Find Academic Supervisors

• Pedestrian Trajectory Prediction
• Crowd Monitoring Systems
• Computer Vision
• +6 more

Muhammad Haris Kaka Khel is a part-time lecturer at Atlantic Technological University's Department of Computing in Ireland while pursuing his Ph.D. in pedestrian trajectory prediction. His research focuses on developing adaptive systems that analyze human behavior, social interactions, and environmental dynamics to forecast multiple possible pedestrian paths using the PATHS framework. He works under supervisors Dr. Kevin Meehan and Dr. Paul Greaney at ATU Donegal, with additional guidance from Dr. Marion McAfee (ATU Sligo) and Dr. Sandra Moffet (Ulster University). B.S. in Electrical Engineering (Communication specialization) from UET Peshawar, Pakistan (2019) Research master's at University of Kuala Lumpur, Malaysia (completed 2022) with Erasmus exchange experience at Politecnico di Torino, Italy Ph.D. candidate at Atlantic Technological University (expected completion 2026) Research Interests Khel's work addresses critical gaps in pedestrian trajectory prediction by modeling multiple possible paths rather than single deterministic outcomes. This approach enhances robustness in dynamic environments like urban streets and public spaces, with applications in: Autonomous vehicle navigation safety Urban planning and walkability improvements Event crowd management systems Smart surveillance for anomaly detection Accessible navigation for visually impaired Public transportation optimization Teaching & Collaboration As a part-time lecturer at ATU Donegal, he contributes to computing education while maintaining external collaborations including: Sponsored research with Saudi Arabia's Deputyship for Research and Innovation Graduate research assistant role at University Kuala Lumpur-British Malaysian Institute Former intern at Center of Intelligent Systems and Networks Research

• Integrated Sensing and Communication
• Wireless Communications
• Signal Processing
• +6 more

Dr. Fan Jiang serves as an Associate Senior Lecturer at Halmstad University's School of Information Technology, where he leads research at the intersection of wireless communications and localization technologies. His academic profile reflects strong technical expertise in next-generation wireless systems with practical applications in vehicular networks and satellite communications. His research program focuses on integrated sensing and communication (ISAC) frameworks, with particular emphasis on: Massive MIMO and millimeter wave system design Wireless localization/synchronization algorithms Integration of sensing functionalities within communication protocols Applications for vehicular positioning and satellite networks Analysis of his 15 publications from 2022-2024 reveals consistent output in high-impact venues, with growing emphasis on satellite communications and reconfigurable intelligent surfaces. His work demonstrates strong mathematical rigor in channel modeling and algorithm design while addressing practical implementation challenges like synchronization errors and hardware constraints. Dr. Jiang maintains active research collaboration through co-authorship networks and participates in experimental validation efforts, as evidenced by his 2022 field study on 5G mmWave positioning. His technical approach balances theoretical innovation with real-world applicability across multiple wireless domains.

Domagoj MatijevićView profile

Associate Professor

• Machine Learning
• Optimization
• Bioinformatics
• +5 more

Domagoj Matijević is an Associate Professor at the School of Applied Mathematics and Informatics within Josip Juraj Strossmayer University of Osijek . His academic career spans computational geometry, optimization algorithms, and bioinformatics applications. PhD in Computer Science (Algorithms and Complexity), Max-Planck-Institute for Computer Science, Saarbrücken (2007) MS in Computer Science, Saarland University (2002) BS in Mathematics and Computer Science, University of Osijek (2001) Research interests focus on Machine Learning , Computational Geometry , and Bioinformatics , particularly through software tools like Fortuna for RNA splicing analysis and Trajan for comparing single-cell trajectories. His work bridges theoretical computer science with practical implementations in C++ , Python , and CUDA for high-dimensional data processing. 2023 Best Paper Award at MIPRO's Artificial Intelligence Systems track Key contributor to Neural Network-Based Pollen Prediction and Well-Separated Pair Decomposition implementations Developed Trajan for dynamic pseudotime warping and Fortuna for novel splicing event detection Currently teaches Algorithm Complexity , Computational Geometry , and Embedded Systems . Past projects include NVIDIA-funded GPU implementations and German-Croatian collaborations on kinetic spanners.

• Macroeconomics
• Monetary Policy
• Fiscal Policy
• +3 more

Jinill Kim is a Professor in the Department of Economics at Korea University, Seoul, South Korea. His research focuses on macroeconomic policy, particularly monetary and fiscal interactions, with affiliations to institutions including the Board of Governors of the Federal Reserve System, Bank of Korea, and International Monetary Fund. Primary Affiliation: Department of Economics, Korea University Secondary Affiliations: IMF, Federal Reserve Board, Bank of Finland, Barcelona School of Economics His work spans computational economics, dynamic general equilibrium modeling, and optimal policy design under conditions like zero interest rate bounds and demographic shifts. Recent publications address topics such as: Designing loss functions for central banks Welfare effects of fiscal transfers Extreme event responses in monetary policy Two-sector productivity shocks Kim's methodological expertise includes perturbation techniques, projection methods, and calibration of macroeconomic models.

• High-Temperature Corrosion
• Aerospace Materials
• Power Systems & Turbines
• +6 more

Dr. Simon Gray is a Principal Fellow in High Temperature Corrosion and Mechanical Interactions at Cranfield University's Surface Engineering and Precision Centre. His research focuses on materials performance in extreme environments, particularly for the gas turbine industry, with key areas including high-temperature corrosion fatigue, hydrogen testing, accelerated oxidation, and machine learning applications for non-destructive evaluation. Expertise: Aerospace Materials, Power Systems & Turbines, Surface Engineering, Sustainable Manufacturing Collaborators: Rolls-Royce Holdings PLC, Siemens Energy AG, MTU Aero Engines AG, NASA, UK Atomic Energy Authority His recent work includes stress corrosion cracking of single-crystal Ni-base superalloys, AI-driven crack detection in microscopy images, and oxidation studies of intermetallic compounds. Clients include major aerospace companies and energy authorities. Scientific Contributions: Gray's publications address hot corrosion mechanisms, environmental degradation of superalloys, and data-driven battery health classification systems. His team develops lifing models for turbine components and investigates hydrogen embrittlement effects.

• Operational Research
• Multi-objective Optimization
• Project Management
• +3 more

Helena Gaspars-Wieloch is a Professor in the Department of Operations Research and Mathematical Economics at the Institute of Informatics and Quantitative Economics, Poznan University of Economics and Business (PUEB). With academic progression from Doctor in 2008 to Professor Assistant in 2019, she has established herself as a leading researcher in decision science with interdisciplinary expertise spanning economics and computer science. Her research focuses on operational research , multi-objective optimization , project management , and optimization under uncertainty , with 75% of her work in economics and finance and 25% in computer and information sciences. She has developed innovative methodologies including the Target Decision Rule for uncertain problems and explored connections between one-criterion decision making under uncertainty and multi-criteria decision making under certainty. Professor Gaspars-Wieloch's recent publications (2022-2024) demonstrate continued scholarly productivity with 53 total publications. Her work shows strong integration of scenario planning with decision rules, particularly for new and risky projects where traditional probability assessments are unavailable. She has made significant contributions to applications of AHP, TOPSIS, and Goal Programming in novel contexts for economic decision making. Total publications: 53 Promoted theses: 1 Activities: 18 Achievements: 6 Total ministerial score: 1,397 As an educator, she has contributed significantly to teaching quantitative subjects at economics universities, developing Excel-based operational research materials and reflecting on e-learning experiences. Her research has practical applications in project management for innovative projects with short life cycles, resource allocation under uncertainty, sustainable development analysis, and risk management frameworks.

• Operations Management
• Supply Chain Management
• Inventory Management
• +8 more

Suresh Chand is a Professor and the Louis A. Weil Jr. Chair of Management at the Daniels School of Business, Purdue University . He serves as Department Head of Supply Chain and Operations Management. Ph.D., Industrial Administration, Carnegie Mellon (1979) M.S., Industrial Engineering, University of Texas (1976) M. Tech. & B. Tech., Mechanical Engineering, IIT Kanpur (1974, 1972) His research focuses on production process optimization across manufacturing and healthcare sectors. Key areas include: Capacity and production planning for volume flexibility Supply chain modeling to align supply with demand Reduction of patient flow time in healthcare systems Learning/forgetting effects in scheduling and setups Statistical process control and inventory management Professor Chand has published over 50 articles in Operations Research , Management Science , and similar journals. His work spans theoretical advancements in lot sizing, scheduling algorithms, and practical applications in healthcare and global supply chains. He has served as Associate Editor for Management Science (1986-2008), Area Editor for Production and Operations Management (1992-2003), and Senior Editor for Manufacturing and Service Operations Management (1999-2004). He was General Chair for the POM 2005 international conference. Professor Chand teaches core and elective courses in Operations Management to MBA, undergraduate, and doctoral students. He has taught at Purdue's Hannover campus and led Summer Study Abroad programs at TVS Motors in India.

AHMET ÜNVERENView profile

Assistant Professor

• Evolutionary Algorithms
• Optimization
• Artificial Intelligence
• +5 more

Ahmet Ünveren is an Assistant Professor in the Department of Computer Engineering at Eastern Mediterranean University's Faculty of Engineering. He maintains an active research profile in computational intelligence and optimization algorithms while supervising numerous graduate students. Education: PhD in Computer Engineering (1996-2002), Eastern Mediterranean University MS in Applied Mathematics & Computer (1994-1996), Eastern Mediterranean University BS in Applied Mathematics and Computers (1989-1994), Eastern Mediterranean University Dr. Ünveren's research focuses on evolutionary algorithms, optimization techniques, and artificial intelligence. His work spans multi-objective optimization, metaheuristics, and computational intelligence applications. He has developed novel approaches to problems like the quadratic assignment problem, vehicle routing, and nurse scheduling through hybrid algorithmic frameworks. His most recent publications demonstrate a continued focus on enhancing optimization algorithms through memory structures, hybrid approaches, and multi-objective frameworks. His research shows a progression from basic evolutionary algorithms to sophisticated hybrid methods incorporating reinforcement learning elements in recent work. Supervision: Dr. Ünveren has supervised numerous graduate students, including multiple PhD candidates and over 20 Master's students. His students' research covers evolutionary algorithms, optimization techniques, machine learning applications, and various computational intelligence methods. Current PhD students continue to explore reinforcement learning integration with evolutionary algorithms for complex optimization problems.

Álvaro MorenoView profile

Researcher

• Fluid Mechanics
• Aerodynamics
• Machine Learning
• +5 more

Álvaro Moreno is a postdoctoral researcher at Universidad Carlos III de Madrid (UC3M), following a postdoctoral position at Massachusetts Institute of Technology (MIT) and a PhD in Physics of Fluids at Universiteit Twente. His academic training includes a Bachelor’s and Master’s in Aerospace Engineering from Universidad de Sevilla and Technische Universität München, respectively.

• Internal Combustion Engines
• Emissions Reduction
• Computational Fluid Dynamics (CFD)
• +3 more

Prof. Dr. Mehmet Zafir Gül is a Professor at the Department of Thermodynamics and Heat, Faculty of Engineering, Marmara University. He holds a Ph.D. in Mechanical Engineering from the University of Manchester Institute of Science and Technology (UMIST). His career includes roles as Rector of Marmara University (2010–2014), Head of Mechanical Engineering Department (2005–2010), and has served on national academic committees like YÖK. Education: Bachelor's in Mechanical Engineering from Istanbul Technical University (1982) Ph.D. in Mechanical Engineering from UMIST (1994) Research Interests: Prof. Gül specializes in Internal Combustion Engines , focusing on emissions reduction, combustion optimization, and CFD modeling. His work integrates advanced numerical simulations with experimental validation to address challenges in engine efficiency and environmental impact. Awards: Fulbright Scholarship (2002) Ulakbim Yayın Teşvik (UBYT) Award (2009) Advising & Grants: Supervised 15+ theses and led projects funded by TÜBİTAK, Fulbright, and industry partners. His research spans alternative fuels, engine geometry optimization, and low-emission technologies. Labs/Teams: Active in automotive and energy sectors, collaborating on projects like biodiesel production, solar-powered car design, and SCR systems for emission control.

Xiangjie ChenView profile

Lecturer

• Clean Energy
• Renewable Energy Systems
• Battery Technology
• +5 more

Dr. Xiangjie Chen is a Lecturer in Clean Energy at the Wolfson School of Mechanical, Electrical and Manufacturing Engineering, University of Leicester, and a member of the Centre for Renewable Energy Systems Technology (CREST). She joined in 2019 after serving as a Research Fellow at the University of Nottingham, where she developed strong industrial connections across the UK and Europe. Her expertise spans sustainable energy systems, battery technology, solar thermal applications, and building retrofitting strategies. Education : PhD in Sustainable and Renewable Energy, University of Nottingham (2009–2013) MSc in Building Engineering (First Class), Politecnico di Milano, Italy (2006–2008) BEng in Civil Engineering (First Class), Beijing Jiaotong University, China (2002–2006) Research Interests : Dr. Chen focuses on advancing clean energy technologies, including lithium-ion battery degradation modeling, solar-driven desalination systems, and holistic approaches to building energy retrofitting. Her work integrates computational modeling with experimental validation to address challenges in energy storage, thermal management, and sustainable architecture. Recent projects emphasize decarbonisation pathways for UK housing and innovative materials for thermochemical energy storage. Key Contributions : She has pioneered the Energy-Flow based Ensemble Calibration model for energy retrofit predictions and developed the Motivation-Objective-Criteria (MOC) framework to engage homeowners in low-energy renovations. Her research on hybrid hydrogen production systems and membrane-based dehumidification technologies has furthered practical applications in renewable energy integration. Awards : Best Research Paper Award, Engineering Research Showcase 2019, University of Nottingham Labs & Collaborations : Active at CREST, Dr. Chen collaborates with industry partners to translate research into commercial solutions. Her work bridges academic innovation with real-world sustainability challenges in energy systems and built environments.

Alessandro AgnetisView profile

Professor

• Combinatorial Optimization
• Scheduling Algorithms
• Operations Research
• +3 more

Alessandro Agnetis is a Full Professor at the Department of Information Engineering and Mathematical Sciences, University of Siena, with expertise in combinatorial optimization and scheduling theory. His research spans operations research, multi-agent systems, and lean management applications, with recent publications focusing on adversarial bilevel scheduling, drone emergency logistics, and fairness-utility trade-offs in two-agent systems. Current Courses: Optimization Methods, Combinatorial Optimization, Production Planning (Master's in Engineering Management) Contact: alessandro.agnetis@unisi.it Research Trends: His work addresses complex scheduling problems across manufacturing, healthcare, and logistics, particularly emphasizing: Multi-agent coordination and Nash equilibrium models Parallel machine scheduling with unreliable jobs Emergency delivery optimization using drones Time-critical testing and cross-docking systems Lean management integration in healthcare operations

Chandra KambhamettuView profile

Professor

• Computer Vision
• Robotics
• Autonomous Systems
• +3 more

Chandra Kambhamettu is a Professor in the Department of Computer and Information Sciences at the University of Delaware, and Director of the Video/Image Modeling and Synthesis (VIMS) Lab. His research focuses on computer vision, robotics, and autonomous systems with applications in biomedical imaging, remote sensing, and multimedia analysis. Education: PhD in Computer Science and Engineering from the University of South Florida (1991-1994), M.S. in Computer Science and Engineering from the same institution (1989-1991), and B.S. from Osmania University (1985-1989). Research Themes: His work spans salient object detection, 3D point cloud analysis, thermal imaging, and biomedical applications such as sickle cell retinopathy detection. Recent projects include deep learning frameworks for SAR imagery analysis, autonomous systems for polar environments, and medical image segmentation. Publications: Recent articles emphasize advancements in neural network architectures (e.g., SODAWideNet++), thermal material classification, and Arctic sea ice motion estimation. Themes include fusion of RGB-IR imagery, salient object detection without pre-training, and transformer-based models. Labs and Teams: Leads the VIMS Lab, which develops cutting-edge solutions for video modeling, image synthesis, and multispectral data analysis.

Carlos LoustoView profile

Professor

• Computational Relativity
• Gravitation
• Gravitational Waves
• +8 more

Carlos Lousto is a Professor of Mathematics and Statistics at the College of Science, Rochester Institute of Technology (RIT). He specializes in computational relativity, gravitational waves, and black hole dynamics, contributing to LIGO-Virgo-KAGRA collaborations. His research leverages advanced numerical simulations and radio astronomy techniques, particularly through the Argentine Institute of Radio Astronomy (IAR). Education: Ph.D., Universidad Nacional de la Plata (Argentina) Ph.D., Universidad de Buenos Aires (Argentina) Research Interests: Computational Modeling of Black Hole Mergers Gravitational Wave Astronomy Pulsar Timing Arrays Extreme-Mass-Ratio Inspirals Numerical Relativity Techniques General Relativity Tests Article Trends: Recent work focuses on high-energy black hole collisions, pulsar glitches, and observational campaigns for continuous gravitational waves. He explores eccentric orbits and ultralight dark matter signatures while refining detector sensitivity and multimessenger astronomy methods. Grants & Advising: Grants: Supported by NSF via XSEDE (2012-2013) for computational resources. Teaching: Advises graduate students through thesis courses (ASTP-790, ASTP-890). Labs/Teams: Active member of LIGO-Virgo collaborations, IAR, and NANOGrav. Develops numerical relativity catalogs and leads pulsar timing initiatives in South America.

Alexander SchwingView profile

Associate Professor

• Machine Learning
• Computer Vision
• Robotics
• +7 more

Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations to the Coordinated Science Laboratory and the Computer Science Department. His research focuses on machine learning, computer vision, and structured prediction, emphasizing algorithms for deep networks, multivariate distributions, and 3D scene understanding. He has held postdoctoral positions at the University of Toronto and completed his PhD at ETH Zurich. Educations: PhD in Computer Science (ETH Zurich, 2014) Diploma in Electrical Engineering & IT (Technical University of Munich, 2010) Research interests include generative modeling, embodied agents, video segmentation, and reinforcement learning. He has developed influential frameworks like XMem for video object segmentation and MaskRNN for instance-level tracking. His work emphasizes reproducibility and open-source releases. Key awards include the NSF CAREER Award, Amazon Research Award, and NVIDIA GPU donations. He has advised over 25 students, many of whom have pursued roles at top tech firms and academia. Current research explores structured prediction, multi-agent systems, and 3D reconstruction. His labs collaborate with industries like Samsung and Adobe, and he teaches courses on machine learning and pattern recognition.