جست‌وجوی استاد و پژوهشگر

• Hydrological Modeling
• Statistical Hydrology
• Hydroclimatology
• +۶ مورد دیگر

Assoc. Prof. Dr. Alper Serdar Anli is an academic at Ankara University, Faculty of Agriculture, Department of Agricultural Structures and Irrigation. His career spans over two decades, including roles as Research Assistant (2002–) and Vice Dean of the Faculty of Agriculture (2020–2023). His research focuses on hydrological modeling, drought and flood analysis, agrometeorology, and sustainable irrigation systems. Doctorate: Ankara University (2009) - Regional Frequency Analysis of Rainfall Using L-Moments Master's: Ankara University (2003) - Hydrological Drought via SPEI Bachelor's: Ankara University (2000) - Agricultural Structures and Irrigation Dr. Anli’s research explores climate change impacts on water resources, hydrological drought risk modeling, and innovative irrigation techniques. His work integrates statistical hydrology (L-moments, SPEI, RDI) with GIS and data mining for regional climate analysis. He has contributed to drought prediction frameworks and sustainable agricultural water use in semi-arid regions. His recent publications address flood frequency analysis in Susurluk Basin (2025), climate-resilient irrigation practices (2024), and regional drought modeling using SPEI (2023). Scholarships from MASHAV (Israel) and ICTP (Italy) supported his international training. He has supervised postgraduate theses on drought indices and water management. MASHAV Scholarship (2005) Abdus Salam ICTP Scholarship (2008) Dr. Anli’s managerial roles include Vice Director of Water Management Institute (2018–) and leadership positions in his department and faculty. His interdisciplinary work bridges agricultural engineering, hydroclimatology, and sustainable development goals.

• Pediatric Cardiology
• Fetal Cardiology
• Congenital Heart Disease
• +۴ مورد دیگر

Frédéric Dallaire is a Professor of Pediatrics at the Université de Sherbrooke and a leading Pediatric and Fetal Cardiologist at the Centre Hospitalier Universitaire de Sherbrooke (CHUS). He directs the Mother-Child Research Axis at CHUS and founded the Canadian Congenital and Pediatric Cardiology Research Network . His research focuses on congenital heart disease risk factors, fetal echocardiography, pediatric cardiovascular reference standards, and clinical knowledge integration. Education : MD (2006) and PhD in Epidemiology (2006) from Université Laval Leadership : Director of CHUS Mother-Child Research Center, founder of Canadian Pediatric Cardiology Network Research Interests span congenital heart disease , fetal cardiology , pediatric echocardiography , and epidemiological studies . He investigates risk factors in repaired tetralogy of Fallot, prenatal detection of heart defects, and vascular parameter normalization. His recent articles highlight machine learning applications in MIS-C/Kawasaki disease prediction epidemiological trends in congenital heart disease standardization of pediatric cardiovascular imaging gender disparities in Kawasaki disease outcomes Scientific Awards include: 2024 RECMUS Award for graduate student supervision 2023 FRQS Clinical Scholar (Senior) 2021 Emerging Academic Leader Award Multiple Dean's Honor List recognitions (2016-2023) His grants secure over $10 million in funding, including major awards from CIHR and Heart & Stroke for congenital heart disease research and rare disease network development.

Erhard Rahmمشاهده پروفایل

استاد

• Data Integration
• Knowledge Graphs
• Privacy-Preserving Data Analysis
• +۴ مورد دیگر

Prof. Dr. Erhard Rahm is a leading computer science academic at the University of Leipzig , affiliated with the Institute of Computer Science under the Faculty of Mathematics and Computer Science. With over 35 years of research contributions, he specializes in scalable data integration, privacy-preserving record linkage (PPRL), and graph analytics for knowledge graphs and digital twins. Key research areas: Data Integration for Knowledge Graphs, Privacy-Preserving Data Analysis, Dynamic Graph Management Recent work focuses on hybrid digital twins, medical image analysis, and secure health data processing 2016-2021: Speaker of GI's Database Systems and Information Systems division Scientific Contributions: His 15 most recent publications (2023-2025) span: Privacy-preserving machine learning (federated learning, PPRL with LLMs) Graph-based data transformations (Temporal Knowledge Graphs, Property Graph Streams) Automated schema matching and entity resolution systems Biomedical data applications (cell confluence assessment, stress detection) Selected Honors: VLDB Ten-Year Best Paper Award (2011) ICDE Influential Paper Award (2013) MSR Outstanding Collaborator Award (2016) Leadership Roles: Currently Vice President of the Gesellschaft für Informatik (since 2022), former DFG reviewer (2016-2024), and active in NFDI infrastructure (2019-2022). He leads research at ScaDS.AI, focusing on big data integration and scalable graph analytics.

• modeling of the educational process
• information technology in education
• databases
• +۳ مورد دیگر

Natalia Volodymyrivna Shcherbak serves as a Senior Lecturer in the Department of Computer Systems and Networks at Zaporizhia State Technical University's Faculty of Computer Science and Technology, where she also holds the position of Deputy Dean. With over two decades of academic experience since joining the university in 2001, she has established herself as a dedicated educator and researcher in computer science. Dr. Shcherbak graduated from Zaporizhia State Technical University in 2001 with a specialty in "Computer systems and networks" and a qualification as "systems engineer." Her educational background has provided the foundation for her academic career focused on computer science education and research. Her research interests span several key areas including modeling of the educational process, information technology in education (particularly Learning Management Systems), databases, web services, and Service-Oriented Architecture (SOA). Her scholarly work demonstrates a consistent connection between theoretical computer science concepts and practical educational applications, with particular emphasis on developing methodologies for assessing educational quality and optimizing learning experiences through technology. Analysis of her publication trends from 2014-2024 reveals a strong research trajectory in web services quality assessment, decision-making methods for service selection, and educational process modeling. Her recent work (2022-2024) advances multi-criteria decision-making methodologies for QoS-based web service environments, while her earlier publications (2016-2018) focus on test task quality assessment methods and educational resource development. Quality assessment methodologies for web services Educational process modeling and optimization Test task quality and complexity evaluation Computer science curriculum development Throughout her career, Dr. Shcherbak has taught a diverse range of computer science subjects including Assembly language programming, database organization, algorithms and calculation methods, programming, computer logic, and modern programming methods. Her teaching portfolio demonstrates expertise across both theoretical foundations and practical applications in computer science education.

Wolfgang Tressمشاهده پروفایل

استاد

• Organic Electronics
• Perovskite Solar Cells
• Device Physics
• +۴ مورد دیگر

Prof. Dr. Wolfgang Tress is a faculty member at the ZHAW Zurich University of Applied Sciences , affiliated with the School of Engineering . He leads the Novel Semiconductor Devices Team, focusing on Organic Electronics and Photovoltaics. His research spans perovskite solar cells, device physics, optoelectronic characterization, and mixed ionic-electronic conductors. PhD in Physics from Technical University of Dresden (2012) Master in Electrical Engineering from University of Ulm (2007) His research explores critical aspects of perovskite materials, including device stability, ionic conductivity, and efficiency optimization through advanced modeling and experimental techniques. Key projects involve correlative optoelectronics, interface tailoring, and lead-free perovskite development. Recent publications highlight trends in machine learning applications for perovskite stability analysis, ion migration dynamics, and multifunctional material design. He has authored over 20 peer-reviewed articles in journals like Advanced Energy Materials , Nature Energy , and Science . 2023 : Web of Science Highly Cited Researcher 2019 : ERC Starting Grant 2019 : SPS Award in Applied Physics 2016 : Zeno Karl Schindler Award As project leader, he drives initiatives in perovskite-on-silicon tandem cells, defect engineering, and laboratory digitization. His work combines experimental fabrication with computational modeling to address photovoltaic reliability challenges.

Michael E. Roseمشاهده پروفایل

پژوهشگر ارشد

• Economics of Science
• Innovation
• Academic Labor Market
• +۴ مورد دیگر

Michael E. Rose is a Senior Research Fellow at the Max Planck Institute for Innovation and Competition, specializing in Innovation and Entrepreneurship Research. He holds a Ph.D. from the University of Cape Town with a dissertation on "Collaboration Networks in Economic Science." His research focuses on the Economics of Science, Innovation, Academic Labor Market, Social Network Analysis, and Machine Learning. Rose earned his Bachelor of Science in Economics and Business Administration from the University of Jena (2009-2012), followed by a Master of Science in Quantitative Economics from Kiel University (2012-2014), where he also participated in the Advanced Studies Program in International Economic Policy Research at the Kiel Institute for the World Economy. He completed his doctoral studies at the University of Cape Town (2015-2018). His academic journey includes research visits to the Deutsche Bundesbank and Georgia Institute of Technology. His research interests center on understanding how social structures and networks influence scientific production and careers. Rose has made significant contributions to bibliometrics, academic collaboration networks, and the application of machine learning to economic analysis. His work on informal collaboration through acknowledgements has revealed patterns of gender differences and reciprocity in academic networks. He has also investigated how advisor connectedness affects student placement outcomes in the economics job market, demonstrating the importance of social networks even in markets with minimal information frictions. Rose has received notable recognition through the University of Cape Town Murray-Jelks Scholarship for International Travel (2017-2018) and the AIFMRM PhD Dissertation Scholarship (2015-2018). His research has been published in prestigious journals including The Review of Economics and Statistics, Journal of European Consumer and Market Law, SoftwareX, and Scientometrics, with numerous forthcoming publications in 2025. He is actively involved in supporting Ukrainian scholars through initiatives like #ScienceForUkraine, which he co-founded following Russia's invasion of Ukraine. The initiative has become a central hub connecting displaced Ukrainian researchers with support offers from institutions worldwide, recording over 2,600 support listings. Rose has also developed several open-source software tools for bibliometric analysis, including pybliometrics for Scopus data, Sprynger for Springer Nature data, and sosia for creating control groups of scientists, significantly enhancing research reproducibility and data integrity in the Science of Science field.

• Big Data
• Artificial Intelligence
• Cloud Computing
• +۵ مورد دیگر

Prof. Dr.-Ing. Christoph P. Neumann is an Associate Professor at Amberg-Weiden University of Applied Sciences (OTH) in the Faculty of Electrical Engineering, Media Technology and Computer Science, holding a W2 professorship in Big Data and Cloud Computing for AI since 2021. Previously, he served as a guest lecturer at Friedrich-Alexander-University Erlangen-Nuremberg (FAU) from 2013 to 2020, accumulating over thirteen years of university teaching experience. Education: Neumann earned his doctorate (Dr.-Ing.) from FAU, where his dissertation on distributed data management, system integration, and process management received a top grade and nomination for the GI Dissertation Award. Research Interests: His work focuses on Big Data infrastructure , AI-driven analytics , and cloud-native software architecture , with emphasis on scalable data-centric applications. He aims to establish nationally visible Big Data research integrated with OTH's Faculty EMI, ensuring AI students receive industry-relevant curricula. Key innovations include data lake technologies for BI modernization and graph databases for content management systems. Scientific Awards: Nomination for GI Dissertation Award Two-time recipient of FAU's best course award (Exercises/GOP subject) Ranked among OTH's Top-5 scientists (out of 200+ professors) Best Paper Award (2024, with Patrick Levi) IARIA Fellowship (2025) Best Paper Award (2025, with Philipp Stangl) Advising and Grants: Supervised 25 student theses at FAU and secured approximately 900,000€ in external funding through three cluster projects, creating additional PhD positions. Industry projects included 768,000€ revenue contribution at EXASOL and 2.7 million € budget responsibility at Teambank AG for BI modernization. Labs and Teams: Speaker of the Research Cluster Robotics & Big Data (RBD-OTH) and member of the Innovation and Competence Center Artificial Intelligence (IKKI). Also serves on INDIGO consortium's steering committee and advises four profile schools for computer science (PIZ), with a guest professorship at Hefei University (China) teaching Big Data principles.

Klavs Martin Sørensenمشاهده پروفایل

دانشیار

• Process Analytical Technology
• Food Quality and Safety
• Vibrational Spectroscopy
• +۷ مورد دیگر

Klavs Martin Sørensen is an Associate Professor at the Department of Food Science, Faculty of Science, University of Copenhagen. His research focuses on applying advanced chemical sensors combined with Process Analytical Technology (PAT) and Deep Learning/AI to optimize food manufacturing processes, ensuring high food quality and safety while minimizing environmental footprint. His educational background includes: Ph.D. in Food Technology, University of Copenhagen (2015) Participant in the DNATF-funded Pasteur-program (project manager education), Harvard University (2012) Engineering Technician, Electronics, Frederiksberg Tekniske Skole, Denmark (1998) With over 20 years of industry experience in developing measurement equipment for the food industry prior to obtaining his PhD, Professor Sørensen brings a unique practical perspective to his academic work. His research centers on vibrational spectroscopy, chemometrics, gas chromatography & mass spectrometry, and sustainable food production technologies. He has developed expertise in applying spectral sensors in online scenarios for process optimization, with special focus on Quality by Design and Process Analytical Technology frameworks. His recent publications demonstrate a strong focus on applying spectroscopic techniques and data analysis methods to food quality assessment across various food matrices including meat, cheese, wine, and protein side streams. His work bridges the gap between theoretical chemometrics and practical industrial food manufacturing applications. His notable awards include: Niels Foss Talent Prize (2019) Course of the year award by Department of Food Science for Rapid Food Analysis Method (2019) Nomination as Faculty teacher of the year by B.Sc. students (2020) Professor Sørensen has led multiple significant research projects including Better utilization of protein side streams (2016-2028), QC-CREW (2018-2020), and SayCheese (2018-2021). He has served as a censor for the BSc/MSc course Quantitative Food Spectroscopy since 2009 and was responsible for the course Rapid Food Quality Analysis Methods starting in 2017. He was a key organizer for the 2017 International Conference On Near-Infrared Spectroscopy (ICNIRS 2017). His technical expertise spans vibrational spectroscopy apparatus design, chemometric tools (PCA, PLS, PLSDA), GC/MS techniques, ultrasonic measurement and imaging, and artificial intelligence modeling for time series data. He also possesses extensive software development skills with over 20 years' experience in C++, C, Python, MATLAB, and various database systems, which he applies to real-time on-line data processing systems for food manufacturing.

Lydia E. Kavrakiمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Computational Biomedicine
• +۱۱ مورد دیگر

Lydia E. Kavraki is the Noah Harding Professor of Computer Science and Professor of Bioengineering, Electrical and Computer Engineering, and Mechanical Engineering at Rice University . As Director of the Ken Kennedy Institute , she leads initiatives in AI, data science, and computing across seven schools and 27 departments. B.A., University of Crete (1989) Ph.D., Stanford University (1994) Her research bridges robotics , AI , and computational biomedicine . In robotics, she pioneered sampling-based motion planning (OMPL library) and recently developed microsecond-level planning frameworks. In biomedicine, her work focuses on peptide-HLA interactions , personalized immunotherapy , and drug metabolite prediction , with tools like APE-Gen and DINC. Recent publication trends highlight her interdisciplinary work: 2022 PNAS Nexus study on HLA charge interactions , 2020 Frontiers in Immunology work on TCR cross-reactivity , and 2017 structural modeling of immune checkpoints. Themes include structure-based drug discovery , AI-driven motion planning , and ethical AI considerations . Scientific honors include: IEEE Frances E. Allen Medal (2023) ACM-AAAI Allen Newell Award (2020) ACM Athena Lecturer (2017) Three National Academy memberships (NAS/NAE/NAM) NSF CAREER, Sloan Fellowship, Whitaker Investigator Kavraki actively mentors students and postdocs, with over 40 alumni in academia and industry. Her Ken Kennedy Institute leadership spans AI in Health Conference organization and industry partnerships .

Maragatha Kuchibhatlaمشاهده پروفایل

استاد

• Statistical research methodology
• Analysis of repeated measurements
• Latent growth curve models
• +۴ مورد دیگر

Maragatha Kuchibhatla is Professor of Biostatistics and Bioinformatics and Professor in Psychiatry and Behavioral Sciences at Duke University School of Medicine. She serves as Senior Fellow in the Center for the Study of Aging and Human Development and leads the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core within the Division of Biostatistics. Her educational background includes a Ph.D. from Texas A&M University (1992). Ph.D., Biostatistics, Texas A&M University, 1992 Kuchibhatla's research specializes in advanced statistical methodologies for complex clinical data, with emphasis on longitudinal modeling (latent growth curve/class models), classification algorithms, and psychiatric clinical trial design. Her work bridges biostatistical innovation with practical applications in trauma recovery, geriatric care, and health disparities research, particularly in comorbid populations. Recent publications (2024-2025) demonstrate strong interdisciplinary focus across trauma surgery, hematology, global health, and emergency medicine. Key trends include development of predictive models for bleeding/thrombosis risks, age-neutral surgical protocols, and implementation of machine learning in resource-limited settings—showcasing her ability to translate statistical theory into life-saving clinical tools. She currently directs 9 major grants totaling over $20 million in active funding: POWER: Predicting Obesity with Enhanced EHR Resources (NICHD, 2022-2027) Machine learning for thyroid cytopathology in LMIC (NCI, 2021-2027) Personalized Targeted Nutrition via StructurEd Delivery (DoD, 2022-2026) Ultrasound-guided nerve block training (SAEM, 2025-2026) Bystander interventions in opioid cardiac arrests (EMF, 2025-2026) Lung transplantation CT-based selection (RSNA, 2025-2026) Trauma nutrition optimization (Fresenius Kabi, 2024-2026) Elderly trauma ventilation care (NIH, 2020-2025) Nasopharyngoscope cancer screening network (NIH, 2019-2025) Her grant portfolio reflects strategic focus on trauma recovery, health equity, and diagnostic innovation. Kuchibhatla collaborates extensively through Duke's BERD Core and the Center for the Study of Aging, contributing biostatistical leadership to multidisciplinary teams addressing critical gaps in trauma care, psychiatric treatment, and global health access.

• Automatic text analysis
• Information search
• IT project management
• +۲ مورد دیگر

Natalia Sergeevna Belova is an Associate Professor in the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. She has been working at HSE since 2012 with 13 years of scientific and teaching experience. Education: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.11 "Mathematical and software support for computing machines, complexes and computer networks" 2009: Postgraduate study from Moscow State University of Instrument Engineering and Computer Science 2005: Engineer qualification in "Computers, complexes, systems and networks" from Moscow State Academy of Instrument Engineering and Computer Science Research Interests: Dr. Belova's research focuses on automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work bridges theoretical computer science with practical applications in software engineering education and industry. She has made significant contributions to pattern recognition, face identification systems, and the development of computationally efficient methods for intelligent systems processing multimedia information. Publications Trend: Dr. Belova's research has evolved from foundational work on embedded databases to more recent contributions in computer vision, affect recognition, and AI applications. Her early work (2009-2013) focused on database systems reliability and project-based learning methodologies. Since 2015, her research shifted toward computer vision, pattern recognition, and deep learning applications. Her most recent publications (2022-2025) demonstrate expertise in AI applications across various domains including transportation systems and video analysis. Scientific Achievements: Recipient of the Grant of the President of the Russian Federation for young doctors of science MD-306.2017.9 (2017-2018) Gratitude from the Vice-Rector of HSE University (February 2025) Gratitude from the Faculty of Computer Science, HSE University (July 2023) Multiple publication bonuses for journals in List A and international peer-reviewed publications (2016-2025) Best Teacher award for 2016-2017 Member of the High Professional Potential Group (HSE personnel reserve) Teaching and Mentoring: Dr. Belova has supervised over 25 bachelor's theses on diverse software engineering topics including mobile applications, web platforms, biometric identification systems, and educational software tools. She teaches courses on "Group Dynamics and Communications in Professional Software Engineering Practice" and "Economics of Software Engineering." Her commitment to project-based learning methodology is evident in both her research and teaching practice. Professional Development: Dr. Belova actively participates in advanced training programs, including recent courses on modernization of educational programs in software engineering (2025) and problems of training modern specialists in software engineering (2024).

Mizuho Iwaiharaمشاهده پروفایل

استاد

• Database Systems
• Web Information Systems
• Text Mining
• +۳ مورد دیگر

Professor Mizuho Iwaihara is affiliated with Waseda University's Faculty of Science and Engineering and Graduate School of Information, Production, and Systems. Her research focuses on database systems, web information retrieval, text mining, security/privacy, and social media analysis. She has led significant projects on Wikipedia edit history analysis, knowledge graph construction, and privacy-preserving frameworks. Key research areas: Database Query Processing, Web Information Systems, Text Mining, Knowledge Management, Social Media Her recent publications address semantic analysis of collaborative content, topic evolution tracking, and privacy behavior modeling. Over 85 papers with 349 citations reflect her impact in database and social media research. Scientific achievements include: Best Demo Award (2014) for WikiReviz Best Paper Award (2008) at IFIP e-Business Conference EC-Web2006 recognition Grants from Japan Society for the Promotion of Science (JSPS) span multiple projects on knowledge graph development, social content analysis, and privacy-preserving systems. She supervises numerous graduate students and leads the Data Engineering Laboratory at Waseda University.

Sebastian Manethمشاهده پروفایل

استاد

• Databases
• Automata Theory and Applications
• Data Compression
• +۳ مورد دیگر

Sebastian Maneth is a Heisenberg Professor at the Universität Bremen , affiliated with the Faculty of Mathematics and Computer Science and the Department of Computer Science . His research focuses on Databases , Automata Theory and Applications , and Data Compression , with significant contributions to tree transducers and XML processing. Editor of Theory of Computing Systems (TOCS) Associate Editor for Frontiers in Computer Science Editor of Algorithms His recent work explores the boundaries of tree transducers , regular expressions , and grammar-based compression , with decidable properties in formal language theory. He organizes major conferences like the Dagstuhl Seminar on Regular Expressions and serves on numerous program committees (CIAA, ICALP, FoIKS, etc.). Current projects include FO-Query Enumeration over compressed data, shape-preserving tree transducers , and user identification via eye-tracking data . His editorial and organizational roles highlight leadership in theoretical computer science communities.

Kay E Holekampمشاهده پروفایل

استاد

• Behavioral Ecology
• Evolutionary Biology
• Mammalian Behavior
• +۳ مورد دیگر

Kay E Holekamp is a University Distinguished Professor in the Department of Integrative Biology at Michigan State University's College of Natural Science. Her research focuses on the complex interplay between social, ecological, and endocrine factors in mammalian behavioral development and evolution, particularly in spotted hyenas. Smith College (B.A.) University of California, Berkeley (Ph.D.) Michigan State University (1986-present) For over three decades, Holekamp's lab has conducted longitudinal fieldwork in Kenya's Maasai Mara, maintaining an exhaustive database tracking hyena behavior, social networks, and physiological traits. This work has produced groundbreaking insights into social inheritance, parasite-induced behavioral changes, and the evolutionary basis of animal cognition. Notable scientific recognitions include: 2019 Distinguished Animal Behaviorist Award (Animal Behavior Society) 2017 MSU Graduate School Outstanding Faculty Mentor Award Holekamp has mentored numerous students and researchers, including: Dr. Lily Johnson-Ulrich (PhD in Animal Behavior) Dr. Eli Strauss (J. Animal Ecology, PNAS publications) Dr. Julie Turner (Ethology cover study on personality) Dr. Zachary Laubach (Nature Communications epigenetics work) The Holekamp Lab maintains the Mara Hyena Project, one of the most extensive animal behavior databases globally, and collaborates with institutions including University of Pennsylvania, Bar-Ilan University, and University of Nebraska.

• Distributed Database Design
• Query Optimization
• Evolutionary Algorithms
• +۸ مورد دیگر

Prof. Ahmet Cosar is a faculty member in the Department of Computer Engineering at the Middle East Technical University (METU), Ankara, Turkey. He holds a PhD in Computer Science from the University of Minnesota (1996), an MS in Computer Engineering from Bilkent University (1988), and a BS in Computer Engineering from METU (1986). His research focuses on distributed database design, query optimization, evolutionary algorithms, computer networks, and cloud computing. He leads the Intelligent Data Analysis Group (IDAG) and teaches courses such as CENG 240, CMPE 275, and CENG 280. Education: PhD, Computer Science, University of Minnesota, 1996 MS, Computer Engineering, Bilkent University, 1988 BS, Computer Engineering, METU, 1986 Research Interests: Query optimization in distributed databases Evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) Cloud computing and data warehouse design Machine learning applications in cybersecurity Wireless sensor networks and data fusion Publications: Over 50 peer-reviewed articles in journals like Computers & Industrial Engineering and Neurocomputing , focusing on optimization algorithms, cloud resource allocation, and machine learning techniques. Recent work includes island-parallel metaheuristics for graph coloring and reinforcement learning for adaptive interventions. Advising & Grants: Supervised over 30 graduate students (PhD and MS) in areas like evolutionary algorithms, cloud databases, and sensor networks. Active in research grants related to distributed systems and big data. Labs/Teams: Leads the Intelligent Data Analysis Group (IDAG), collaborating on projects in optimization, machine learning, and cloud computing.