematological malignancies. University of Cambridge: MA, MMath in Mathematics University of Warwick: PhD in Mathematics University of Lancaster: MSc in Medical Statistics Dr. Crouch specializes in the statistical modeling of complex epidemiological data, with particular focus on hematological malignancies. His research encompasses predictive modeling, event history analysis, and machine learning applications in cancer epidemiology. He has made significant contributions to understanding myelodysplastic syndromes, lymphoma classification, and survival analysis in blood cancers through population-based studies. His work often involves collaboration with international registries including the European Myelodysplastic Syndromes Registry (EUMDS) and the Haematological Malignancy Research Network. Analysis of his recent publications reveals a strong focus on myelodysplastic syndromes (MDS), with particular attention to risk stratification, survival analysis, and treatment outcomes. His work increasingly incorporates genomic and molecular data to refine disease classification and prediction models. The trend shows progression from purely statistical methodology development toward integrated translational research that combines clinical, genomic, and epidemiological data to improve patient outcomes. Extensive publication record with 113 research outputs including 66 articles, 21 patents, and numerous meeting abstracts Active participation in major international research consortia including MDS-RIGHT and ImmunAID Significant contributions to the development of statistical methodologies for cancer epidemiology Dr. Crouch actively supervises PhD students in mathematical and statistical modeling applied to cancer epidemiology, with particular interest in time-to-event models, complex longitudinal models, and simulation techniques. His research has been supported through multiple projects, including the European Myelodysplastic Syndromes Registry and the MDS-RIGHT project focused on facilitating informed decision-making in hemato-oncology. He contributes to the Advanced Health and Social Statistics module for postgraduate students at the University of York.

Truong Q. Nguyenمشاهده پروفایل

استاد

• Image/Video Processing
• 3D Video Technology
• Wavelets and Filter Banks
• +۵ مورد دیگر

Truong Q. Nguyen is a Professor in the Electrical and Computer Engineering (ECE) Department at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He holds positions at the Center for Wireless Communications and the California Institute for Telecommunications and Information Technology. His research focuses on image/video processing, wavelets, 3D video technology, and applications in healthcare and robotics. He has authored influential textbooks like Wavelets & Filter Banks and pioneered low-power video processing algorithms for mobile devices. Nguyen earned his B.S., M.S., and Ph.D. in Electrical Engineering from the California Institute of Technology (1985–1989). He held roles at MIT Lincoln Laboratory and Boston University before joining UCSD in 1998. His honors include the IEEE Signal Processing Paper Award (1992), NSF Career Award (1995), IEEE Fellow (2005), and UCSD’s Distinguished Teaching Award (2019). His research interests span 3D video processing, machine learning for health monitoring, and biomedical imaging. Notable contributions include wavelet-based compression techniques and AI-driven medical image analysis. He leads the UCSD Video Processing Lab, exploring computer vision, robotics, and generative AI applications. Nguyen is committed to educational innovation, co-creating programs like the Hands-on Curriculum, Summer Research Internship Program (SRIP), and Project-in-a-Box (PIB) for K-12 students. Nguyen’s work bridges academia and industry, with patents in wavelet design and signal analysis. Recent projects include NSF-funded initiatives to develop inclusive engineering curricula and collaborate on graduate pathways programs through the Inclusive Engineering Consortium (IEC).

Rong-Hao Liangمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Tangible User Interfaces
• Intelligent Sensing Systems
• +۷ مورد دیگر

Dr. Rong-Hao Liang is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with both the Future Everyday Group (Department of Industrial Design) and the Signal Processing Systems Group (Department of Electrical Engineering). His research bridges intelligent sensing systems and user interface technology for ubiquitous computing and embodied human-computer interaction . He co-organized international ACM conferences (CHI, UIST, DIS) and has over 70 peer-reviewed publications and 10 patents. PhD in Computer Science (2014) and MSc in Electrical Engineering (2010) from National Taiwan University Founded GaussToys Inc. in 2015, focusing on magnetic-field sensors Cross-appointed to Electrical Engineering in 2021 His research explores intelligent sensing systems , tangible user interfaces , and physiological sensors for real-world challenges. Key trends in his work include ubiquitous health monitoring (e.g., preterm infants), wearable technology , and innovative interaction design . Articles like GaussBits and NFCStack demonstrate his focus on magnetic and RFID-based tangible systems . Scientific awards include the ACM CHI 2013 Best Paper Award , 2014 Honorable Mentions , and the ACM SIGGRAPH Asia 2012 Emerging Technologies Prize . He mentors passion-driven projects in user interface design and embedded systems , emphasizing technical rigor and creativity. His STRAP project (2020–2025) addresses heart disease prevention via big data and AI . Labs and teams include the Future Everyday Group and Signal Processing Systems Group , with collaborations across healthcare , education , and technology startups . His work aligns with UN Sustainable Development Goals , particularly good health and well-being .

Mohamed Khalifaمشاهده پروفایل

پژوهشگر ارشد

• Health Informatics
• Artificial Intelligence in Healthcare
• Clinical Decision Support Systems
• +۳ مورد دیگر

Dr. Mohamed Khalifa is a Visiting Fellow at the Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney. He holds a PhD in Health Innovation from Macquarie University (2020) and an MSc in Health Informatics from the University of Edinburgh (2012). His expertise spans health informatics, AI-driven healthcare solutions, and strategic healthcare management. He has led multidisciplinary teams in developing evidence-based frameworks like GRASP for clinical predictive tools. Affiliations: Visiting Fellow, Macquarie University Director of Studies, College of Health Sciences (Education Centre of Australia) Former Digital Health Officer, Australian Digital Health Agency (2020–2021) His research focuses on AI applications in healthcare, clinical decision support systems, and health analytics. Over 20 years, he has published 60+ peer-reviewed papers and holds an innovation patent (2018). He has received awards including the IMIA Best Paper (2020) and ICIMTH Best Paper (2015). Dr. Khalifa’s work emphasizes improving healthcare efficiency through technology, including projects on predictive tools, emergency room performance, and diabetes management. He is a Fellow of the Australasian Institute of Digital Health and certified in healthcare information systems (CPHIMS).