جست‌وجوی استاد و پژوهشگر

Kexin Rongمشاهده پروفایل

استادیار

• Database systems
• Machine learning
• Data analytics
• +۳ مورد دیگر

Kexin Rong is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. She leads the D2I Lab, which is part of the Georgia Tech database group, and maintains an affiliation with VMware Research Group as an affiliated researcher. Dr. Rong received her Ph.D. in Computer Science from Stanford University in 2021 (advised by Peter Bailis and Philip Levis) and her B.S. in Computer Science from California Institute of Technology in 2015. Her academic journey includes recognition as an EECS Rising Star by UC Berkeley and an Honorable Mention for the SIGMOD Jim Gray Doctoral Dissertation Award. Her research focuses on democratizing data science through systems that simplify large-scale data analytics for non-experts. She synthesizes techniques from data management, machine learning, and human-computer interaction to improve both computational and human efficiency. Current research directions include analytics over unstructured data (Video Labeling with SketchQL and Image Understanding with VCR) and data infrastructure for AI (Data preprocessing with LOTUS and DiffPrep, Checkpointing for DL training with Inshrinkerator). Her recent publications show a strong trend toward building user-friendly systems that bridge complex data analytics and non-expert users, with significant impact demonstrated by adoption of systems like ASAP by industry players including Datadog and TimescaleDB for time series visualizations. Amazon Research Award recipient (2024) SIGMOD 2024 Distinguished PC Award Multiple NSF awards (2023-2024) Best Paper Finalist at IISWC 2024 Catherine M. and James E. Allchin Early Career Professorship Dr. Rong actively mentors students, with recent PhD graduates including Peng Li (VLDB'23 Best Paper Award winner), Hantian Zhang, and Renzhi Wu now at leading tech companies. She teaches core database courses including CS6400 Database Systems Concepts and Design and CS4440 Emerging Database Technologies. The D2I Lab under her direction is committed to providing an inclusive environment that welcomes applicants from diverse backgrounds, especially those underrepresented in computer science.

• Biomedical Engineering
• EEG Signal Processing
• Machine Learning
• +۵ مورد دیگر

Marin Kołodziej is an Associate Professor at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems within the Faculty of Electrical Engineering at Warsaw University of Technology. His research program bridges biomedical engineering with artificial intelligence, focusing on practical applications in healthcare and human-computer interaction systems. He maintains an active laboratory presence in room GE 205 and leads the Biomedical Signal Processing and Analysis Team. Research Focus His primary research domains include: Biomedical signal processing with specialization in EEG analysis Machine learning applications for emotion recognition from speech and facial expressions Deep learning architectures (CNN, LSTM) for biomedical signal denoising Computer vision systems for agricultural and safety applications Brain-computer interface development and optimization Epileptic seizure detection using intracranial electroencephalography Publication Trends Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on hybrid deep learning approaches, particularly CNN-LSTM combinations for EEG artifact removal and emotion recognition. His work shows increasing interdisciplinary reach, with computer vision applications emerging in agricultural technology (seed detection) and driver safety systems (fatigue detection). The consistent thread through all publications is the application of advanced signal processing techniques to solve real-world biomedical challenges. Academic Impact Dr. Kołodziej maintains strong bibliometric indicators with an h-index of 14 (Scopus) and 11 (Web of Science), Total SNIP of 35.471, and Total CiteScore of 144.57. His 101 publications and 182 supervised theses reflect substantial scholarly output and mentorship impact. The Cumulative ministry score of 3,067 places him among the institution's highly productive researchers. Academic Leadership As leader of the Biomedical Signal Processing and Analysis Team, he directs research on advanced algorithms for biomedical applications. His 7 documented activities suggest engagement in academic service and collaborative projects, though specific grant details are not provided in source materials. His extensive thesis supervision (182 theses) demonstrates significant commitment to training the next generation of engineers and researchers.

Francisco Charte Ojedaمشاهده پروفایل

دانشیار

• Machine Learning
• Multilabel Classification
• Autoencoders
• +۶ مورد دیگر

Francisco Charte Ojeda is a Titular de Universidad (Associate Professor) at the University of Jaén, Spain, working in the Department of Computer Science within the Faculty of Experimental Sciences. He is affiliated with the Andalusian Inter-University Institute in Data Science and Computational Intelligence and leads research in the Intelligent Systems and Data Mining group. His academic journey includes earning a doctorate from the University of Granada with a thesis on hybrid flexible computing methods for multilabel classification. His research interests span machine learning with particular emphasis on multilabel classification, autoencoders, feature learning, time series forecasting, and dimensionality reduction. Professor Charte Ojeda has developed several R packages including mldr, mldr.datasets, ruta, and predtoolsTS that have become valuable tools in the machine learning community. His work often focuses on solving practical challenges in data science through innovative algorithmic approaches and software implementations. Analysis of his recent publications reveals a strong focus on autoencoder architectures, multilabel learning techniques, and time series forecasting methods. His research demonstrates a consistent pattern of developing practical software tools alongside theoretical contributions, bridging the gap between academic research and real-world applications. The trend shows increasing emphasis on explainable AI, ensemble methods, and efficient implementations for big data environments. Professor Charte Ojeda has been involved in multiple research projects funded by Spanish national grants (PID2019-107793GB-I00/AEI, TIN2015-68854-R). His collaborative network includes prominent researchers in the field such as Antonio J. Rivera, Francisco Herrera, and María J. del Jesús. He maintains an active presence in academic communities through his personal website (fcharte.com), GitHub repositories, and contributions to open-source software. His educational contributions include textbooks on programming languages, operating systems, and computational tools, reflecting his commitment to both research and teaching excellence.

• R Programming
• Data Visualization
• Cloud Computing
• +۴ مورد دیگر

Pavol Sojka is a PhD student at the Department of Applied Informatics , Faculty of Economic Informatics , University of Economics in Bratislava. He teaches courses such as Algoritmy a programovanie 1 , Jazyk R , and Python , focusing on programming and data analysis.

• Control Systems
• Wireless Communications
• Network Optimization
• +۳ مورد دیگر

Dr. Michael Reyer is a Professor and holds the Chair of Intelligent Control Systems at the Faculty of Electrical Engineering and Information Technology, RWTH Aachen University. He serves as Deputy Chair and is based at the ICT Cube 1 in the Electrical Engineering building (room 302) at Kopernikusstr. 16, Aachen. His primary research areas include: Control Systems and Intelligent Automation Wireless Communications (LTE, OFDM, MIMO) Network Optimization and Resource Allocation Machine Learning for Communication Networks Natural Language Processing for Social Media His publication record from 2011 to 2021 demonstrates a dual focus: advancing wireless network technologies (including cognitive radio, LTE planning, and MIMO channel modeling) and developing NLP techniques for social media analysis. This interdisciplinary work bridges electrical engineering and computer science, with applications in 4G/5G networks and user-generated content processing. As head of the Chair of Intelligent Control Systems, he leads research efforts in intelligent control and communication systems at RWTH Aachen University.

• Spatial data management
• Spatio-temporal statistics
• Machine learning in GIS
• +۳ مورد دیگر

Felix Kunde is a Researcher at the Beuth University of Applied Sciences Berlin, contributing to the ExCELL and MAGDa projects. He holds a Bachelor's degree in Geography and a Master's degree in Geoinformation Science. His professional experience includes roles as a database developer for 3D city models and teaching geodatabase systems at universities. He actively participates in conferences such as FOSSGIS, FOSS4G, and WhereCamp, presenting work on topics like traffic forecasting, geospatial data management, and PostGIS extensions. Research interests focus on spatial data management (RDBMS/NoSQL/HDFS), spatio-temporal statistics, linear referencing, machine learning in GIS, and 3D city modeling. Current projects address short-term traffic forecasting with STARIMA/SVM/ANN methods, handling missing data in time series, and scalable geostatistical algorithms on Hadoop. He also works on open data models for traffic in OpenStreetMap and mobile databases using GeoPackage. Education: Bachelor of Geography Master of Geoinformation Science Key Contributions: Co-developer of pgMemento for spatial database auditing Contributor to the 3DCityDB project Lead researcher on ExCELL mobility platform Teaching: Lectures on geodatabases at multiple universities PostGIS workshops at conferences His publications span topics from geodatabase systems to traffic prediction algorithms, with notable work in spatial-temporal data analysis and geoinformatics applications. He advises multiple students on projects related to mobility analytics, data mining, and geospatial technology integration.

Laurent LE BRUSQUETمشاهده پروفایل

دانشیار

• Multivariate Data Analysis
• Tensor Decomposition
• Statistical Learning
• +۶ مورد دیگر

Laurent LE BRUSQUET is an Associate Professor at CentraleSupélec's Laboratoire des Signaux et Systèmes (L2S), specializing in multivariate data analysis, tensor decomposition, and statistical learning. His research focuses on applications in health informatics, energy systems, and Industry 4.0, with notable contributions to the RGCCA package and exposome research. He collaborates on the AP-HP/INRIA/CentraleSupélec chair addressing frailty in aging. Active in teaching, he leads courses on machine learning, optimization, and multivariate data analysis for engineering students. His team includes PhD students Abdelhak Boudehane, Fabien Girka, and Lucas Sort. Research Interests: Multiway data analysis, tensor factorization, joint analysis of heterogeneous data, health biomarkers, and stochastic modeling. Key Projects: ATHLETE (exposome research), 3D stochastic dosimetry for electromagnetic fields, and UAV path optimization. Publications span journals like Biometrics and IEEE Journal of Electromagnetics , emphasizing methodological advancements and interdisciplinary applications. Teaching: Courses on statistics, machine learning, optimization, and probability across CentraleSupélec's engineering programs. Labs/Teams: Modelling and Estimation team, SYCOMORE, Inverse Problems Group.

• Computer Vision
• Deep Learning
• Point Cloud Processing
• +۳ مورد دیگر

ABOUELAZIZ Ilyass is a Researcher-Lecturer at CESI, specializing in Computer Sciences. He holds a PhD from Mohammed V University (2014-2020) and a Master's in Computer Sciences and Telecommunications (2012-2014). His research focuses on Point Cloud Processing, Mesh Quality Assessment, Deep Learning, Building Information Modeling, and Computer Vision. He currently supervises PhD candidate Hafsa Benallal on Point Cloud Segmentation and Denoising for BIM. PhD Topic: No-reference mesh visual quality assessment using deep learning and visual saliency Master's Topic: Computer Sciences and Telecommunications Teaching activities include courses on Algorithms, Web Development, Programming (C, JAVA, Python), Artificial Intelligence, and Image Processing at 2nd to 5th-year engineering levels. He employs active pedagogy, lectures, and practical sessions (TP/TD). Scientific contributions include 20+ peer-reviewed publications in venues like IEEE Access, Pattern Recognition, and conferences such as ICIP and SITIS. He serves as a reviewer for Optics Express and IEEE ICIP, and has organized sessions at ATSIP (2017), SETIT (2016-2017), and URAC Day (2015). Research highlights include pioneering work on graph-based deep learning for mesh quality assessment and developing outlier-removal techniques for point clouds. His work bridges computer vision with sustainable energy solutions, such as photovoltaic prediction and building decarbonization. Future projects emphasize BIM integration with deep learning for smart building applications and enhancing 3D mesh quality metrics using novel neural architectures.

Zahra Nematzadehمشاهده پروفایل

استادیار

• Machine Learning in Medical Applications
• Digital Biomarkers
• AI-Driven Healthcare Solutions
• +۴ مورد دیگر

Zahra Nematzadeh is an Assistant Professor at the Department of Electrical Engineering and Computer Science, Florida Institute of Technology. Her work bridges machine learning and healthcare, focusing on digital biomarkers and AI-driven medical solutions. She holds a PhD and Master's in Computer Science from Universiti Teknologi Malaysia. Education: PhD in Computer Science, Universiti Teknologi Malaysia Master's in Computer Science, Universiti Teknologi Malaysia Research interests include machine learning applications in medical diagnostics (e.g., laryngeal cancer detection via voice analysis), digital biomarkers for early disease detection, and interdisciplinary AI solutions. Her methods often involve hybrid models combining clustering algorithms (e.g., k-means, fuzzy C-means) with classification techniques. Publications span noise detection in datasets, medical classification systems (e.g., breast cancer diagnostics), and workflow reliability analysis using Petri nets. Recent work emphasizes high-dimensional medical data analysis through genetic algorithms and feature selection. Advising/Grants: No specific grants or advisees listed, but her research likely involves mentoring students in AI/healthcare projects. Lab involvement not detailed in provided text.

Joe Stanleyمشاهده پروفایل

استاد

• Computational Intelligence
• Biomedical Image and Signal Processing
• Data Fusion
• +۳ مورد دیگر

Dr. Joe Stanley is a Professor and Associate Chair of Academic Affairs and Computer Engineering Coordinator in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology (Missouri S&T). He holds a PhD in Computer Engineering and Computer Science from the University of Missouri-Columbia. His research focuses on biomedical image/signal processing, computational intelligence, and medical informatics, with a strong emphasis on developing tools for clinical decision support. He has advised 30 MS and 8 PhD students and mentored 11 undergraduates. Stanley is a recipient of numerous awards, including the IEEE-HKN C. Holmes MacDonald Outstanding Teaching Award (2016) and multiple teaching excellence recognitions from Missouri S&T. Education: PhD in Computer Engineering and Computer Science (UM-Columbia), MS/BS in Electrical Engineering (UM-Columbia). Research Interests: Computational Intelligence, Biomedical Image/Signal Processing, Machine Learning/Deep Learning, Medical Informatics, and STEM Education. Collaborations include the National Library of Medicine and development of tools for dermatology, pathology, and cervical cancer analysis. Awards: Over 10 teaching awards, including Sustained Excellence in Teaching (2019), IEEE Outstanding Educator (2015–2016), and involvement in K-12 STEM outreach through Project Lead the Way. Grants and Contributions: Led NSF-funded initiatives, developed algorithms for medical image analysis, and co-authored over 50 peer-reviewed publications. Active in academic advising and curriculum development, including the LEAD program for students. Labs/Teams: Image Processing Laboratory at Missouri S&T, collaborating on humanitarian demining, traffic monitoring, and bioinformatics projects. Co-inventor of patents in melanoma diagnosis.

Kelly Streetمشاهده پروفایل

استادیار

• Biostatistics
• Computational Biology
• Single-Cell RNA Sequencing
• +۷ مورد دیگر

Kelly Street is an Assistant Professor in the Division of Biostatistics at the Keck School of Medicine of the University of Southern California (USC), where she conducts research at the intersection of biostatistics, computational biology, and genomics. Her work focuses on developing statistical and computational methods for analyzing high-dimensional biological data, particularly from single-cell sequencing technologies. Her educational background includes a PhD in Biostatistics with a Designated Emphasis in Computational Biology (2018), an MA in Biostatistics (2014), both from the University of California, Berkeley, and a BS in Mathematics (2012) from the University of Chicago. Kelly Street's research interests center on single-cell RNA sequencing (scRNA-seq), with a strong emphasis on trajectory inference to model continuous biological processes such as cell differentiation. She develops methods for differential expression and differential progression across conditions, enabling comparisons of developmental trajectories between experimental groups. Her work extends to multi-omics integration , combining data from diverse assays including ATAC-seq, WGBS, CyTOF, and TCR-seq to gain a more comprehensive understanding of cellular states and functions. She is an active contributor to the Bioconductor project, creating and maintaining software tools that support reproducible and scalable single-cell data analysis. The analysis of her recent publications reveals a consistent focus on improving computational frameworks for single-cell genomics, particularly in modeling dynamic biological processes, enhancing data quality (e.g., CyTOF data cleaning), and enabling cross-condition comparisons in trajectory analysis. Her work bridges statistical theory with practical applications in immunology and cancer research. Invited speaker at major institutions including MD Anderson Cancer Center, Harvard T.H. Chan School of Public Health, and Northwestern University. Regular presenter at BioC and EuroBioc conferences, highlighting her active role in the Bioconductor community. Organizer and instructor for educational workshops such as the Healthcare Innovation Replicathon and Bioconductor training sessions. Kelly Street has advised and collaborated on training programs for undergraduate and graduate students through REU programs and has contributed to open-source software development. While specific grant details are not mentioned, her active research program and workshop leadership suggest ongoing funding support. She has also collaborated with researchers at Dana-Farber Cancer Institute, as indicated by her email address and seminar presentations. Her laboratory or research group appears to focus on developing statistical methods and software for single-cell analysis, likely involving a team of graduate students, postdocs, and collaborators working on trajectory inference, multi-omics integration, and immune profiling applications.

• Empirical Software Engineering
• Code Quality Analysis
• Software Maintenance
• +۷ مورد دیگر

Rocco Oliveto is a Professor in the Department of Computer Science at the University of Salerno, Italy, with a distinguished research career spanning over two decades in empirical software engineering. His work bridges theoretical software engineering principles with practical applications, with recent expansion into healthcare informatics and machine learning applications. His research interests focus on code quality assessment, software maintenance practices, developer behavior analysis, and empirical studies of software engineering phenomena. He has made significant contributions to understanding code smells, bug prediction, API compatibility issues, and more recently, container technologies and smart contract analysis. His recent work demonstrates a strategic expansion into healthcare applications, leveraging software engineering techniques for medical diagnostics and rehabilitation systems. Oliveto's publication pattern shows consistent productivity with multiple high-impact publications each year across top venues including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, and Empirical Software Engineering journal. His recent articles (2023-2025) reveal a growing interest in applying software engineering techniques to healthcare domains while maintaining strong contributions to core software engineering topics. The research demonstrates sophisticated methodological approaches combining empirical studies with machine learning techniques. His collaborative network includes prominent researchers such as Simone Scalabrino, Gabriele Bavota, and Andrea De Lucia, with whom he has co-authored numerous high-impact publications. This collaboration spans both traditional software engineering topics and emerging interdisciplinary applications in healthcare.

Hongyu Zhangمشاهده پروفایل

مدرس

• Geographic Information Science (GIScience)
• GeoAI
• Geoprivacy
• +۷ مورد دیگر

Hongyu Zhang is a Lecturer in the Department of Earth, Geographic, and Climate Sciences at the University of Massachusetts Amherst, where he contributes to the Geographic Information Science and Technology (GIST) program. He is based at the Mount Ida Campus and is actively engaged in research and teaching at the intersection of geography, technology, and ethics. Education: PhD in Geography, McGill University, 2024 MSc in Geography, Western University, 2017 Bachelor of Environmental Studies (BES) in Geomatics, University of Waterloo, 2015 (with minor in Computer Science and Diploma of Excellence in GIS) Hongyu's research focuses on geoprivacy , GeoAI , and the ethical dimensions of spatial data . He investigates how individuals disclose location information on social media, particularly in Chinese digital environments like Weibo, using mixed methods to understand the sociotechnical dynamics of privacy. His work aims to promote responsible spatial data science by bridging GIScience with human behavior and digital ethics. His recent publications and open-source contributions reflect a strong trend in social media analysis , geoprivacy discourse , and computational ethics . He develops tools and datasets to analyze privacy-related language and behavior online, with a focus on Chinese platforms. His work also extends to GIS education , where he emphasizes project-based learning to improve student employability in geospatial fields. Scientific Contributions: Development of lexicons for analyzing geoprivacy in Chinese social media Creation of datasets on microblog content and user comments Open educational resources in data science and GIS Research on algorithmic price discrimination and digital surveillance Hongyu is committed to open science and student mentorship. While no formal list of advisees is available, his teaching and project-based approach suggest active engagement with students. He has no listed scientific awards, but his research output and GitHub activity indicate a growing scholarly presence in geospatial ethics and digital society. He maintains an active research website and GitHub profile, where he shares code, datasets, and educational materials, reflecting a transparent and collaborative research philosophy.

Laika Satishمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• Artificial Intelligence Ethics
• Machine Learning Applications
• +۳ مورد دیگر

Laika Satish is an Assistant Teaching Professor in the Department of Computer Science at the University of Missouri–St. Louis , affiliated with the College of Arts and Sciences . Her office is located in 330 ESH, and she maintains regular student consultation hours through both Zoom and in-person sessions. She earned her MS in Computer Applications from the University of Mumbai in 2006. Her research spans interdisciplinary domains including: Computer Science Education : Developing AI-enhanced pedagogical tools, process visualization techniques, and integrity-preserving frameworks for programming education AI Ethics & Accountability : Examining trust models, algorithmic transparency, and societal impacts of generative AI Biomedical Informatics : Creating machine learning approaches for Alzheimer's biomarker discovery and COVID-19 proteomic analysis Algorithmic Foundations : Optimizing data cleaning processes and analyzing computational complexity of evolutionary algorithms Publication analysis reveals three dominant themes across her recent work (2021-2025): Innovations in computer science education leveraging explainable AI and process visualization Ethical frameworks for algorithmic accountability and trust in AI systems Computational methods for biomarker discovery in neurodegenerative and viral diseases Her work consistently bridges theoretical computer science with applied domains including healthcare, education, and social informatics.

Ilaria Lucrezia Ameriseمشاهده پروفایل

دانشیار

• Multivariate Analysis
• Time Series
• Nonparametric Statistics
• +۳ مورد دیگر

Ilaria Lucrezia Amerise is an Associate Professor in the Department of Economics, Statistics and Finance 'Giovanni Anania' (DESF) at the University of Calabria (UNICAL). Her research focuses on multivariate analysis, time series, nonparametric statistics, and statistical methods for complex/high-dimensional data including functional and spatial data. Editor-in-Chief of JP Journal of Biostatistics (ANVUR Area 13) Editorial Board Member of International Journal of Statistics and Systems (ANVUR Area 13) Recent research involves: Statistical preprocessing of crowdsourced data for Nigerian food prices Quantile regression with heteroskedasticity and non-crossing constraints Electricity demand forecasting via Reg-SARMA models Exchange rate prediction using simultaneous prediction intervals Time series outlier detection and smoothing techniques She contributes to academic governance through the Laboratorio Statistico Informatico (Statistical Informatics Lab) within DESF. Teaching includes undergraduate and graduate courses in Statistics, with materials available in both Italian and English.