Konstantin Korovin is an Associate Professor and Reader in Formal Methods at the University of Manchester. He leads the Formal Methods Research Group and is a core developer of the iProver theorem prover, a tool for automated reasoning in first-order logic with applications to verification, neuro-symbolic systems, and machine learning integration. His work focuses on combining automated reasoning techniques with machine learning, particularly in areas like premise selection, neural architecture for term synthesis, and hybrid verification systems. Affiliations: Centre for Digital Trust and Society, SCorCH Project (Secure Code for Capability Hardware) Research Beacons: Digital Futures Key research interests include automated theorem proving, verification of machine learning models, non-linear constraint solving, and neuro-symbolic reasoning. He has contributed to tools like ESBMC (for C++ program verification) and SMLP (a symbolic machine learning prover). His work spans theoretical advancements in superposition calculus and practical applications in hardware verification and systems biology. Collaborations include projects on DNA-based computing, robotic scientific discovery (e.g., Genesis), and formal methods for industrial hardware verification. Korovin’s research is supported by grants from the Engineering and Physical Sciences Research Council (EPSRC) and industry partnerships.
Dr Michael Short is an Associate Professor of Process Systems Engineering at the University of Surrey , affiliated with the School of Chemistry and Chemical Engineering and the Surrey Institute for Sustainability . His research focuses on mathematical optimisation tools for sustainable process design, bioenergy systems, renewable energy integration, and pandemic risk modelling. Current projects include AI-driven biogas production optimisation and carbon-negative chemical synthesis. EPSRC-funded £1.7M project on AI for biogas production Co-I in £5M Supergen Bioenergy Impact Hub Developed open-source DECO2 software for ASEAN decarbonisation His team applies mixed-integer nonlinear programming (MINLP) and machine learning to industrial challenges in pharmaceuticals, aquaculture, and microbreweries. Collaborations span Eli Lilly, Pfizer, and universities in Japan, Malaysia, and Brazil. Supervised projects address grid-scale energy storage, CO2 utilisation, and rural electrification. Scientific Awards : 2021 EPSRC Impact Acceleration Account Commercialisation Fellow Best Speaker Award at 2020 Sustainable Process Integration Lab Conference Editorial Board Member of Journal of Water Process Engineering Recent publications examine direct air capture integration, waste-to-energy brewing processes, and whole-system energy models. Supervises students in distributed energy systems, catalytic processes, and sustainable design.
Jamie Fairbrother is a Lecturer in Operational Research (Optimisation) at the Data Science Institute , Lancaster University , within the Department of Management Science . His research integrates optimisation, applied probability, and statistics to solve real-world problems in logistics and telecommunications, with a particular focus on stochastic programming and scenario generation. Education: Jamie Fairbrother completed his PhD research in scenario generation for stochastic programming, which laid the foundation for his subsequent work in optimisation under uncertainty. Research Interests: His expertise spans several domains: Optimisation & Operational Research: Development of mathematical models and algorithms for complex decision-making problems. Applied Probability & Statistics: Use of probabilistic methods to model uncertainty in planning and operations. Logistics & Telecommunications: Practical applications in mail centre staffing, airport slot scheduling, and wireless communication networks. Stochastic Programming: Scenario generation and robust optimisation techniques for strategic planning under uncertainty. Research Funding & Projects: Dr Fairbrother has led or contributed to multiple collaborative projects, including: Coordination of Strategic and Tactical Interventions for Reducing Air Traffic Delays (2023–2024) – a case study at Heathrow Airport. STOR-i: Optimising In-Store Price Reductions (2022–2025) – with PhD student Katie Howgate. Resource Allocation under Uncertain Demand in Royal Mail Mail Centres (2020–2023). Industrial Mathematics KTP with BT Research (2013) – modelling TV whitespace interference. PhD Supervision: He currently supervises Katie Howgate , a PhD student in Bayesian and Computational Statistics within the STOR-i Centre for Doctoral Training . Professional Affiliations: Dr Fairbrother is affiliated with the STOR-i Centre for Doctoral Training , the Centre for Transport & Logistics (CENTRAL) , and the OR-MASTER project on airport resource allocation.
Professor Iskander Aliev holds a Personal Chair in the School of Mathematics at Cardiff University. His research spans integer optimization, discrete mathematics, and number theory, with significant contributions to the geometry of numbers and Diophantine approximations. Research Interests: Integer Optimization using Algebraic and Geometric Methods Discrete Mathematics including Geometry of Numbers and Discrete Geometry Number Theory with focus on Diophantine Approximations and Additive Number Theory His recent publications demonstrate strong trends in integer programming, particularly examining sparsity properties, proximity bounds, and integrality gaps. His work connects discrete geometry with optimization theory, often focusing on lattice structures and their applications to computational problems across mathematics and computer science. Scientific Recognition: Editor of Beiträge zur Algebra und Geometrie Editor of Combinatorics and Number Theory Recipient of EPSRC grant EP/Y032551/1 for "Average-case proximity for integer optimisation" (June 2024-May 2025) Professor Aliev is actively involved in research within the Operational Research, Mathematical Analysis, and Discrete Mathematics and Data Science groups at Cardiff University. His teaching includes MA3007 Coding Theory and MA3603 Optimisation courses. His extensive publication record spans from 1998 to 2025, reflecting sustained contributions to his fields of expertise. His PhD was completed at IM PAN under Prof. Andrzej Schinzel, and his MSc at St.-Petersburg State University under Prof. Yuri Matiyasevich, establishing strong foundations in mathematical theory that continue to inform his research.
Jonas Schnidrig is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the School of Architecture, Civil and Environmental Engineering (ENAC). He works as an External Employee in the SCI STI FM Group at EPFL Valais Wallis, based at the IPESE institute in Sion. His research focuses on sustainable energy policy and planning, specializing in energy system modeling, optimization, and decision support for the energy transition. He actively bridges research, industry, government, and society by developing strategies that balance ecological, social, and economic dimensions of energy transitions. Dr. Schnidrig's educational background includes: PhD in Mechanical Engineering (Energy and Technologies) from EPFL-HES-So Valais Wallis (2020-2024) Master's in Mechanical Engineering (Thermodynamics & Energetics) from EPFL (2018-2020) Bachelor's in Mechanical Engineering from EPFL (2013-2018) His research interests span sustainable energy system modeling, life cycle assessment, renewable energy planning, rational use of energy, and thermoeconomic optimization. He specializes in energy system integration, incorporating sustainability metrics into energy planning, policy support for sustainable energy transitions, and renewable energy finance and infrastructure planning. His work emphasizes a life-cycle perspective to evaluate the ecological, social, and economic dimensions of energy systems. Analysis of his recent publications reveals a strong focus on decentralized energy systems, multi-actor energy planning, and the integration of environmental metrics into energy modeling. His research demonstrates how strategic decentralization can reduce system costs by up to 10% while increasing self-consumption by up to 68%. He has pioneered methodologies that integrate life-cycle impact assessment into energy system modeling, showing potential for 15-47% cost reductions alongside 31-81% reductions in environmental impacts. His notable scientific achievement includes: Zanelli Prize for outstanding contribution to sustainable development (2020) Dr. Schnidrig coordinates the EnergyScope Community, which brings together over 50 researchers and practitioners, and contributes to major international projects including EnergyScope Community, Net Zero Valais, and collaborations with Polytechnique Montréal, Hydro Québec, and Swiss energy institutions. He supervises semester and master projects at EPFL and teaches the course "Sustainability, climate and energy" (ENV-421). He leads research on digital twins for cities within the Blue City project and focuses on understanding the role of infrastructure in high share renewable energy systems, investigating synergies across different scales, and developing sustainable energy systems that align economic efficiency with environmental sustainability.
Justin Pearson is an Associate Professor at the Department of Information Technology; Division of Computing Science at Uppsala University . He is a member of the university's optimisation group and coordinates the IT department's mentor programme for new employees . Research Interests include Constraint Programming , Combinatorial Optimisation , Artificial Intelligence , Software Testing , and Complexity Theory . His work focuses on theoretical and practical aspects of constraint satisfaction, local search algorithms, and symmetry breaking in constraint programming. Teaching involves courses such as Algorithms and Data Structures II (1DL231) and Introduction to Machine Learning for Bachelor Students (1DL034), with a PhD-level course on Category Theory offered periodically based on demand. Scientific Contributions span publications in constraint programming for air traffic management, sensor networks, and industrial applications. Recent work includes parameterised treewidth in constraint models, time-series constraints, and symmetry breaking techniques. Students supervised include PhD candidates Frej Knutar Lewander (co-supervised with Pierre Flener), Yi Zhao (co-supervised with Di Yuan), and previously graduated students Gustav Björdal , María Andreína Francisco Rodríguez , and Joseph Scott .
Micah Nehring is a Senior Lecturer at the School of Mechanical and Mining Engineering , The University of Queensland . He leads the High Performance Surface Mining Research Group , focusing on optimizing In-Pit-Crusher-Conveyor (IPCC) systems and advancing mine planning methodologies. His work bridges mathematical programming with practical mining challenges, particularly in ultimate pit limit determination and production scheduling . Research interests include: Mine Planning Production Scheduling Optimization IPCC Systems Integration Cut-off Grade Methodologies ESG Risk Incorporation Recent publications highlight trends in machine learning applications for coal mine roof characterization, electrification of surface mining fleets, and multi-objective optimization for sustainable underground operations. His work often combines mixed integer programming with discrete event simulation to address complex mining scenarios. Advising spans projects like: Forecasting Market Capitalisation via Neural Networks Battery Trolley Haulage Systems Hydro-Economic Modeling for Water Management Funding collaborations include Anglo American , NewCrest Mining , and Caterpillar Global Mining , with grants focused on carbon tax impacts , roof displacement prediction , and rock characteristics research.
Dr. Asef Nazari is a Senior Lecturer in Mathematics at Deakin University's School of Information Technology, Faculty of Science Engineering and Built Environment. With over two decades of experience, his work spans mathematical modeling, optimization, and AI applications in energy systems, supply chains, and cybersecurity. Education : PhD (University of Ballarat), MSc (Amirkabir University of Technology), BSc (University of Tabriz), Graduate Certificate in Higher Education Teaching (Deakin University) Research : Focuses on Electricity network planning with renewables Optimization techniques (non-smooth, integer programming) AI/ML for cryptocurrency markets and causal inference Community battery systems for EV charging Soft happy coloring in network analysis Supply chain and project scheduling optimization Grants : Leads industry collaborations like the REACH Scholarship (2025–2028) and has secured funding from Monash University/ClimateWorks and The Ian Potter Foundation. Teaching : Developed the 'Mathematics for AI' master's unit, emphasizing real-world problem-solving in optimization, operations research, and data analysis. Supervision : Currently guides 8 PhD students in topics like AI-driven authentication security and land-use modeling. Awards : Recognized as a Fellow of the Higher Education Academy (FHEA)
Nargiz Sultanova is a Lecturer in applied mathematics and statistics at Federation University Australia, working within the Institute of Innovation, Science and Sustainability (IISS). She holds a PhD and MSc from the University of Ballarat and completed her undergraduate studies at Baku State University in Baku, Azerbaijan. Her research focuses on optimization, particularly nonsmooth optimization and its various applications. Her work spans theoretical mathematical optimization as well as practical applications in water distribution systems. She has published extensively on topics including minimax problems, nonsmooth DC optimization, and water network optimization. Sultanova's publication record shows consistent research output from 2010 through 2021, with particular emphasis on optimization techniques applied to water distribution systems. Her work frequently involves collaboration with researchers including A.M. Bagirov, H. Mala-Jetmarova, and D. Savić, demonstrating strong interdisciplinary connections between mathematical theory and engineering applications. She is a member of the Australian Mathematics Society and ANZIAM, reflecting her active participation in the mathematical community. Her research has appeared in journals spanning mathematics, engineering, and computational science. As a lecturer, she contributes to the academic numeracy development of undergraduate students at regional universities, with publications examining student preparedness and mathematical education in regional Australian contexts.
Associate Professor Melih Ozlen serves as Deputy Head of Department (Academic Operations) in the Department of Mathematical and Geospatial Science within RMIT University's School of Science at the City Campus in Australia. With a strong academic background in operations research and optimization, he has established himself as a leading researcher in combinatorial optimization, mathematical programming, and multi-objective optimization. His research interests focus on practical applications of optimization techniques across various domains including healthcare logistics, wildfire management, network optimization, and power systems. Ozlen's work bridges theoretical optimization methods with real-world challenges, particularly in resource allocation problems under uncertainty. The publication record reveals a consistent research trajectory with emphasis on decomposition methods, evolutionary algorithms, and matheuristic approaches for solving complex optimization problems. His recent work shows increasing focus on healthcare applications while maintaining strong contributions to wildfire management and network optimization problems. As an active reviewer for prestigious journals including European Journal of Operational Research and Computers and Operations Research, as well as the Australian Research Council, Ozlen contributes significantly to the academic community. His professional memberships span key organizations including INFORMS, Australian Mathematical Society, and Australian Society for Operations Research. Ozlen actively supervises graduate students with current projects focusing on home care optimization, multi-objective mixed integer programming, and vehicle routing problems with applications to wildfire management. His teaching interests align with his research expertise in operations research, combinatorial optimization, mathematical programming, and multi-objective optimization.
Dr Ripon Kumar Chakrabortty is a Decision Scientist at the School of Systems & Computing, UNSW Canberra . His research focuses on applying Artificial Intelligence and Optimization Models to complex systems, particularly in Global Supply Chains, Logistics, and Medical Informatics . He coordinates the Master of Decision Analytics program and leads the Artificial Intelligence Community of Practice at the Australian Institute of Project Management. PhD in Computer Science, UNSW (2017) M.Sc. & B.Sc. in Industrial Engineering, BUET Diploma in Logistics, Monarch Institute Certified by Chartered Institute of Logistics and Transport Australia His research portfolio includes over 5 million AUD in Category 1 grants from Australian Research Council , Department of Defence , and other government agencies. He has supervised 24 PhD/Master's students to completion or ongoing supervision, with expertise in Explainable AI , Healthcare Analytics , and Smart Manufacturing . Recent scientific recognitions include a Highly Cited Paper , Best Paper at IEEE IEEM , and Champion at IEEE CEC Competition . His editorial roles span prestigious journals like IEEE Transactions on Industrial Informatics and Mathematics (MDPI) .
Silvia Colabianchi is a researcher and assistant professor („Ricercatore“) at the Department of Computer, Control and Management Engineering „Antonio Ruberti“ of Sapienza University of Rome, where she teaches the Smart Factory course in the Master’s programmes in Management and Mechanical Engineering. Born in Rome in 1994, she obtained her PhD in Industrial and Management Engineering from the same university in 2023 with a dissertation on human-centred cyber-resilience of socio-technical systems. Education: PhD in Industrial and Management Engineering, Sapienza University of Rome, 2023 MSc in Management Engineering, Sapienza University of Rome, 2019 Scientific high-school diploma, Liceo Scientifico “PNI”, Rome Research interests revolve around the integration of cutting-edge digital technologies into manufacturing from a human-centric, Industry 5.0 perspective. She combines computer vision, large language models and immersive virtual reality to improve quality inspection, operator assistance and occupational safety, while investigating cyber-resilience, organisational learning and sustainable/circular product design. Her 2023-2025 publications show a clear trend toward AI-enabled manufacturing support (digital assistants, LLMs, conversational agents), advanced training technologies (VR, Safety-II, ergonomic pose detection) and resilience assessment (fuzzy maturity models, storytelling-based studies, warehouse and supply-chain disruptions). Interdisciplinary work spans from industrial engineering and operations management to behavioural cybersecurity and space manufacturing logistics. She is member of the scientific sector ING-IND/17 “Industrial Mechanical Systems” and actively collaborates on national and international projects that apply machine-learning models, data-analysis dashboards and immersive tools to real industrial environments. Office: Room A125, Via Ariosto 25, 00185 Rome, Italy Phone: +39 06 7727 4036 (int. 35036) Email: silvia.colabianchi@uniroma1.it
Safia Kedad-Sidhoum is a Full Professor at the Conservatoire National des Arts et Métiers (CNAM), affiliated with the CEDRIC Laboratory's Combinatorial Optimization team. Her research focuses on Operations Research, particularly in combinatorial optimization, scheduling, lot-sizing, energy management, and supply chain optimization. She holds a Ph.D. from École Centrale Paris (1997) and an HDR from UPMC (2010). She has held academic positions at UPMC from 1999 to 2017, including roles as Associate and Assistant Professor, before becoming a Full Professor at CNAM in 2018. She has extensive industrial experience, including as a Supply Chain Planning Project Manager at Dynasys (1998–1999). She co-leads the Master Parisien de Recherche Opérationnelle (MPRO) and has advised numerous Ph.D. students. Her work includes projects funded by Google, CNRS, and FUI initiatives, addressing energy optimization, remanufacturing, and supply chain efficiency. She actively participates in academic societies such as EURO/ROADEF, IWLS, and serves on editorial boards. Her teaching spans scheduling, optimization, and mathematical tools at CNAM and UTC.
Professor Regina Berretta is an Honorary Professor at the University of Newcastle's School of Information and Physical Sciences, specializing in Computing and Information Technology. With expertise spanning computer science, mathematics, and applied optimization, she has established herself as a leading researcher in metaheuristic methods for solving complex combinatorial problems. Currently serving as a Chief Investigator at the ARC Training Centre for Food and Beverage Supply Chain Optimisation, she applies her mathematical modeling skills to address critical challenges in food industry supply chains, with the Centre receiving over $2 million in funding to train next-generation researchers. Professor Berretta earned her PhD, Master of Engineering (Electrical), Bachelor of Mathematics, and Teachers Certificate from Universidade Estadual de Campinas in Brazil. Her educational background in computational and applied mathematics has provided the foundation for her extensive research career focused on integer programming and metaheuristic approaches to optimization problems. Her academic journey includes progression from Lecturer at the University of Newcastle (2003-2007) to various leadership positions including Head of Discipline of Computer Science and Software Engineering (2011-2014) and Assistant Dean of Equity, Diversity and Inclusion (2019-2022). Her research expertise centers on the design of mathematical models and development of efficient computational techniques to tackle large and complex combinatorial optimization problems across diverse application areas. Professor Berretta has made significant contributions to bioinformatics through her work on genetic signature identification from gene expression datasets, and to supply chain optimization through her research on perishable food inventory management, lot sizing, and scheduling problems. Her methodological specialties include memetic algorithms, evolutionary computation, and integer programming approaches that have proven effective for problems that are otherwise computationally intractable. Analysis of her recent publications reveals a strong interdisciplinary focus, with optimization techniques increasingly applied to food supply chain challenges while maintaining connections to bioinformatics applications. Her work demonstrates a consistent pattern of translating theoretical optimization methods into practical solutions for industry problems, particularly in the agricultural sector. Additionally, her more recent publications show growing engagement with gender equity issues in STEM fields, reflecting her leadership in relevant initiatives. Co-founder of HunterWISE, promoting girls and women in STEM Leader of Google CS4HS project for five consecutive years Recipient of over $4 million in research funding through 35 grants Author of more than 80 papers and book chapters Chief Investigator at ARC Training Centre for Food and Beverage Supply Chain Optimisation Professor Berretta has demonstrated exceptional leadership through her administrative roles and community initiatives. As co-founder of HunterWISE, she has developed a comprehensive approach to increasing female participation in STEM through school programs and professional networking events. Her leadership of the Google CS4HS project has directly impacted high school computer science education in the Hunter region. Her research collaborations span multiple disciplines and institutions, reflecting her ability to bridge theoretical computer science with practical industry applications, particularly in the food supply chain sector where her optimization models have demonstrated significant cost and waste reduction potential.
Dr. Emir Demirović is an Assistant Professor in the Department of Computer Science at Delft University of Technology (TU Delft), The Netherlands. He leads the Constraint Solving ("ConSol") research group and co-directs the Explainable AI in Transportation Lab ("XAIT") as part of Delft AI Labs. Prior roles: Postdoc at University of Melbourne (2017-2020), PhD at Vienna University of Technology (2017) Collaborations: Civil Engineering, QuTech, and industry partners Funding sources: Dutch national funding agency, TU Delft, VoestAlpine Research Focus: Constraint programming and combinatorial optimisation Explainable AI methods for decision-making systems Integration of optimisation with machine learning Robust/resilient optimisation for industrial applications Optimal decision trees with dynamic programming Quantum computing scheduling techniques Scientific Contributions: Pioneered "Pseudo-Boolean Reasoning" for algorithm certification Developed "Blossom" algorithm for optimal decision trees Advances in "Predict+Optimise" frameworks Created Pumpkin constraint programming solver Bridge between SAT/CP and Machine Learning Awards: First Place, MaxSAT Evaluation 2018+ First Place, ROADEF/EURO 2012 Adoption of methods in Google OR-Tools Collaborations: Research visits to EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien Participated in Dagstuhl seminars, Lorentz workshops, and Simons-Berkeley programme
