جست‌وجوی استاد و پژوهشگر

• Electrical Engineering
• Computer Science
• Control Systems
• +۴ مورد دیگر

Kaplienko Oleksandr Olehovych serves as a Senior Lecturer at Zaporizhzhia Polytechnic University, where his academic contributions span electrical engineering and computer science disciplines through teaching software design tools for electromechanical systems, computer engineering, and industrial electronics. His professional focus integrates computational methods with traditional engineering to address challenges in digital transformation and system optimization. His educational credentials include: Zaporizhia National Technical University, Faculty of Electrical Engineering, 2004: Master in Electromechanics (specialty: Electrical machines and devices) Dr. Kaplienko's research encompasses Electrical Engineering, Computer Science, Control Systems, Renewable Energy, Blockchain Technology, Virtual Reality, and Fuzzy Logic, with practical applications in transformer insulation monitoring, warehouse lighting design, indoor navigation systems, and renewable energy modeling. His methodological approach frequently employs neural networks and fuzzy logic to enhance system robustness and predictive accuracy. Analysis of his 2017-2021 publications reveals a pronounced shift toward digital ecosystem innovation, particularly in blockchain implementations for intellectual property management in educational contexts, virtual reality solutions for industrial design, and data-driven frameworks for energy system balancing. This trajectory underscores his commitment to interdisciplinary convergence between legacy engineering practices and emerging computational paradigms. Scientific recognition: No documented scientific awards, fellowships, or medals in the provided materials Regarding academic mentorship and research funding, the source text contains no references to graduate students, grant awards, or sponsored research projects under his direction. No laboratory facilities, research teams, or collaborative institutional partnerships were specified in the available documentation.

• Economic Sciences
• Finance
• Accounting
• +۲ مورد دیگر

Băncilă Natalia is a Professor at the Faculty of Economics of the European University of Moldova. She holds a PhD habilitate in economic sciences and is employed under a cumulative (combined) employment condition. University: European University of Moldova School: Faculty of Economics Department: Economy and Tourism Her research focuses on general economic sciences, with specific interests in finance, accounting, economic policy, and tourism economics. No additional details about her academic activities, students, or publications are available in the provided texts.

• General Surgery
• Medical Education
• Vascular Diseases
• +۵ مورد دیگر

Mitchell A. Cahan, MD is an Adjunct Professor in the Department of Surgery at UMass Chan Medical School, affiliated with the T.H. Chan School of Medicine. He also holds a position at the Tan Chingfen Graduate School of Nursing. His academic career spans multiple disciplines within surgery and medical education. Dr. Cahan received his BA in History and MLS in Library Science from the University of Maryland, College Park, his MD from Johns Hopkins University School of Medicine, and an MBA from the University of Tennessee in 2014. His educational background reflects a unique combination of medical training, historical perspective, and business administration. Dr. Cahan's research interests focus on several key areas of surgical practice and education. His work in General Surgery encompasses acute care, emergency procedures, and surgical education methodologies. He has made significant contributions to understanding Healthcare Disparities , particularly regarding Medicaid versus non-Medicaid patients across various surgical conditions including appendicitis, cholecystitis, and facial fractures. His research in Vascular Surgery includes innovative studies on venous thromboembolism using unique models such as hibernating black bears. Additionally, he has published extensively on Medical Education , developing and evaluating curricula for surgical clerkship students, with a particular focus on human factors training and critical thinking development. Analysis of Dr. Cahan's recent publications reveals consistent themes across surgical care equity, innovative educational approaches, and physiological research with translational applications. His work frequently examines how insurance status affects surgical outcomes and care pathways, while also exploring novel biological models for understanding coagulation. The interdisciplinary nature of his research bridges clinical practice, medical education, and basic science investigation. Dr. Cahan has received numerous prestigious awards throughout his career, including: Multiple Outstanding Medical Educator Awards from University of Massachusetts Medical School classes (2008-2017) Physician Recognition Program Award from UMass Memorial Medical Center (2009) Educational Achievement (Star) Award for Outstanding Individual Achievements in Medical Education (2009) Champions of Excellence Team Award for Open Access Patient Centered Care (2009) Alpha Omega Alpha Honor Medical Society membership (2003) Multiple honor society memberships throughout his academic career Dr. Cahan has been actively involved in surgical education and curriculum development, particularly through his work on human factors training for surgical students and residents. His research collaborations span multiple departments and institutions, with frequent co-authorship with colleagues in surgery, nursing, and medical education. He has contributed to significant advancements in understanding surgical care pathways, particularly for emergency general surgery conditions. While specific laboratory information isn't detailed in the available text, Dr. Cahan's research on hibernating black bears for venous thromboembolism research suggests involvement with specialized physiological research teams. His work in surgical education indicates leadership in simulation-based training programs and curriculum development initiatives at UMass Chan Medical School.

Thomas J. Zegaمشاهده پروفایل

استاد

• Planetary Sciences
• Meteoritics
• Cosmochemistry
• +۳ مورد دیگر

Thomas J. Zega is Professor of Planetary Sciences at the University of Arizona, with joint appointments in the Lunar and Planetary Laboratory and the Department of Materials Science and Engineering. He is a member of the Graduate Faculty and conducts research at the Kuiper Materials Imaging and Characterization Facility. Education: Specific degree details not provided in the text. Research Interests: Professor Zega specializes in the atomic-scale characterization of planetary materials, meteorites , and presolar grains . His work integrates transmission electron microscopy (TEM) , electron energy-loss spectroscopy (EELS) , and focused ion beam (FIB) techniques to unravel the formation and alteration histories of extraterrestrial samples. Key themes include nebular thermodynamics , parent-body hydrothermal alteration , and space weathering . Across his recent publications, Zega has emphasized in-situ analytical techniques applied to asteroidal regolith , calcium-aluminum-rich inclusions (CAIs) , and ordinary and carbonaceous chondrites . Studies of Itokawa particles , Miller Range 07687 , and hibonite thermobarometry highlight an integrated approach combining experimental petrology , isotope cosmochemistry , and quantitative EELS . Awards & Honors: No specific awards listed in the provided text. Advising & Grants: Professor Zega mentors graduate students as part of the UA Graduate Faculty. While individual student names are not listed, he is active in training the next generation of planetary scientists and engineers through hands-on work at the Kuiper facility. Laboratories & Teams: He leads research efforts within the Kuiper Materials Imaging and Characterization Facility , a state-of-the-art laboratory for coordinated electron and ion microscopy of planetary materials.

• Immunology
• B Cell Biology
• Antibody Responses
• +۴ مورد دیگر

Ashley Priddey is a Postdoctoral Researcher in the Bannard Group (B Cell Immunology) at the MRC Weatherall Institute of Molecular Medicine (WIMM), part of the Nuffield Department of Medicine within the University of Oxford's Medical Sciences Division. Her work focuses on deciphering the germinal centre reaction mechanisms that generate high-affinity antibody responses critical for infection defense and disease prevention. She earned her PhD and completed her initial postdoctoral training at the University of Cambridge under Dr. Vasilis Kosmoliaptsis, where she developed innovative antibody characterization methodologies to enhance immunological risk assessment in solid organ transplantation. Her research centers on B cell dynamics within germinal centres, specifically investigating somatic hypermutation, clonal expansion, selection processes, and inter-zonal B cell cycling that produce affinity-matured plasma cells and memory B cells. This work bridges fundamental immunology with translational applications in vaccine design and disease progression understanding, emphasizing humoral immunity mechanisms. No scientific awards were documented in the source material. Information regarding student supervision or research grant funding was not provided in the available text. Dr. Priddey operates within the Bannard Group at the MRC WIMM's Target Identification Division Unit (TIDU), where her laboratory work examines germinal centre microanatomy and B cell fate decisions to advance therapeutic interventions for immune-related conditions.

Michael Langbergمشاهده پروفایل

استاد

• Information Theory
• Coding Theory
• Network Communication
• +۶ مورد دیگر

Michael Langberg is a Professor in the Department of Electrical Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research centers on the mathematical foundations of information, focusing on algorithmic and combinatorial challenges in communication, data representation, and storage systems. Langberg's research spans information theory, coding theory, network communication, and network coding, with significant contributions to 'Big Data' representation via geometric clustering and probabilistic methods in combinatorics. His work addresses fundamental problems in point-to-point communication, network reliability, and succinct data structures, emphasizing theoretical rigor while maintaining practical relevance for modern communication systems. Key themes include adversarial channel modeling, secure network coding, and energy-efficient communication protocols. His recent publications (2023-2025) reveal a strong trajectory in adversarial channel theory, secure network coding, and privacy-preserving data analysis. Langberg consistently bridges theoretical information theory with combinatorial optimization, particularly in characterizing capacity limits under adversarial constraints, developing computationally efficient coding schemes, and exploring differential privacy applications in group testing. His work demonstrates increasing interdisciplinary reach, connecting traditional information theory with machine learning systems like federated learning over O-RAN architectures. Scientific Awards: IEEE Fellow Langberg teaches core electrical engineering courses including EE 178 (Digital Principles), EE 439/539 (Information Theory and Coding), and EE 531 (Probability and Stochastic Processes), with consistent offerings through Spring 2026. While the provided text lacks specifics on research grants or student advising beyond course instruction, his extensive publication record indicates active leadership in theoretical communications research. No laboratory or research team affiliations are explicitly mentioned in the source material.

• AI Explainability
• AI Transparency
• Web Science
• +۳ مورد دیگر

Marco Brambilla is a Full Professor of Computer Science and Engineering at Politecnico di Milano, Italy. He leads the Data Science Lab within the DEIB (Department of Electronics, Information and Bioengineering) and serves as director of the Computer Science and Engineering B.Sc. and M.Sc. curricula at the university. His research focuses on AI Explainability and Transparency , Web Science , Big Data Analysis , Social Media Analytics , and Model-driven Development . He is the co-inventor of the Interaction Flow Modeling Language (IFML) standard by the OMG and holds 2 patents on crowdsourcing and multi-domain search. His recent scholarly work demonstrates expertise across multiple domains of computer science, with particular emphasis on graph-based RAG architectures for energy efficiency, human-AI interaction studies, and interpretable network visualizations for CNN-based image classification. His publications show a consistent focus on practical applications of theoretical computer science concepts. Co-inventor of IFML standard by OMG 2 patents on crowdsourcing and multi-domain search GSE Academic Award for Excellence for correlating twitter sentiment analysis and stock price variations Author of multiple books including Model Driven Engineering in Practice , Web Information Retrieval , and Model-Driven UI Engineering of Web and Mobile Apps with IFML Professor Brambilla has been involved in the creation of four startups: WebRatio, Servitly, Fluxedo, and Quantia. He teaches Enterprise ICT Architectures, Systems and Methods for Big and Unstructured Data, Web Science, Digital Innovation Lab, and Model-driven Engineering courses. His Data Science Lab at Politecnico di Milano drives research in multiple cutting-edge areas of computer science and AI.

Haileleol Tibebuمشاهده پروفایل

استادیار

• Responsible AI
• AI Policy and Governance
• Algorithmic Fairness
• +۳ مورد دیگر

Haileleol Tibebu serves as a Teaching Assistant Professor in the School of Information Sciences at the University of Illinois Urbana-Champaign, holding a PhD in Artificial Intelligence from Loughborough University London. His office is located in Room 5152 at 614 E. Daniel St., with contact via htibebu@illinois.edu. His educational background centers on Artificial Intelligence, culminating in a PhD from Loughborough University London. Professor Tibebu's research investigates the societal dimensions of AI systems, with emphasis on Responsible AI frameworks , algorithmic fairness , and AI policy governance . He examines AI's impact on marginalized communities while developing accountability mechanisms and ethical deployment models. His work bridges technical innovation with regulatory frameworks to ensure equitable AI applications across public and private sectors. Recent publications (2021-2025) reveal an evolution from computer vision and autonomous systems toward critical AI studies, with current work dominating in ethical frameworks, bias mitigation, and societal governance. The 2025 papers specifically address intersectional bias and decision-support accountability, signaling a strategic pivot toward policy-relevant AI research. Key recognitions include: Certificate of Honor, IEEE IAS (2023) EPSRC National Productivity Improvement Fund (2019) Dean’s Enterprise Award, Loughborough University (2018) He secured the EPSRC National Productivity Improvement Fund grant in 2019 and actively mentors students, evidenced by participation in the 2025 Undergraduate Research Symposium. His Fall 2025 teaching load includes six sections of Ethics & Policy for Data Science and Machine Learning Cloud courses. As a 2025 Public Voices Fellow, he engages in public discourse on AI's societal implications.

Julia Hockenmaierمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Grounded Language Processing
• +۴ مورد دیگر

Julia Hockenmaier is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Her research focuses on Natural Language Processing (NLP), particularly in grounded language processing, collaborative dialogue systems, and vision-language interfaces. She leads a research group exploring linguistically expressive yet computationally tractable grammars for wide-coverage parsing. Willett Faculty Scholar award recipient Published extensively in ACL, EMNLP, and JAIR venues Active in interdisciplinary research connecting NLP with computer vision and robotics Her recent work investigates the limitations of large language models in code understanding and the role of grounded dialogue systems in Minecraft environments. She has advised numerous PhD and MS students, including Alice Lai, Yonatan Bisk, and Rajarshi Haldar. Key article trends include semantic code search, multi-modal NLP, and grammar induction. Her scientific contributions span from computational linguistics to protein folding analysis with awards recognizing her faculty excellence.

Vishal M. Patelمشاهده پروفایل

استادیار

• Signal and image processing
• Computer vision
• Machine learning
• +۲ مورد دیگر

Vishal M. Patel is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, with research focusing on signal processing, computer vision, and biomedical applications. His work bridges machine learning with medical imaging and biometrics. Ph.D. in Electrical Engineering, University of Maryland, College Park (2010) Dr. Patel's research centers on medical image analysis, surgical video processing, and bias mitigation in generative models. He develops diffusion-based frameworks for ophthalmic applications like cataract surgery analysis and nystagmus video generation, while advancing face recognition systems and image restoration techniques under unconstrained conditions. His recent work integrates vision-language models for zero-shot classification in satellite imagery and maritime surveillance. His 2025 publications reveal a strong emphasis on medical AI, particularly in surgical video understanding and neurological disease phenotyping using eye movement data. Key trends include physics-aware video generation guided by large language models, identity-preserving face restoration, and robust bias mitigation in diffusion-based synthesis. The work consistently addresses real-world challenges in medical imaging, remote sensing, and biometric security. Major recognitions include: 2016 ONR Young Investigator Award 2016 Jimmy Lin Award for Invention A. Walter Tyson Assistant Professorship Award Best Paper Awards at IEEE AVSS 2017 and IEEE BTAS 2015 Two Best Student Paper Awards at IAPR ICPR 2018 Best Poster Awards at BTAS 2015 and 2016 As an Associate Editor for IEEE Signal Processing Magazine and IEEE Biometrics Compendium, Dr. Patel shapes scholarly discourse in his field. His ONR Young Investigator Award supports high-impact research in active learning for surgical videos, while editorial roles and technical committee memberships demonstrate leadership. Though student names aren't specified, his lab trains graduate researchers in medical computer vision. Dr. Patel leads a research laboratory focused on computer vision for biomedical applications, developing frameworks like GenVOG for nystagmus video generation and StepAL for surgical step detection. The lab pioneers diffusion models for medical image synthesis, surgical skill assessment, and robust biometric systems, with strong ties to ophthalmology and neurology applications.

• Minimally Invasive Surgery
• Gastrointestinal Surgery
• Esophageal Surgery
• +۵ مورد دیگر

Dr. Ellen H. Morrow, MD, MS, FACS is a Clinical Associate Professor in the Department of Surgery at the University of Utah School of Medicine. She practices at both University of Utah Hospital and Veteran's Hospitals, specializing in minimally invasive gastrointestinal, foregut, and bariatric surgery. With expertise in esophageal surgery and pathophysiology diagnosis, Dr. Morrow performs high-volume laparoscopic procedures for reflux, paraesophageal hernias, and achalasia, and is certified in weight loss surgery including gastric bypass and sleeve gastrectomy. Dr. Morrow received her B.S. in Molecular, Cellular, & Developmental Biology from Yale University, her M.D. from Stanford University Medical School, completed her Surgery Residency at Stanford University Hospital, pursued a Senior Fellowship in Minimally Invasive Surgery at University of Washington, and earned an M.S.C.I. in Clinical Investigation from University of Utah. Her educational journey reflects a progressive specialization in surgical techniques and clinical research methodology. Her research focuses on minimally invasive gastrointestinal surgery outcomes, particularly in bariatric procedures and esophageal disorders. She investigates patient-reported outcomes, surgical techniques for hiatal hernias, and the psychological aspects of both surgical patients and healthcare providers. Notably, her work extends beyond traditional surgical research to include studies on healthcare professional well-being, burnout, and resilience during the pandemic, demonstrating a holistic approach to surgical care that considers both patient and provider mental health. Analysis of Dr. Morrow's recent publications reveals a strong pattern connecting surgical outcomes with psychological factors. Her research spans from technical surgical questions (like hiatal hernia repair during gastric bypass) to broader healthcare system issues (like provider well-being measurement). This interdisciplinary approach bridges clinical surgery with psychological science, particularly evident in her mindfulness intervention studies for healthcare professionals and resilience research during the pandemic. American Board of Surgery Certification Dr. Morrow provides patient-centered care with a focus on helping patients improve their health and lives through surgical intervention. While specific grant information isn't detailed in the provided text, her participation in the PCORnet Bariatric Study Collaborative indicates involvement in large-scale, multi-institutional research. Her clinical practice encompasses minimally invasive gastrointestinal surgery, gastric/esophageal surgery, upper gastrointestinal tract surgery, and hernia surgery using open, laparoscopic, and robotic techniques. Patient reviews generally rate her highly (4.9/5 based on 52 reviews), noting her knowledge, experience, and thorough explanations, though some comments indicate variability in patient experiences regarding communication style.

Nicholas Kavoussiمشاهده پروفایل

استادیار

• Minimally Invasive Surgery
• Image-Guided Surgery
• Robotic Surgery
• +۴ مورد دیگر

Nicholas Kavoussi, MD serves as an Assistant Professor in the Department of Urology at Vanderbilt University School of Medicine, specializing in minimally invasive surgical techniques through the Division of Endourology and Stone Disease. He is an active member of the Vanderbilt Institute of Surgery and Engineering (VISE), driving interdisciplinary collaboration between surgical innovation and engineering disciplines. His academic foundation includes: B.S. in Applied Mathematics, Columbia University School of Engineering and Applied Science (2008) M.D., State University of New York Downstate College of Medicine (2013) Residency in Urology, University of Texas Southwestern Medical Center (2018) Fellowship in Endourology and Minimally Invasive Surgery, Vanderbilt University Medical Center (2020) Dr. Kavoussi's research program centers on the integration of advanced technologies into urologic surgery, with particular emphasis on image-guided procedures and surgical innovation. His work bridges engineering principles with clinical urology to develop novel solutions for kidney stone disease, prostate conditions, and urologic cancers. He actively investigates real-time optical tracking systems, computer vision applications for tumor segmentation, and machine learning models for predicting stone disease complications. Analysis of his recent publications reveals a cohesive trajectory in surgical technology translation, consistently applying engineering methodologies to enhance precision in endoscopic and robotic procedures. His 2021-2024 work demonstrates progressive refinement of image-guided navigation systems, with increasing sophistication in artificial intelligence applications for intraoperative decision support and surgical outcome optimization across stone disease, prostate surgery, and kidney cancer interventions. No scientific awards or fellowships are documented in the provided materials. While student mentorship details are unavailable, Dr. Kavoussi's research is conducted within Vanderbilt's collaborative ecosystem, leveraging institutional resources for surgical innovation without explicit grant documentation in the source texts. As a core contributor to Vanderbilt's Robotic Surgery, Medical Devices, and Image-Guided Surgery research group within VISE, he participates in developing next-generation surgical tools and navigation systems, maintaining active partnerships with engineering faculty to advance the integration of real-time data analytics into the surgical workflow for complex urologic conditions.

Alexander Eitelمشاهده پروفایل

استاد

• Multimedia Learning
• Educational Technology
• Cognitive Science
• +۵ مورد دیگر

Prof. Alexander Eitel is a Professor in the Department of Educational Psychology at Justus-Liebig-Universität Gießen. His research focuses on multimedia learning, seductive details, video-based learning, and teacher education. He leads projects funded by the DFG and Robert-Bosch Stiftung, exploring topics like self-regulated learning and emotional design effects. His work emphasizes the cognitive and instructional factors influencing learning outcomes, particularly in digital environments. Collaborations include researchers from Freiburg, Erfurt, and Tübingen. Key research interests include: (1) How multimedia elements affect learning retention and cognitive load; (2) The impact of seductive details on instructional effectiveness; (3) Applications of eyetracking for understanding learning processes. Current projects investigate boundary conditions of seductive details and adaptive refutation strategies for teacher misconceptions. Publications span journals like Journal of Computer Assisted Learning and Applied Cognitive Psychology , addressing topics from testing effects to instructional signal design. His team includes postdoctoral researchers like Dr. Marie-Christin Krebs and M.Sc. Anna-Sophia Dersch, focusing on teacher training and educational technology innovation.

David Cruz-Uribeمشاهده پروفایل

استاد

• Harmonic Analysis
• Partial Differential Equations
• Weighted Inequalities
• +۳ مورد دیگر

David Cruz-Uribe is a Professor in the Department of Mathematics at the University of Alabama. His research focuses on harmonic analysis, with emphasis on weighted and variable exponent Lebesgue spaces, extrapolation theory, and applications to partial differential equations, particularly degenerate elliptic equations. He has authored/co-authored multiple books, including The Stieltjes Integral (2023) and Variable Lebesgue Spaces: Foundations and Harmonic Analysis (2013). His work bridges abstract harmonic analysis with practical applications in PDEs, contributing to the understanding of maximal operators, singular integrals, and fractional operators in nonhomogeneous spaces. His research interests include the study of classical harmonic analysis operators (maximal functions, Hilbert transform, fractional integrals) on weighted spaces, and their role in solving degenerate elliptic and parabolic equations. He also explores the interplay between weighted norm inequalities and functional spaces like Orlicz and variable exponent spaces. Key contributions include advancements in the theory of Rubio de Francia extrapolation, reverse Hölder inequalities for matrix weights, and the development of modular inequalities in variable Lebesgue spaces. These tools underpin his work on PDEs, where he addresses regularity of solutions and existence under non-standard conditions. He has secured grants such as the RUI: Harmonic Analysis on Weighted Lebesgue Spaces (2014). His publications span over two decades, reflecting sustained innovation in his field. While no specific awards are listed, his extensive publication record and authorship of foundational texts highlight his scholarly impact.

Xinwei Dengمشاهده پروفایل

استاد

• Statistical Learning
• Uncertainty Quantification
• High-Dimensional Data Analysis
• +۷ مورد دیگر

Xinwei Deng is a Professor of Statistics and Data Science Faculty Fellow at Virginia Tech, affiliated with the Department of Statistics in the College of Science. He also co-directs the VT Statistics and Artificial Intelligence Laboratory (VT-SAIL). He earned his Ph.D. from Georgia Institute of Technology in 2009 under Professors C.F. Jeff Wu and Ming Yuan, and joined Virginia Tech in 2011. Education: PhD in Statistics (Georgia Tech, 2009), B.S. in Mathematics (Nanjing University, China, 2003). Research Interests: Focuses on the interface between experimental design and machine learning, uncertainty quantification, covariance matrix estimation, high-dimensional data analysis, and applications in nanotechnology, manufacturing, and healthcare. His work bridges statistical theory with practical challenges in emerging technologies like tissue engineering and environmental science. Publications: Over 80 peer-reviewed articles in top journals such as Journal of the American Statistical Association , Technometrics , and Proceedings of the National Academy of Sciences . Recent work emphasizes scalable algorithms, AI resilience in manufacturing, and uncertainty quantification in complex systems. Awards/Honors: Includes Data Science Faculty Fellowships, ISI election (2017), and multiple best paper awards. Recognized for contributions to statistical methodologies in quality engineering and AI robustness. Teaching & Advising: Teaches advanced courses like Data Analytics, Experimental Design, and Multivariate Analysis. Mentors students in interdisciplinary projects spanning statistics, computer science, and engineering. Labs/Teams: Leads VT-SAIL, fostering AI-driven statistical solutions for real-world problems. Collaborates with industry partners (e.g., P&G, Deloitte) and government agencies (NSF, AFRL) on applied research.