جست‌وجوی استاد و پژوهشگر

Timo Laukkanenمشاهده پروفایل

پژوهشگر

• Energy Engineering
• Industrial Energy Systems
• Biomass Utilization
• +۶ مورد دیگر

Timo Laukkanen is a Staff Scientist at Aalto University's Department of Energy and Mechanical Engineering. He holds a Doctoral degree in Engineering and Technology (2012) from Aalto University, a Licentiate (2003), and a Master's (1999) from Helsinki University of Technology. His work focuses on industrial energy systems, process optimization, and sustainable energy solutions, contributing to UN Sustainable Development Goals related to climate action and industry innovation. Key research areas include thermal energy storage, biomass utilization, hydrogen production, and carbon capture. He leads projects like OptStorage (2023-2025), exploring renewable energy storage, and has contributed to forest industry energy efficiency studies. His work integrates advanced modeling techniques (e.g., machine learning, AspenPlus) for process optimization. Recent publications (2023-2025) highlight innovations in energy hubs, solar-assisted gasification, and oxy-fuel combustion systems. He has engaged in doctoral thesis assessments and serves on editorial boards. His research bridges academia and industry, emphasizing practical applications in sustainable energy systems.

Alaa Marshanمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Data Science
• Healthcare Informatics
• +۷ مورد دیگر

Dr. Alaa Marshan is a Senior Lecturer in the Department of Computer Science at the University of Surrey, affiliated with the Nature Inspired Computing and Engineering Research Group and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. He holds a PhD in Computer Science from Brunel University London (2018), an MSc in Business Systems Integration (2012), and a BSc in Computer Engineering (2003) from the University of Aleppo. His research focuses on machine learning, data science, and their applications in healthcare, finance, and social network analysis. Key projects include the Semantic Credit Risk Assessment of Business Ecosystems (SCRIBE) and the KTP project 'Emotion AI for trading on Financial Markets.' He teaches modules such as Practical Business Analytics, Data Science Principles, and MSc Dissertation coordination. Marshan’s publications explore diverse topics, including healthcare AI, sentiment analysis, privacy concerns, and pandemic modeling. His work bridges technical innovation with societal impact, addressing challenges in ethical AI, privacy, and humanitarian data science. He advises on grants and collaborates with interdisciplinary teams, contributing to both academic and industry-driven research. His lab affiliations and teaching roles highlight his commitment to advancing data-driven solutions.

Karol Binkowskiمشاهده پروفایل

مدرس ارشد

• Statistical Modelling
• Financial Mathematics
• Stochastic Processes
• +۳ مورد دیگر

Karol Binkowski is a Senior Lecturer in Statistics at the School of Mathematical and Physical Sciences, Macquarie University. He holds a PhD in Statistics (2009) from Macquarie University and a Master of Science in Mathematical Finance (2003) from Jagiellonian University. With seven years of experience as a Quantitative Analyst at Westpac Group, he transitioned to academia with a focus on statistics education and leadership. His teaching portfolio includes courses such as Introductory Statistics, Applied Statistics, and Statistical Inference, delivered in both face-to-face and online formats. He actively contributes to academic governance through roles in committees like the School Equity, Diversity, and Inclusion Committee and the Faculty AI Working Group 2024. Research interests span applications of statistics to financial markets (e.g., state-space models for crude oil futures) and pedagogical innovations, including peer instruction and mastery learning. His work bridges statistical theory with practical education challenges, emphasizing student engagement and threshold concept mastery. Key recognitions include the 2023 Award for Excellence in Early Career Teaching and collaboration awards for inter-school initiatives. He has supervised research projects on liquidity management for automated market makers and contingent claims frameworks, reflecting his dual focus on finance and education. Binkowski’s publications span statistical pedagogy and financial modelling, with recent work addressing peer instruction strategies and online learning effectiveness in service units.

Damian Kozburمشاهده پروفایل

دانشیار

• Econometrics
• Machine Learning
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Prof. Damian Kozbur is an Associate Professor of Econometrics at the University of Zurich's Department of Economics, affiliated with the Digital Society Initiative. He holds a PhD from the University of Chicago (2014) and has been at UZH since 2016. His research focuses on integrating economic theory with machine learning tools, particularly in high-dimensional econometric models. Key areas include estimation techniques for weak instruments, panel data analysis, and clustering methods for statistical inference. He has contributed to journals like Econometrica and the Journal of Business and Economic Statistics. His work addresses applications such as market dynamics, causal inference in complex data, and policy analysis. Education: PhD in Econometrics from the University of Chicago Booth School of Business (2014), BA in Mathematics (2008). Previous roles include ETH Zurich Fellow (2014–2016). Teaching includes advanced econometrics and machine learning courses for doctoral students. Research Interests: High-dimensional econometrics, model selection, statistical inference, and applications of machine learning to economic problems. His recent articles tackle spatial dependence robustness, debiased machine learning, and forecast hedging with random forests. Professional Service: Associate Editor of the Journal of Business and Economic Statistics since 2022. Active in reviewing for top econometric outlets like Journal of Econometrics and Econometric Theory.

Pantelis Pipergias Analytisمشاهده پروفایل

دانشیار

• Decision Making
• Computational Modeling
• Social Influence
• +۵ مورد دیگر

Pantelis Pipergias Analytis serves as an Associate Professor at the Department of Business and Management within the Faculty of Business and Social Sciences at the University of Southern Denmark (SDU), while also holding a senior fellowship at the Danish Institute of Advanced Study. His academic journey spans multiple institutions and disciplines, having studied economics at the University of Athens, cognitive science at EHESS in Paris, and earned his PhD in psychology from Humboldt University in Berlin. Before joining SDU, Analytis conducted research as a predoctoral researcher at the Max Planck Institute for Human Development and as a postdoctoral researcher in the Computer and Information Science department at Cornell University. His scholarly fingerprint reveals significant contributions to decision making (100%), search engine psychology (80%), social learning (80%), individual-level analysis (40%), human-computer interfaces (40%), and strategic choice (40%). Analytis's research program centers on developing mathematical and computational models of individual and collective behavior, employing experimental methods, big-data analysis, and large-scale simulations. His work investigates how algorithms shape human decision-making and how human behavior influences algorithmic outcomes, with particular attention to bias, diversity, and efficiency in digital systems. Current research examines social influence in recommender networks, strategic sophistication in interactive decision-making, and the dynamics of collective intelligence. His publication record demonstrates consistent scholarly output across computer science, psychology, and management disciplines. Recent work includes examinations of social influence in online rating systems, algorithmic bias in recommender systems, and the mathematical properties of ranking-based cumulative advantage processes. His research has practical implications for designing fairer and more efficient digital platforms and decision support tools. Analytis leads the active research project 'Ranking-based cumulative advantage' (2023-2026), investigating how ranking mechanisms influence attention allocation in competitive environments. He regularly participates in research seminars globally, with upcoming presentations scheduled through May 2025, demonstrating his active scholarly engagement. As an educator, he previously taught 'DS817: Algorithms we live by' (2020-2022), exploring how algorithms shape human experience and decision-making processes.

Pietro Millossovichمشاهده پروفایل

دانشیار

• Actuarial Science
• Financial Risk Management
• Stochastic Modeling
• +۳ مورد دیگر

Pietro Millossovich is an Associate Professor (Reader) in Actuarial Finance at Bayes Business School, part of the Faculty of Actuarial Science and Insurance at City St George's, University of London. He concurrently holds a Lecturer position at the University of Trieste, Italy. His academic background includes a PhD in Mathematics from the University of Trieste, a DEA in Probability and Finance from the University of Paris VI, and a BSc in Statistics and Actuarial Science from the University of Trieste. His research focuses on actuarial science, financial risk management, stochastic modeling, and demographic analysis. Key areas include mortality forecasting, sensitivity analysis, variable annuities valuation, and longevity risk assessment. He has contributed to developing R packages like StMoMo for stochastic mortality modeling and SWIM for sensitivity analysis. Recipient of the 2020 Peter Clark Prize for Best Paper (for Reverse Sensitivity Testing ) and the 2018 ICA Best Paper Award, his work bridges theoretical advancements with practical applications in insurance and finance. He is a Fellow of the Italian Society of Economic and Social Sciences (AMASES).

Matheus Grasselliمشاهده پروفایل

استاد

• Financial Mathematics
• Stochastic Analysis
• Probability Theory
• +۵ مورد دیگر

Matheus Grasselli is a Professor of Financial Mathematics at the Department of Mathematics and Statistics, McMaster University, where he currently serves as Deputy Provost since July 2022. He is affiliated with the PhiMac research group and co-leads the Systemic Risk Analytics initiative at the Fields Institute. His previous roles include Deputy Director at the Fields Institute (2012–2016) and Director of the Centre for Financial Industries (2017–2020). Grasselli’s research spans Financial Mathematics, focusing on stochastic analysis, systemic risk, climate-economic modeling, and asset price bubbles. He has advised numerous graduate and undergraduate students, including PhD candidates and postdoctoral fellows, contributing to over 50 academic publications. His teaching includes advanced courses in financial mathematics, numerical methods, and real options, alongside contributions to the Integrated Sciences program. Grasselli holds a PhD in Information Geometry from King’s College London and has collaborated internationally on projects integrating economic theory with climate science. Education: PhD in Information Geometry, King’s College London. Leadership Roles: Deputy Provost (2022–present), Deputy Director (Fields Institute, 2012–2016), Director (Centre for Financial Industries, 2017–2020). Research Interests: Financial Mathematics, systemic risk, climate-economic models, asset price bubbles, and stochastic processes. His work bridges theoretical frameworks with practical applications in policy analysis and financial stability. Teaching: Courses include graduate programs like the McMaster Financial Mathematics (MFM) Master’s, numerical methods for finance, and undergraduate courses in calculus and linear algebra. He pioneered the design of the Integrated Sciences program. Collaborations: Active in interdisciplinary projects, including climate-economic modeling and systemic risk analysis. His research often employs agent-based computational economics and dynamical systems approaches.

Edouard Amourouxمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Higher Education
• Modelling and Simulation
• +۳ مورد دیگر

Edouard Amouroux is a Senior Lecturer in Computer Science at the School of Computing Technologies, RMIT University, Australia. His research spans computer vision, higher education pedagogy, and agent-based modeling. He focuses on interdisciplinary applications such as avian brood parasitism analysis, fraud detection systems, and energy consumption optimization through agent-based simulations. Research Interests: His work integrates computational methods with real-world challenges, including: Computer vision techniques for pattern recognition and biological studies Agent-based modeling for disease spread and energy systems Pedagogical innovation in project-based learning and interdisciplinary education Multi-scale simulation frameworks for complex systems Publications: His recent work highlights advancements in AI-driven fraud detection, SCADA-integrated energy systems, and epidemic modeling. He has developed the GAMA simulation platform, a widely used tool for multi-agent and spatially explicit modeling. Collaboration & Supervision: Open to collaborative projects in technology education and industry partnerships. Supervises research on converging technology-pedagogy for startups and improving feedback processes in computing education. Contact: Available for advisory roles, speaking engagements, and media inquiries via email or ORCID (0000-0002-0547-5051).

Erol Orelمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Infectious Disease Epidemiology
• +۵ مورد دیگر

Erol Orel is an econometrician and data scientist at the University of Geneva's Institute of Global Health, part of the Faculty of Medicine. He works in the Division of Infectious Diseases and Mathematical Modelling, focusing on applying machine learning and data science to global health challenges. Erol holds a Master of Science in Quantitative Economics, a D.E.A. in Econometrics, and a PhD in Biomedical Sciences (Global Health track) from the University of Geneva. Before transitioning to global health, he spent over a decade in finance as a quantitative analyst and risk manager, specializing in portfolio risk and alternative investments. He teaches statistics and epidemiology in the MAS en santé publique program at the Faculty of Medicine. His research emphasizes HIV epidemiology, including socio-behavioral risk prediction, disease surveillance, and machine learning applications. He contributed to WHO, FOPH, and HUG collaborations on pandemic modeling and developed LiteRev, an NLP-driven tool for automating literature reviews. Key projects include analyzing HIV viral suppression in Malawi, predicting antiretroviral treatment interruptions in South Africa, and assessing SARS-CoV-2 antibody prevalence in Cameroon. His work bridges econometric methods with public health, aiming to improve data-driven decision-making in global health systems.

Andreas W. Bettمشاهده پروفایل

استاد

• Photovoltaics
• Solar Energy
• Semiconductor Materials
• +۱۲ مورد دیگر

Prof. Dr. Andreas W. Bett is Director of the Fraunhofer Institute for Solar Energy Systems ISE in Freiburg, Germany, and holds the chair in »Solar Energy - Materials and Technologies« at the Faculty of Mathematics and Physics, University of Freiburg. He has been a key figure at Fraunhofer ISE since 1987, serving in leadership roles including head of the III-V epitaxy group, division director, deputy director, and since 2017, full director. Dipl. in Physics and Mathematics, University of Freiburg (1988–1989) PhD in Physics, University of Konstanz (1992) His research focuses on photovoltaic materials and devices, including silicon and III-V semiconductors, epitaxial growth, solar cell fabrication, concentrator photovoltaics (CPV), perovskite-silicon tandem cells, and system integration. He is a pioneer in high-efficiency solar conversion and industrialization of CPV technology. His recent publications emphasize tandem solar cells, advanced characterization, and energy system transformation. He actively contributes to standardization through IEC TC82 WG7 and the European PV Platform WG3. Prof. Bett has received numerous accolades, including: European Becquerel Prize (2009) Joseph von Fraunhofer Prize (2010) EARTO Prize (2010) German Environmental Prize (2012) WSS Research Award (2023) He has advised and collaborated with numerous researchers, many of whom are co-authors on his publications. He co-founded Concentrix Solar and has led major R&D initiatives in solar manufacturing, recycling, and policy. His work bridges fundamental research, industrial application, and energy policy, positioning him as a leading voice in the global transition to sustainable energy.

Enrique Ter Horstمشاهده پروفایل

دانشیار

• Quantitative Finance
• Bayesian Statistics
• Statistical Modeling
• +۱۲ مورد دیگر

Enrique Ter Horst is an Associate Professor in the Faculty of Management at Universidad de los Andes, Colombia, affiliated with the Finance department. His research spans quantitative finance, Bayesian statistics, sentiment analysis, and interdisciplinary applications in marketing, psychology, and public policy. He teaches courses such as Derivatives Markets, Multivariate Models, and Computational Methods in Finance at both undergraduate and graduate levels. PhD in Bayesian Statistics, Duke University, 2003 Maitrise de Sciences Economiques, Université Louis Pasteur, 1999 Licence d'Économétrie, Université Louis Pasteur, 1998 Ter Horst's research focuses on developing advanced statistical and machine learning models, particularly using Bayesian frameworks and maximum entropy methods. His work applies to diverse domains including financial risk modeling, art market analysis, climate change news impact, and brand ethics. He has made significant contributions to understanding sentiment-driven portfolio hedging, brand commitment during crises, and topic modeling in academic publishing. His recent publications (2020–2025) reveal a strong trend in applying Bayesian and entropy-based methods to interdisciplinary problems, especially in finance, marketing, and social media analytics. He frequently employs computational techniques such as multinomial inverse regression and deep reinforcement learning to analyze large-scale textual and financial data. Financial Risk Manager (GARP) Secretary, Economics, Finance and Business Section, ISBA (2015–2016) Ter Horst advises doctoral students such as Manuel Andrés Martínez Patiño and has previously mentored Jovelyn Ferrer Garcia. His industry experience includes quantitative research roles at Credit Suisse First Boston and Morgan Stanley, where he co-edited the 'Quant Strategist' research series. He has consulted for the European Central Bank, Eurostat, and the Inter-American Development Bank. He is a member of the Finance and Financial Economics research group at Uniandes, which supports collaborative work in asset pricing, risk management, and behavioral finance.

• Sustainable Food Systems
• Food Waste Reduction
• Food Security
• +۷ مورد دیگر

Dr. Christian Reynolds is a Professor at the Centre for Food Policy, School of Health and Psychological Sciences, City, University of London. He is a leading researcher in sustainable food systems, with a strong focus on food waste, food security, citizen science, and the environmental impacts of dietary choices. His work bridges academic research and policy, evidenced by his frequent contributions to UK parliamentary inquiries and international organizations. His research interests span sustainable food systems, food waste reduction, food governance, public health nutrition, and the integration of citizen science into food policy. He investigates how cooking practices, food provisioning, and consumer behaviors influence sustainability outcomes. His interdisciplinary approach combines environmental science, social science, and data analytics to address complex food system challenges. The 15 most recent publications highlight a consistent focus on food waste, sustainable diets, policy coherence, and environmental impacts. His work employs diverse methodologies including life cycle assessment, discrete event simulation, machine learning, and qualitative analysis. Key themes include the environmental footprint of diets, the effectiveness of waste reduction interventions, the role of packaging, and the social dimensions of food security and equity. Dr. Reynolds has contributed to policy through numerous written evidences submitted to UK parliamentary committees on issues such as food security, trade policy, obesity, and soft power. He has also co-authored influential reports for organizations like the Food Standards Agency, WWF, and the World Bank. He actively disseminates research through conference presentations globally and has developed practical tools such as a food loss and waste water footprint calculator for organizations in the UAE. His collaborative network is extensive, involving researchers from across the UK, Europe, Brazil, and Australia. Dr. Reynolds leads and contributes to projects that engage citizens in food system research, emphasizing lived experience and equitable food environments. His work on citizen science aims to democratize data collection and empower communities in shaping food policy.

• Data Science
• Computational Statistics
• Bayesian Models
• +۵ مورد دیگر

Antonietta Mira is a Full Professor of Statistics at the University of Italian Switzerland (USI), where she also serves as the founder and director of the Data Science Lab. She holds a part-time professorship at Università dell'Insubria and has held visiting roles at institutions such as the University of Cambridge and Université Paris-Dauphine. Her academic leadership includes a Vice-Dean role in the Faculty of Economics (2013-2015). She earned a PhD in Computational Statistics from the University of Minnesota (1998), a Master's in Statistics (1996), and a Doctorate in Methodological Statistics from the University of Trento (1995). Her research focuses on Bayesian statistical models, computational statistics, and interdisciplinary applications in healthcare, finance, and social sciences. Mira is a Fellow of the International Society for Bayesian Analysis (ISBA) and the Istituto Lombardo Accademia di Scienze e Lettere. Her work has been recognized with awards for both research and teaching excellence. She leads Swiss National Science Foundation-funded projects and serves on editorial boards of journals like Bayesian Analysis and Statistica Sinica . Her publications span topics such as approximate Bayesian computation (ABC), spatio-temporal modeling of health events, and data science applications in public policy. Mira also engages in public outreach, including scientific exhibitions like Numbed by Numbers! and lectures on data-driven societal issues.

• Agricultural Systems Modelling
• Climate Change Impacts and Adaptation
• Sustainable Agricultural Development
• +۷ مورد دیگر

Dr Ke Liu is an Agricultural Systems Modeller at the Tasmanian Institute of Agriculture (TIA), University of Tasmania, where he conducts cutting-edge research on climate change adaptation, sustainable agriculture, and advanced biophysical modelling. He plays a key role in teaching sustainability courses and supervising doctoral students focused on waterlogging, AI in agriculture, and soil carbon dynamics. Dr Liu earned his PhD in Agricultural Science from the University of Tasmania between 2018 and 2022. His research focuses on improving crop growth models, integrating machine learning with process-based models, and developing strategies to mitigate the impacts of extreme climatic events such as waterlogging and drought. His research interests include agricultural systems modelling, climate change impacts and adaptation, sustainable agricultural development, soil waterlogging simulation, machine learning in agriculture, and soil organic carbon dynamics. He has pioneered novel algorithms for simulating soil waterlogging, significantly enhancing model responsiveness to extreme conditions. His 2023 Nature Communications paper on this topic is a Highly Cited and Hot Paper, with over 160 citations and a widely viewed Nature -commissioned science video. The recent publications reflect a strong trend toward integrating AI and hybrid modelling approaches with traditional biophysical models to address complex agricultural sustainability challenges. Key themes include waterlogging and drought resilience, soil carbon prediction, food-energy-water-carbon nexus analysis, and climate-smart farming systems. These works demonstrate a consistent focus on developing practical, scalable solutions for global agricultural sustainability. Tasmanian 2025 Young Tall Poppy Wheat Initiative Early Career Travel Awards Agronomy 2023 Outstanding Reviewer Award Vice-Chancellor's Sustainability Award College Sustainability Award for the Systems Modelling Team Hubei Crop Science Society Outstanding Academic Paper Award (2023, 2022) The CoSE Executive Dean’s Award for Outstanding Performance (2022) National Scholarship, Ministry of Education of China (2020, 2017) The third prize of Postgraduate Forum on Hybrid Rice Science (2020) Dr Liu actively supervises PhD students on topics such as soil waterlogging simulation and AI-driven soil carbon gains. He is a key investigator on major funded projects including the Future Drought Fund collaborations with Charles Sturt University and the University of Melbourne, and a GRDC-funded project on waterlogging impacts in barley, canola, and fababean. These projects total over $16 million in funding and involve multidisciplinary systems analysis to identify win-win-win solutions in productivity, environmental impact, and farm prosperity. He contributes to the scientific community as an Associate Editor for Frontiers in Plant Science and editorial board member for European Journal of Agronomy , npj Sustainable Agriculture , Resources, Environment and Sustainability , and Agricultural Systems , highlighting his growing academic leadership in sustainable agriculture and modelling.

Zoltan Buttمشاهده پروفایل

مدرس

• Actuarial Science
• Mortality Modeling
• Health Insurance
• +۶ مورد دیگر

Zoltan Butt is a Lecturer in Actuarial Science at the Faculty of Actuarial Science and Insurance within City St George's, University of London. He teaches Financial and Investment Mathematics and applications of information technology to undergraduate students. His research focuses on mortality and morbidity models, risk analysis in health insurance, and the application of data analysis to insurance software development. Education: BSc Hons (1st class) in Mathematics with Statistics from Middlesex University, London (1998), winning the Institute of Mathematics and its Applications Prize. PhD in Actuarial Science from City University, London (2014), focusing on actuarial models for insurance applications. Research Interests: His work includes developing methodologies for estimating employment risk reduction factors (collaborating with the Ogden Working Party), creating simulation-based actuarial models for income protection and long-term care insurance, and analyzing longitudinal labor force data. Notable projects include contributions to the UK Ogden Tables used in courts for loss of earnings compensation. Publications: His research spans mortality forecasting, work life expectancy calculations, and frailty-based models. Key areas include the application of Markov models for long-term care projections and Lee-Carter variants for mortality analysis. Professional Activities: He has presented at conferences such as the Royal Statistical Society and the National Association of Forensic Economics. His collaborations include work on disability annuities and co-morbidity prevalence projections using the Health Improvement Network (THIN) data. Labs/Teams: Involved in interdisciplinary teams developing actuarial software tools and contributing to policy-relevant research on compensation valuation and insurance risk modeling.