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Livia A. CarvalhoView profile

Senior Lecturer

• Neuropsychopharmacology
• Psychoneuroimmunology
• Precision Medicine
• +7 more

Dr. Livia A. Carvalho is a Senior Lecturer in Neuropsychopharmacology at Queen Mary University of London's Faculty of Medicine and Dentistry, within the Clinical Pharmacology and Precision Medicine department at the William Harvey Research Institute. She holds an honorary Senior Lecturer position at the University of Malaga's Department of Psychobiology and is a Fellow of both the Institute of Clinical Research and the Higher Education Academy. Education: BSc in Pharmacology and Biochemistry from University of São Paulo, Brazil MSc in Neurosciences from University of São Paulo PhD in Pharmacology from University of São Paulo Postdoctoral training at King's College London (with Prof. Carmine Pariante) and University College London (with Prof. Andrew Steptoe) Research Interests: Her work focuses on immunophenotyping depression to improve disease stratification and identify novel drug targets. This involves laboratory-based experiments, clinical trials, and epidemiological studies. Key themes include inflammation-driven mental health pathways, precision medicine for mental illnesses, and translational approaches linking molecular and clinical data. Teaching & Mentoring: Dr. Carvalho leads the 'Brain Behaviour and Immunity' module and co-leads 'Clinical Pharmacology and Drug Safety Assessment'. She advises MBBS and Biomedical Sciences students and has mentored over 20 researchers, many of whom have achieved academic or industry success. Notable trainees include Daisy Fancourt (UCL) and Ann Liljn (University Hospital of Northern Sweden). Awards & Recognition: Young Investigator Awards from NARSAD, ECNP, and the British Association for Psychopharmacology American Psychosomatic Society Award Fellowships in Clinical Research and Higher Education Collaborations & Funding: Her collaborations include Prof. Magdi Yaqoob and Prof. Helen Warren (internal) and Prof. Glyn Lewis (external). Sponsors include Barts Charity. She reviews for journals like Brain, Behavior, and Immunity and evaluates grants for the Medical Research Council and National Institute of Health Research. Labs & Teams: Active in the Psychobiology Group at UCL and the Clinical Pharmacology team at Queen Mary. Her research group includes members like Luis de Souza Teodoro and Christopher Clesse.

• Nutritional Immunology
• Maternal and Infant Nutrition
• Lipid Metabolism
• +3 more

Catherine Field is a Professor in the Department of Agricultural, Food and Nutritional Science at the University of Alberta, affiliated with the Faculty of Agricultural, Life & Environmental Sciences. She holds a PhD from the University of Alberta and is a Fellow of the Canadian Academy of Health Sciences (FCAHS) and a Canada Research Chair (CRC) Tier 1 in Human Nutrition and Metabolism. Her primary research focuses on nutritional immunology, particularly the impact of lipids like docosahexaenoic acid (DHA) and arachidonic acid (ARA) on immune system development, cancer therapy, and maternal-infant health. Key educational background includes her PhD in Nutrition from the University of Alberta. Her work spans clinical and preclinical studies, including investigations into DHA's role in enhancing cancer treatments, maternal nutritional status during pregnancy, and the prevention of allergies through early-life dietary interventions. She leads the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort study, examining maternal and infant nutrition's long-term effects. Research interests include lipid metabolism, nutritional programming of immunity, and the interplay between diet and cancer. Awards include the prestigious CRC Tier 1 Chair, recognizing her leadership in human nutrition research. Her lab is located in the Li Ka Shing Centre for Health Research Innovation. Publications highlight her contributions to understanding DHA's effects on breast cancer cells, immune tolerance in allergy models, and maternal vitamin D and iron status during pregnancy. Collaborations include studies on fiber interventions in pediatric populations and the gut microbiome's role in health outcomes.

• Family Well-being
• Social Policy Analysis
• Health Inequities
• +3 more

Dr. Deanna Williamson is an Associate Professor in the Department of Human Ecology at the University of Alberta, within the Faculty of Agricultural, Life and Environmental Sciences. Her research focuses on family well-being, particularly examining how social, economic, and political conditions influence family functioning in diverse populations. She leads a critical ethnographic project exploring family processes among families with young children facing socioeconomic challenges and ethno-cultural diversity. Williamson has secured significant grants, including a SSHRC grant (2013–2018) totaling $489,000 and a Teaching Enhancement Fund grant for field supervisor engagement in practicum programs. Her work critiques welfare-to-work policies and their impacts on low-income families, emphasizing health inequities, child development, and systemic marginalization. She collaborates with interdisciplinary teams and has contributed to randomized controlled trials evaluating service integration approaches for disadvantaged families. Williamson actively supervises graduate students researching topics like urban Indigenous family dynamics, disability-inclusive family processes, and poverty reduction strategies. Publications span themes such as work-life balance, maternal employment effects, policy fragmentation, and cross-cultural research methodologies. She advocates for participatory approaches in community-based research, particularly with immigrant/refugee populations, ensuring marginalized voices inform policy and practice. Williamson’s scholarship bridges academic research with actionable insights for social and health systems reform.

Javier OrlandiView profile

Assistant Professor

• Neuroscience
• Complexity Science
• Statistical Physics
• +5 more

Javier Orlandi is an Assistant Professor in the Department of Physics and Astronomy at the University of Calgary, where he leads the Complex Neural Systems Lab as a Canada Research Chair. His research bridges systems neuroscience, complexity science, and statistical physics, focusing on understanding distributed coding principles in the brain and their impact on perception and learning. He employs computational and experimental approaches, including large-scale calcium imaging and optogenetic manipulation, in collaboration with institutions such as the Hotchkiss Brain Institute and the Computational Neuroscience Platform. Orlandi earned his Ph.D. in Physics from the University of Barcelona (2015), studying spontaneous activity in neuronal cultures. Subsequent postdoctoral research included work at the University of Barcelona (2015–2016) on Huntington’s disease, followed by the University of Calgary (2016–2019) and RIKEN Center for Brain Science in Japan (2019–2022), where he explored cortical processing during decision-making. His research has addressed criticality in neural systems, network inference, and the impact of genetic mutations on brain activity. Key areas of interest include self-organized criticality, neuro-glia communication, and the development of tools like NETCAL for analyzing calcium imaging data. Orlandi has organized challenges to advance connectomics and holds the Eyes High Postdoctoral Fellowship and Canada Research Chair awards. His work integrates theoretical models with experimental data to uncover fundamental principles of neural computation. Affiliations: Complexity Science Group, Hotchkiss Brain Institute, Computational Neuroscience Platform Education: Ph.D. in Physics (University of Barcelona, 2015) Grants: Canada Research Chair, Eyes High Postdoctoral Fellowship Labs: Complex Neural Systems Lab at University of Calgary

Jeremy FoxView profile

Professor

• Community Ecology
• Ecological Restoration
• Mathematical Ecology
• +4 more

Jeremy W. Fox is a Professor in the Department of Biological Sciences at the University of Calgary. He holds a PhD in Ecology and Evolutionary Biology from Rutgers University (2000) and a BA from Williams College (1995). His work focuses on community assembly processes, combining experimental and theoretical approaches with microbial systems. Key interests include dispersal effects, ecosystem function, and the dynamics of competitive interactions. Research highlights include challenging the Intermediate Disturbance Hypothesis (2013), quantifying biodiversity's role in ecosystem stability (2013), and analyzing meta-analytic approaches in ecology (2022). His lab explores how environmental fluctuations and species traits influence community structure and stability. Fox has been recognized with awards including the British Ecological Society Early Career Award (2007) and Alberta Ingenuity grants (2005-2007). Teaching includes courses on quantitative biology (ECOL 425, BIOL 315). His work emphasizes experimental rigor, theoretical frameworks, and open science practices. The Fox Lab's research spans ecological restoration, food web dynamics, and evolutionary ecology, with a focus on long-term community processes and their applied implications.

Wendy ChanView profile

Assistant Professor

• Educational Statistics
• Generalization of Research Findings
• Statistical Methods in Education
• +3 more

Wendy Chan is an Assistant Professor in the Human Development and Quantitative Methods Division at the University of Pennsylvania's Graduate School of Education. Her expertise lies in educational statistics, focusing on improving statistical models to enhance the generalization of research findings from small-scale studies to broader populations. She holds a Ph.D. in Statistics from Northwestern University and has experience teaching mathematics in New York City through Teach for America. Education: Ph.D. in Statistics, Northwestern University Teach for America, New York City middle school teaching experience Research Interests: Dr. Chan's work addresses challenges in scaling up educational interventions and improving generalization from localized trials. She explores statistical methodologies such as propensity score methods, robust estimation, and small-area estimation to tackle issues like model assumption violations and limited data. Her current projects include assessing generalization assumptions, evaluating robust estimation approaches, and analyzing heterogeneous treatment effects. Grants & Projects: Spencer Foundation: Investigating covariates, matching, and longitudinal population changes in generalization. IES: Conceptual replication of the HOPS program for ADHD organizational skills. PCORI: Reducing disparities in ADHD behavioral therapy access for low-income families. Labs/Teams: Collaborates on interdisciplinary projects, including the longitudinal study of novice teachers' role-identities and behavioral health interventions in pediatric care.

Alexandre PoirierView profile

Associate Professor

• Econometrics
• Causal Inference
• Quantile Regression
• +3 more

Alexandre Poirier is the Provost’s Distinguished Associate Professor of Economics at Georgetown University, where he has held positions since 2018. He previously served as an Assistant Professor at the University of Iowa (2013–2018). He holds a Ph.D. in Economics from UC Berkeley (2013), an M.A. in Statistics (2011), and a B.Sc. in Mathematics and Economics from Université de Montréal (2007). His research focuses on econometric theory, causal inference, and sensitivity analysis, with publications in top journals like Econometrica , Journal of Econometrics , and Quantitative Economics . He develops statistical software tools, including Stata modules regsensitivity and tesensitivity , and serves as an Associate Editor for Journal of Business & Economic Statistics and Econometric Reviews . His work emphasizes robustness to unconfoundedness assumptions and omitted variable bias. He teaches advanced econometrics at the Ph.D. and undergraduate levels, including courses on causal inference and panel data methods. Education Ph.D. in Economics, UC Berkeley, 2013 M.A. in Statistics, UC Berkeley, 2011 B.Sc. in Mathematics and Economics, Université de Montréal, 2007 Research Interests Prof. Poirier’s research bridges econometric theory and applied methods, with a focus on identifying causal effects under relaxed assumptions. Key areas include quantile regression, instrumental variables, sensitivity analysis for unobserved confounding, and panel data models. His work often addresses practical challenges in policy evaluation, such as omitted variable bias and endogeneity. Recent contributions include frameworks for comparing sensitivity parameters and assessing robustness of treatment effect estimates. Grants and Software Developed regsensitivity and tesensitivity Stata packages for regression and treatment effect sensitivity analysis Recipient of grants from the National Science Foundation and UC Berkeley’s Graduate Division Professional Service Associate Editor, Journal of Business & Economic Statistics (2021–present) Associate Editor, Econometric Reviews (2024–present) Moderator, arXiv Econometrics Section (2025–present) Referee for leading journals including American Economic Review , Econometrica , and Journal of the American Statistical Association

Peter CholakView profile

Professor

• Computability Theory
• Definability
• Recursion Theory
• +3 more

Peter Cholak is a Professor and Associate Chair in the Department of Mathematics at the University of Notre Dame. He holds a B.S. from Union College (1984), and M.S., M.A., and Ph.D. from the University of Wisconsin (1988, 1991). His research group specializes in logic and computability theory. Cholak's research explores the relationship between computability and definability, with focus areas including automorphisms of computably enumerable sets, Ramsey theory, reverse mathematics, and algorithmic randomness. His work establishes deep connections between computational complexity and definability in arithmetic, such as the classification of orbits in c.e. sets and the computational strength of combinatorial principles. His publications demonstrate consistent focus on computability-theoretic problems with recent expansions into machine learning theory. Key trends include definability in c.e. sets, reverse mathematics of combinatorial theorems, and computational aspects of geometric measure theory. Unifying themes include lattice automorphisms, degree structures, and the limits of formal systems. He has supervised 12 doctoral dissertations in computability theory and related areas. Students have explored topics including algorithmic randomness, lattice embeddings, reverse mathematics, and applications to computer science. Cholak leads the Logic Research Group at Notre Dame, collaborating with researchers globally on problems at the intersection of computability, combinatorics, and foundations of mathematics. Recent work includes computability-theoretic analysis of geometric projections and transformer network expressivity.

Eric JutkowitzView profile

Associate Professor

• Aging
• cost effectiveness
• cost-effectiveness analysis
• +10 more

Eric Jutkowitz is an Associate Professor in the Department of Health Services, Policy and Practice at Brown University. His research focuses on evaluating the value and quality of care for vulnerable populations, particularly through analyzing long-term care systems for older adults and caregivers. He specializes in cost-effectiveness studies of non-drug interventions, Medicare expenditure impacts of dementia, and predictive modeling of dementia progression. His methodologies include systematic reviews, microsimulation, and Markov models. Education: PhD in Health Services Research, Policy, and Administration from the University of Minnesota, with a focus on health decision science. Key research areas include cost-effectiveness analysis, pharmacoeconomics, longitudinal data analysis, and observational research methods. He has developed innovative tools like a dementia progression microsimulation and conducted meta-analyses to assess interventions preventing cognitive decline. Grants and advising details are not explicitly listed in the provided text. His work is affiliated with Brown University’s Health Services, Policy and Practice department, with additional research visibility through platforms like Google Scholar and ResearchGate.

Abdus S. WahedView profile

Professor

• Personalized Medicine
• Dynamic Treatment Regimes
• Statistical Methodology
• +5 more

Abdus S. Wahed is a Professor of Biostatistics and Associate Chair in the Department of Biostatistics and Computational Biology at the University of Rochester. His research focuses on developing statistical methods for dynamic treatment regimes (DTRs), particularly in Sequential Multiple Assignment Randomized Trials (SMARTs), and their applications in hepatitis B, cancer, and obesity. He also works on longitudinal data analysis, survival analysis, and causal inference. Key Roles: Associate Chair, Professor of Biostatistics Affiliations: University of Rochester Medical Center His research interests include: Personalized medicine and adaptive treatment strategies Methodologies for analyzing longitudinal and time-to-event data Clinical collaborations in hepatitis B, liver diseases, and cancer treatment Recent work emphasizes statistical innovations in SMART trials, including interim monitoring and sample size adjustments. His collaborations span virology, oncology, and public health, addressing issues like hepatitis B treatment discontinuation and arsenic exposure effects. Awards: No specific awards listed in provided text. Advising & Grants: Active in mentoring graduate students and leading research grants focused on biostatistical methodologies. Collaborates with organizations like the Hepatitis B Research Network and the Longitudinal Assessment of Bariatric Surgery study. Labs/Teams: Engaged in multidisciplinary teams addressing chronic diseases and statistical trial design.

Yu ChengView profile

Professor

• Dynamic Treatment Regimes
• SMART (Sequential Multiple Assignment Randomized Trials)
• Causal Inference
• +6 more

Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.

Benjamin C. PierceView profile

Professor

• Programming Languages
• Type Systems
• Formal Verification
• +3 more

Benjamin C. Pierce is Henry Salvatori Professor of Computer and Information Science at the University of Pennsylvania, with appointments in the School of Engineering and Applied Science. A Fellow of the ACM, his research spans programming languages, formal verification, and security-privacy technologies. He directs the DeepSpec project on verified systems infrastructure and leads climate computing initiatives. Research interests focus on: Formal methods for reliable software via proof assistants like Coq Bidirectional programming and data synchronization Language-based security and differential privacy Publication trends show consistent contributions to type theory foundations, with recent emphasis on property-based testing methodologies and real-world verification. Articles frequently appear in top PL/SEC venues with practical applications in compilers and secure systems. Scientific Awards: ACM Fellow (systems verification) SIGPLAN Distinguished Educator Award (textbook innovations) Advises graduate students through the Penn PL Club. PI for NSF Expeditions in Sustainable Computing. Leads the VERSE project for verified C code and Unison file synchronizer. Directs the Penn Programming Languages Research Group collaborating with industry partners including Amazon and Microsoft Research.

• Quantum Cognition
• Cognitive Psychology
• Decision Making
• +7 more

Emmanuel M. Pothos is a Professor in the Department of Psychology at City, University of London, within the School of Arts and Social Sciences. He has held prior academic positions as Senior Lecturer at City, Reader at Swansea University, and Lecturer at both the University of Edinburgh and Bangor University, reflecting a sustained and progressive academic career in cognitive psychology. PhD in Psychology, University of Oxford (1995–1998) BSc in Physics, Imperial College London (1992–1995) His research primarily focuses on quantum cognition, where he applies the mathematical formalism of quantum theory to model cognitive phenomena such as decision-making, judgment, and similarity perception. He investigates violations of classical probability in human cognition, context effects, and the role of ambiguity and conflict in choices. His interdisciplinary work draws from psychology, physics, and computational modeling. The recent trend in his publications reveals a strong emphasis on formal modeling of cognitive processes using quantum probability, with applications extending into behavioral health, financial systems, and foundational questions in decision theory. His work frequently appears in high-impact journals across psychology, physics, and interdisciplinary sciences, indicating a broad scholarly impact. While no formal awards are listed in the provided data, his extensive publication record in top-tier journals and frequent peer-review activities for leading outlets such as Nature , Psychological Review , and PNAS reflect significant academic recognition and service. Dr. Pothos actively collaborates with researchers internationally and has contributed to theoretical advances in understanding how quantum-like structures can explain human cognitive biases and complexities. Although no direct mention of student supervision is made, his role as a professor and prolific researcher implies involvement in mentoring graduate students and leading research initiatives. He has not indicated any lab or research group name, but his work clearly involves collaborative projects in cognitive modeling and decision science.

• Network Analysis
• Random Graph Theory
• High-Dimensional Statistics
• +5 more

Can M. Le is an Associate Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. His research lies at the intersection of statistics, network science, and high-dimensional data analysis, with a focus on theoretical and applied aspects of network modeling and inference. Ph.D. in Statistics, University of Michigan, Ann Arbor His research interests include network analysis, random graph theory, community detection, high-dimensional statistical inference, and regularization of network data. He develops methods for analyzing noisy, complex network structures and has contributed significantly to spectral methods and low-rank approximations in network science. His work bridges theoretical statistics with practical applications in social and biological networks. The recent publications demonstrate a strong trend in modeling and inference for network-linked data, with emphasis on robustness, adaptivity, and concentration properties of random graphs. His work combines deep probabilistic analysis with statistical methodology, particularly in community detection and network estimation under noise and heterogeneity. His research is supported by the National Science Foundation (NSF) grant DMS-2015134, indicating active funding and ongoing contributions to the field. While no formal list of advisees is provided, his collaborative work with leading statisticians such as Elizaveta Levina and Roman Vershynin suggests an active research group and mentoring role. He has no listed scientific awards in the provided text. However, his consistent publication record in top journals (JASA, JRSSB, Annals of Statistics, JMLR) underscores his scholarly impact. Dr. Le's work is closely tied to theoretical and applied statistical research on networks, likely involving a research lab or team focused on network data science, though specific lab names or team structures are not mentioned in the text.

Davide La VecchiaView profile

Professor

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +3 more

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.