Sara A. Solla is a Professor of Physics and Neuroscience at Northwestern University, conducting interdisciplinary research that bridges statistical physics and computational neuroscience. Her work applies theoretical frameworks from physics to model complex neural systems and cognitive processes. Her core research areas include: Theoretical neural network modeling using spin-glass systems for associative memory Statistical mechanics of supervised and incremental learning algorithms Emergence of generalization capabilities in adaptive systems Computational approaches to sensory processing and motor control Professor Solla's significant contributions have been recognized through prestigious honors: Election as Fellow of the American Physical Society Membership in the American Academy of Arts and Sciences She maintains active professional engagement through memberships in the Society for Neuroscience, New York Academy of Sciences, and Society for the Neural Control of Movement, reflecting her commitment to advancing interdisciplinary neuroscience research.
Hugo Lavenant is an Assistant Professor in the Department of Decision Sciences at Bocconi University in Milan, Italy. His academic journey includes a PhD in mathematics from Université Paris-Sud under Filippo Santambrogio (2016-2019) and a postdoctoral fellowship at the University of British Columbia (2019-2020) working with Young-Heon Kim, Brendan Pass, Geoffrey Schiebinger, and Dave Schneider. Professor Lavenant's research spans theoretical and applied aspects of mathematical analysis, with a focus on optimal transport theory , calculus of variations , and Bayesian statistics . His work explores the geometry of the Wasserstein space, numerical solutions to dynamical optimal transport problems, and applications to biological data analysis. He has made significant contributions to understanding harmonic mappings in the Wasserstein space, connections between optimal transport and nonlinear elasticity, and the application of optimal transport to trajectory inference in biological systems. Analysis of Professor Lavenant's recent publications reveals a strong trend toward interdisciplinary applications of optimal transport, particularly in statistics and biology. His work bridges pure mathematical theory with practical computational methods, with increasing focus on developing tractable statistical tools based on optimal transport distances. The research spans theoretical mathematics, computational methods, and applications to real-world data analysis problems. Professor Lavenant actively supervises graduate students, currently advising PhD candidates George Kanchaveli and Francesco Mascari (both co-advised with Marta Catalano), as well as Master's students Mathis Hardion and Niccolò Bargellini. His teaching portfolio includes Mathematical Analysis 2, Real Analysis I, and Advanced Analysis and Optimization 1 at Bocconi University. His scholarly contributions demonstrate a consistent focus on advancing both the theoretical foundations and practical applications of optimal transport, with growing emphasis on statistical methodology and biological applications in recent years.
Matteo Dellacasagrande serves as a Researcher at the Department of Mechanical, Energy, Management and Transport Engineering (DIME) within the Polytechnic School of the University of Genoa. His academic appointments include membership on the Joint Teacher-Student Commission and teaching responsibilities for advanced courses in aircraft propulsion systems. His research focuses on fluid dynamics in turbomachinery , particularly low-pressure turbine optimization, separated flow modeling, and aircraft engine design. Key methodologies include computational fluid dynamics, experimental validation using large databases, and statistical modeling techniques like Bayesian Lasso for flow prediction. His work bridges theoretical fluid mechanics with practical aerospace engineering applications. Recent publications demonstrate consistent focus on turbine blade aerodynamics (2024-2025), with significant contributions to loss mechanism analysis in low-pressure turbines and novel approaches to modeling separation bubbles. His research integrates experimental data with advanced statistical methods to improve prediction accuracy in complex flow scenarios. Teaching activities: AIRCRAFT ENGINES (Master's Degree in Mechanical Engineering - Energy and Aeronautics) AIRCRAFT PROPULSION (Master's Degree in Mechanical Engineering - Energy and Aeronautics) DESIGN OF MACHINES AND ENERGY SYSTEMS Professional engagement: Member of the Joint Teacher-Student Commission at the Polytechnic School, with office hours by appointment via institutional email.
Nicola Righetti serves as Assistant Professor at the Department of Communication Sciences, Humanities and International Studies (DISCUI) at the University of Urbino Carlo Bo. A computational social scientist with extensive expertise in data analysis and digital methodologies, he bridges technical approaches with social scientific inquiry. His research examines how digital media platforms shape communication, public discourse, and political behavior through rigorous data-driven investigation. Methodologically, he specializes in: Large-scale data analysis Network analysis techniques Machine learning applications Natural language processing Advanced statistical modeling Righetti teaches Digital Methods and Sociology of Cultural and Communicative Processes courses across multiple degree programs including Information, Media and Advertisement, and Modern Languages and Cultures. His scholarly work investigates the intersection of communication technologies with democracy, power structures, and social transformation. He has established significant research partnerships with: European Commission Media Authority of North Rhine-Westphalia These collaborations focus on critical contemporary issues including misinformation ecosystems, digital influence operations, and platform governance frameworks. Previously, he contributed to the Computational Communication Science Lab at the University of Vienna's Department of Communication.
Tiziana Ciano (born April 21, 1991) serves as a tenured Researcher and Assistant Professor at the Department of Economic and Political Sciences, University of Aosta Valley. She holds a PhD in Law and Economics & Doctoral Europaeus from Mediterranean University of Reggio Calabria & University of Portsmouth, and a Master's Degree in Economics obtained with highest honors. Her academic appointments include Contract Professor at DICEAM Department, Mediterranean University of Reggio Calabria, and membership in Decisions_Lab scientific laboratory. Dr. Ciano's research spans mathematical economics with strong AI applications: Artificial Intelligence and Applied Mathematics Machine Learning and Deep Learning for socio-economic systems Stochastic Programming and Multi-objective Optimization Dynamic Systems and Epidemic Modeling Decision Support Systems for tourism and finance Her recent publications reveal a consistent focus on applying advanced mathematical methods to portfolio optimization, pandemic dynamics, climate change sustainability, and tourist flow forecasting. She has developed tri-objective optimization models for financial decision-making and fuzzy fractional-order models for pandemic dynamics, demonstrating methodological innovation across multiple domains. Dr. Ciano has received multiple prestigious recognitions including the Best Poster Award at DySES2022 conference for her work on fake news sentiment analysis during pandemics, a Research Award for her paper on duopoly game analysis published in Symmetry, and the Anassilaos Giovani Ricerca Award under the patronage of the President of the Republic of Italy. She serves on editorial boards of high-impact journals including Mathematics (MDPI) Financial Mathematics section (Q1, IF 2.592), Applied Mathematical Sciences, and International Journal of Contemporary Mathematical Sciences. As Guest Editor, she has curated special issues on mathematical economics, differential games, and symmetry in machine learning algorithms. She regularly referees for Mathematical Problems in Engineering, Information Processing & Management, and Scientific Reports (NATURE). Dr. Ciano actively participates in Decisions_Lab research laboratory and leads multiple collaborative projects including MONTUR (Real-time Monitoring of Tourist Flows in Aosta Valley), MEC – Marketplace Ecosostenibile Calabria, and IUSTIT-IA. She has organized numerous international conferences including AMASES 2023, NUMTA2023, and special sessions on predictive analytics and machine learning applications in socio-economic systems.
Consuelo R. Nava is Associate Professor of Economic Statistics at the University of Aosta Valley (University of Valle d'Aosta) in the Department of Economic and Political Sciences. She also serves as a visiting professor at the Catholic University of the Sacred Heart in Milan and the University of Turin. Additionally, she is a member of the Cross-Border Center on Tourism and Mountain Economies. Dr. Nava earned her undergraduate degree (laurea triennale) in Business Economics from the University of Aosta Valley and Economics from the University of Turin, both with highest honors (110 e lode). She completed her PhD in Economics with a focus on applied mathematics and statistics at the University of Turin's Vilfredo Pareto Doctoral School in 2014. Following her doctorate, she worked as a research fellow at the Department of Economics and Statistics Cognetti de Martiis at the University of Turin (2014-2016) and as a research grant recipient with the Department of Translational Medicine at the University of Eastern Piedmont (2016-2017). Dr. Nava's research focuses on econometrics, time series analysis in the frequency domain, price indices, Bayesian inference, and discrete choice models. Her work bridges theoretical statistical methods with practical applications in economics, energy markets, tourism, and public health. She has developed innovative approaches to price index construction, switching behavior analysis in electricity markets, and Bayesian methods for modeling complex economic phenomena. Her publications demonstrate a strong interdisciplinary approach, with research spanning economic statistics, public health applications, tourism economics, and energy market analysis. Recent work shows an increasing focus on applying machine learning techniques alongside traditional econometric methods, particularly in analyzing startup survival during economic crises and modeling consumer behavior in liberalized markets. Dr. Nava has received several prestigious awards and recognitions: TEM Summer School Grant, Aosta, Italy Civil Society Talents Fellowship, Giovanni Goria Foundation, Asti, Italy Junior DESPINA Fellow, Big Data Lab, Turin, Italy Google Early Career Researchers Travel Grant, ISBA Socialis Prize, 7th edition, for her thesis on Corporate Social Responsibility Honor Student Scholarship, Collegio Carlo Alberto, Moncalieri, Italy As an educator, Dr. Nava has taught extensively across multiple institutions including Statistics, Quantitative Methods for Management, Econometrics, and Bayesian Statistics. She has served as a reviewer for several academic journals including Economics of Innovation and New Technology, Italian Journal of Applied Statistics, L'Industria, and Bayesian Analysis. Her research projects include studies on cruise ship impacts on destinations, illegal gambling analysis, switching behavior in European electricity markets, and Bayesian methods for analyzing asbestos exposure effects on mesothelioma risk. Her work with the Cross-Border Center on Tourism and Mountain Economies reflects her commitment to regional economic development research.
Giulia Cereda serves as Associate Professor in the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) at the University of Florence since 2025. Her academic journey includes prior roles as Fixed-term Researcher (RTD-b, 2022-2024), Research Fellow (2021-2022), and Swiss National Science Foundation Postdoc Mobility Fellow (2019-2021) at Leiden University and University of Florence. She holds a Joint PhD in Statistics from Leiden University and University of Lausanne (2011-2016), complemented by Master's and Bachelor's degrees in Mathematics from the University of Milan. Her research spans forensic statistics with focus on rare type match problems in DNA evidence evaluation, medical statistics applied to SARS-CoV-2 pandemic modeling, and machine learning implementations for biogeographical ancestry prediction. Recent publications demonstrate methodological innovations in Bayesian approaches for forensic evidence, compartmental modeling of epidemic dynamics, and supervised learning applications in population genetics. Analysis of her 14 most recent publications (2020-2025) reveals dual research thrusts: (1) forensic statistics addressing DNA mixture interpretation and rare haplotype matching through Bayesian frameworks, and (2) epidemiological modeling of smoking dynamics and SARS-CoV-2 transmission using compartmental models with uncertainty quantification. Her work bridges theoretical statistics with practical public health and forensic applications, frequently employing machine learning for complex prediction tasks. Supported by the Swiss National Science Foundation for postdoctoral research (2019-2021), she has contributed to pandemic response through Tuscan regional modeling and school-based screening strategies. Current office hours are Thursdays 3:00-4:00 PM by appointment, with ongoing research in forensic identification systems and epidemic forecasting methodologies.
Emanuela Dreassi is a Full Professor of Statistics at the University of Florence, where she currently serves as Director of the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) from November 1, 2024, to October 31, 2028. She is affiliated with the School of Economics and Management and has held various institutional roles including Director of the Bachelor's Program in Statistics (2014-2022) and Vice-President of the School of Economics and Management (2019-2022). Her research spans hierarchical Bayesian models and spatial statistics, with methodological advancements in specifying and estimating models for multilevel data, missing data, and latent variable models. She has made significant contributions to robust analysis in small area estimation, compatibility of conditional distributions, semicontinuous data modeling, Bayesian predictive inference, and knockoffs construction. Her work bridges theoretical statistics with practical applications in epidemiology, environmental studies, and medical research. Dreassi's recent publications (2020-2025) reveal a strong focus on methodological innovations in Bayesian statistics, spatial analysis, and knockoff filters for variable selection. She has published extensively in top statistical journals while also collaborating on interdisciplinary research in medical fields including plastic surgery and epidemiology. Her work demonstrates a consistent trajectory of advancing statistical methodology while maintaining strong connections to real-world applications across multiple domains. As an active referee for numerous prestigious journals including Biometrics, Journal of the Royal Statistical Society, and Statistics in Medicine, Dreassi contributes significantly to the scholarly community. She has coordinated research units for PRIN and HORIZON2020 projects and participated in numerous national and international conferences as organizer, scientific committee member, and session chair.
