Dr. Lecturer İnci Umakoglu is an Assistant Professor at the Faculty of Engineering , Kütahya Dumlupınar University . She holds a PhD in Electrical-Electronics Engineering from the same university and has been a Research Assistant in multiple laboratories since 2015, including Analog & Digital Communications, Measurement & Circuit, and Logic Design. Her research focuses on Wireless Communication Systems (NOMA, OTFS modulation) Antenna Design for 5G and beyond UAV-assisted network architectures IoT device security protocols She has led and advised numerous student research projects in avionics, telemetry, and low-altitude defense systems under the TÜBİTAK 2209-A and Scientific Research Project (BAP) programs. Recent publications highlight her work in deep learning for signal detection , LoRa-based telemetry systems , and IoT security methods , with a strong emphasis on practical implementations in 5G and UAV technologies. She is an active member of IEEE and the Applied Computational Electromagnetics Society (ACES) .
Mustafa Altun is a Professor at Istanbul Technical University in the Department of Electronics and Communication Engineering. With an h-index of 11 and 78 research outputs, he has established himself as a prominent researcher in electronics engineering and neural network hardware implementations. His research focuses on key areas including: Nano-crossbar Arrays and Switching Lattices Logic Synthesis and Boolean Function Realization Neural Network Hardware Implementation Low-voltage Circuit Design Fault Tolerant Computing Electrodeposition for Li-ion Battery Anodes Dr. Altun's recent work demonstrates a strong emphasis on energy-efficient hardware implementations of artificial neural networks using approximate arithmetic blocks to reduce power consumption while maintaining acceptable accuracy levels. His research on silicon anode electrodeposition has explored how artificial light-induced processes can increase silicon loading for improved battery performance. These dual research directions reflect his commitment to advancing both computing hardware and energy storage technologies. His scientific achievements have been recognized with prestigious awards including the ODTÜ Mustafa N. Parlar Research Incentive Award (2022), EU H2020 Success Award (2015), and the Young Scientist Award (2020). His work has been supported by multiple projects funded by TUBITAK and SRP. Dr. Altun has supervised 16 students according to available data and has led numerous research projects focusing on novel circuit architectures for efficient computing, particularly in neural networks and fault-tolerant systems. His laboratory work continues to push the boundaries of nano-crossbar array computing and its practical applications.
Prof. ZAFER ŞİAR is a Professor of Mathematics at the Department of Mathematics, Faculty of Arts and Sciences, Bingöl University. He holds a Ph.D. in Mathematics from Sakarya University (2012), followed by promotions to Associate Professor (2015) and Full Professor (2021). His academic career includes roles as a Research Assistant at Bilecik University (2012–2013) and an instructor at Dershaneler between 2006–2009. His research focuses on Number Theory, particularly Diophantine equations, Fibonacci/Lucas/Pell sequences, and repdigit properties. Notable contributions include studies on exponential equations, integer solutions, and sequence-based number theory. He has supervised multiple thesis students, including Fatih ERDUVAN (2021), İbrahim ERDURAN (2022), and Merve NADİROĞLU (2024). Recent publications (2020–2023) emphasize perfect powers in sequences, repdigit analyses, and solutions to complex Diophantine equations. His work bridges algebraic and computational number theory, with applications in discrete mathematics. He serves as a reviewer for Mathematical Reviews and over 10 international journals since 2016.
Assistant Professor Süha Tuna is affiliated with Istanbul Technical University at the Institute of Informatics in the Computational Science and Engineering department. She holds a postdoctoral degree from the University of Strathclyde and has been actively involved in academic roles since 2010.
Elvin Çoban serves as Associate Professor and Chair of the Industrial Engineering Department within Ozyegin University's Faculty of Engineering, specializing in optimization applications for healthcare systems, humanitarian logistics, and service operations. Her research bridges theoretical operations research with real-world implementation in critical infrastructure domains. Education Background: PhD in Operations Management and Manufacturing, Carnegie Mellon University (2012) MS in Operations Management and Manufacturing, Carnegie Mellon University (2010) MS in Industrial Engineering, Sabanci University (2008) BS in Manufacturing Systems Engineering, Sabanci University (2006) Dr. Çoban's research program focuses on developing mathematical models for complex decision-making under uncertainty, with significant contributions to disaster response logistics using drone technology and healthcare operations management. Her work integrates machine learning with combinatorial optimization to solve scheduling problems in medical facilities and vaccine distribution networks, while also exploring energy systems through electric vehicle charging infrastructure modeling. Current projects emphasize predictive analytics for post-disaster damage assessment and resource allocation during pandemics. Analysis of her 15 most recent publications (2019-2024) reveals three dominant research thrusts: (1) humanitarian logistics (35% of output), particularly drone routing for disaster assessment; (2) healthcare operations (50%), including operating room scheduling and vaccine distribution; and (3) energy systems (15%), focused on electric vehicle infrastructure optimization. Methodologically, her work combines stochastic programming, metaheuristics, and machine learning to address data-scarce environments. Research Funding and Advisory: Principal Investigator for TUBITAK 1001 project (2021-2024): Information Gathering and Damage Prediction for Post-Disaster Drone Assessment Researcher for TUBITAK 3501 project (2013-2016): Blood Supply Chain Optimization Consultant for TEA Networks (2018-2021) and TUBITAK 1507 workforce management project (2014-2016) Advises three graduate students: Kian Farajkhah (PhD candidate), Birce Adsanver (MSc 2020), and Gulsah Alper (MSc 2015) on optimization applications in disaster response and healthcare She leads an active research group at Ozyegin University that collaborates with government agencies and industry partners on projects involving real-time decision support systems. Current initiatives focus on integrating real-world constraints into optimization models for medical logistics and developing scalable algorithms for drone-based disaster assessment networks, with ongoing recruitment for TUBITAK-funded humanitarian logistics research positions.
Özgün Ekici is an Associate Professor of Economics at the Faculty of Business Administration, Ozyegin University in Istanbul, Türkiye. He holds a PhD in Economics (2011) from Carnegie Mellon University, an MSc in Economics (2007) from the same institution, and a BSc in Industrial Engineering (2003) from Bilkent University. His academic journey also includes an MSc in Economics and Management Sciences (2005) from Humboldt University. Research Interests: Dr. Ekici specializes in Matching Theory, Mechanism Design, and Market Design, developing algorithms for applications like kidney allocation and public school quota systems. His work spans Microeconomics, Game Theory, and Portfolio Selection, with recent publications focusing on pair-efficiency in reallocation systems, equilibrium analysis in coalition structures, and optimal toll design for urban infrastructure. Teaching: He has taught courses in Microeconomics, Macroeconomics, and Game Theory. Contact: Email: ozgun.ekici@ozyegin.edu.tr
Assoc. Prof. Meltem Adıyaman is a faculty member at Dokuz Eylül University, Department of Mathematics, College of Science. She has held academic positions since 2006, advancing from Research Assistant (2006-2011), Assistant Professor (2011-2024), and currently Associate Professor (2024-present). Her research focuses on numerical methods for differential equations, eigenvalue problems, and computational mathematics, with an emphasis on residual methods, Bézier curve applications, and nonlinear systems. Doctorate in Mathematics (2009), Dokuz Eylül University Postgraduate in Mathematics (2005), Dokuz Eylül University Undergraduate in Mathematics (2003), Dokuz Eylül University Her research interests include numerical analysis, differential equations, eigenvalue problems, and computational mathematics, particularly applied to nonlinear and singular systems. She has published 25 WoS-indexed articles and 6 in Scopus, with a Scopus h-index of 38. Her work frequently employs residual methods, Bézier control points, and Taylor decomposition techniques. Assoc. Prof. Adıyaman has advised 5 theses, including doctoral and master’s students A. Beler, M. Kemal, V. Öğer, and B. Noyan. She has served as a peer reviewer for journals like Computational Methods for Differential Equations and participated in 15+ international conferences. Her career spans teaching across all levels, from calculus to advanced numerical methods.
Kadir Can Erbaş serves as a Lecturer in the Biomedical Engineering Program at Başkent University, Ankara, Turkey. Holding a PhD in Physics from Middle East Technical University (METU), he bridges theoretical physics with practical engineering applications across diverse domains including materials science, biomedical engineering, and computational geometry. His academic foundation includes: PhD in Physics (2013), METU Master's Degree in Physics (2008), METU Master's Degree in Physics Education (2005), METU Bachelor's Degree in Physics Education (2001), METU Dr. Erbaş specializes in Mathematical Physics and Thermal Analysis , with pioneering work on Newtonian thermal analysis for casting processes. His research has expanded into Chaos Theory applications for modeling romantic relationships and Geometric Pattern Design in Islamic art. His methodology consistently combines analytical solutions with experimental validation, demonstrating exceptional versatility across physics subdisciplines. Analysis of his 15 most recent publications reveals a clear trajectory from core materials science (2014-2016) toward interdisciplinary applications. Early work focused on solidification modeling in metallurgy, while recent publications explore geometric algorithms and emotional dynamics, indicating strategic expansion into computational art and social physics. This evolution highlights his ability to transfer fundamental physics principles to novel domains. Scientific recognition: No formal awards documented in current records Teaching responsibilities include General Physics I/II and associated laboratory courses. No information is available regarding graduate student supervision, research grants, or dedicated laboratory facilities. His active publication record through 2025 confirms ongoing research productivity within Başkent University's academic framework.
Coşar GÖZÜKIRMIZI is an Assistant Professor in the Department of Computer Engineering at Beykent University's Faculty of Engineering and Architecture. He has been serving at Beykent University since 2016 as a Doctor Lecturer and previously worked as a Research Assistant at Istanbul Technical University's Institute of Information from 2014. His research focuses on probabilistic evolution theory, numerical methods for ordinary differential equations, and multivariate analysis. His work particularly emphasizes: Probabilistic evolution approaches to ODE systems Fluctuation expansion techniques Numerical integration methods Enhanced Multivariance Products Representation Telescope matrices and space extension techniques Dr. GÖZÜKIRMIZI's publication record shows consistent theoretical development from foundational numerical integration techniques (2008-2012) to sophisticated probabilistic evolution frameworks (2015-2025). His recent work has concentrated on probabilistic evolution theory for explicit autonomous ordinary differential equations, with particular attention to squarified telescope matrices and optimal space extension. His research demonstrates strong theoretical foundations combined with practical numerical implementations, primarily published in international computational mathematics conferences. Dr. GÖZÜKIRMIZI maintains an active research profile with publications spanning from 2008 to 2025. He frequently collaborates with Metin Demiralp (on most publications), Melike Ebru Kirkın, and Elif Tataroğlu. His work bridges theoretical mathematics with computational applications, particularly in solving complex differential equation systems through innovative probabilistic approaches.
Bengisen Pekmen is a Professor in the Department of Mathematics at TED University's Faculty of Arts and Sciences, serving concurrently as Vice Head of the Department. She holds a Scopus ID 57200550143 and teaches a wide range of mathematics courses, including Calculus, Linear Algebra, Numerical Methods, and Senior Project modules. Her research focuses on numerical analysis, fluid dynamics, magnetohydrodynamics (MHD), and machine learning applications in fluid flow modeling. Education: B.Sc./M.Sc./Ph.D. in Mathematics (with Physics minor) from Middle East Technical University (METU), Ankara, Turkey. Her research interests span numerical simulation of fluid dynamics problems, bioconvection flow, nanofluid behavior, and MHD effects in porous media. She employs techniques like radial basis functions (RBF), differential quadrature methods (DQM), and machine learning for predictive modeling. Publications (selected 2023–2025) analyze MHD Stokes flow, nanofluid convection in wavy enclosures, entropy generation, and bioconvection with magnetotactic bacteria. These studies emphasize interdisciplinary applications in engineering and environmental science.
Merve Gürbüz Çaldağ is an Assistant Professor in the Department of Mathematics at TED University. She specializes in numerical analysis and computational fluid dynamics, particularly focusing on magnetohydrodynamics (MHD), nanofluid dynamics, and machine learning applications in fluid mechanics. Her research integrates advanced numerical methods like Radial Basis Functions (RBF) and boundary element techniques to solve complex fluid flow problems under magnetic fields. Dr. Çaldağ has taught a wide range of courses, including Advanced Calculus, Linear Algebra, Differential Equations, and Probability Theory, across multiple academic terms from 2022 to 2026. Her teaching emphasizes both theoretical foundations and practical computational skills. Her research interests revolve around modeling fluid dynamics in constrained geometries, such as lid-driven cavities and constricted channels, under varying magnetic field conditions. Recent work involves applying machine learning to enhance predictive accuracy in MHD flow simulations and analyzing bioconvection dynamics involving magnetotactic bacteria. She has also explored numerical stability and optimization of RBF-based methods for solving unsteady MHD flow equations. Despite no listed awards, her contributions to computational fluid dynamics and MHD modeling are evident through over 15 peer-reviewed articles since 2015. She currently holds no grants or lab affiliations mentioned in the provided texts.
Francesco Silvestri is an Associate Professor in Computer Engineering at the Department of Information Engineering, University of Padova. Previously, he held roles including Assistant Professor (2016-2019) and post-doctoral positions at the University of Padova and IT University of Copenhagen. He also served as a part-time lecturer at IT University of Copenhagen (2013-2014) and as a visiting scholar at the University of Texas at Austin (2006-2007). He earned his Ph.D. in Computer Engineering from the University of Padova in 2009. His research focuses on algorithms and data structures, emphasizing big data efficiency, high performance computing, and modern architectures. Key areas include similarity search, dimensionality reduction, graph mining, and parallel algorithms. He supervises PhD students and collaborates with researchers globally. His work has been recognized with awards such as the ICDT Best Paper Award (2023) and the ACM SIGMOD Research Highlights Award (2020). Teaching includes courses like Big Data Computing and Algorithm Design at the University of Padova and IT University of Copenhagen. He has served on program committees for major conferences like Euro-Par, IPDPS, and SIGMOD. His contributions span academic publications and industrial collaborations, including work on mobility data analysis and resilient algorithms. He is affiliated with the AIDA Lab (Algorithms for Intelligent Data Analytics) and actively contributes to the academic community through editorial roles, such as being an Associated Editor of the ACM Transactions on Parallel Computing.
Burcu Tunga is an Associate Professor in the Department of Mathematics at Istanbul Technical University, Faculty of Science and Letters. She holds a PhD in Computational Science and Engineering from the same institution and has been an active academic since 2005, progressing from Lecturer to her current rank. She has held key administrative roles, including Deputy Head of Department and membership in university-wide promotion and doctoral committees. PhD: Computational Science and Engineering, Istanbul Technical University (2004–2010) BS: Mathematics, Istanbul Technical University (1995–2000) Her research lies at the intersection of applied mathematics and computer science, with a strong emphasis on image processing, data modeling, and High Dimensional Model Representation (HDMR) . She develops and applies advanced mathematical models for image denoising, color-to-gray conversion, hyperspectral analysis, and machine learning-based diagnostics. Her work integrates fractional calculus, deep learning, and tensor decomposition to solve complex problems in medical imaging, agriculture, and materials science. Recent publications (2022–2025) show a strong trend toward hybrid models combining HDMR with deep learning and fractional operators , applied to tasks such as coffee leaf disease detection, wood defect imaging, and hyperspectral anomaly detection. These reflect a consistent focus on enhancing image quality, feature extraction, and pattern recognition through novel mathematical frameworks. Her scientific contributions are recognized through active research funding and a growing publication record. Key projects include: Anomaly Detection in Hyperspectral Images with HDMR (BAP-funded, 2024–2025) Disease Detection in Coffee Leaves with Deep Learning (completed, 2024) Content-Based Image Retrieval with HDMR (completed, 2018–2021) Burcu Tunga advises several graduate students and collaborates widely across disciplines. She leads research involving tomographic reconstruction, stress wave analysis in trees, and financial time series prediction. Her lab focuses on developing non-destructive evaluation techniques, medical CAD systems, and intelligent image processing algorithms , often leveraging HDMR as a core methodological framework.
Melike Nikbay is a Professor in the Department of Aerospace Engineering at Istanbul Technical University, affiliated with the College of Engineering. Her research focuses on aeroacoustics, aeroelasticity, multi-fidelity modeling, and sonic boom mitigation in supersonic aircraft design. She leads projects on machine learning applications in aerospace problems and has pioneered Bayesian optimization techniques for flow simulations. Nikbay has been recognized with the AIAA Associate Fellow Award (2017) and Young Contributor Award (2016). Her research interests include computational fluid dynamics (CFD), uncertainty quantification, and multidisciplinary design optimization. Notable projects include low-boom supersonic transport design and low-observable UAV development. She has published extensively on turbulence model calibration, wind tunnel testing, and surrogate modeling for aerodynamic optimization. Nikbay has secured 18 funded projects as Principal Investigator, including initiatives on machine learning validation, cavity flow analysis, and high-fidelity computational tools. Her work bridges theoretical advancements and practical aerospace engineering challenges, emphasizing reproducibility and industrial relevance.
İlker Murat Koç is a Professor in the Department of Mechanical Engineering at Istanbul Technical University. His research focuses on advanced sensor design, optimization techniques, tribology, and control systems. He has led multiple projects as Principal Investigator, including initiatives on force/moment sensor dynamics, unmanned aerial vehicle control, and medical robotic sensor development. Key research areas include: Multi-axis force/moment sensor design and optimization Genetic algorithm applications in engineering systems Tribological interactions in mechanical components L1 adaptive control methodologies He actively supervises academic research, having advised 23 theses. Current research projects involve sensor error analysis, inertial measurement unit compensation, and capacitive sensor development for medical robotics. His work bridges theoretical mechanical engineering with practical applications in aerospace, automotive, and medical technologies.
