Find Academic Supervisors

Gang LiView profile

Associate Professor

• Supply Chain Management
• Operations Management
• Optimization
• +12 more

Gang Li is an Associate Professor of Supply Chain and Operations Management at the Department of Management, Bentley University, with affiliate faculty status at Georgia State University. He holds two PhDs: one in Supply Chain and Operations Management from the University of Texas at Austin and another in Systems Engineering from the Beijing University of Aeronautics and Astronautics. Teaching Interests: Operations & Supply Chain Management, Decision Methods, Quality Management Research Interests: Service Process Optimization, Supply Chain Social Impact, Decision Support Systems His research focuses on model and algorithm development for service operations, with emphasis on sustainability integration and societal/environmental impact. Article trends include supply chain sustainability , optimization , service equity , and decision analytics . Award: ASQ Certified Six Sigma Black Belt

Dario Landa-SilvaView profile

Professor

• Computational Optimization
• Operations Research
• Artificial Intelligence
• +4 more

Dario Landa-Silva is a Professor of Computational Optimization at the University of Nottingham and a key member of the Computational Optimization and Learning (COL) Lab . He serves as Director of Teaching and Learning in the Faculty of Science and co-leads the Smart Systems and Operations initiative within the TMC Initiative . His academic journey began with a BEng and MSc from institutions in Mexico, followed by a PhD and PHCHE from the University of Nottingham. Education: BEng (Mexico) MSc (Mexico) PhD (University of Nottingham) PHCHE (University of Nottingham) Landa-Silva's research focuses on the intersection of computer science, operations research, and artificial intelligence , applying metaheuristics, hyper-heuristics, and machine learning to solve constrained combinatorial problems like vehicle routing, personnel scheduling, and space allocation. He has pioneered methods integrating optimization with data science, human computation, and computer simulation, particularly in logistics and healthcare applications. His recent publications highlight innovations in renewable energy forecasting , autonomous systems for oil exploration, and hybrid AI optimization techniques. Notable trends include multi-objective approaches , stochastic scenario bundling , and evolutionary algorithms for imbalanced data classification. Scientific Awards: Fellow of Advance Higher Education FHEA (Fellow of the Higher Education Academy) Landa-Silva has supervised numerous PhD students in topics such as workforce scheduling , nurse rostering , and logistics optimization . He has led over £1.2M in research grants from EPSRC , Innovate UK , and industry partners like Unilever , E.ON Energy , and Netacea Ltd . He is affiliated with the COL Lab and the Smart Systems and Operations team, emphasizing practical translation of research into business impact through Knowledge Transfer Partnerships and REF impact case studies .

Flavia MicotaView profile

Senior Lecturer

• Optimization Algorithms
• Cloud Computing
• Evolutionary Computation
• +3 more

Dr. Flavia Micota is a Senior Lecturer at the Faculty of Mathematics and Computer Science of the West University of Timisoara. Her academic work bridges theoretical computer science with practical applications in optimization and data analysis. University: West University of Timisoara Faculty: Faculty of Mathematics and Computer Science Her research focuses on: Optimization algorithms and metaheuristics Cloud computing resource allocation Evolutionary computation Medical data analysis using taxonomy-based measures Task scheduling in heterogeneous systems Algorithm design for static and dynamic environments Analysis of her publications reveals trends in applying hybrid optimization techniques to cloud computing challenges, refining metaheuristics for industrial scheduling, and developing taxonomy-driven similarity/dissimilarity measures for medical data classification. Common methodologies include differential evolution, symmetry breaking, and constraint satisfaction frameworks. No scientific awards are explicitly mentioned in the provided data.

• Mechanobiology
• Tissue Engineering
• Augmented Reality
• +4 more

Antonio Boccaccio is an Associate Professor in the Department of Mechanics, Mathematics and Management at the Polytechnic University of Bari . His research focuses on mechanobiology, tissue engineering, and augmented reality applications. Academic rank: Associate Professor Department: Mechanics, Mathematics and Management Research areas: Scaffold design, Finite element modeling, Stem cell biomechanics His recent work includes: Optimizing lattice spring models for mechanobiology Designing porous scaffolds for bone regeneration Developing augmented reality tools for dental and STEM applications

Samira Shirzaei NicholsView profile

Assistant Professor

• Operations Research
• Public Health
• Gender Equity
• +4 more

Samira Shirzaei Nichols serves as an Assistant Professor in the Department of CISA (Computer Information Systems and Analytics) at the College of Business, University of Central Arkansas (UCA), with contact details including email sshirzaei@uca.edu, office COB 305D, and phone (501) 450-5331. Her academic role integrates quantitative analytics with real-world problem-solving across public health, social equity, and business operations. Her research spans Operations Research, Public Health, and Gender Equity, employing machine learning, statistical modeling, and simulation to address epidemiological forecasting (HIV/COVID-19), gender pay disparities in academia/business, and service system optimization. Key methodologies include ARIMA/SARIMA time-series analysis, RNN neural networks, mixed-methods approaches, and Society 5.0 simulation frameworks, demonstrating interdisciplinary rigor in translating theoretical models to societal challenges like refugee health access and grocery store operational efficiency. Analysis of her 15 publications (2014-2025) reveals an accelerating focus on public health crises since 2020, with 60% of recent work dedicated to pandemic response modeling and gender equity analysis. While early research centered on supply chain optimization (2014-2018), post-2020 outputs pivot toward urgent health applications—evidenced by 5 COVID-19 forecasting studies—and structural inequities, using comparative data (2018 vs. 2021) to quantify gender pay gaps across academic disciplines. Her work consistently bridges technical analytics with human-centric outcomes. No information is available regarding student advising, grant funding, laboratory facilities, or research teams from the provided sources, though her prolific publication record indicates active scholarly engagement.

Yelena YeshaView profile

Professor

• Data Science
• Artificial Intelligence
• Machine Learning
• +5 more

Yelena Yesha serves as Professor and Knight Foundation Endowed Chair of Data Science and AI at the University of Miami's College of Arts and Sciences, with dual appointments in the Department of Computer Science and Department of Radiology. She holds several leadership positions including Innovation Officer at the Frost Institute for Data Science and Computing (IDSC), Director of IDSC AI + Machine Learning, Board Member and Senior Liaison for Industry Interaction at the Institute of the Mathematical Sciences of the Americas (IMSA), and Founding Director of NSF CARTA. Her research spans multiple interdisciplinary domains with strong emphasis on artificial intelligence and data science applications. She develops machine learning models for civil engineering problems like concrete strength prediction, creates blockchain solutions for IoT and government applications, and applies natural language processing to medical imaging and healthcare analytics. Her work demonstrates exceptional ability to bridge computer science with practical applications in engineering, healthcare, and public policy. Analysis of her recent publications reveals a strong trend toward practical AI implementations across diverse fields, with particular focus on healthcare applications (NLP for breast imaging, infectious disease monitoring) and engineering applications (3D-printed concrete, CFRP-confined structures). Her research increasingly incorporates explainable AI techniques like SHAP analysis and develops user-friendly interfaces for practical deployment of complex models. Knight Foundation Endowed Chair of Data Science and AI As Innovation Officer at IDSC and Director of multiple research initiatives, Dr. Yesha oversees significant research programs connecting academic work with industry applications. Her leadership in NSF CARTA indicates involvement in major federally funded research projects. She has served as principal investigator or key collaborator on numerous interdisciplinary grants spanning computer science, engineering, and healthcare domains. Dr. Yesha directs the IDSC AI + Machine Learning initiative and serves as Senior Liaison for Industry Interaction at IMSA, indicating leadership in research teams focused on data science applications. As Founding Director of NSF CARTA, she leads a consortium-level research effort connecting multiple institutions around data science and computing. Her work with IDIOMS demonstrates leadership in healthcare-focused research teams developing infectious disease monitoring systems.

• Operations Research
• Production Planning
• Scheduling
• +3 more

Semra Tunalı is a Professor at İzmir University of Economics' Faculty of Economics and Administrative Sciences, Department of Business Administration since 2010. She holds a Ph.D. in Computer Sciences from Ege University and has held academic positions at Ege University, Dokuz Eylül University, and as Visiting Fellow at GINTIC Institute of Manufacturing Technology, Northwestern University, and Loyola University. B.Sc. in Industrial Engineering, Ege University (1978) M.Sc. in Applied Statistics, Ege University (1980) MBA, University of Missouri (1985) Ph.D. in Computer Sciences, Ege University (1991) Her research focuses on production optimization, with emphasis on: Buffer allocation in unreliable production lines Capacitated lot sizing with setup carryover Scheduling flexible mixed-model assembly lines Hybrid metaheuristics for manufacturing systems Simulation optimization in industrial applications Combinatorial optimization in production planning Her recent publications (2010-2019) demonstrate expertise in applying heuristic algorithms like genetic algorithms and tabu search to complex production problems, particularly in lot sizing and assembly line optimization. Common themes include: Integration of simulation and analytical modeling Hybrid algorithm development for manufacturing systems Capacity planning improvements through optimization Reliability analysis in production line design

Andreas KloseView profile

Associate Professor

• Operations Research
• Combinatorial Optimization
• Integer Programming
• +3 more

Andreas Klose is an Associate Professor at the Department of Mathematics, Aarhus University, Faculty of Science. His work focuses on combinatorial optimization and integer programming, particularly for logistics applications. Active in exact solution approaches for bi-objective and uncertain facility location problems Develops metaheuristics for fixed charge transportation challenges Teaches master courses on location planning, vehicle routing, and optimization methods Research Trends: Recent publications emphasize facility location under uncertainty, exact algorithms for capacitated problems, and metaheuristic approaches. Topics span both theoretical and applied operations research. Teaching: Offers courses on Multiple Criteria Optimization , Location Planning , Vehicle Routing , and Metaheuristics , alternating between fall and spring semesters. Collaborations: Frequently collaborates with researchers like Simon Gadegaard and Kim Jörnsten on facility location and transportation problem frameworks.

Michal WeiszerView profile

Lecturer

• Optimisation
• Metaheuristics
• Operational Research
• +5 more

Dr. Michal Weiszer is a Teaching Fellow at the School of Engineering and Materials Science, Queen Mary University of London, specializing in optimization, metaheuristics, and operational research with applications in airport operations. His research has significantly contributed to the development of the Active Routing concept for airport ground movement, integrating aircraft engine performance and airframe dynamics with routing and scheduling to achieve greener operations. Dr. Weiszer's research interests focus on routing and scheduling, multi-objective optimisation, simulation, and intelligent transportation systems . His work bridges theoretical optimization techniques with practical aviation applications, particularly in airport ground movement. He has pioneered approaches that integrate aircraft performance models with routing algorithms to reduce emissions and improve efficiency in airport operations. His research spans both algorithmic development and practical implementation, with strong industry connections. Analysis of Dr. Weiszer's publication record shows a consistent focus on airport ground movement optimization using evolutionary algorithms and multi-objective approaches. His research has evolved from basic routing algorithms to more sophisticated integrated systems incorporating aircraft dynamics, emissions modeling, and uncertainty handling. Recent work has expanded into multimodal transportation and open-source simulation platforms, demonstrating the broadening impact of his research beyond traditional airport operations. Dr. Weiszer has received several notable awards and funding: QMUL Flexible Innovation Fund as Principal Investigator EPSRC sponsored TRANSIT project (EP/N029496/1) as Research Co-Investigator Impact Acceleration projects with Avisu as Co-Investigator Impact Acceleration projects with NATS (UK air traffic control) as Co-Investigator His grant portfolio demonstrates strong industry-academia collaboration, with projects translating theoretical research into practical applications. The funding from Royal Society, EPSRC, and industry partners highlights the recognition of his work's potential impact on sustainable aviation. Dr. Weiszer has successfully secured both early-stage innovation funding and larger collaborative projects, showing progression in his research career.

Filipe RodriguesView profile

Assistant Professor

• Optimization under Uncertainty
• Stochastic Programming
• Robust Optimization
• +2 more

Filipe Rodrigues is an Assistant Professor in the Mathematics Department at the Lisbon School of Economics & Management (ISEG), University of Lisbon, since 2020, specializing in Operational Research with emphasis on optimization under uncertainty. His educational background includes a PhD in Operations Research (2019), Master's in Operations Research (2015), and Bachelor's in Mathematics (2013), all from the University of Aveiro. Dr. Rodrigues' research centers on stochastic programming and robust optimization methodologies applied to port terminal operations and maritime transportation, addressing complex problems like berth allocation, quay crane scheduling, and vehicle routing under uncertain conditions. His work bridges theoretical advances with practical logistics solutions. His publication record shows consistent focus on maritime logistics optimization, with 15 recent articles in premier journals including European Journal of Operational Research and Transportation Research Part B. Key trends include distributionally robust modeling for port operations and advanced heuristics for vehicle routing under uncertainty. He has supervised nine Master's students on diverse topics including ship allocation under uncertainty, inspection routing for power lines, climate change investment impacts, and road freight pricing simulation. As an active researcher at ISEG, he contributes to the Data Lab and Policy Lab, supporting interdisciplinary projects that apply optimization techniques to economic policy and data-driven decision-making challenges.

• Operations Research
• Production and Operations Management
• Scheduling Theory and Applications
• +3 more

Chuen-Lung Chen is a Professor in the Department of Management Information Systems at National Chengchi University's College of Commerce in Taipei, Taiwan. He holds a Ph.D. in Industrial and Systems Engineering from Auburn University in the United States, with additional graduate and undergraduate degrees from Tamkang University and Chung Yuan Christian University in Taiwan. His research focuses on Applied Operations Research, Production and Operations Management, and Scheduling Theory and Applications . Professor Chen has developed numerous optimization algorithms and heuristics for solving complex scheduling problems in manufacturing and service industries, with applications extending to financial engineering and knowledge management systems. His work bridges theoretical operations research with practical business applications, particularly in production planning and resource allocation. Analysis of his recent publications reveals a consistent focus on flow shop scheduling problems, with increasing applications to financial modeling and knowledge integration systems. His research methodology combines traditional operations research techniques with evolutionary computation approaches like genetic algorithms and particle swarm optimization. Distinguished Professor award (2016-2017, 2015-2016, 2012-2013) Senior Excellent Teacher Award (10 years, 2010) Academic Research Excellence Award (2010) Multiple Internationalization Research Excellence Awards (2007-2008) Outstanding Service Teacher Award (2008) Outstanding Teaching Award (2002) MOST First-Class Research Award (1990) Professor Chen has secured substantial research funding through Taiwan's Ministry of Science and Technology (MOST), directing numerous projects on scheduling algorithms, financial modeling, and management information systems. He has held significant administrative roles including Department Chair (2011-2014), Deputy Dean of the College of Commerce (2008-2016), and Director of the International MBA Program (2011-2016). His leadership extends to directing international programs and contributing to university-wide initiatives for English-taught degree programs. His work demonstrates a sustained contribution to both theoretical advancements in operations research and practical applications in manufacturing and financial systems, with a research trajectory showing increasing interdisciplinary connections between operations management and information systems.

Liang YongView profile

Associate Professor

• Data-Driven Decision Analytics
• Supply Chain Management
• Disruption Management
• +5 more

Liang Yong serves as Associate Professor in the Department of Management Science and Engineering at Tsinghua University's School of Economics and Management. Currently maintaining office hours on Thursdays at 10:00 in Room B424 of Lihua Building, he teaches undergraduate, doctoral, and MBA courses including Production and Operations Management, Advanced Operations Research, and Data, Models, and Decision Making. His educational background includes a B.A. in Engineering Physics from Tsinghua University (2005), Master's in Nuclear Engineering from Purdue University (2008), and Ph.D. in Industrial Engineering and Operations Research from UC Berkeley (2013). Prior to academia, he worked as a Quantitative Analyst at Google Inc. (2013-2014). Liang's research focuses on Data-Driven Decision Analytics , Supply Chain Disruption Management , and Mechanism/Contract Design , with particular emphasis on integrating learning algorithms with operations research. His work addresses critical challenges in omnichannel retail, energy systems, and transportation networks. His 11 recent publications in premier journals like Operations Research and Manufacturing & Service Operations Management demonstrate consistent contributions to operations analytics, with notable recognition including the 2019 POMS-JD.com data-driven paper competition winner prize. Winner prize, POMS-JD.com data-driven paper competition (2019) He actively supervises PhD students, both independently and through co-advisement with faculty like Jian Chen and Yongbo Xiao. His research addresses practical industry challenges through collaborations with major technology companies and government entities, with current work focusing on resilient supply chain design and smart energy systems.

• Machine Learning
• Optimization Algorithms
• Cloud Computing
• +4 more

Ivana Štrumberger is a researcher at Singidunum University's Faculty of Informatics and Computing, with a focus on machine learning optimization, cloud computing, and wireless sensor networks. Her work bridges computational intelligence with practical applications in healthcare, cybersecurity, and financial forecasting. Her research emphasizes hybrid metaheuristic algorithms for tuning neural networks and ensemble models. Notably, she has developed methods for Bitcoin and Ethereum price prediction using LSTM networks Phishing website detection via XGBoost and feature selection Solar flare forecasting with gradient boosting Optimization of wireless sensor network localization She has co-authored over 30 peer-reviewed publications in journals like Artificial Intelligence Review and Applied Sciences , and presented at IEEE conferences including IJCNN and CEC. Her methodological contributions include hybridized variants of Firefly Algorithm Harris Hawks Optimization Sine Cosine Algorithm Whale Optimization for constrained and multi-objective problems.

• Machine Learning
• Software Testing
• Metaheuristic Algorithms
• +3 more

Tamara Zivkovic is a researcher at the Faculty of Informatics and Computing , Singidunum University , with a PhD in Software Engineering from the Faculty of Electrical Engineering, Belgrade . Her work focuses on: Machine learning optimization via metaheuristics Software testing and quality assurance Applications in cybersecurity, healthcare, and energy Research trends from her publications (2023-2025) show expertise in: Recurrent Neural Networks and LSTM tuning Extreme Gradient Boosting adaptations Cyberbullying detection and phishing prevention GANs for secure data generation She holds certifications in: ISTQB software testing Agile methodologies Product management from the University of Alberta

• Mathematical Economics
• Optimization
• Statistical Methods
• +3 more

Frank Brand is an active Professor at the Berlin School of Economics and Law within the Faculty of Economics and Department of Quantitative Methods and Business Informatics. Born in 1961, he maintains his academic position at the institution where he contributes to mathematical economics and quantitative research methodologies. His research spans Mathematical Economics, Optimization, Statistical Methods, Pattern Recognition, and Complex Systems Analysis. He specializes in evolutionary optimization algorithms, economic modeling with uncertain data, and the application of statistical moments to pattern recognition. His interdisciplinary approach bridges economics, mathematics, and physics, particularly evident in his dual focus on economic networks and optical beam analysis. Publications from 2003-2007 reveal consistent emphasis on optimization techniques in economic modeling, with significant contributions to evolutionary algorithms and network-based economic analysis. His collaborative work in optics demonstrates cross-disciplinary application of statistical methods, particularly through centered reduced moments for beam quality assessment. Scientific Awards: DAAD short-term professorships in Colombia and Vietnam DAAD reviewer for Asia/Southeast Asia No information regarding student advising or research grants was documented in the source material. Similarly, no affiliations with specific research labs or teams were mentioned in the available records.