جست‌وجوی استاد و پژوهشگر

Tim Schwartzمشاهده پروفایل

استاد پژوهشی

• Human-Robot Interaction
• Multi-modal Interaction
• Industry 4.0
• +۳ مورد دیگر

Dr. Tim Schwartz is an Associated Member at the German Research Center for Artificial Intelligence (DFKI) located at the Saarland Informatics Campus in Saarbrücken, Germany. He is affiliated with the Ubiquitous Media Technology Lab (UMTL) where he conducts research at the intersection of human-robot interaction, multimodal interfaces, and industrial applications. His work spans over two decades with significant contributions to the fields of robotics, augmented reality, and Industry 4.0 implementations. Dr. Schwartz's research interests focus primarily on Human-Robot Interaction , Multi-modal Interaction , and Industry 4.0 applications. His work explores how humans and robots can effectively collaborate in industrial settings, with particular attention to communication modalities, task division, and intuitive interfaces. Recent projects include human-robot collaboration in assembly cells, social cognitive robots for warehouse environments, and augmented reality applications for aircraft manufacturing. Analysis of his publication history reveals a clear trajectory from foundational work in multimodal interfaces and context-aware computing (2005-2015) toward increasingly applied research in industrial robotics and human-robot collaboration (2016-2024). His most recent publications demonstrate a strong emphasis on practical implementations in manufacturing and warehouse environments, with a particular focus on optimizing work dynamics between humans and robots. Dr. Schwartz actively engages in academic supervision, offering thesis opportunities such as the user-study on optimal work dynamics in human-robot collaboration at the Power4Production Hall in Saarbrücken. His collaborations extend to institutions including ZeMA (Zentrum für Mechatronik und Automatisierungstechnik gGmbH), indicating strong industry-academia partnerships. At DFKI, he works within a vibrant research community that includes numerous colleagues in the Ubiquitous Media Technology Lab, contributing to a collaborative environment focused on cutting-edge research in human-robot interaction, multimodal systems, and industrial applications of artificial intelligence.

Christopher Bishopمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Probabilistic Models
• +۶ مورد دیگر

Christopher Bishop is a Microsoft Technical Fellow and Director of Microsoft Research AI for Science, concurrently serving as Honorary Professor of Computer Science at the University of Edinburgh and Fellow of Darwin College, Cambridge. His distinguished career spans theoretical physics, neural computing, and leadership in AI research. Fellow of the Royal Academy of Engineering (2004) Fellow of the Royal Society of Edinburgh (2007) Fellow of the Royal Society (2017) Founding member of UK AI Council Member of Prime Minister's Council for Science and Technology (2019) Delivered Royal Institution Christmas Lectures (2008) His research focuses on probabilistic models and machine learning, with significant contributions to AI for scientific discovery. Bishop pioneered the concept of the fifth paradigm of scientific discovery , where AI transforms traditional research methodologies across natural sciences. His work bridges theoretical computer science with practical applications in fusion energy, materials science, and computational biology. Analysis of his recent publications reveals a strategic shift toward AI-driven scientific infrastructure , with emphasis on machine learning foundations that endure technological evolution. His 2024 textbook became Springer Nature's top-selling publication, demonstrating exceptional impact in both academic and industrial contexts. Deep Learning: Foundations and Concepts (2024) Pattern Recognition and Machine Learning (2006) Neural Networks for Pattern Recognition (1995) Bishop leads Microsoft's global AI for Science initiative, establishing research teams in Berlin and coordinating interdisciplinary projects that apply machine learning to climate science, fusion energy, and molecular biology. His leadership in the Prime Minister's Council shapes national AI strategy while maintaining active engagement in public science communication through lectures and media appearances.

Dejan Markovicمشاهده پروفایل

استاد

• Implantable neuromodulation systems
• Domain-specific compute architectures
• Design methodologies
• +۳ مورد دیگر

Dejan Markovic is a Professor of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA), holding the Mukund Padmanabhan Term Chair in Electrical Engineering and serving as Area Director for Circuits and Embedded Systems. His research spans implantable neuromodulation systems, domain-specific compute architectures, and energy-efficient design methodologies for biomedical and embedded applications. Dr. Markovic earned his PhD (2006) and MS (2000) from UC Berkeley and BS (1998) from the University of Belgrade, Serbia. His research focuses on ultra-low-power integrated circuits for neural interfaces, including artifact-free stimulation/sensing platforms and neural recording front-ends, alongside domain-specific architectures for sparse linear algebra in IoT and mobile computing. Recent work demonstrates strong trends in medical neuromodulation and energy-efficient VLSI for biomedical signal processing and wireless communications. His scientific contributions have been recognized with prestigious awards including IEEE Fellow (2021), ISSCC Lewis Award (2014), ISSCC Jack Raper Award (2010), and NSF CAREER Award (2009). Additional honors include UC Berkeley's David J. Sakrison Memorial Prize (2007) and multiple best paper awards. Dr. Markovic co-founded semiconductor IP startup Flex Logix Technologies (2014) and leads the DMGroup research lab at UCLA. He teaches core courses including ECE 115C (Digital Integrated Circuits), ECE M216A (VLSI Design), and BME M260 (Neuroengineering), mentoring students in cutting-edge VLSI design for medical and communication applications. The DMGroup lab specializes in developing implantable neuromodulation systems, domain-specific compute engines, and novel design methodologies, with recent projects featured in UCLA's coverage of brain-computer interface technology and Anari AI's $2M funding for personalized AI hardware.

• Architecture
• Urban and regional planning
• Design
• +۵ مورد دیگر

Professor Alan Penn is a leading academic at University College London's The Bartlett School of Architecture , where he holds the title of Professor in Architectural and Urban Computing. He previously served as Dean of the Bartlett Faculty of the Built Environment from 2009 to 2019 and has been instrumental in establishing Space Syntax Ltd , a UCL knowledge transfer spin-out company. His affiliations include membership in the Space Syntax Laboratory, board membership of UCL Consultants Ltd, and trustee status at Shakespeare North Trust. Education: BSc (1978), Dip Arch (1980), MSc (1983) - all from University College London Alan Penn’s research investigates how spatial design influences social and economic behaviors through innovative space syntax methodologies . Key areas include: Agent-based simulations of human behavior Spatio-temporal representations of built environments Urban spatial network analysis Urban sustainability across multiple dimensions Cognitive markers in architectural design Historical urban growth modeling His recent publications demonstrate a strong focus on computational urbanism, evolutionary city patterns, and behavioral architecture. Research trends show interdisciplinary approaches combining architectural theory with: Machine learning applications Network science analysis Behavioral psychology insights Historical GIS techniques Complex systems modeling Public health considerations Scientific recognition includes: HEFCE Business Fellowship (2001-2005) KTP SE Region Award (2010) Multiple UCL Enterprise awards ‘Spirit of Enterprise’ Award (2008) As Principal Investigator he leads the £5m EPSRC-funded Urban Dynamics Lab , demonstrating sustained research excellence. His work extends to public engagement through: Shakespeare North Trust educational theatre development Media appearances (New Scientist, Slashdot) Public policy contributions

Ghyslain Gagnonمشاهده پروفایل

استاد

• Radiofrequency circuits and antennas
• Microelectronics
• Wireless communications
• +۴ مورد دیگر

Ghyslain Gagnon is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) in Montreal, Canada. He leads research activities within the LACIME – Communications and Microelectronic Integration Laboratory, focusing on cutting-edge developments in microelectronics, sensors, and communication systems. His work bridges theoretical research and practical applications across multiple domains including health technologies, wireless communications, and quantum engineering. Education: B.Ing. from École de technologie supérieure M.Ing. from École de technologie supérieure Ph.D. from Université de Carleton Professor Gagnon's research spans several interconnected domains with emphasis on Radiofrequency circuits and antennas, Microelectronics, Wireless communications, Sensors and monitoring systems, Machine learning applications, Health technologies, and Quantum engineering. His work demonstrates a strong commitment to translating theoretical concepts into practical solutions with real-world impact, particularly in the areas of health monitoring systems and advanced communication technologies. His recent publications reveal a clear trajectory toward increasingly interdisciplinary research, combining traditional electrical engineering with machine learning, health monitoring, and quantum technologies. The trend shows growing emphasis on practical applications in automotive safety systems, wireless communications for next-generation networks, and health monitoring technologies that leverage flexible electronics and novel sensor designs. Professor Gagnon has successfully supervised numerous graduate students through their doctoral and master's research, with recent theses focusing on smart hearing protection devices, machine learning applications, energy monitoring systems, and flexible sensor technologies. His supervision record demonstrates consistent productivity and relevance to contemporary engineering challenges. He is an active member of the LACIME research laboratory, which focuses on six key areas: Functional materials, Micro- and nanofabrication processes, Conception and design of integrated circuits, Design and fabrication of hybrid components, Photonic and electronic microsystems, and Signal processing and communication. This environment provides students with access to cutting-edge tools and fosters innovation through interdisciplinary collaboration.

Arianna Bisazzaمشاهده پروفایل

دانشیار

• Natural Language Processing
• Multilingualism
• Low-resource Language Modeling
• +۷ مورد دیگر

Arianna Bisazza is an Associate Professor in the Computational Linguistics Group at the University of Groningen, where she leads the InClow research group focused on Interpretable, Cognitively inspired, Low-resource language models. Her work bridges computational linguistics, cognitive science, and language acquisition to develop more robust and interpretable language processing algorithms that can adapt to diverse linguistic phenomena worldwide. Dr. Bisazza's research interests span statistical modeling of human languages in multilingual contexts, with particular focus on improving language model performance for "challenging" or low-resource languages. Her work explores how insights from human language acquisition can inform better language modeling techniques, and she investigates methods to make state-of-the-art NLP systems more interpretable and transparent. As a cross-disciplinary researcher, she actively seeks to enhance our understanding of human language processing and evolution through computational modeling tools. Her recent publications reveal a strong emphasis on multilingual evaluation frameworks (like TurBLiMP and MultiBLiMP), interpretability of language models, and connections between human language acquisition and neural network learning. Her work consistently addresses the challenge of making language technology more robust across diverse linguistic structures and typological features. Outstanding Paper Award at the BabyLM Challenge (CoNLL'24 Shared Task) for "BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency" Dr. Bisazza currently leads a Vidi project funded by the Dutch Research Council (NWO) on improving low-resource language modeling through child language acquisition insights. She is also part of two national consortium projects funded by NWA-ORC initiatives: InDeep (Interpreting deep learning models for language, speech & music) and LESSEN (Low Resource Chat-based Conversational Intelligence). She supervises multiple PhD students, including two China Scholarship Council (CSC)-funded researchers working on simulating human patterns of language learning and change. Her earlier research was supported by a Veni grant (2017-2021) focused on understanding and improving the encoding of linguistic structure in Neural Machine Translation models. As head of the InClow research group, Dr. Bisazza oversees a team investigating interpretable, cognitively inspired approaches to low-resource language modeling. The group's work combines insights from cognitive science and linguistics with cutting-edge NLP techniques to develop language models that better reflect human language processing capabilities, particularly in resource-constrained settings.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

• Medical Informatics
• Statistical Science
• Bioinformatics
• +۵ مورد دیگر

Dr. Masato Inoue is a Professor at the Faculty of Science and Engineering , School of Advanced Science and Engineering at Waseda University. He holds a Doctor of Medical Science from Kyoto University. Education: 2003 - Kyoto University Graduate School of Medicine 2003 - Kyoto University His research spans multiple disciplines at the intersection of Medical Informatics , Bioinformatics , and Statistical Mechanics . Key areas include: Medical Imaging : Developing Bayesian super-resolution algorithms and Prior Ensemble Learning for improved MRI reconstruction Voice Analysis : Creating innovative voice quality quantification systems for clinical diagnostics Genetic Analysis : Advancing haplotype inference methods and gene network modeling Signal Processing : Applying statistical mechanics to diverse problems from coding theory to neuroscience His recent publications (2021-2012) demonstrate consistent contributions to medical imaging algorithms , voice disorder classification , and genetic data analysis . Notable collaborations include work with Kyoto University researchers , Swedish medical institutions , and cross-disciplinary teams in bioengineering.

• Nuclear Physics
• Theoretical Physics
• Interdisciplinary Physics
• +۷ مورد دیگر

Jan Ryckebusch is a Senior Full Professor and Department Chair at Ghent University's Faculty of Sciences, Department of Physics and Astronomy . His research bridges Nuclear Physics and Interdisciplinary Physics , with notable contributions to quantum mechanics, statistical mechanics, and machine learning applications. Key research areas include Short-Range Nuclear Correlations , Neutrino-Nucleus Scattering , and Social Network Dynamics . He has supervised multiple PhD students in projects related to Quantum Computing , Agent-Based Modeling , and Statistical Physics of Social Systems . His recent work explores Econophysics (e.g., wealth-income mobility studies) and Opinion Dynamics in social networks. Scientific Awards : No specific awards mentioned in the provided data. Grants & Collaborations : Active in interdisciplinary projects with co-authors across Physics , Economics , and Computer Science , including Luis E C Rocha, Koen Schoors, Wim Cosyn, and others.

• Bioinformatics and Computational Biology
• Machine Learning
• Data Science
• +۳ مورد دیگر

Fardina Alam is a Lecturer in the Department of Computer Science at the University of Maryland, College Park. She holds a Ph.D. from George Mason University (2023), where she specialized in Structural Bioinformatics and Machine Learning. Her academic rank reflects UMD's Professional Track Faculty, equivalent to an Assistant Professor of Teaching. Dr. Alam's research focuses on deep learning applications in protein structure prediction, generative AI, and ethical data practices. She has contributed to projects funded by an NSF FET Grant (#1900061) and published in journals like Biomolecules. Awards include the 2023 Outstanding Dissertation Award and 2022 Editor's Choice Article recognition. Education: Ph.D. (Computer Science, George Mason, 2023); M.S. (Computer Science, George Mason, 2019); B.S. (Computer Science and Engineering, Military Institute of Science and Technology, Bangladesh, 2013). Her teaching emphasizes data science ethics and interdisciplinary applications, including a new course for UMD's Data Science minor. Professional roles include Associate Guest Editor at Bioinformatics Advances (2024), Faculty Advisor to the Bangladeshi Graduate Student Association, and Program Co-Chair for the ACM-BCB Computational Structural Bioinformatics Workshop (2023). Research Interests: Structural Bioinformatics, Generative AI, Responsible AI Ethics, Deep Learning, Machine Learning, and Data Science. Her work bridges computational biology and AI, with a focus on equitable data practices and protein structure analysis. Recent projects address challenges in generating physically-realistic protein structures and improving question-answering systems through equitable data strategies. Awards: Recipient of the 2023 Outstanding Dissertation Award (George Mason University), 2023 Best Paper Award, and 2022 Biomolecules Editor's Choice Article. These accolades highlight contributions to protein structure prediction and bioinformatics methodology. Advising & Grants: NSF FET Grant #1900061 supported her computational biology research. She advises the Nobanno graduate student group and chairs workshops in her field. Her teaching and research aim to promote inclusivity, particularly for underrepresented groups in STEM. Labs/Teams: Active contributor to the Computational Biology Lab at George Mason University and collaborates with UMD's interdisciplinary teams on data ethics and AI applications. Her work aligns with UMD's vision for data-driven education and innovation.

• Machine Learning
• Interactive Machine Learning
• Healthcare Applications
• +۵ مورد دیگر

Dr. Brian Mac Namee is an Associate Professor at the School of Computer Science, University College Dublin (UCD), and UCD Site Director at the Insight SFI Research Centre for Data Analytics since 2024. His research focuses on interactive machine learning, emphasizing human-centered model training, with applications in healthcare, agriculture, space technology, and virtual reality. He co-authored the textbook Fundamentals of Machine Learning for Predictive Data Analytics , translated into five languages. Education includes a PhD (2004) and BA (Mod) from Trinity College Dublin. He previously co-founded the Applied Intelligence Research Centre at Technological University Dublin and was a founding co-PI at CeADAR. He chairs the Artificial Intelligence Association of Ireland and directs training at Krisolis Ltd. Notable awards include the 2016 Data Science Award for Best Academic Research Team and UCD's Fiosraigh Research Excellence Award (2013). His teaching spans advanced machine learning, data science projects, and programming courses. He leads grants such as the SFI-funded Machine Learning & Virtual Reality for Pilot Training and co-directs the SFI Centre for Research Training in Machine Learning.

Alex Gittensمشاهده پروفایل

استادیار

• Machine Learning
• Computational Linear Algebra
• Randomized Numerical Linear Algebra
• +۳ مورد دیگر

Alex Gittens is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), joined in 2017. His research focuses on algorithmic trade-offs between computational efficiency and accuracy in large-scale linear algebra and machine learning contexts. He has expertise in kernel methods, randomized numerical linear algebra, and low-rank approximation techniques. Education: PhD in Applied and Computational Mathematics, Caltech (2013) Industry Postdoc at eBay Research Labs (2013-2015) Postdoctoral Scholar at International Institute of Computer Science (2015-2016) His research explores scalable machine learning algorithms, nonlinear and multilinear sketching applications, and sampling for low-rank tensor/matrix approximation. Current technical interests include attention mechanisms for knowledge graph completion, federated learning trade-offs, and causal inference in adversarial settings. Recent publication trends show active contributions in federated learning (privacy-fairness optimization), causal information extraction (financial text analysis), and adversarial machine learning (robustness-security trade-offs). His work emphasizes trustworthy ML systems and computational efficiency in high-dimensional data processing. Teaching includes foundational discrete mathematics (CSCI 2200) and advanced machine learning courses (CSCI 6968/4968). He offers advising through Slack channels and via email, focusing on course selection, research opportunities, and graduate school preparation.

Jun Baiمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۲ مورد دیگر

Jun Bai is an Assistant Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. His research focuses on Machine Learning, Deep Learning, Medical Image Analysis, AI-driven diagnostics for cancer and diseases, and drug discovery. He holds a Ph.D. in Computer Science and Engineering from the University of Connecticut (2023), an M.S. in Computer Science from the University of Dayton (2019), and an M.S. in Interdisciplinary Studies in Education (2015). His work emphasizes applying AI to healthcare challenges, such as robust mammogram classification, 3D biomedical image registration, and peptide generation for drug discovery. Recent studies include hybrid transformer models for medical imaging and weakly-supervised systems for prostate cancer diagnosis. His computational methods span molecular dynamics simulations and graph neural networks. Despite his prolific research output, no specific grants, advising roles, or lab affiliations are explicitly listed in the provided data. Contact: Rhodes Hall 891, Cincinnati, OH | Email: baiju@ucmail.uc.edu

• Autonomous Driving
• Machine Learning
• Wireless Communication
• +۱۱ مورد دیگر

Dr. Tao (Kevin) Huang is a researcher at James Cook University's College of Science and Engineering, with expertise spanning autonomous driving, wireless communication systems, and medical imaging applications. His work integrates machine learning, sensor fusion, and multimodal data analysis to address complex challenges in vehicular networks, environmental monitoring, and healthcare technology. Research Interests: Dr. Huang's research focuses on Autonomous driving perception systems IoT-enabled vehicular networks AI for medical diagnostics and environmental sensing Signal processing and privacy-preserving communication protocols Recent Publications: His 2025 work emphasizes advancements in V2X cooperative perception, radar-LiDAR-camera fusion, and diffusion models for medical imaging. Key trends include cross-modal robustness, real-time processing for autonomous systems, and AI applications in sustainability.

Tse-Hsun Chenمشاهده پروفایل

دانشیار

• Software Engineering
• AIOps
• Software Testing
• +۳ مورد دیگر

Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal. He leads the Software PErformance, Analysis, and Reliability (SPEAR) lab, focusing on improving software quality through log analysis, AIOps, and mining software repositories. His research collaborates with companies like Microsoft, BlackBerry, and Ericsson. Education: PhD, MSc, and BSc in Computer Science from Queen's University and the University of British Columbia. Awards include the Gina Cody Research Award (2021) and recognition as one of the world's most active software engineering researchers (JSS study). Research interests include software testing, DevOps, and leveraging LLMs for SE tasks. Recent work emphasizes log parsing with LLMs (e.g., LibreLog) and fault localization. Graduates from his lab hold academic positions at institutions like York University and DePaul University. Teaching includes courses on software verification, testing, and process management. Active in program committees for ICSE, FSE, and MSR. Over 50 publications in top venues like TSE, ICSE, and FSE.