Find Academic Supervisors

Filippo PellegrinoView profile

Research Fellow

• Finance
• Macroeconomics
• Monetary Policy
• +4 more

Filippo Pellegrino is a Research Fellow in Sustainable Finance at the Business School, Imperial College London (2023–present). He will join the Adam Smith Business School, University of Glasgow, as an Assistant Professor of Finance in 2025/26. His research focuses on econometrics, empirical asset pricing, and macroeconomic data analysis, leveraging time series methods and unstructured datasets to study asset dynamics and policy implications. Education: PhD in Statistics, London School of Economics and Political Science (2023) MSc in Global Finance (Double Degree), Fordham Gabelli School of Business (2015–2016) MSc in Economics and Finance (Distinction), LUISS Guido Carli (2013–2015) BSc in Economics and Management, LUISS Guido Carli (2010–2013) Research Interests: His work spans econometrics, macroeconomics, and financial markets. Key themes include inflation modeling, dynamic factor models, short-selling frictions, and climate-driven commodity price analysis. He emphasizes empirical methods to bridge theory and policy. Articles Trends: Recent publications address inflation dynamics (structural models, central bank perspectives), commodity markets under climate risk, and cross-sectional momentum in equity markets. A common thread is the integration of macroeconomic data and advanced time-series techniques. Awards: None explicitly listed. However, his MSc at LUISS was completed with distinction. Advising & Grants: No explicit details on advisees or grants provided in the text. However, his role as a Research Fellow suggests active participation in academic research projects. Labs/Teams: Affiliated with Imperial College’s Business School research groups focused on sustainable finance and macroeconomics. Collaborations may extend to policy institutions like Now-Casting Economics (non-academic experience).

• Financial Econometrics
• Asset Pricing
• Credit Derivatives
• +5 more

Andras FULOP is a Professor of Finance at ESSEC Business School in France, specializing in financial econometrics, asset pricing, and credit derivatives. He holds a Ph.D. in Finance from the Rotman School of Management (2006), an M.A. in Economics from the University of Toronto (2000), and an M.Sc. in Economics from Budapest University of Economic Sciences (1999). His career includes roles as Co-Head and Head of the Finance Department at ESSEC (2016–2019), Visiting Researcher at the Bundesbank (2016–present), and Research Consultant at the Hungarian National Bank (2012–2013). Research interests focus on Bayesian methods, structural credit risk modeling, market liquidity, and macrofinance. Notable works include studies on transparency initiatives in CDS markets, long-run risk models, and self-exciting jumps in asset prices. FULOP has received awards such as the 2023 EUROFIDAI-ESSEC Best Paper Award and the 2019 ESSEC Foundation White Project Award. He serves as an Associate Editor for the Journal of Financial Econometrics and Subject Editor for the Journal of Multinational Financial Management . His research articles span topics like real-time Bayesian learning in bond markets, news indices for country fundamentals, and computational methods for filtering diffusions. FULOP has advised multiple students on empirical asset pricing and chairs thesis juries at ESSEC. He has organized numerous conferences, including workshops on Bayesian econometrics and empirical finance.

Carl Peter Leslie SchultzView profile

Associate Professor

• Answer Set Programming
• Software Development Tools
• Building Information Modeling
• +5 more

Carl Peter Leslie Schultz is an Associate Professor at the Department of Electrical and Computer Engineering - Software Engineering & Computing systems at Aarhus University. His research focuses on computational tools for construction and building design, including Answer Set Programming, Digital Twin Technology, and Social Sustainability. He leads and collaborates on projects such as PROBONO (H2020) and COGITO, addressing energy efficiency and optimal construction management. Research Interests: Development of spatial reasoning systems for architectural design Optimization of construction processes using digital twins Integration of sustainability metrics in building projects Notable Contributions: Published 103+ research outputs since 2006 Recipient of multiple awards including Best Paper Awards (2011, 2015) and the Best Ph.D. Thesis Award (2010) Advising & Grants: Lead PI on PROBONO (2022-2026) focusing on energy-efficient neighborhood development Co-PI on Optimal Construction Management & Production Control (2020-2024) He is actively involved in the DesignSpace Group (2015-2018) and collaborates internationally on topics like smart cities and community-driven land documentation.

Adam GriffinView profile

Researcher

• Epidemic Models
• Mathematical Statistics
• Monte Carlo Simulation
• +4 more

Adam Griffin is a Researcher at the Mathematics Institute of the University of Warwick. He holds an MMath from the same institution and is currently pursuing a MASDOC PhD, supervised by Simon Spencer and Gareth Roberts. His research focuses on epidemic models, quasi-stationary distributions, Monte Carlo methods, and probability theory, with applications to influenza dynamics and neglected tropical diseases. He has contributed to advancements in resampling techniques for Sequential Monte Carlo methods, such as Regional Resampling and Combine-Split Resampling. Education: MMath (University of Warwick) Research Interests: Epidemic modeling and quasi-stationary distributions Monte Carlo simulation techniques Probability theory and stochastic processes Quantum turbulence and fluid dynamics Teaching: SO243: Practice of Quantitative Research (2015) ST202: Stochastic Processes seminar teacher (2014–15) First-year mathematics supervisor (2011–12, 2013–14) Activities: Cellist in the university symphony orchestra Member of the university folk society's ceilidh band Founder of The Night Before folk dance band

• Spatial Statistics
• Quantile Regression
• Healthcare Data Analysis
• +5 more

Professor Ji Meng Loh is a faculty member in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on spatial statistics, quantile regression, healthcare data analysis, and software testing methodologies. He has led multiple federally funded projects including 'Scalable Inference of Quantile Regression for Large-Scale Health Care Data' (2019-2022) and 'Spatial Inference with Application to Astronomy' (2005-2010). His work bridges theoretical statistics with applications in astronomy, biomedicine, and software engineering. Research Interests: Spatial point processes and bootstrap methods Statistical computing and machine learning Healthcare policy and data analysis Formal software testing and mutation analysis Astronomy data modeling Recent Research Trends: His 2020-2025 publications emphasize methodological advancements in: Mortality modeling with neural networks Biostatistical analysis of EEG data Formal verification of software loops Mutation testing metrics He has also contributed to healthcare decision-making frameworks and public health policy research. Grants & Collaboration: Co-PI on NSF-funded projects investigating local public goods allocation (2006-2012) and aerospace engineering cost modeling (2005-2013). Active collaborations span NJIT's departments of Computer Science, Public Health, and Biomedical Engineering. Professional Contributions: Developed novel statistical methods for analyzing spatial data in astronomy and public health contexts. His work on 'equivalent mutants' in software testing has influenced industry standards. Currently oversees statistical consulting initiatives for NJIT researchers.

Fotis PapailiasView profile

Senior Lecturer

• Time Series Econometrics
• Financial Forecasting
• Macroeconomic Analysis
• +4 more

Fotis Papailias is a Senior Lecturer in Banking & Finance at King’s Business School and Deputy Director of the Data Analytics for Finance and Macro (DAFM) Research Centre. His research focuses on time series econometrics, financial and macroeconomic forecasting, resampling procedures, portfolio selection, and technical trading strategies. He has consulted for hedge funds, Oxford Economics, and the European Commission, applying his academic work to investment strategies via the quantf research website. Education: Advanced qualifications in economics and econometrics (specific details not publicly disclosed). Affiliations: King’s Business School, DAFM Research Centre. His research interests emphasize the development of methodologies for analyzing financial and macroeconomic data, including big data applications for nowcasting, volatility discovery, and structural break analysis. Key contributions include enhancing forecasting accuracy through novel econometric techniques and improving portfolio allocation via covariance averaging. Recent publications highlight trends in leveraging alternative datasets (e.g., Airbnb) and central bank communication for real-time economic assessment. His work bridges academic rigor and practical investment strategies, with a focus on trend-following strategies and hedging mechanisms. Grants & Consulting: Collaborations with the European Commission, Oxford Economics, and small hedge funds. Labs/Teams: Leads the DAFM Research Centre, focusing on quantitative finance and macroeconomic research.

Haiyan BaiView profile

Professor

• Quantitative Research Methods
• Statistical Analysis
• Propensity Score Methods
• +4 more

Dr. Haiyan Bai is a Professor in the Department of Learning Sciences and Educational Research at the University of Central Florida's College of Community Innovation and Education. She holds a Ph.D. in quantitative research methodology from the University of Cincinnati. Her expertise spans statistical methods, research design, and measurement, with a focus on applications in education and behavioral sciences. Dr. Bai teaches graduate-level courses in research methods, measurement, and statistics, delivered via face-to-face, online, and hybrid formats. She actively chairs doctoral dissertation committees and provides statistical consultation to colleagues and local school districts. Her service includes federal/state grant reviewing, journal/book proposal reviewing, and involvement in international educational initiatives. Her research emphasizes statistical innovations such as propensity score analysis and resampling techniques, applied to educational and behavioral datasets. She has secured grants from the National Science Foundation (NSF) and U.S. Department of Education, contributing to large-scale educational research projects.

Thomas GeorgeView profile

Professor

• Market Microstructure
• Asset Pricing
• Behavioral Finance
• +3 more

Thomas George is the Senior Associate Dean for Faculty Affairs and Bauer Professor of Finance at the University of Houston's Bauer College of Business. He previously held positions at Ohio State University, the University of Iowa, and Northwestern University. His research focuses on market microstructure, asset pricing, and behavioral finance, with publications in top journals like the Journal of Finance and Journal of Financial Economics. He has won the Best Paper Prize from the Journal of Financial Intermediation and been nominated for the Smith-Breeden Prize. George leads faculty recruitment, retention, and promotion processes at Bauer College, directs the AIM Center for Investment Management, and oversees the Cougar Investment Fund. He co-developed the Graduate Certificate in Financial Services Management and teaches in Bauer's Executive MBA program. His consulting work includes designing educational programs on project valuation and performance evaluation. He served as Interim Dean during the 2018-19 academic year. Research interests include equity and fixed-income investment management, pricing errors, and liquidity dynamics. His work bridges theoretical finance and practical market mechanisms, addressing puzzles like momentum investing and post-earnings drift. Recent studies explore remote work productivity post-COVID-19 and China's stock market dynamics. Awards: Best Paper Prize (Winner/Runner-Up), Smith-Breeden Nominee Labs/Initiatives: AIM Center for Investment Management, Cougar Investment Fund Consulting: Corporate educational program design for valuation and risk management

Friedrich LeischView profile

Professor

• Statistical Computing
• Cluster Analysis
• Bioprocess Modeling
• +3 more

Friedrich Leisch was a Professor at the Institute of Statistics, University of Natural Resources and Life Sciences, Vienna. He held leadership positions including Head of the Department of Landscape, Spatial and Infrastructure Sciences and was a core developer for the R programming language. His career included positions at Ludwig-Maximilians-Universität München and Vienna University of Technology, where he earned his doctorate and habilitation. Education includes: PhD in Applied Mathematics, Vienna University of Technology (1999) Habilitation in Statistics, Vienna University of Technology (2005) Leisch's research focused on statistical computing methodologies with applications across diverse scientific domains. His primary interests included: Development of resampling methods for cluster analysis and validation Bioprocess modeling for pharmaceutical manufacturing Causal modeling frameworks for scientific inference Statistical computing and R package development His recent publications demonstrate a strong focus on biotechnological applications of statistical learning, developing methods for real-time process monitoring in pharmaceutical production. Articles frequently address machine learning explainability, temporal clustering, and environmental impact assessment of agricultural practices. Scientific awards include: Peter Keller Award for tourism research Supervised numerous doctoral and master's students across statistics and biotechnology domains. Led multiple research projects including: Mobile Field Phenotyping for Agronomy and Breeding Statistical evaluation of downstream processing data Market Segmentation Methodology projects Collaborated extensively with bioprocessing research groups including the Austrian Centre of Industrial Biotechnology (ACIB), focusing on statistical modeling of fermentation processes and purification systems.

Yan GaoView profile

Assistant Professor

• Biostatistics
• Clinical Trials
• Bayesian Methods
• +7 more

Dr. Yan Gao is an Assistant Professor of Biostatistics at the Medical College of Wisconsin (MCW), affiliated with the Cancer Center and Department of Medicine. Her research focuses on Bayesian survival analysis, experimental design in clinical trials, and accelerating MCMC algorithms. She holds a PhD in Biostatistics from the University of Illinois at Chicago (2021) and prior master's degrees in Biostatistics, Social Medicine, and Public Health. PhD: Biostatistics, UIC (2021) MSc: Biostatistics, UIC (2016) MSc: Social Medicine, Fudan University (2009) BMedSc: Public Health, Harbin Medical University (2006) Research interests include immunology, oncology, numerical integration, and algorithm design. She collaborates with MCW's Department of Medicine and Cancer Center, contributing statistical expertise to NIH grants and protocol reviews. Key methodological work involves joint models, multiple testing procedures, and clinical trial design.

Shan-Lu LiuView profile

Professor

• Molecular Virology
• Virus-Host Interaction
• Innate Immunity
• +3 more

Shan-Lu Liu is a Professor and Director of the Viruses and Emerging Pathogens Program at the Infectious Disease Institute, The Ohio State University. His research focuses on molecular virology, virus-host interactions, innate immunity, and cell signaling. He holds an M.D. from Zhengzhou University School of Medicine (1989) and a Ph.D. in Microbiology from the University of Washington School of Medicine (2003), followed by postdoctoral training at the University of Washington and Fred Hutchinson Cancer Research Center. Research interests include host restriction mechanisms against viral infections, viral cell-to-cell transmission, and immune responses to SARS-CoV-2. Key projects involve IFITM proteins' role in viral entry inhibition, Ebola virus fusion mechanisms, and HIV Nef's interaction with TIM proteins. He investigates SARS-CoV-2 variants' immune evasion and cell-cell fusion dynamics, with recent work on Omicron subvariants and vaccine development. Dr. Liu's affiliations include the Center for Retrovirus Research, Biomedical Sciences Graduate Program, and Molecular, Cellular, and Developmental Biology Program. His work spans viral pathogenesis, host defense mechanisms, and translational strategies to combat emerging pathogens.

Theo PeplerView profile

Lecturer

• Biostatistics
• Epidemiology
• Multivariate Analysis
• +9 more

Dr Theo Pepler is a Lecturer and Lead Biostatistician at the Robertson Centre for Biostatistics, University of Glasgow. He holds academic appointments at both the University of Glasgow and Stellenbosch University, specializing in biostatistics, epidemiology, and multivariate analysis. His work bridges academia, industry, and government policy through roles such as co-leading data initiatives at the Scottish Government’s EPIC Centre. Education PhD in Statistics (Stellenbosch University, 2014) MComm in Statistics (specializing in factorial experiments) BComm Honours in Statistics BComm in Economic & Management Sciences Research Interests focus on statistical computing, open science, and applications in veterinary public health. Key areas include biomarker discovery for mastitis management, transition cow health optimization, and genomic epidemiology of livestock diseases. His work emphasizes practical statistical solutions for real-world agricultural challenges. Grants & Projects EPIC Centre of Expertise in Animal Disease Outbreaks (Scottish Govt, 2022-2025) Movenet Livestock Movement Toolkit (EPSRC, 2024) Mastitis Diagnosis Innovations (Innovate UK, 2018-2020) His research integrates statistical methodologies with veterinary medicine, contributing to evidence-based practices in dairy farming and infectious disease control.

Francesca ChiaromonteView profile

Professor

• Statistics
• Bioinformatics
• High-dimensional Data Analysis
• +5 more

Francesca Chiaromonte is a Professor of Statistics and the Dorothy Foehr Huck and J. Lloyd Huck Chair in Statistics for the Life Sciences at the Pennsylvania State University. She holds a courtesy affiliation with the Department of Public Health Sciences and leads the Institute for Genome Sciences within the Huck Institutes of the Life Sciences. She is also affiliated with the Sant’Anna School of Advanced Studies in Pisa, Italy, where she coordinates the EMbeDS initiative and contributes to PhD programs in Data Science and AI for Society. Education: She earned a Laurea (cum laude) in Statistics and Economic Sciences from the University of Rome La Sapienza (Italy) and a Ph.D. in Statistics from the University of Minnesota (USA). Her research focuses on statistical methodologies for high-dimensional data, with applications in genomics, climate science, and economics. Key areas include dimension reduction, feature selection, and functional data analysis. Research Interests: Her work bridges statistical theory and interdisciplinary applications, particularly in 'Omics' sciences, meteorology, and economics. She develops computational techniques for analyzing complex datasets, such as resampling methods and latent structure modeling. Recent projects include studies on mitochondrial mutations, child obesity biomarkers, and the economic impacts of natural disasters. Articles Trends: Her publications emphasize methodological innovations in statistics (e.g., group elastic net algorithms) and their applications in genetics (e.g., L1 transposition dynamics) and public health (e.g., metabolomic obesity studies). She also explores non-B DNA structures and their genomic implications. Awards: She is a Fellow of the American Statistical Association (2016) and the Institute of Mathematical Statistics (2022), recognized for her contributions to statistical methodology and interdisciplinary research. Advising & Grants: While specific grant details are not listed, her leadership roles and collaborative projects suggest significant contributions to funding and mentoring. She directs the Institute for Genome Sciences and collaborates globally, including with NYU, UCLA, and the Santa Fe Institute. Labs/Teams: Active in the Center for Computational Biology and Bioinformatics, Center for Medical Genomics, and the Institute of Economics at Sant’Anna School. Her work integrates computational biology, statistical theory, and interdisciplinary training programs.

İbrahim Halil GÜMÜŞView profile

Professor

• Matrix Theory
• Operator Inequalities
• Optimization
• +3 more

Prof. Dr. İbrahim Halil GÜMÜŞ is a distinguished faculty member at Adıyaman University, Faculty of Arts and Sciences, Department of Mathematics, where he currently holds the position of Professor (since 2023). Previously, he served as Associate Professor (2017-2023) and Assistant Professor (2011-2017) at the same institution. Before his academic career, he worked as a Teacher at Public Schools under the Ministry of National Education from 2002 to 2011. He earned his educational qualifications from Selçuk University: B.Sc. in Mathematics (1998-2002), M.Sc. in Mathematics (2002-2005), and PhD in Mathematics (2005-2011). His Master's Thesis focused on 'On the Hadamard Product of Matrices' (2005), while his PhD Thesis examined 'Bounds on arithmetic, geometric and Heinz means of positive definite matrices' (2011). Prof. GÜMÜŞ's research primarily centers on Matrix Theory and Operator Inequalities, with significant contributions to Positive Operators, Matrix Analysis, and Optimization. His work demonstrates a strong theoretical foundation in mathematical inequalities with increasing applications in data science and medical informatics. He has published extensively in high-impact journals such as Linear and Multilinear Algebra, Journal of Mathematical Analysis and Applications, and Operators and Matrices. His publication record shows a clear evolution from theoretical matrix inequalities toward practical applications in data analysis, particularly evident in his recent work on synthetic data generation for imbalanced datasets using geometric means and Heinz averages. This interdisciplinary approach bridges pure mathematics with machine learning applications, especially in medical data analysis. TÜBİTAK Publication Incentive Award, 2012 TÜBİTAK Publication Incentive Award, 2015 TÜBİTAK Publication Incentive Award, 2017 TÜBİTAK Publication Incentive Award, 2018 TÜBİTAK Publication Incentive Award, 2019 TÜBİTAK Publication Incentive Award, 2021 Prof. GÜMÜŞ has successfully supervised multiple Master's theses on topics including inequalities for positive multilinear mappings, geometric inequalities via majorization methods, determinants of positive semi-definite matrices, and mathematical approaches to synthetic data sampling. He serves as a referee for prestigious journals including Journal of Inequalities and Applications and Mathematical Reviews/MathSciNet. His administrative roles include Farabi Coordinator, Erasmus Coordinator, Bologna Coordinator for both the Faculty of Arts and Sciences and Institute of Science, and Mevlana Exchange Program Institutional Coordinator. His work bridges theoretical mathematics with practical applications in data science, particularly in addressing challenges related to imbalanced datasets in medical informatics through innovative mathematical approaches.

Alper Kürşat UysalView profile

Associate Professor

• Text Classification
• Feature Selection
• Pattern Recognition
• +2 more

Alper Kürşat Uysal is an Associate Professor of Computer Engineering at Alanya Alaaddin Keykubat University. His research develops advanced text classification algorithms and feature selection methods, with applications ranging from spam detection to educational analytics. Research foci: Novel feature selection metrics for text data Imbalanced text classification techniques Short text analysis methodologies Multilingual spam detection systems Affective computing in educational contexts Uysal's publications introduce innovative approaches to feature selection, including the Extensive Feature Selector method and specialized metrics for short text datasets. His work addresses practical challenges in Turkish-language social media content moderation and develops automatic classification systems for educational applications. Recent research examines how feature selection impacts classifier performance on imbalanced text corpora.