جست‌وجوی استاد و پژوهشگر

Vardges Melkonianمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Network Design Problems
• Approximation Algorithms
• +۷ مورد دیگر

Vardges Melkonian is an Associate Professor in the Department of Mathematics at Ohio University, part of the College of Arts and Sciences. His academic work bridges theoretical and applied mathematics, with a strong emphasis on optimization and algorithmic problem-solving. Research Interests: Dr. Melkonian specializes in combinatorial optimization, network design, approximation algorithms, and applications of operations research. His research integrates mathematical programming and discrete modeling to solve complex real-world scheduling and allocation problems. These interests are reflected in his extensive publication record spanning sports leagues, hybrid work, exercise routines, and social partitioning. Publication Trends: Over the past decade, his scholarly output has consistently focused on developing integer programming and optimization models for diverse domains—from recreational puzzles like KenKen to large-scale logistical challenges in manufacturing and public policy. His work demonstrates a unifying theme: transforming practical problems into formal mathematical frameworks for efficient solution. Scientific Awards: No awards are mentioned in the provided text. Advising and Grants: The available information does not list any students or grant funding. However, his research contributions suggest active engagement in academic mentorship and potential involvement in funded projects, though specifics are not disclosed. Labs and Research Teams: There is no mention of specific labs, research groups, or collaborative teams associated with Dr. Melkonian in the provided content.

Fredrik Manneمشاهده پروفایل

استاد

• Parallel Computing
• Distributed Computing
• Graph Algorithms
• +۳ مورد دیگر

Fredrik Manne is a Professor at the Department of Informatics, University of Bergen, Norway. His work focuses on parallel and distributed computing, particularly in combinatorial scientific computing and self-stabilizing algorithms. He has contributed extensively to graph algorithms, including matching, coloring, and clustering, with applications in high-performance computing, numerical optimization, and wireless networks. His research involves designing algorithms for parallel architectures, including multi-core and GPU-based systems. He has co-authored numerous publications on topics such as spanning forests, vertex cover heuristics, and b-matching. His work often integrates theoretical and applied approaches, addressing challenges in sparse matrix computations and wireless mesh network communication. The recent articles highlight his exploration of graph neural networks for parallel coloring, efficient multithreaded matching algorithms, and GPU-accelerated clustering methods. These studies emphasize scalability, optimization, and real-world applications in computational science.

Evangelos E. Miliosمشاهده پروفایل

استاد

• Visual Text Analytics
• Modelling and Mining of Networked Information Spaces
• Text Mining
• +۸ مورد دیگر

Evangelos E. Milios is a Professor in the Faculty of Computer Science at Dalhousie University , Halifax, Nova Scotia. He has been a faculty member since 1998 and leads the MALNIS (Machine Learning and Networked Information Spaces) research group. He is affiliated with the Institute of Big Data Analytics and served as Scientific Director of DeepSense , an innovation hub for ocean data analytics. Education: PhD in Electrical Engineering and Computer Science, MIT (1986) SM & EE, MIT (1983) Dipl. Eng. in Electrical Engineering, NTUA, Greece (1980) His research focuses on visual text analytics, text mining, graph mining, social network analysis, and machine learning . He has made significant contributions to modeling and mining of networked information spaces, with applications in data science and AI. The recent publications reflect a strong trend in data mining, robotics, pattern recognition, and semantic analysis , particularly in log analysis, pose estimation, and information retrieval. His work bridges theoretical algorithms with practical applications in robotics and web technologies. Scientific Awards and Honors: Distinguished Research Professor (2017–2022) Killam Chair in Computer Science (2006–2011) Senior Member, IEEE Professional Engineer, Ontario (1998–2024) He has served in key administrative roles including Associate Dean, Research (2008–2017) and Director of the Graduate Program (1999–2002) . He has supervised numerous graduate students and taught a wide range of courses in AI, machine learning, data science, and networking. His research is supported by major grants and collaborations, including NSERC and industry partnerships. Research Labs and Teams: MALNIS – Focuses on machine learning and networked information spaces. DeepSense – Ocean data analytics and AI innovation. Institute of Big Data Analytics – Cross-disciplinary big data research.

Joana Braga Pereiraمشاهده پروفایل

دانشیار

• Brain Connectivity
• Neurodegenerative Disorders
• Alzheimer's Disease
• +۱۱ مورد دیگر

Joana Braga Pereira is an Associate Professor (Docent in Neurosciences) and Principal Researcher at the Department of Clinical Neuroscience, Karolinska Institutet, where she leads the Brain Connectomics research group. Her work focuses on brain connectivity measures derived from structural MRI, functional MRI, diffusion tensor imaging, and other neuroimaging modalities in patients with neurodegenerative disorders. Education: Docent in Neurosciences, Karolinska Institutet, 2021 PhD in Biomedicine, University of Barcelona, 2012 Master in Neurosciences, University of Barcelona, 2008 Postdoctoral Fellow in Neuroimaging, Karolinska Institute, 2017 Dr. Pereira's research focuses on understanding brain connectivity and network topology in neurodegenerative disorders, particularly Alzheimer's and Parkinson's diseases. Her work integrates multiple neuroimaging modalities with biomarker analysis to identify early signs of disease and track progression. She specializes in applying graph theory, deep learning, and novel imaging sequences to analyze complex brain networks and develop precision medicine approaches. Her research spans multimodal brain connectivity, dynamic brain connectivity, functional gradients, and proteomics to understand disease mechanisms. Her recent publications demonstrate a strong focus on computational approaches to understanding neurodegenerative diseases, with particular emphasis on the relationship between brain connectivity patterns, biomarkers, and clinical outcomes across aging and various neurological conditions. She has pioneered methods like delayed correlation-based approaches for dynamic connectivity analysis and developed BRAPH 2.0 software for brain connectivity analysis. Scientific Awards and Grants: Marie Curie Intra-European Fellowship Swedish Research Council Alzheimerfonden Hjärnfonden StratNeuro New Technologies Grant 2025 Swedish Foundation for Strategic Research Senior Research Faculty Position Dr. Pereira actively mentors the next generation of neuroscientists, currently supervising 3 PhD students and 3 postdoctoral researchers, with additional co-supervision of 4 PhD students from other institutions. She has successfully guided 4 PhD students to completion of their theses. She also organizes the yearly conference 'Emerging Topics in Artificial Intelligence' since 2020. She leads the Brain Connectomics research group, which is part of the Department of Clinical Neuroscience at Karolinska Institutet. The group is highly interdisciplinary, combining expertise from medicine, engineering, physics, and computer science to tackle complex questions in neuroscience. The group has developed BRAPH 2.0, a comprehensive software for brain connectivity analysis using graph theory and deep learning.

David S. Choiمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Network Analysis
• +۵ مورد دیگر

David S. Choi is an Associate Professor at Heinz College, Carnegie Mellon University, which houses the Schools of Public Policy and Information Systems. He maintains a courtesy appointment in the Department of Statistics at the same institution. His academic credentials include a Ph.D. in Electrical Engineering from Stanford University under Benjamin Van Roy. Professional experience encompasses roles as a technical staff member at MIT Lincoln Laboratory, postdoctoral research at Harvard University's School of Engineering, and a visiting postdoc at UC Berkeley's Department of Statistics. Choi's research centers on statistical and machine learning methodologies for network-structured data. His work pioneers community detection techniques, latent variable network modeling, unsupervised learning frameworks, and causal inference methods that account for interference in social networks. These innovations address fundamental challenges in analyzing interconnected systems across public policy, healthcare, and social sciences. Publication analysis reveals consistent contributions to causal inference in networked experiments, particularly in characterizing interference and spillover effects. His scholarship spans network clustering algorithms, biological network modeling, and policy evaluation methodologies, with impactful work published in Journal of the American Statistical Association, PNAS, and Annals of Statistics. No scientific awards were documented in the source material. The available information does not reference doctoral students supervised or research grants managed by Professor Choi. Specific research laboratories or collaborative teams were not mentioned in the provided documentation.

Gerard de Meloمشاهده پروفایل

استاد

• Artificial Intelligence
• Large Language Models
• Natural Language Processing
• +۹ مورد دیگر

Gerard de Melo is a Professor and Chair of Artificial Intelligence and Intelligent Systems at the Hasso Plattner Institute (University of Potsdam, Germany). He also serves as a member of the Cognitive Sciences Research Focus and ELLIS Unit Potsdam . Research Interests: AI and Machine Learning Natural Language Processing Cross-modal AI (vision-language models, knowledge graphs) Societal and philosophical aspects of AI Medical Informatics Recommendation Systems Graph Neural Networks Recent Article Trends: His work spans generative AI (Vector Grimoire), medical LLM evaluation (CliMedBench), distributed training (Efficient Parallelization), and social media analysis (WallStreetBets) with a focus on multimodal systems and ethical AI . Scientific Recognition: Rutgers CSGSS Best Professor Award (Teaching, Advising) Best Paper Awards: NeurIPS Workshop, CIKM, EACL LANTERN IEEE TCSE Distinguished Paper Award First Runner-Up ACM WebSci Advising & Grants: Mentored students like Rohan Sawahn (Better World Award) and Maximilian Schall (IEEE TCSE Award). Secured major funding including German BMBF Grant for AI Service Center Berlin-Brandenburg and DARPA SocialSim Program support.

Kamesh Munagalaمشاهده پروفایل

دانشیار

• Algorithm design
• Discrete optimization
• Approximation algorithms
• +۸ مورد دیگر

Kamesh Munagala is an Associate Professor of Computer Science at Duke University, where he has been employed since 2004. His research spans theoretical computer science with applications in e-commerce, databases, data analysis, and networks. He serves as an Area Editor for PeerJ Computer Science and has held leadership roles including Director of Graduate Studies for the Duke CS department from 2012 to 2015. Education: PhD in Computer Science from Stanford University (2003) BTech from IIT Bombay (1998) Munagala's research focuses on algorithm design and discrete optimization, particularly in scenarios with uncertain inputs. His work encompasses approximation algorithms, online algorithms, and algorithmic game theory. Recent research has concentrated on two main themes: (1) Persuading optimizers or learners toward certain objectives through information revelation and pricing, and (2) Ensuring fairness to groups based on proportionality and stability in resource allocation and societal decision making contexts. His theoretical work has practical applications in designing data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Analysis of Munagala's recent publications reveals a strong trend toward interdisciplinary research at the intersection of computer science, economics, and social choice theory. His work shows increasing sophistication in handling fairness constraints across multiple dimensions, particularly in societal decision-making contexts like school assignment, participatory budgeting, and redistricting. There is also a notable expansion into the emerging area of large language models for combinatorial optimization, reflecting the field's evolution toward AI-assisted algorithmic solutions. His research consistently bridges theoretical foundations with real-world applications requiring nuanced approaches to resource allocation under uncertainty. Scientific Awards: NSF CAREER Award (2008) Alfred P. Sloan Research Fellowship (2009) Best paper award at WWW 2009 conference Munagala has served as Director of Graduate Studies for the Duke CS department from 2012 to 2015 and was a Visiting Research Professor at Twitter in 2012. His research has been supported by prestigious grants including the NSF CAREER award. He actively mentors students, with many of his publications featuring student co-authors across his two main research thrusts. His work addresses fundamental challenges in computing efficient solutions under uncertainty, designing pricing and incentive mechanisms for selfish agents, and developing fairness-preserving algorithms for societal decision making. Munagala leads research projects in two broad categories: proportionality in resource allocation and societal decision making, and asymmetric information and persuasion in learning and optimization. His work on proportionality explores fairness concepts like proportionality or core-stability across various societal contexts, while his research on asymmetric information addresses how parties with incomplete information collaborate or compete to achieve optimization objectives through strategic information revelation.

Pasi Hyytiäinenمشاهده پروفایل

پژوهشگر

• Theology
• Biblical Studies
• Textual Criticism
• +۳ مورد دیگر

Pasi Hyytiäinen is a Postdoc researcher and Grant-funded researcher at the Faculty of Theology , focusing on textual criticism and digital humanities. His work integrates computational methods with biblical scholarship, particularly analyzing New Testament manuscripts. Current affiliation: Faculty of Theology Active projects: EXPRECCE, The Spread of Early Christianity in Cultural Evolutionary Perspective Research interests include: Textual Criticism: quantifying relationships between manuscripts Network Analysis: mapping co-occurrence of theological concepts Cultural Evolution: studying adaptive changes in early Christian texts Software Development: creating tools like Relate for textual scholarship Article trends highlight interdisciplinary approaches combining theology, linguistics, and computational modeling. His work emphasizes quantitative methods, manuscript classification, and semantic drift in biblical texts. Key projects involve cultural evolutionary perspectives on early Christianity, analyzing martyr narratives, and outgroup hostility. He has presented extensively on textual drift and software tools at international conferences. Collaborations include partnerships with researchers like Nikki, Luomanen, and Söderholm, contributing to teams exploring manuscript evolution and cultural dynamics in religious traditions.

Jean-Cédric Chappelierمشاهده پروفایل

مدرس ارشد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Jean-Cédric Chappelier is a Senior Lecturer and Researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences. He works in computational linguistics and natural language processing, with a focus on robust parsing techniques, semantic indexing, and clustering algorithms. His research interests span computational linguistics, natural language processing, machine learning, tree substitution grammars, and semantic indexing. His publications from 2000-2006 demonstrate expertise in stochastic parsing, ontology-based indexing, and community structure analysis in complex networks. He has supervised multiple EPFL PhD students including Florian Seydoux and Emmanuel Eckard. Jean-Cédric holds an Ms.Sci. and Ph.D. in Computer Science from École Nationale Supérieure des Télécommunications de Paris. He teaches courses on object-oriented programming, computer systems, and natural language processing at EPFL.

Cristina G. Fernandesمشاهده پروفایل

دانشیار

• Algorithms
• Combinatorial Optimization
• Graph Theory
• +۷ مورد دیگر

Cristina G. Fernandes is an Associate Professor in the Department of Computer Science at the Institute of Mathematics and Statistics, University of São Paulo (IME-USP), where she conducts research in theoretical computer science, combinatorial optimization, and graph theory. She teaches advanced courses such as Advanced Data Structures and Topics in Algorithm Analysis. Her research interests include algorithms, combinatorial optimization, graph theory, approximation algorithms, data structures (persistent, retroactive, kinetic, succinct), and computational geometry. She applies theoretical methods to solve complex problems in network design, clustering, and discrete structures. The recent publications reflect a strong focus on structural and extremal graph theory, approximation algorithms, and combinatorial optimization. Key themes include tree and path packing, dominating sets, Steiner-type problems, and combinatorial properties of graphs. Her work often involves deep structural analysis and algorithmic design for NP-hard problems. Cristina G. Fernandes has not been mentioned with any specific scientific awards in the provided texts. She has supervised numerous postdoctoral researchers, PhD, MSc, and undergraduate students, many funded by FAPESP, CAPES, and CNPq. She leads significant research projects, including CAPES/MATH/STIC/CLIMAT-AMSU on energy efficiency in distributed computing and CNPq Universal projects on partitioning and connectivity. She is actively involved in academic advising and grant-funded research. She leads or participates in the Research Group in Theoretical Computer Science, Combinatorics and Combinatorial Optimization at IME-USP, fostering collaborative research in algorithms and discrete mathematics.

Laxman Dhulipalaمشاهده پروفایل

استادیار

• Parallel Algorithms
• Graph Processing
• Parallel Clustering
• +۷ مورد دیگر

Laxman Dhulipala is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a research scientist at Google Research in the Graph Mining team. He holds a Ph.D. from Carnegie Mellon University, advised by Guy Blelloch, and was a postdoctoral researcher at MIT with Julian Shun. Ph.D., Carnegie Mellon University Postdoctoral Research, MIT His research focuses on efficient parallel algorithms, particularly for graph processing and clustering. He explores theoretical and practical models of parallel computation aligned with modern hardware. His work spans parallel graph algorithms, computational geometry, and scalable systems for massive datasets. The recent publications demonstrate a strong trend in scalable and dynamic graph algorithms, with a focus on hierarchical clustering, connectivity, and benchmarking. Key themes include batch-dynamic updates, memory-efficient data structures, and practical parallel implementations for massive-scale problems. Many works appear in top venues such as SPAA, VLDB, NeurIPS, and SIGMOD. Best Paper Award at VLDB'25 Best Paper Award at SPAA'22 Best Paper Runner Up at VLDB'22 Distinguished Paper Award at PLDI'19 Best Paper Award at SPAA'18 Memorable Paper Award Finalist at NVMW'20 Honorable Mention, CMU SCS Dissertation Award Nominated for ACM Dissertation Award Dhulipala has advised and collaborated with numerous students and researchers, including Quinten De Man, Shangdi Yu, Jessica Shi, and others, contributing to influential projects such as Aspen, ParGeo, GBBS, and ParlayLib. He has received recognition for both theoretical and practical contributions to parallel computing. He teaches courses such as CMSC858N (Scalable Parallel Algorithms and Data Structures) and CMSC451 (Design and Analysis of Computer Algorithms). He is actively involved in building tools and frameworks for parallel algorithm development and evaluation, including benchmark suites and graph processing systems. His dual affiliation with academia and Google Research enables impactful, scalable research bridging theory and practice.

Mariam Adedoyin-Oloweمشاهده پروفایل

مدرس

• Data Mining
• Data Science
• Artificial Intelligence
• +۳ مورد دیگر

Mariam Adedoyin-Olowe is a Lecturer and Programme Leader for the MSc Artificial Intelligence at Birmingham City University . Her research focuses on Data Mining, Data Science, and Artificial Intelligence, particularly in analyzing social media data for event detection and decision-making support. Doctorate in Computing Science from Robert Gordon University First degree from University of Portsmouth Research Interests center on applying data mining techniques to Twitter data for tracking real-world events, sentiment analysis, and customer segmentation. Her work bridges computational methods with practical applications in politics, sports, and healthcare domains. Key Publications include studies on: Transformer-based transfer learning for cross-lingual news events AI-driven mental fatigue detection systems Temporal clustering methods for social media event resolution Deep learning approaches for sound classification Rule dynamics frameworks for political event detection Academic Affiliations include membership in BCU's Data Mining Group and Data Analytics & Artificial Intelligence Research Group, as well as former involvement with RGU's Data Science and Information Retrieval groups. She regularly reviews for international conferences and journals.

Petra Hermankovaمشاهده پروفایل

استادیار

• Digital Epigraphy
• Greek Epigraphy
• Latin Epigraphy
• +۵ مورد دیگر

Petra Hermankova serves as an Assistant Professor in Classical Archaeology at Aarhus University's School of Culture and Society, where she also contributes to the Social Resilience Lab. Her academic appointment is active with ongoing projects through 2025 and recent publications in 2025 confirm her current faculty status. Her research focuses on digital epigraphy and the application of computational methods to ancient inscriptions. She employs text mining, machine learning, and social network analysis to study inscription functions across Graeco-Roman societies, economic history patterns, and labor specialization. Her work emphasizes FAIR and Open Science principles in archaeological data management, particularly through leadership in the Epigraphy.info initiative which develops international standards for digital epigraphy. Hermankova's publication trends reveal increasing engagement with computational approaches to ancient social networks and epigraphic big data , with recent work integrating SPARQL querying, RDF modeling, and network visualization techniques. Her research bridges traditional archaeological methods with cutting-edge digital humanities tools, focusing on Mediterranean communities from Thrace to the Roman Empire. She actively participates in major collaborative projects including The Past Social Networks Project (Carlsberg Foundation, 2022-2025), PPAP: Perachora Peninsula Archaeological Project (2019-2024), and SDAM: Small data - Big Challenges (2019-2023). Hermankova regularly organizes international workshops through Epigraphy.info and teaches practical courses in digital epigraphy methodology.

Yarema Okhrinمشاهده پروفایل

استاد

• Statistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Prof. Dr. Yarema Okhrin serves as Full Professor at the Department of Statistics and Data Science within the Faculty of Economics at the University of Augsburg. He leads the Chair of Statistics and Data Science, which is part of the Business Analytics & Operations and Finance, Accounting, Controlling & Taxation clusters. His research group includes Dr. Sebastian Heiden, Dr. Rui Ren, and several research assistants working on advanced statistical methods and their applications in business and economics. Professor Okhrin's research spans mathematical and statistical models for data-driven problems in business and economics, with particular expertise in statistics, data science, and machine learning. His work focuses on developing methodologies for time series analysis, portfolio optimization, risk management, and statistical process control. Recent research has expanded into high-dimensional statistics, image analysis, and network monitoring applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary applications of statistical methods, particularly in finance and process monitoring. His work demonstrates increasing integration of machine learning techniques with traditional statistical approaches, especially in portfolio optimization and risk assessment. A notable pattern is the development of specialized statistical methods for high-dimensional data analysis across multiple domains. Professor Okhrin supervises bachelor's and master's theses at the Department of Statistics, with research topics including data mining, time series modeling, forecasting methods, regression analysis, and statistical data analysis. His department offers thesis opportunities in specialized areas such as asymmetric dependencies in financial markets, forecasting, investor sentiment analysis, medical statistics, volatility modeling, multivariate distributions, sustainable investing, and portfolio optimization. The Chair of Statistics maintains active research collaborations and offers academic consulting on statistical issues and data analysis. The department hosts regular research seminars to facilitate exchange of ongoing research projects and maintains connections with industry partners for practical applications of statistical methods.

Paul Boniolمشاهده پروفایل

پژوهشگر

• Time Series Analytics
• Anomaly Detection
• Machine Learning
• +۲ مورد دیگر

Paul Boniol is a researcher at Inria, affiliated with the VALDA project-team—a collaboration between Inria Paris, École Normale Supérieure, and CNRS. His work focuses on time series analytics, anomaly detection, and machine learning applications. Ph.D. in Computer Science and Applied Mathematics (University of Paris, EDF R&D) Visiting Ph.D. at University of Chicago Education: Grenoble INP ENSIMAG Engineering School Research interests span: Unsupervised anomaly detection in large time series Time series management systems Machine learning for predictive maintenance Graph-based time series analysis Explainable AI for temporal data Recent publications emphasize advancements in: Weakly supervised anomaly localization Graph embedding techniques Model selection frameworks Interactive visualization tools Smart meter data analysis Scientific recognition: Paul Caseau Thesis Prize 2022 Lambdamu Congress Research-Industry Prize 2022 BDA & INFORSID Ph.D. Prizes 2022