جست‌وجوی استاد و پژوهشگر

Serkan GÜLDALمشاهده پروفایل

استادیار

• Density Functional Theory
• Quantum Computing
• Molecular Physics
• +۵ مورد دیگر

Serkan GÜLDAL is an Assistant Professor at Adıyaman University's Department of Physics, Faculty of Arts and Sciences. He holds a PhD in Systems Modeling and Information Theory from the University of Alabama, Birmingham (2016) and master's and bachelor's degrees in Physics. His research focuses on Density Functional Theory, Quantum Computing, Molecular Physics, and Machine Learning applications in UAV safety and materials modeling. He is a lifetime member of the Golden Key International Honor Society and a Wolfram Technology Associate (2016). Education: Bachelor's in Physics (2006), Erciyes University Master's in Density Functional Theory (2011), Mississippi State University PhD in Systems Modeling & Information Theory (2016), University of Alabama, Birmingham Research Interests: Perovskite materials modeling using DFT Machine learning for UAV collision detection and data imbalance Quantum computing and molecular systems analysis Systems modeling in communication theory and STEM education Publications Overview: Over 30 peer-reviewed articles spanning materials science, machine learning, and UAV systems. Recent work includes drone safety models, perovskite crystal structures, and imbalanced data techniques. Awards: 15th IKSAD Science Award (202X) Grants & Leadership: Lead researcher on projects like 'ABO3 Perovskite Modeling' (2018-2019) and 'U-Space Separation in Europe' (2021-2022). Served as Department Head (2019) and currently holds Vice Dean roles. Labs & Teams: Active in open-source software contributions (LibreOffice/Firefox) and interdisciplinary collaborations on UAV systems and materials science.

İbrahim Halil GÜMÜŞمشاهده پروفایل

استاد

• Matrix Theory
• Operator Inequalities
• Optimization
• +۳ مورد دیگر

Prof. Dr. İbrahim Halil GÜMÜŞ is a distinguished faculty member at Adıyaman University, Faculty of Arts and Sciences, Department of Mathematics, where he currently holds the position of Professor (since 2023). Previously, he served as Associate Professor (2017-2023) and Assistant Professor (2011-2017) at the same institution. Before his academic career, he worked as a Teacher at Public Schools under the Ministry of National Education from 2002 to 2011. He earned his educational qualifications from Selçuk University: B.Sc. in Mathematics (1998-2002), M.Sc. in Mathematics (2002-2005), and PhD in Mathematics (2005-2011). His Master's Thesis focused on 'On the Hadamard Product of Matrices' (2005), while his PhD Thesis examined 'Bounds on arithmetic, geometric and Heinz means of positive definite matrices' (2011). Prof. GÜMÜŞ's research primarily centers on Matrix Theory and Operator Inequalities, with significant contributions to Positive Operators, Matrix Analysis, and Optimization. His work demonstrates a strong theoretical foundation in mathematical inequalities with increasing applications in data science and medical informatics. He has published extensively in high-impact journals such as Linear and Multilinear Algebra, Journal of Mathematical Analysis and Applications, and Operators and Matrices. His publication record shows a clear evolution from theoretical matrix inequalities toward practical applications in data analysis, particularly evident in his recent work on synthetic data generation for imbalanced datasets using geometric means and Heinz averages. This interdisciplinary approach bridges pure mathematics with machine learning applications, especially in medical data analysis. TÜBİTAK Publication Incentive Award, 2012 TÜBİTAK Publication Incentive Award, 2015 TÜBİTAK Publication Incentive Award, 2017 TÜBİTAK Publication Incentive Award, 2018 TÜBİTAK Publication Incentive Award, 2019 TÜBİTAK Publication Incentive Award, 2021 Prof. GÜMÜŞ has successfully supervised multiple Master's theses on topics including inequalities for positive multilinear mappings, geometric inequalities via majorization methods, determinants of positive semi-definite matrices, and mathematical approaches to synthetic data sampling. He serves as a referee for prestigious journals including Journal of Inequalities and Applications and Mathematical Reviews/MathSciNet. His administrative roles include Farabi Coordinator, Erasmus Coordinator, Bologna Coordinator for both the Faculty of Arts and Sciences and Institute of Science, and Mevlana Exchange Program Institutional Coordinator. His work bridges theoretical mathematics with practical applications in data science, particularly in addressing challenges related to imbalanced datasets in medical informatics through innovative mathematical approaches.

Alper Kürşat Uysalمشاهده پروفایل

دانشیار

• Text Classification
• Feature Selection
• Pattern Recognition
• +۲ مورد دیگر

Alper Kürşat Uysal is an Associate Professor of Computer Engineering at Alanya Alaaddin Keykubat University. His research develops advanced text classification algorithms and feature selection methods, with applications ranging from spam detection to educational analytics. Research foci: Novel feature selection metrics for text data Imbalanced text classification techniques Short text analysis methodologies Multilingual spam detection systems Affective computing in educational contexts Uysal's publications introduce innovative approaches to feature selection, including the Extensive Feature Selector method and specialized metrics for short text datasets. His work addresses practical challenges in Turkish-language social media content moderation and develops automatic classification systems for educational applications. Recent research examines how feature selection impacts classifier performance on imbalanced text corpora.

• Machine Learning
• Bioinformatics
• Computational Biology
• +۶ مورد دیگر

Zafer Aydın is an Associate Professor at the Department of Computer Engineering, Graduate School of Engineering and Science, Abdullah Gul University. He holds a PhD in Electrical and Computer Engineering from Georgia Institute of Technology and has held academic positions at Bahcesehir University and Abdullah Gul University. Research Interests: Machine learning applications in bioinformatics, biomedical engineering, health informatics, and industrial problem-solving (network intrusion detection, traffic flow prediction, sentiment analysis). Broad research areas: Protein structure prediction, respiratory virus modeling, medical imaging analysis, network security Recent projects focus on deep learning, ensemble methods, and feature engineering for biological and industrial datasets Scientific Achievements: First place in multiple DREAM Challenge leaderboards (COVID-19, Parkinson's disease) Second place in Respiratory Viral DREAM Challenge T0 phase He has mentored 4 PhD and 6 Master's students, and participated in 12 funded research projects including TUBITAK grants. His lab develops AI solutions for biomedical and industrial applications while maintaining active collaborations with international researchers.

Huanjing Wangمشاهده پروفایل

استاد

• Machine Learning
• Big Data Analytics
• Feature Selection
• +۲ مورد دیگر

Huanjing Wang is a Professor of Computer Science in the Ogden College of Science and Engineering at Western Kentucky University, where she also serves as CS Undergraduate Program Coordinator. She holds a Ph.D. in Computer Science from The University of Alabama (2005). Her research specializes in machine learning and big data analytics, with focus areas in feature selection techniques, fraud detection systems, and software defect prediction. She develops methods for handling high-dimensional data in applications ranging from healthcare fraud to software quality assurance. Recent publications (2019-2023) demonstrate sustained focus on fraud detection applications using explainable AI and ensemble feature selection methods. Her work provides comparative frameworks for evaluating feature selection stability and performance across diverse datasets. She serves as Associate Editor for the Journal of Big Data and has chaired multiple IEEE conferences including ICMLA 2019. Teaching responsibilities include Algorithms, Database Systems, Machine Learning, and Data Mining courses at undergraduate and graduate levels. Departmental service includes chairing the Undergraduate Curriculum Committee since 2015 and leadership in accreditation activities.

• Statistical Machine Learning
• Bayesian Statistical Inference
• Statistical Ecology
• +۷ مورد دیگر

Jabed Tomal is an Associate Professor in Statistics and Data Science at Thompson Rivers University (TRU), Canada. Previously, he held positions as an Assistant Professor at TRU (2018–2023) and the University of Toronto Scarborough (2014–2018), and a Postdoctoral Fellow at the University of British Columbia (2014). He earned a Ph.D. in Statistics (2013) from UBC, specializing in statistical machine learning, and dual M.Sc. degrees in Statistics (University of Windsor, 2007) and Biostatistics (University of Dhaka, Bangladesh). His research focuses on ensemble methods, Bayesian inference, and statistical ecology, with applications in drug discovery, protein homology, and environmental modeling. Key research interests include developing ensemble models for high-dimensional data, Bayesian methods for breakpoints detection in housing markets and ecological systems, and statistical approaches in healthcare. Notable contributions include work on QSAR studies, Bayesian hierarchical modeling of pandemic impacts, and ecological threshold detection. Tomal has secured grants such as the NSERC Discovery Grant ($102,500) and TRU internal funds for projects in big data and environmental thresholds. He has advised numerous graduate and undergraduate students on topics ranging from machine learning in healthcare to single-cell RNA sequencing analysis. His teaching spans courses like Bayesian Machine Learning, Multivariate Statistics, and Theoretical Machine Learning at the graduate level, alongside foundational statistics and calculus courses at TRU and the University of Dhaka. Administrative roles include Chair of the Award and Scholarship Committee for TRU’s Master of Data Science program and membership in Senate Research Committee. Education: Ph.D., Statistics (2013), UBC Vancouver M.Sc., Statistics (2007), University of Windsor M.Sc., Biostatistics (2005), University of Dhaka Awards: NSERC Discovery Grant (2021–2026) Research Training Recognition Fund (TRU,多次) SSC 2013 Talk Honourable Mention Labs/Teams: Active in interdisciplinary projects at TRU’s Department of Mathematics and Statistics, focusing on data science applications in ecology, healthcare, and genetics.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Dr. Bassma Al-Jubouri is a Lecturer in Computer Science at York Business School, York St John University. She holds an MSc in Artificial Intelligence from the University of Southampton and a PhD in Machine Learning from Bournemouth University, where her doctoral research was part of the EU INFER project. Prior to her current role, she worked at the Norwegian University of Science and Technology (NTNU) from 2019 to 2024, first as a Postdoctoral Researcher and later as a Researcher, focusing on practical engineering applications of machine learning. Her academic background includes teaching modules related to Artificial Intelligence, Machine Learning, Deep Learning, and their real-world applications. Her research interests span AI, machine learning, and data science, with a focus on addressing imbalanced datasets and enhancing regression models' accuracy through extreme value prediction techniques. She aims to develop practical AI solutions for diverse domains. While no specific scientific awards or grants are listed, her professional trajectory reflects a strong commitment to advancing machine learning applications in industry and academia. No lab affiliations or collaborative teams are explicitly mentioned in the provided information.

• Medical Statistics
• Artificial Intelligence in Healthcare
• Machine Learning
• +۳ مورد دیگر

Lei Clifton is a Research Fellow and Official Fellow in AI & Machine Learning at Reuben College, University of Oxford. She leads a team of researchers in the Nuffield Department of Population Health and contributes to the Pandemic Science Institute’s preparedness efforts. With a PhD in Statistical Machine Learning (2007, University of Manchester), her expertise spans large-scale clinical data analysis and healthcare AI deployment. She advises the NIHR Research Support Service on study design and collaborates with Prof David Clifton’s 'AI for Healthcare' CHI Lab. Her research focuses on medical statistics and AI applications in observational studies and electronic health records, including synthetic data generation, immune protein biomarker discovery, and scalable AI implementation in healthcare systems. She co-leads projects like the Implementation framework for AI deployment and dysregulated immune protein prediction in Multiple Myeloma. Key areas of contribution include healthcare AI ethics, cross-institutional model validation, and LLM applications in medicine. Her work bridges statistical rigor with real-world clinical challenges, emphasizing generalizability and fairness in machine learning systems.

Erick Moreno-Centenoمشاهده پروفایل

دانشیار

• Discrete Optimization
• Network Optimization
• Linear Programming
• +۶ مورد دیگر

Dr. Erick Moreno-Centeno is an Associate Professor in the Department of Industrial & Systems Engineering at Texas A&M University's College of Engineering. His research bridges discrete optimization, network optimization, and computational mathematics, focusing on developing exact algorithms for solving linear systems with applications in energy systems, decision theory, and machine learning. Ph.D., Industrial Engineering & Operations Research, University of California, Berkeley (2010) His work emphasizes eliminating roundoff errors in computational methods while addressing real-world challenges such as power grid reliability, radiation therapy quality assurance, and social media impact during extreme events. He has contributed to software tools like SPEX and EAGER projects. Key trends in his publications include exact factorization techniques for sparse/dense linear systems, heuristic algorithms for power flow optimization, and axiomatic methods for data aggregation. His research spans both theoretical advancements and practical implementations. Scientific Awards & Honors: Volunteer Service Award - Meritorious Level, INFORMS (2017) Dr. Hamed K. Eldin Outstanding Early Career Industrial Engineer in Academia Award, IIE (2016) Distinguished Award in Teaching - College Level, Association of Former Students (2016) Honorable Mention - INFORMS Junior Faculty Interest Group paper competition (2015) Selectee for National Academy of Engineers’ Frontiers of Engineering Education Symposium (2014) Professor of the Year Award - IIE student chapter (2013) Montague-Center for Teaching Excellence Scholar (2012) He has secured grants like the EAGER project for power grid topology control and contributed to teaching excellence through awards and symposium participation. No student advising details are publicly available in the provided text.

Hamzah Syedمشاهده پروفایل

استادیار

• Statistical Genetics
• Bioinformatics
• Multi-Omics Studies
• +۳ مورد دیگر

Dr. Hamzah Syed is an Assistant Professor of Statistical Genetics and Bioinformatics at Koç University's School of Medicine. He leads research integrating statistical methods and machine learning into multi-omics studies, particularly targeting cancer and neurodegenerative diseases like ALS. His work focuses on overcoming drug resistance mechanisms in cancers such as prostate cancer and triple-negative breast cancer through epigenetic and genomic approaches. Dr. Syed holds a PhD in Biostatistics from the University of Liverpool and conducted postdoctoral research at University College London (UCL), specializing in rare disease datasets and bioinformatics tools. He is the principal investigator on a CIHR-funded project exploring microbiomic biomarkers for ALS and has secured over ten TÜBİTAK grants. His research portfolio includes developing computational platforms like EPIKOL for epigenetic vulnerability screening and MOPower for multi-omics power analysis. He manages clinical and multi-omic data for Koç University's high-performance computing infrastructure, ensuring robust data integration across medical research projects. Key research themes include drug resistance in solid tumors, epigenetic regulation in glioblastoma, and applying phenome-wide association studies (PheWAS) to understand disease mechanisms. His work bridges statistical rigor with translational applications, aiming to advance precision medicine strategies.

Rohitash Chandraمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Climate Science
• +۱۳ مورد دیگر

Dr. Rohitash Chandra is a Senior Lecturer in Data Science at the University of New South Wales (UNSW) School of Mathematics and Statistics. He leads research in AI methodologies (e.g., Bayesian deep learning, neuroevolution) with applications to climate extremes, geoscience models, mineral exploration, biomedicine, and ancient religious text analysis. He is the Data Theme Lead of the ARC ITTC Training Centre for Data Analytics in Minerals and Resources (2020–2025) and a Chief Investigator in NHMRC projects on AI-driven drug repurposing for COVID-19. Education: PhD in Artificial Intelligence (Victoria University of Wellington, 2012), MSc (University of Fiji, 2008), BSc (University of the South Pacific, 2006). Research Interests: His work spans AI-driven climate modeling (cyclone forecasting, paleoclimate analysis), geoscience applications (remote sensing for mineral exploration), medical diagnosis (skin cancer detection, drug repurposing), and AI in humanities (sentiment analysis of religious texts like the Bhagavad Gita). He pioneers methods like Bayeslands for geoscientific parameter estimation and Bayesreef for reef evolution modeling. Articles Trends: Recent work focuses on LLM applications in cultural analysis, economic forecasting frameworks, and Bayesian neural networks for uncertainty quantification. His publications bridge AI with interdisciplinary challenges in earth sciences, healthcare, and social sciences. Grants & Awards: Recipient of the UNSW Science Silverstar Award (2022), Sydney Fellowship (2017–2019), and over $10M in ARC/industry funding. He has supervised 40+ students across PhD, masters, and honours programs. Labs & Teams: Founder of the Transitional Artificial Intelligence Research Group (t-AI). Collaborates with EarthByte Group, UN agencies, and institutions like Imperial College London on projects ranging from Mars landscape modeling to Hindu philosophy LLMs like Vedanta-GPT.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Prof Kai Qin is a Professor of AI and Data Science at Swinburne University, affiliated with the School of Science, Computing and Emerging Technologies. He holds roles including Director of the Intelligent Data Analytics Lab, Deputy Director of the Swinburne Space Technology and Industry Institute, and Vice President for Education at IEEE Computational Intelligence Society (CIS). Qin earned his B.Eng. from Southeast University (2001) and PhD from Nanyang Technological University (2007). His research focuses on Computational Intelligence (CI), encompassing Neural Networks, Evolutionary Computation, and Fuzzy Systems, with applications in Machine Learning, Remote Sensing, and Pervasive Computing. His work has garnered over 20k Google citations and recognition such as IEEE Fellow (2025). Key achievements include the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and leadership in conferences like IJCNN 2022. He pioneered the Master of Data Science program at Swinburne (2018–2020) and leads initiatives in federated learning, onboard AI for satellite missions, and AI-driven medical diagnostics. Qin’s professional contributions span editorial roles in journals like Swarm and Evolutionary Computation and leadership in IEEE technical committees. His grants include SmartSat CRC projects for satellite AI and ARC-funded research on gravitational lensing and traffic analytics.

Giovanni Steaمشاهده پروفایل

دانشیار

• Quality of Service
• Wireless Networks
• Network Calculus
• +۳ مورد دیگر

Giovanni Stea is an Associate Professor at the Department of Information Engineering within the College of Engineering at the University of Pisa. His research focuses on Quality of Service, resource allocation in wireline and wireless networks, performance evaluation through simulation and analytical techniques, and traffic engineering. Dr. Stea's research spans network calculus, wireless systems, and telecommunications infrastructure. His recent work demonstrates strong emphasis on 5G/6G networks, AI integration in telecommunications, and optimization of network performance. Key trends include: Advancements in network calculus methodologies Innovations in mobile edge computing applications Development of trustworthy AI systems for next-generation networks Cross-disciplinary approaches combining telecommunications and machine learning His publications consistently address computational efficiency in network analysis and practical implementations of theoretical models in real-world telecommunications environments.

• Machine Learning
• Optimization Techniques
• Healthcare Operations Management
• +۵ مورد دیگر

Michele Samorani is an Associate Professor in the Department of Information Systems & Analytics at the Leavey School of Business, Santa Clara University, and Program Director for the Master of Science in Information Systems (MSIS). He holds a PhD in Operations and Information Management from the University of Colorado Boulder and degrees in Computer Science from the University of Bologna. His research focuses on combining machine learning and optimization to improve business processes and healthcare systems. Key areas include reducing racial disparities in medical appointment scheduling, relational data mining, and metaheuristic algorithms. Collaborations include healthcare organizations dedicated to Black women, mental health nonprofits, and pharmaceutical companies. His work has been featured in MIS Quarterly , Manufacturing & Service Operations Management , and INFORMS Journal on Computing . Award highlights include the 2021 INFORMS Minority Issues Forum Paper Competition, AACSB's 2021 Innovations That Inspire, and inclusion in a United Nations report. Media features include Forbes and WIRED . He teaches courses such as Data Science with Python, Natural Language Processing, and Business Intelligence. His research emphasizes practical impact, addressing social equity through algorithmic fairness and operational efficiency in healthcare and retail. Ongoing projects explore ethical AI applications and decision support systems for vulnerable populations.

Dongjin Songمشاهده پروفایل

استادیار

• Continual Learning
• Graph Machine Learning
• Time Series Analysis
• +۲ مورد دیگر

Dr. Dongjin Song is an Assistant Professor in the School of Computing at the University of Connecticut, specializing in continual learning, graph representation, and time series analysis. He holds a PhD from UC San Diego (2016) and previously worked at NEC Labs America. His NSF CAREER award (2024) supports research on evolving graph learning for applications in healthcare, energy, and transportation. Recognized with the Frontiers of Science Award (2024) and UConn AAUP Excellence Award (2025), he develops algorithms addressing catastrophic forgetting and generalization in dynamic systems. Research interests include: Robust representation learning for time-series and graph data Meta-knowledge distillation for heterogeneous systems Privacy-preserving federated learning LLM-enhanced multimodal forecasting He actively contributes as Area Chair at NeurIPS and Associate Editor for Neural Networks . Educational initiatives integrate research into AI/ML courses, and outreach targets K-12 STEM engagement. Current projects explore power outage prediction, clinical time-series analysis, and cross-platform mental health monitoring.