Angela Andreella is an Assistant Professor in the Department of Economics and Management at the University of Trento. She holds a Ph.D., M.Sc., and B.Sc. in Statistics from the University of Padova. Her prior roles include Assistant Professor at Ca' Foscari University of Venice (2022–2024) and postdoctoral positions at the Universities of Padova and Insubria. Research Interests: Her work spans multivariate analysis, selective inference, social statistics, and permutation tests, with applications in biostatistics, neuroscience, and data analytics. She specializes in high-dimensional data, robust statistical methods, and quantitative approaches to social and medical research. Professional Memberships: Italian Statistical Society Italian Society of Biometry International Society of Biometry International Organization for Human Brain Mapping Institute of Mathematical Statistics Italian Association of Psychology
Alex Tong is an incoming Assistant Professor at Duke University (as of 2025) and will join Aithyra in Vienna as a Principal Investigator. Previously, he completed postdoctoral research at Mila under Yoshua Bengio and a visiting postdoc at Oxford with Michael Bronstein. Education : PhD (2021) and MPhil (2020) in Computer Science from Yale University; BS/MS (2017) from Tufts University Research Interests span generative modeling , deep learning , optimal transport , and graph signal processing applied to protein design and single-cell biology . His work bridges mathematical formalism (e.g., SE(3) flows, Wasserstein manifolds) with biological discovery. Publication Trends (2023-2025) focus on diffusion models for protein structure prediction , optimal transport in single-cell analysis , and geometric machine learning for molecular dynamics . Collaborations include labs like Mila, Oxford, and Yale. Awards : Best Paper (Gen Bio @ ICML 2025), Outstanding Paper (DELTA @ ICLR 2025), Best Student Paper (IEEE MLSP 2020)
Dr. Andre Waschka serves as an Assistant Professor of Statistics at Mercer University's College of Liberal Arts and Sciences, specializing in applied statistical methodologies with biomedical applications. His work bridges data science, machine learning, and causal inference to address complex healthcare challenges. His educational credentials include: Ph.D. in Statistics, University of California, Berkeley M.A. in Biostatistics, University of California, Berkeley B.S. in Applied Mathematics, North Carolina State University B.S. in Economics, North Carolina State University Research focuses on developing semi-parametric and parametric models for high-dimensional longitudinal data, emphasizing causal frameworks for treatment optimization in precision medicine. Current projects target positivity violations in complex studies, semi-parametric bootstrap simulations, and treatment rule development for ICU settings using targeted maximum likelihood estimation. His 2020 publication on hydrocortisone therapies for septic shock demonstrates his application of statistical rigor to critical care medicine. No scientific awards, student advisement records, or laboratory affiliations were documented in the source material.
Dr. Huong Ha is a tenured Senior Lecturer in Computer Science and Program Manager of the Bachelor of Computer Science at the School of Computing Technologies, RMIT University, Australia. Previously, she worked at the Applied Artificial Intelligence Institute (A2I2), Deakin University, Australia. She received her PhD from the University of Newcastle, Australia in May 2017. Her educational background includes: PhD in Computer Science from the University of Newcastle, Australia (2017) Dr. Ha's research focuses on the intersection of Machine Learning and Software Engineering, specializing in Automated Machine Learning (particularly Bayesian Optimization), Trustworthy Machine Learning, and Data-driven Software Engineering. Her work addresses critical challenges in intelligent incident management of software systems and quality prediction and optimization for software systems. She has made significant contributions to root cause analysis for microservice systems using causal inference and Bayesian optimization techniques. Her publication record shows a strong trend in applying advanced machine learning techniques to software engineering problems, particularly in microservices architecture. Her recent work demonstrates expertise in developing benchmarking frameworks (RCAEval) and optimization methods (MOCA-HESP) that bridge theoretical advancements with practical applications in software systems. Dr. Ha has received numerous awards and recognitions: Top High-volume Course at RMIT (Semester 1, 2025) Outstanding Reviewer of KDD 2025 Research Track Special Commendation for 2024 RMIT STEM College Learning and Teaching Award ACM SIGSOFT Best Artifact Award at FSE 2024 Top Reviewer of NeurIPS 2022-2023 As Program Manager of the Bachelor of Computer Science, Dr. Ha plays a key role in curriculum development and academic leadership. She actively supervises PhD students including Thuan (Solving Mathematical Problems with LLMs) and Thanh (Fraud Detection for Blockchain Networks), and has secured multiple research grants including the OpenAI Researcher Access Grant (10,000 USD) and several RMIT RACE Merit Allocation Scheme grants. Her academic service includes serving on program committees for major conferences in both machine learning (NeurIPS, ICML) and software engineering (ASE, ICSE).
Stefano Sarao Mannelli is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. He also holds a Visiting Lecturer position at the University of the Witwatersrand. His research group focuses on fundamental aspects of learning in biological and artificial systems, with emphasis on bias generation, optimization dynamics, and comparative neuroscience. Education: Ph.D. in Theoretical Physics, Université Paris-Saclay (2020) M.Sc. in Electronic Engineering, Politecnico di Torino (2017) M.Sc. in Physics of Complex Systems, Politecnico di Torino/SISSA (2016) M2 in Physique Théorique, Paris Diderot/UPMC/ENS Cachan (2016) B.Sc. in Mathematics for Engineering, Politecnico di Torino (2014) Research: Dr. Mannelli develops model-based approaches to reduce complex machine learning problems into analytically tractable frameworks. His core interests include: 1) Bias amplification mechanisms in AI systems, 2) Learning differences between biological and artificial neural networks (continual/transfer/curriculum learning), and 3) Optimization in high-dimensional landscapes. His work bridges statistical physics, neuroscience, and deep learning theory. Publication Trends: Recent articles (2024-2025) predominantly analyze curriculum learning dynamics, bias propagation in optimization, and theoretical comparisons between biological and artificial learning systems. Methodologically, they combine statistical physics frameworks with control theory and high-dimensional analysis. Awards: Academic Grant (CM Lerici Foundation, 2025) Travel Grants (Guarantor of Brains, G-Research 2024) UK–IT Trustworthy AI Exchange Programme (Alan Turing Institute, 2023) SCGB Conference Award (Simons Foundation, 2023) Ph.D. Scholarship (CEA, 2017-2020) Team & Funding: Leads a research group with 2 PhD students and 1 postdoc. Secured significant funding for international workshops including Analytical Connectionism (£42K, 2023; $152K, 2024) and High-Dimensional Methods (135,500 SEK, 2025).
Ron Yurko is an Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University , where he focuses on methods at the intersection of statistical inference and machine learning. He serves as Director of the Carnegie Mellon Sports Analytics Center (CMSAC) , overseeing initiatives like the sports analytics research lab, CMSACamp , and the annual Sports Analytics Conference. Research Interests His work bridges Sports Analytics and Natural Language Processing , with applications in Biostatistics , Public Policy , and Statistical Pedagogy . Recent projects include evaluating NFL player positioning ( NFL Ghosts ), analyzing language patterns in LLM outputs, and developing fractional tackle metrics for defensive evaluation. Google Scholar articles reveal deeper methodological contributions to Bayesian modeling for directional movement analysis, multilevel models for snap timing, and spatio-temporal frameworks in football event modeling. Academic Background PhD in Statistics (2022) from Carnegie Mellon University BS in Statistics with University Honors (2015) from Carnegie Mellon Additional Contributions He is developing a textbook Statistical Methods in Sports Analytics (2027), maintains the Statistical Thinking in Sports Analytics newsletter, and teaches course 36-460/660 at CMU.
Nikolas Nüsken is a Lecturer in Mathematical Data Science and Computational Mathematics at King’s College London’s Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD from Imperial College London (2018) and previously worked at the Alan Turing Institute and the University of Potsdam’s Collaborative Research Centre 'Scaling Cascades in Complex Systems'. His research focuses on computational Bayesian inference, stochastic analysis, optimal control, and kernel methods. Key interests include interacting particle systems, optimal transport, and partial differential equations (PDEs). His work bridges theoretical mathematics with applications in machine learning and data science. Recent research trends involve advancing Monte Carlo methods, Stein variational gradient descent, and tensor-based discretization schemes for solving complex PDEs and stochastic systems. His studies often intersect with robust filtering, deep learning for boundary value problems, and geometric analysis of optimization algorithms. Nüsken’s contributions include 39 cited publications, with notable work on low-rank maximum likelihood estimation, robust regression for BSDEs, and controlled Monte Carlo diffusions. He collaborates extensively on topics like rough dynamics, ensemble Kalman filtering, and neural Schrödinger–Föllmer flows. He is affiliated with King’s Probability and Statistics research groups, contributing to experimental design, MCMC methods, and probabilistic modeling. His work emphasizes interdisciplinary applications, blending mathematical rigor with computational innovation.
Bala Rajaratnam serves as a Professor in the Department of Statistics at the University of California, Davis, with his office located in the Mathematical Sciences Building at One Shields Avenue, Davis, CA 95616. His primary research focuses on advanced statistical methodologies, including: Covariance Estimation for complex datasets Graphical models in probabilistic systems High dimensional inference techniques Bayesian methods for uncertainty quantification Environmental Statistics applications Positivity constraints in causal inference His work bridges theoretical statistics with practical environmental data analysis, emphasizing robust computational approaches for modern statistical challenges. Professional correspondence may be directed to brajaratnam@ucdavis.edu.
Doudou Zhou is an Assistant Professor of Statistics & Data Science at the National University of Singapore. Previously, he was a Postdoctoral Research Fellow in Biostatistics at Harvard University (2022-2024), and earned a Ph.D. in Statistics from UC Davis (2022), advised by Prof. Hao Chen. He holds dual B.S. in Statistics and B.E. in Computer Science from USTC (2019). His research focuses on developing statistical methods and machine learning techniques for electronic health records (EHR) data, including federated learning, reinforcement learning, graph neural networks, and high-dimensional statistics. Key interests include representation learning for multi-institutional data harmonization and precision medicine applications. Notable awards include the 2023 Harvard Data Science Initiative Postdoctoral Fellowship and 2022 ICSA Student Poster Award. He teaches Applied Natural Language Processing (ST5230) and actively contributes to journal reviewing (e.g., JASA, Biostatistics). His work spans algorithm development in federated reinforcement learning and transfer learning for heterogeneous domains. He leads a research group exploring topics such as knowledge graph integration, multimodal EHR analysis, and fair federated learning systems. His code contributions include implementations for federated offline RL and change-point detection methods.
Marc Mezard is a Professor of Theoretical Physics at Bocconi University, where he leads the newly established Department of Computational Sciences. Previously, he served as Research Director at CNRS in Paris and held roles at Université Paris Sud. He earned his PhD in Physics from École Normale Supérieure in Paris in 1984. His research focuses on statistical physics of disordered systems, with applications to machine learning, information theory, computer science, and biophysics. His work bridges theoretical physics and interdisciplinary fields, including neural networks and deep learning, where he explores the impact of data structure on learning strategies. He teaches undergraduate courses in statistical and quantum physics and a doctoral course on complex systems. Key research themes include emergent phenomena in complex systems, with contributions to spin glass theory, compressed sensing, and algorithmic solutions for random satisfiability problems. His publications span foundational topics in statistical mechanics and modern applications in data science. Marc Mezard has collaborated extensively with institutions and researchers globally, contributing to the theoretical foundations of computational and physical sciences. His academic leadership includes directing École Normale Supérieure from 2012 to 2022, fostering interdisciplinary research initiatives.
Yuexi Wang is an Assistant Professor in the Department of Statistics at the University of Illinois. Their research focuses on Bayesian methodology, approximate Bayesian computation, and deep learning applications in statistical inference. Key areas include uncertainty quantification, sparse deep learning, and statistical modeling for count data. Education: Not explicitly stated in text. Research interests span Bayesian analysis, with emphasis on developing scalable methods for posterior approximation, adversarial simulation, and generative models. They have contributed to variable selection via Bayesian forests and uncertainty quantification in sparse neural networks. Recent work explores optimal transport-based methods for posterior sampling and Pochhammer priors in count models. Publications emphasize methodological advances in Bayesian deep learning, including data augmentation techniques and adversarial approaches. Articles often bridge theory and application in machine learning and computational statistics. No scientific awards explicitly mentioned. Advising and grants information unavailable in provided text.
Neill Campbell is a Professor of Visual Computing and Machine Learning in the Department of Computer Science at the University of Bath . He is the Director of the Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) and co-director of the Centre for Mathematics and Algorithms for Data (MAD) . He holds an external position as Honorary Associate Professor at University College London and is a Royal Society Industry Fellow. His research spans visual computing, machine learning, and their applications in graphics, vision, and healthcare. His educational background includes a Master of Engineering and a Doctor of Philosophy in Engineering from the University of Cambridge, with his PhD focusing on Automatic 3D Model Acquisition from Uncalibrated Images. Neill Campbell’s research interests lie at the intersection of shape modeling , machine learning , and computer vision . He develops probabilistic and deep learning models to understand and generate visual content, with applications in digital humans, virtual production, biomechanics, and medical imaging. His work emphasizes uncertainty quantification, generative modeling, and alignment learning using Gaussian processes and Bayesian nonparametrics. He leads interdisciplinary projects that bridge computer science and mathematical sciences. The recent publications reflect a strong trend in probabilistic modeling , geometric deep learning , and medical applications . Topics include Gaussian process-based shape modeling, anomaly detection in safety-critical systems, and sparse approximations for geometric representations. His work increasingly integrates theoretical machine learning with real-world applications in health, engineering, and creative industries. Royal Society Industry Fellow Neill Campbell actively supervises PhD students across multiple Centres for Doctoral Training (ART-AI, SAMBa, CDE) and leads significant research grants such as MyWorld (UKRI Strength in Places Fund) and REMODEL (EPSRC). His group collaborates with industry leaders like Rolls-Royce, NVIDIA, Adobe, and DNEG, and he supports student internships and industrial placements. He is also involved in spin-out activities, including contributions to Forceteck Ltd. He leads the Visual Computing Group and is deeply embedded in research centers including CAMERA , MAD , and MyWorld . His team includes postdoctoral researchers and PhD students working on 3D reconstruction, biomechanics, inverse problems, and generative models, fostering a collaborative and interdisciplinary research environment.
Deniz Baglan is an Associate Professor of Economics and serves as the Director of Undergraduate Studies in the Department of Economics at Howard University, which is part of the College of Arts and Sciences. He holds a Ph.D. in Economics from the University of California, Riverside, and contributes significantly to econometric theory and its applications in macroeconomics and finance. Research Interests: His primary research lies in nonparametric and semiparametric econometric methods, particularly applied to panel data models. He investigates issues related to unobserved heterogeneity, dynamic modeling, and structural inference in economic and financial contexts. His work spans both theoretical developments and empirical applications, focusing on macroeconomic dynamics and empirical finance. Publication Trends: His recent scholarly output shows a consistent focus on advancing nonparametric techniques for panel data, including identification, estimation, and testing in models with fixed effects, time-varying parameters, and nonlinear structures. These contributions are published in notable journals such as Journal of Macroeconomics and Studies in Nonlinear Dynamics and Econometrics , reflecting a strong trajectory in methodological econometrics. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While specific students and grant details are not listed, his role as Director of Undergraduate Studies suggests active involvement in academic mentoring and curriculum development. As a faculty member publishing in top-tier econometrics journals, he likely contributes to research supervision at graduate levels and may participate in externally funded research projects. Labs and Research Teams: There is no mention of specific labs, research centers, or collaborative teams in the provided information.
Bikramjit Das is an Associate Professor and Associate Head of Pillar (Graduate Programme) at Singapore University of Technology and Design (SUTD). He holds a PhD in Operations Research from Cornell University and prior to SUTD, was a postdoctoral researcher at ETH Zurich’s RiskLab. His research focuses on extreme events analysis using applied probability, optimization, and statistical learning, with applications in finance, telecommunications, federated learning, and climate modeling. He teaches courses in Probability, Stochastic Modeling, and Analytics, and directs the Master of Science in Technology and Design (Data Science) program. Education: PhD in Operations Research (Cornell University), B.Stat & M.Stat (Indian Statistical Institute). Research emphasizes heavy-tailed distributions, risk contagion, and network modeling. Key areas include risk analysis in financial networks, robust optimization under uncertainty, and extreme value theory. His work bridges theoretical probability and real-world applications in data science and public policy. Notable contributions include studies on asymptotic independence in high dimensions, robust newsvendor models, and inference techniques for heavy-tailed data. His articles explore topics ranging from federated learning under noise to climate modeling and congestion phenomena in sparse networks. Collaborations include visiting positions at MIT and the Karlsruhe Institute of Technology. Active in academic leadership, he has contributed to technical reports on healthcare provider choice analysis and probabilistic flood risk assessments for nuclear power plants.
Professor Michael Pitt is a faculty member in the Department of Mathematics at King's College London, holding the rank of Professor of Statistics within the Faculty of Natural, Mathematical & Engineering Sciences. He completed his doctorate in Statistics at the University of Oxford (Nuffield College) in 1997, followed by postdoctoral research at Imperial College London. His career includes roles at the University of Warwick (1999-2016) as Assistant and Associate Professor before joining King's College. Education: PhD in Statistics, University of Oxford (1997) Postdoctoral Research, Imperial College London (1997-1999) Research Interests: Focuses on advanced statistical methodologies including particle filtering, sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), and their applications in financial econometrics and biostatistics. Key areas include: Development of computationally efficient algorithms for Bayesian inference Analysis of financial time series and stochastic volatility models Multivariate copula models for dependence structures Clinical outcome analysis in cardiology through statistical revascularization studies Publications: Recent work emphasizes methodological advancements in pseudo-marginal methods, correlated particle filtering, and adaptive sampling techniques. Notable contributions include applications in cardiac clinical outcomes and high-dimensional density modeling. Labs/Teams: Active member of the Research Centre for Non-Equilibrium Science (CNES) , focusing on interdisciplinary non-equilibrium systems, and part of the Statistics Group within the Department of Mathematics.
