جست‌وجوی استاد و پژوهشگر

Paul Boersmaمشاهده پروفایل

استاد

• Phonetics
• Phonology
• Computational Linguistics
• +۷ مورد دیگر

Paul Boersma is a Professor of Phonetic Sciences at the University of Amsterdam within the Faculty of Humanities. His research explores how phonetic, phonological, and morphological phenomena emerge through computational modeling using artificial neural networks and Optimality Theory, with a focus on multi-level constraint interactions and distributional learning. University of Amsterdam Faculty of Humanities Phonetic Sciences Key research areas include: Computational Modeling : Simulations of phonological category emergence from phonetic data Optimality Theory : Gradual Learning Algorithm applications BiPhon Framework : Parallel bidirectional phonology/phonetics models Statistical Learning : Cross-situational and distributional learning mechanisms Recent publications emphasize: 2025: Inclusive speech recognition systems using Whisper model 2025: F0 ratio analysis for creaky voice diagnostics 2024: Prosodic clitics in child speech and checked tones in Shanghai Chinese 2023: Distributional learning in developmental language disorder contexts 2022: Substance-free phonological features and ghost segment phenomena He has also contributed extensively to the Praat software for phonetic analysis, with continuous updates since 1993.

Danielle Butlerمشاهده پروفایل

پژوهشگر ارشد

• Primary Health Care
• Health Services Research
• Linked Administrative Health Data Analysis
• +۵ مورد دیگر

Danielle Butler is a Visiting Fellow at the National Centre for Epidemiology and Population Health, Australian National University, and a part-time General Practitioner/Researcher at the Institute of Urban Indigenous Health. With 20+ years clinical experience and a PhD (2018), her work focuses on healthcare access equity for underserved populations through linked data analysis, mixed-methods research, and telehealth evaluation. Current projects: Enhancing Safe Telehealth , Patient-Centered Medical Homes , Primary Care Data Linkage Key collaborations: ANU, IUIH, Australian Institute of Health and Welfare Her research combines multilevel modeling of administrative data with participatory action research to evaluate primary care innovations. Recent work examines telehealth impacts , out-of-pocket costs , and Aboriginal health service models . Publications span BMJ Open , BMC Health Services Research , and Health Policy , with emphasis on systematic reviews , linked data methodology , and health equity metrics . Research fingerprint shows dominant themes: Primary Health Care (100%), Aboriginal and Torres Strait Islander Health (66%), Health Services Research (49%), and Telehealth (100%).

Carsten Rottمشاهده پروفایل

استاد

• Particle Physics
• Astrophysics
• Neutrino Physics
• +۵ مورد دیگر

Carsten Rott is a Professor in the Department of Physics & Astronomy at the University of Utah and holds the Jack W. Keuffel Memorial Chair until December 2025. His academic journey began with a Ph.D. in Physics from Purdue University (2004), preceded by undergraduate studies at the Universität Hannover. Rott has held academic positions at institutions including The Ohio State University (CCAPP Senior Fellow 2009-2013), Penn State University (postdoc 2005-2008), and Sungkyunkwan University in South Korea (Assistant Professor 2013-2017, Associate Professor 2017-2025). He has been a member of the IceCube Neutrino Telescope collaboration since 2005 and serves on committees like the IceCube-Gen2 Coordination Committee and JSNS2 Speakers Board. His research spans Particle Physics , Neutrino Astronomy , and Dark Matter Detection . Key projects include analyzing IceCube data for sterile neutrino signatures, studying cosmic-ray anisotropy, and investigating terrestrial gamma-ray flashes. Notable achievements include the Bruno Rossi Prize (2021) for high-energy astrophysics contributions. Rott's work involves multimessenger observations (neutrinos, gamma-rays, radio signals) and detector calibration innovations, such as those for the JSNS2 experiment. Recent publications focus on atmospheric neutrino oscillation parameters, TGF spectroscopy, and dark matter constraints. He employs machine learning techniques (CNNs) for event reconstruction and leads initiatives like the IceCube Master Class for student engagement. Grants include funding for IceCube upgrades (2024-2026) and Hyper-Kamiokande collaborations (2023-2026). As department chair since 2023, Rott continues to bridge experimental particle physics with astrophysical discoveries.

Zhibao Mianمشاهده پروفایل

مدرس

• Trustworthy AI
• Machine Learning
• Large Language Models
• +۸ مورد دیگر

Dr. Zhibao Mian is a Lecturer in the School of Computer Science at the University of Hull, UK, and previously held an Associate Professor position at Northwest Normal University. He specializes in trustworthy AI, machine learning, and intelligent maintenance systems. His research integrates AI with IoT, blockchain, and digital twins in Industry 4.0/5.0 contexts. He leads projects on predictive maintenance for offshore wind turbines and AI-driven sustainable energy solutions. Dr. Mian holds a PhD from the University of Hull and an MSc from the University of Nottingham. Research interests include AI ethics, model-based safety analysis, and RCM. He has secured grants such as the CPHC-funded study on AI in software education and oversees multiple PhD scholarships. Notable roles include Editorial Board member of the American Journal of Artificial Intelligence and Reviewer for high-impact journals/conferences like JSS and IEEE. He is a Senior Fellow of the Higher Education Academy and received the Royal Academy of Engineering's 2024 Exceptional Talent designation. Recent publications (2023-2025) focus on ordinal networks, outlier detection, Belt and Road trade analysis, and carbon emissions modeling. He actively advises PhD students on topics like UAV-based anomaly detection and predictive maintenance frameworks.

Bradley Buchsbaumمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Working Memory
• Functional Neuroimaging
• +۵ مورد دیگر

Bradley Buchsbaum is an Associate Professor and Senior Scientist at the Rotman Research Institute, Baycrest , Toronto, Canada. His research focuses on cognitive neuroscience, particularly on working memory, episodic memory, and functional neuroimaging using fMRI. PhD in Cognitive Science (2003) from University of California, Irvine BSc in Bio-Psychology (1997) from University of California, Santa Barbara His lab investigates how memories are stored, represented, and reactivated in the brain, combining functional neuroimaging, eye-tracking, and computational modeling. Key areas include multivariate statistics, machine learning applications in neuroimaging, and quantifying memory fidelity through behavioral and neural data. Recent publications emphasize neuroimaging methodology (e.g., MRI data consistency checks), memory pattern completion mechanisms, and feature-specific neural reactivation during episodic recall. Research trends highlight interdisciplinary approaches merging cognitive neuroscience with advanced statistical techniques. Lab team includes post-doctoral fellows (Stephen Rhodes), graduate students (Carolyn Guay, Corey Loo, Michael Bone, Nick Hoang, Ryan Barker), and research assistants. The lab actively develops open-source tools like rMVPA and neuroim2 for fMRI analysis.

Krishnendu Guhaمشاهده پروفایل

استادیار

• Embedded Systems
• Cybersecurity
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Krishnendu Guha is an Assistant Professor and CONNECT Funded Investigator at the School of Computer Science and Information Technology, University College Cork. His research bridges embedded systems, cybersecurity, and quantum-safe hardware design with AI and bio-inspired strategies. PhD: University of Calcutta (Department of Science and Technology, Government of India) Postdoctoral: University of Florida Past Roles: Research Fellow at Intel India, Visiting Scientist at Indian Statistical Institute, Temporary Assistant Professor at NIT Jamshedpur His research focuses on embedded systems security , real-time security mechanisms , and quantum-safe hardware . He integrates AI (e.g., neural networks) and bio-inspired strategies (e.g., gecko crypsis behavior) into security frameworks for FPGAs and edge platforms. Recent publications highlight trends in blockchain for supply chains , quantum machine learning , secure FPGA architectures , and distributed AI systems . His work addresses energy efficiency, fault detection, and decentralized security in hardware. As a CONNECT Centre member, Dr. Guha contributes to advanced research in reconfigurable systems and cybersecurity. Grants and collaborations span quantum-safe design, cloud FPGA security, and hardware trojan mitigation.

Ryan Giordanoمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Variational Methods
• +۴ مورد دیگر

Ryan Giordano is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. He holds a PhD in Statistics from UC Berkeley (2019), advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc in Econometrics and Mathematical Economics from the London School of Economics (2009), and undergraduate degrees in Mathematics and Theoretical/Applied Mechanics from the University of Illinois at Urbana-Champaign. Prior to academia, he worked as an engineer at Google and HP and served as a Peace Corps volunteer in Kazakhstan. His research focuses on variational methods , Bayesian robustness , sensitivity analysis , and statistical computing , with applications in machine learning, environmental science, and astronomy. He is particularly known for developing scalable Bayesian inference techniques and quantifying the robustness of statistical models to data perturbations. Giordano’s recent work includes studies on Laplace approximation accuracy, MCMC sensitivity to data removal, and robustness metrics for differential expression analysis. He has contributed to open-source statistical software and collaborates with Tamara Broderick’s group at MIT on postdoctoral work (pre-2019 position). His academic trajectory combines theoretical innovation with practical applications, emphasizing reproducibility and computational efficiency in statistical methodology.

Daniel J. Sorinمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• Cache Coherence Protocols
• +۷ مورد دیگر

Daniel J. Sorin is a Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he also serves as Associate Chair of Education. He holds joint appointments in both the Electrical and Computer Engineering department and Computer Science department, and is recognized as a Bass Fellow for his contributions to education and research. His research focuses on computer architecture with specific expertise in memory systems, cache coherence protocols, fault tolerance, and verification-aware design. Dr. Sorin's work bridges theoretical computer architecture with practical implementations, often incorporating coding theory to solve architectural challenges. His research group has made significant contributions to automated protocol generation, hardware acceleration, and robot motion planning systems. Dr. Sorin's publications reveal a consistent focus on memory consistency models, cache coherence protocols, and verification techniques. His recent work has expanded into robot motion planning acceleration, FPGA resource management, and novel error correction techniques for emerging memory technologies. The trend shows increasing interdisciplinary work connecting computer architecture with robotics and machine learning applications. Program Chair of HiPEAC 2017 Co-chair of IEEE Micro's Top Picks selection committee (2016) Lois and John L. Imhoff Distinguished Teaching Award (2011) NSF CAREER Award recipient IEEE Micro Top Pick awards (2011, 2015) ACM Senior Member As an advisor, Dr. Sorin has mentored numerous PhD students who have gone on to successful careers at leading technology companies including Google, Microsoft, Oracle, and Nvidia. His research group maintains strong industry connections and has produced influential work in cache coherence protocols, memory systems, and fault-tolerant architectures. He has also authored the widely-used textbook 'A Primer on Memory Consistency and Cache Coherence' (2nd edition). Dr. Sorin leads an active research laboratory focused on next-generation computer architecture challenges, with ongoing projects in hardware acceleration, memory systems, and robot motion planning. His group collaborates with researchers across multiple disciplines including robotics, coding theory, and semiconductor design.

Paolo Papottiمشاهده پروفایل

دانشیار

• Data Management
• Data Integration
• Data Cleaning
• +۳ مورد دیگر

Paolo Papotti is an Associate Professor of Computer Science at EURECOM (France) since 2017, affiliated with the Data Science department. Previously, he was a senior scientist at QCRI (Qatar) and an assistant professor at Arizona State University (USA). He earned his PhD in Computer Science from the University of Roma Tre (Italy) in 2007, following an MEng in Computer Engineering from the same institution in 2003. His research focuses on scalable data management, data integration, data cleaning, and computational fact-checking. Notable contributions include work on knowledge graph rule discovery (Rudik), fact-checking frameworks (Scrutinizer), and data quality systems. His research has been supported by awards such as the 2020 Google Faculty Research Fellowship. Key publications include advancements in table representation learning, LLM-based data querying, and crowdsourced fact-checking validation. His work spans theoretical foundations and practical tools for improving data quality and information trustworthiness.

Andrea Burattinمشاهده پروفایل

دانشیار

• Process Mining
• Business Process Management
• Petri Nets
• +۶ مورد دیگر

Andrea Burattin is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His work bridges formal methods and practical process analysis, focusing on process mining, business process management, and hybrid modeling techniques. He actively contributes to research in healthcare process optimization, streaming data analysis, and system verification through Petri nets and CCS transformations. UN Sustainable Development Goals: Poverty eradication, environmental protection, and prosperity for all (via process optimization) Active projects: Immersive Process Mining (2024-2027), Usability and Understandability of Hybrid Process Models (2018-2021) His research explores large language model integration with process mining, proposing frameworks like Tiramisù for multi-faceted process visualization and PN2CCS for formal model translation. Recent work emphasizes real-time monitoring, conformance checking, and IoT-driven process analytics. Key trends in his publications include: 1) Streaming process mining pipelines (2022-2025); 2) LLM-plan generation frameworks (2024); 3) Formal verification techniques (Petri nets, CCS); 4) Healthcare process modeling (2019-2023); 5) Behavioral pattern analysis in process compliance. Scientific Awards Best Demo Award (2022, 2016) Best Process Mining Dissertation Award (2014) Best Workshop Paper (EDBA and PODS4H, 2023) As advisor, he supervises PhD projects on process mining and hybrid modeling. His editorial roles include Information Systems reviewer (2024-2025) and past editor for Engineering Applications of AI (2022-2023). Collaborations span Denmark, Italy, and the Netherlands.

Yiorgos Makrisمشاهده پروفایل

استاد

• Hardware Security
• Trusted Systems
• Machine Learning in Semiconductor Manufacturing
• +۹ مورد دیگر

Yiorgos Makris is a Professor in the Department of Electrical and Computer Engineering at the Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas, since July 2011. Previously, he was a faculty member at Yale University for over a decade. He holds a Ph.D. in Computer Engineering from the University of California, San Diego, and a Diploma in Computer Engineering from the University of Patras, Greece. Education: Ph.D. in Computer Engineering, University of California, San Diego (2001) M.S. in Computer Engineering, University of California, San Diego (1997) Diploma in Computer Engineering and Informatics, University of Patras, Greece (1995) Research Interests: Hardware Security and Trustworthiness Statistical Side-Channel Fingerprinting Machine Learning in Semiconductor Manufacturing Trusted and Reliable Integrated Circuits Hardware Trojans in Wireless Cryptographic ICs On-Die Learning and Emergent Technologies His work focuses on enhancing hardware security through statistical methods, machine learning, and formal verification, with applications in analog/RF ICs, post-production calibration, and secure IC design. Key Contributions: Co-Founder and Site-PI of NSF CHEST I/UCRC (Hardware and Embedded System Security and Trust) Leader of the Safety, Security, and Healthcare Thrust at TxACE (Texas Analog Center of Excellence) Director of the Trusted and RELiable Architectures (TRELA) Lab Grants and Funding: NSF, NIH, SRC, ARO, AFRL, AFWERX, DARPA, DOE, and industry partnerships with Boeing, Northrop Grumman, IBM, Intel, Qualcomm, etc. Recent grants include projects on DNA storage security, analog neural networks, and malicious hardware detection. Awards and Recognition: IEEE Fellow (2025) Best Paper Awards at DATE'13, VTS'15, DCAS'22 Best Hardware Demonstration Awards at HOST'16 and HOST'18 Erik Jonsson School Faculty Research Award (2020) Labs and Teams: TRELA Lab (Focus: Secure Hardware Design, Trusted Architectures) CHEST I/UCRC (Industry-University Collaboration)

Sudhir R. Paulمشاهده پروفایل

استاد

• Biostatistics
• Statistical Inference
• Generalized Linear Models
• +۳ مورد دیگر

Dr. Sudhir R. Paul is a Professor in the Department of Mathematics and Statistics at the University of Windsor, Faculty of Science. He holds a Ph.D. from Wales and has received prestigious awards including Fellowships from the American Statistical Association (2006) and the Royal Statistical Society (1982). His research focuses on Biostatistics and Statistical Inference, with expertise in areas such as Generalized Linear Models, Clustered/Longitudinal Data Analysis, and Categorical Data Analysis. He has supervised numerous graduate students and maintains an active research program addressing topics like risk difference estimation, bias correction in statistical models, and applications in environmental and medical contexts. Education: Ph.D. (Wales). Research interests span advanced statistical methodologies, including zero-inflated models, measurement error correction, and dose-response modeling. His work bridges theoretical development and practical applications in epidemiology, clinical trials, and environmental studies. His publications reflect contributions to clustered data analysis, interval estimation, and generalized estimating equations. Awards highlight his impact in advancing statistical science through teaching, research, and service. Advising: Over 30 M.Sc. and Ph.D. students have been supervised, with current students engaged in doctoral and master’s research. Postdoctoral fellows include experts in statistical theory and applications.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

Kaiqun Fuمشاهده پروفایل

استادیار

• Spatial Data Mining
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas. Education: Ph.D. in Computer Science, Virginia Tech, 2021 M.S. in Computer Science, Virginia Tech, 2016 Research Interests: His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including: Graph neural networks for traffic incident prediction Social media analysis for urban challenges Physics-informed models for power grid stability Citation forecasting in scientific publications Grants & Projects: NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026) NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026) SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024) Professional Involvement: He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000). Labs/Teams: Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.

Jonathan Hugginsمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Computation
• Statistical Genetics
• +۳ مورد دیگر

Jonathan Huggins is an Assistant Professor at Boston University, affiliated with the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences. He holds a Ph.D. in Computer Science from MIT (2018) and a B.A. in Mathematics from Columbia University (2012). His research focuses on developing fast, trustworthy machine learning and Bayesian methods that balance computational efficiency and statistical optimality, with applications in ecological forecasting and genomic data analysis. Education: Ph.D. in Computer Science, Massachusetts Institute of Technology (2018) B.A. in Mathematics, Columbia University (2012) Research Interests: Large-scale machine learning and Bayesian computation Robust statistical inference Applications in genomics and ecological modeling Algorithmic development for scalable inference Key Projects: Stochastic Methods for Data Science: A book on stochastic processes and algorithms VIABEL: A Python package for variational inference and diagnostics ShorTeX: A LaTeX package for mathematical writing Recent Articles: Focus on scalable Bayesian methods, error bounds for iterative algorithms, and mutational signature discovery. His work emphasizes reproducibility and robustness in statistical inference. Awards: Blackwell–Rosenbluth Award (Outstanding Junior Bayesian Researcher) Grants & Funding: Supported by NIH, NSF, and the Department of Defense. Active in advising students across multiple BU programs. Labs/Teams: Affiliated with the BU URBAN Program, Program in Bioinformatics, and Department of Computer Science.