Find Academic Supervisors

Laura Mersini-HoughtonView profile

Professor

• Cosmology
• Theoretical Physics
• Quantum Physics
• +6 more

Laura Mersini-Houghton is a Professor of Theoretical Physics and Cosmology at the University of North Carolina at Chapel Hill. She is affiliated with the Gravity, Cosmology, and HEP (GCHEP) theory group and the Institute of Field Physics , supported by the Bahnson Fund. Her research spans three main areas: origins of the universe, dark energy, and quantum black hole physics. Her work includes proposing a quantum landscape multiverse theory to explain the universe's origin, with predictions tested by PLANCK and LHC experiments. She has also demonstrated that Hawking radiation back-reaction prevents singularity formation in collapsing stars. Education : PhD in Physics (University of Wisconsin-Milwaukee, 2000), MSc (University of Maryland-College Park, 1997), Fulbright Scholar (University of Maryland, 1994). Key trends in her recent publications include cosmological tensions, quantum multiverse implications, and time crystal-based dark energy models. She co-authored works on inflationary cosmology and cosmic probes of fundamental physics. Scientific Awards : UWM Distinguished Alumni She has been featured in media coverage and public talks, exploring topics like the arrow of time and cosmic puzzles. Her research integrates theoretical physics, string theory, and observational cosmology, supported by the Bahnson Fund.

• Addictive Disorders
• Genetics
• Substance Use Disorders
• +3 more

Yaira Zoé Núñez is a Researcher in the Division of Human Genetics at the Yale School of Medicine. Her work focuses on genetic and epidemiological factors underlying substance use disorders, including cannabis, opioid, alcohol, and methamphetamine addictions. She collaborates extensively with the Gelernter Lab and other institutions on large-scale genomic studies. Research Interests: Genetic basis of addictive disorders Multi-ancestry genome-wide association studies (GWAS) Epidemiology of polysubstance use Clinical correlates of substance use disorders Key Collaborations: Works with Prof. Joel Gelernter, Renato Polimanti, and international teams. Recent studies include: Multicenter analysis of gambling disorder in methamphetamine users (2025) Multi-ancestry cannabis use disorder GWAS (2023) Genetic risk loci for opioid use disorder (2022) Labs/Teams: Core member of the Gelernter Lab, contributing to translational research bridging genetics and clinical addiction medicine. Active in collaborative networks analyzing large-scale biomedical data.

J. Christopher BrownView profile

Professor

• Human-Environment Geography
• Amazonian Sustainable Development
• Political Ecology
• +5 more

J. Christopher Brown is a Professor of Geography and Environmental Studies at the University of Kansas. He specializes in human-environment interactions, focusing on sustainable development in the Amazon Basin. His research integrates ecological, political, and socio-economic dimensions to address deforestation, agricultural expansion, and NGO-mediated rural development. Affiliations: Department of Geography, University of Kansas; Collaborator with Brazil's Embrapa Agricultural Research Agency Key Projects: Amazonian NGO dynamics and political impacts Soybean and sugarcane expansion impacts Spatial analysis of deforestation and land use Research interests include political ecology, biogeography, and the interplay between global and local scales. He employs remote sensing and interdisciplinary methods to study environmental governance and policy outcomes. His work on NGOs reveals how funding shapes political behavior in Brazil, with findings published in Comparative Political Studies and Political Geography . Recent studies highlight the edge effects of land-use changes and biodiversity impacts on pollinators like orchid bees. Awards: 2016 George and Eleanor Woodyard International Educator Award (University of Kansas) Brown's research bridges academic inquiry and policy, examining how development policies interact with ecological systems in Latin America. Current projects address soy moratorium efficacy, sugarcane expansion in the Cerrado, and the MST landless movement's political influence.

• Psychiatry
• Addiction Medicine
• Mental Health
• +3 more

Hüseyin ÜNÜBOL is an Associate Professor (part-time) at Uskudar University's Department of Psychiatry. He graduated from Ege University Faculty of Medicine in 2008 and completed his PhD in Psychiatry in 2010. He achieved the title of Associate Professor in 2020. His research focuses on behavioral addictions, psychiatric epidemiology, and mental health during crises. Notable areas include ADHD diagnostic methods, pandemic-related anxiety, and addiction patterns in Turkey. Education: Ege University Faculty of Medicine (MD 2008), PhD in Psychiatry (2010) Key Roles: Part-time academic contributor to Uskudar University's Psychiatry Department Research Interests: His work spans addiction medicine, public health surveillance, and the psychological impacts of modern technologies. He has pioneered studies on social media addiction, pandemic mental health dynamics, and the neurocognitive basis of ADHD diagnosis using eye-tracking technology. His recent projects include large-scale national surveys on problematic gaming, shopping addiction, and substance use patterns. Articles Trends: Over 50 peer-reviewed publications since 2012, with recent emphasis on behavioral addictions (sex/gaming/social media), pandemic-related mental health, and diagnostic innovations. His 2020-2024 work shows strong focus on epidemiological studies and technological applications in psychiatry. Awards: None explicitly listed. Grants/Advising: Active in large-scale national health surveys like TURBAHAR but no specific grants/advising details provided here. Collaborates on addiction mapping projects and public health initiatives. Labs/Teams: Involved in multidisciplinary teams studying behavioral addictions and psychiatric epidemiology, though specific lab affiliations not detailed.

Nicola McConkeyView profile

Lecturer

• Neutrino Physics
• Quantum Technology
• Detector Development
• +2 more

Nicola McConkey is an Ernest Rutherford Fellow and Lecturer in Particle Physics at the School of Physical and Chemical Sciences, Queen Mary University of London. She joined the Particle Physics Research Centre in 2024 and leads experimental work in neutrino interactions and detector development. Her affiliations include the Centre for Fundamental Physics and Centre for Experimental and Applied Physics. McConkey is an active member of international collaborations including SBND, DUNE, and MicroBooNE, where she contributed to the assembly of SBND and pioneered high-statistics measurements of electron-neutrino interactions using liquid argon detectors. Her research focuses on three primary domains: neutrino physics (particularly neutrino-argon scattering cross-sections), quantum technology applications for neutrino mass measurement, and liquid argon time projection chamber (LArTPC) detector development. McConkey's investigations aim to advance fundamental particle physics through precision measurements and technological innovation, with emphasis on improving detection capabilities for next-generation neutrino experiments. Publications predominantly explore neutrino interaction dynamics, cross-section measurements, and detector performance optimizations across MicroBooNE, SBND, and DUNE collaborations. Research trends demonstrate consistent focus on refining LArTPC technologies, developing machine learning applications for particle reconstruction, and probing beyond-Standard-Model physics through neutrino interactions. Scientific Awards: Ernest Rutherford Fellowship (2022) McConkey advises two PhD students (Oscar Chow, Yoshita Dabburi) and leads significant research funding including: STFC Grant: 'Piecing together the neutrino mass puzzle' (£431,666; 2024-2027) STFC Outreach Grant: 'Quantum Technologies for Neutrino Mass' (£99,999; 2024-2025) She coordinates research within the Particle Physics Research Centre laboratory and collaborates extensively within the SBND, DUNE, and MicroBooNE international teams, alongside leading the Quantum Technologies for Neutrino Mass collaboration developing novel measurement techniques.

Jedidiah CrandallView profile

Associate Professor

• Internet Security
• Censorship Resistance
• Network Security
• +3 more

Jedidiah Crandall is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with an affiliation to the Biodesign Center for Biocomputing, Security and Society. His research focuses on Internet censorship, network security, and privacy-preserving technologies. Crandall collaborates with journalists and activists to expose surveillance mechanisms, particularly in politically sensitive regions like Russia and China. His work includes analyzing VPN vulnerabilities , decentralized censorship systems , and cross-border data flows . He teaches advanced courses in computer network security and advises graduate students on thesis/dissertation research. Research trends in his publications emphasize measuring state-level information control , attack vectors in modern networks , and secure communication technologies . Notable work includes TSPU: Russia's censorship infrastructure and Hidden Links: Analyzing Secret Families of VPN Apps . Crandall's teaching spans courses like Advanced Computer Network Security and Applied Cryptography , reflecting his commitment to preparing the next generation of security professionals. His Censored Planet project tracks global Internet censorship patterns through large-scale measurements.

• Flood Hydrology
• Hydrology
• Water Resources
• +8 more

Alberto Viglione is an Associate Professor at the Politecnico di Torino , Department of Environment, Land and Infrastructure Engineering (DIATI), and a member of the Interdepartmental Center SmartData@PoliTO. He has been a faculty member since 2019, following a decade as a Research Fellow at the Vienna University of Technology. University: Politecnico di Torino Department: DIATI – Department of Environment, Land and Infrastructure Engineering Rank: Associate Professor Email: alberto.viglione@polito.it His research focuses on flood hydrology, water resources, and hydro-meteorological extremes , integrating statistical analysis, climate change impacts, land use dynamics, and socio-hydrological modeling. He investigates the spatio-temporal dynamics of climatic, hydrological, and human processes in river basins and their implications for extreme event risks. His work emphasizes data integration, conceptual modeling, and risk assessment across scales. The recent publications highlight a strong trend in analyzing European flood dynamics , the impacts of climate change , and the development of socio-hydrological frameworks that incorporate human behavior and societal memory into flood risk modeling. His research spans from statistical hydrology in ungauged basins to large-scale assessments of climate-flood interactions. Scientific Awards and Honors: AMGA Award for best PhD thesis on water resources (2009) Editorial and Professional Service: Associate Editor, Water Resources Research (2014–present) Associate Editor, Hydrological Sciences Journal (2012–2018) Associate Editor, WIRES Water (2012–2020) Associate Editor, Journal of Hydrology and Hydromechanics (2019–present) Scientific Committee Member, European Geosciences Union (2019–2023) Secretary, International Commission on Water Resources Systems, IAHS (2015–present) Teaching and Advising: He teaches courses such as Bayesian Inference , Applied Hydrology , Fundamentals of Environmental Geosciences , and Hydro-meteorological Risk Assessment . He is a PhD supervisor and member of multiple PhD colleges in Civil and Environmental Engineering at Politecnico di Torino. He currently advises PhD students including Tsion Ayalew Kebede , Emanuele Mombrini , Luigi Cafiero , Luca Lombardo , and Matteo Pesce . His research is supported by grants from national (PRIN), EU, and commercial sources, including projects like Clim2FlEx , RETURN , and ATO4WATER . Research Labs and Teams: He is affiliated with the SmartData@PoliTO laboratory, focusing on big data and data science applications in hydrology and environmental systems.

Hugues AschardView profile

Researcher

• Statistical Genetics
• Genome-Wide Association Studies (GWAS)
• Microbiome Research
• +7 more

Hugues Aschard is a Principal Investigator and Structure Manager at the Pasteur Institute in Paris, where he leads research in statistical genetics, microbiome analysis, and computational genomics. He is the principal investigator of the MicMat project, the EpiGenCOV Consortium, and several bioinformatics software initiatives including JASS, RAISS, and MGMM. Research Interests: Statistical and computational methods in genetics Genome-wide association studies (GWAS) Gene-environment interactions Microbiome and host genetics in inflammatory bowel disease Genetic epidemiology of infectious diseases like COVID-19 Development of open-source tools for multi-trait and summary-statistic analysis Recent Research Trends: His recent publications and projects emphasize integrative genetic modeling, multi-trait analysis across diverse populations, and the development of novel computational methods to handle missing data and improve SNP discovery. His work bridges statistical innovation with biological and clinical applications in complex diseases. Scientific Contributions: Development of JASS, RAISS, and MGMM software tools Leadership in large-scale consortia like EpiGenCOV Advancing methods for cross-ancestry genetic studies Advising and Collaboration: He supervises multiple PhD students and postdoctoral fellows, including Christophe Boetto, Antoine Auvergne, and Lucas Chataigner. He collaborates with major institutions such as APHP and CNRGH. His team includes research engineers and administrative staff, indicating an active and well-supported research group. Laboratories and Teams: He is a key member of the Biomaterials and Microfluidics team at the Pasteur Institute, where he contributes to interdisciplinary research involving Bayesian decision processes and genetic modeling.

Murali ManiView profile

Professor

• Database Systems
• Data Provenance
• Generative AI
• +12 more

Murali Mani is a Professor in the Department of Computer Science, Engineering, and Physics at the College of Innovation and Technology, University of Michigan-Flint. He is actively involved in teaching courses such as Database Design (CSC 384, CSC 584) and Independent Graduate Study in Computer Science (CSC 591), and serves as Principal Investigator on multiple research grants focused on computing education and data science. His research interests span database systems, data provenance, generative AI for data augmentation, computing education, and the societal impact of technology . He has developed educational tools including epidemiology calculators and market basket analysis modules to support interdisciplinary learning. His work emphasizes integrating computing skills across disciplines such as health sciences and management. The 15 most recent scholarly contributions reflect a strong focus on data management, AI-augmented data curation, educational technology, and the cognitive aspects of learning programming. These publications appear in venues such as VLDB, IEEE FIE, and ACM conferences, with several under review or in preparation for top-tier journals like Communications of the ACM and the VLDB Journal. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: Murali Mani actively mentors students through independent graduate studies and collaborative research projects. He has secured funding from the National Science Foundation (SGER grant on provenance metadata) and internal university sources, including the CIT/CHS Joint Grant and the Office of Research at UM-Flint, supporting projects on civic literacy, computational skills integration, and AI for social science data archiving. Labs and Teams: While no formal lab name is mentioned, Murali Mani leads a research group focused on data systems and computing education, collaborating with colleagues across departments and institutions. He contributes to initiatives such as the Michigan Institute for Data & AI in Society (MIDAS) and the Academic Data Science Alliance (ADSA), and has presented at conferences including IASSIST, FIE, and ICCTAC.

• Population Health
• Social Epidemiology
• Demography
• +7 more

Pekka Martikainen is a leading researcher in population health and demography, affiliated with the Population Research Unit at the Faculty of Social Sciences, University of Helsinki . He is also actively involved with the MaxHel Center and the Laboratory of Population Health , contributing to major research initiatives such as the Monitoring Mortality Inequalities Consortium and studies on immigrant-native health disparities. His research focuses on social and economic determinants of health and mortality over the life course, using extensive Finnish register data. Key interests include fertility and family dynamics , health inequalities , migration and health , early-life determinants of health, and behavioral factors influencing mortality. His work often explores intergenerational linkages and the role of socioeconomic status in shaping health outcomes. The most recent publications reflect a strong emphasis on mental health , genetic and social interactions , educational and occupational influences on health , and disparities in cancer and reproductive health . Studies frequently employ longitudinal, register-based designs across Nordic and European populations, highlighting cross-national comparative insights. Behavioral Determinants of Health and Mortality Costs and Gains of Postponing Parenthood Immigrant-Native Health Disparities Over the Life Course Linked Lives: Family and Socioeconomic Attainment Medically Assisted Reproduction Mortality Disparities at Subnational Level His research has been published in top journals including The Lancet , Demography , Social Science and Medicine , and European Journal of Epidemiology . He collaborates extensively with researchers at the Max Planck Institute for Demographic Research and leads or contributes to multiple large-scale projects analyzing health and demographic trends in Finland and beyond. Martikainen’s work informs public health policy and contributes to understanding how social structures and individual behaviors interact to shape population health and longevity. He plays a central role in advancing register-based demographic research in Europe.

• Epidemiology
• Women's Mental Health
• Perinatal Depression
• +2 more

Donghao Lu is a Professor at Karolinska Institutet's Institute of Environmental Medicine, where he leads the Epidemiology of Women's Mental Health Lab. His research program focuses on psychiatric epidemiology with specialization in women's reproductive mental health, using large-scale population cohorts to investigate biological mechanisms and health outcomes. Primary research domains include: Risk factors and health consequences of perinatal depression and premenstrual disorders Bidirectional relationships between autoimmune diseases and mood disorders Gender disparities in mental health presentation and outcomes Cardiometabolic comorbidities in reproductive psychiatric conditions His recent publications (2023-2025) demonstrate strong methodological consistency, predominantly using Scandinavian national registries for longitudinal cohort designs. Research themes show progression toward understanding systemic health impacts of reproductive mood disorders, particularly cardiovascular risks and mortality. Over 90% of recent work incorporates population-level data analysis with sample sizes exceeding 10,000 participants. Laboratory focus includes integrating epidemiological methods with biomarker research to bridge obstetrics/gynecology and psychiatry. Current projects investigate inflammatory pathways in perinatal depression and genetic determinants of premenstrual disorder trajectories. The team maintains international collaborations across Scandinavia, China, and North America.

Devin G PopeView profile

Professor

• Behavioral Economics
• Psychology-Economics Intersection
• Observational Data Analysis
• +4 more

Devin G. Pope is the Steven G. Rothmeier Professor of Behavioral Science and Economics at the University of Chicago's Booth School of Business. His research examines psychological biases in economic decision-making using observational data across diverse markets including healthcare, voting, transportation, and consumer behavior. Pope has published extensively in top economics journals (American Economic Review, Quarterly Journal of Economics), general science publications (Science, Nature), and interdisciplinary outlets (Management Science, Psychological Science). His research interests bridge behavioral economics and psychology, focusing on vaccination incentives , racial bias measurement , consumer decision heuristics , and observational data analysis . Recent work leverages smartphone data to study religious attendance patterns, voting wait times, and geographic mobility. Pope's research methodology emphasizes real-world field experiments and large-scale observational datasets to identify psychological biases affecting economic outcomes. Notable scientific contributions include: Co-editing the American Economic Review Amazon Scholar appointment (2019-2021) Robert King Steel Faculty Fellowship Steven G. Rothmeier Professorship Pope advises PhD students and teaches graduate courses including Behavioral Economics and Workshop in Behavioral Science. His research has received significant external funding for pandemic response studies and behavioral interventions. Pope maintains active research collaborations across economics, psychology, and public health disciplines through the Booth School's research centers and workshops.

Devin K. HarrisView profile

Professor

• Structural Health Monitoring
• Smart Cities
• Digital Twins
• +5 more

Devin K. Harris is a Professor and Chair of the Department of Civil and Environmental Engineering at the University of Virginia . His work focuses on large-scale infrastructure systems , combining image-based measurement techniques, simulation , visualization , and data analytics to advance structural health monitoring , smart cities , and digital twins . He also investigates reinforced/prestressed concrete behavior and innovative materials in civil infrastructure. Education : Ph.D., M.S., and B.S. in Civil Engineering from Virginia Tech (2007), Virginia Tech (2004), and University of Florida (1999). Research Interests Structures and Mechanics - Sustainable Infrastructure Systems Infrastructure Condition Assessment Structural Health Monitoring Smart Cities Digital Twins Applied Machine Learning Scientific Awards Delmar L. Bloem Distinguished Service Award (2021) IAspire Leadership Academy Fellow (2020–2022) ASCE Journal of Bridge Engineering Outstanding Reviewer (2013) UVA Teaching Resource Center Excellence in Diversity Fellowship (2012–2013) ACI Young Member Award for Professional Achievement (2011) Grants Principal Investigator for EAGER: Adaptive Digital Twinning: An Immersive Visualization Framework for Structural Cyber-Physical Systems (NSF #2136724) and Performance Characteristics of In-Service Bridges (Virginia Transportation Research Council, 2018–2020). Co-led NCHRP 23-16 on Machine Learning applications in transportation agencies. Labs & Teams Leads the Infrastructure Simulation, Sensing and Evaluation Lab (I-S2EE) , equipped with DIC systems , mobile GPR , thermal imaging , and cyber-physical simulation tools. Collaborates with the Omni-Reality & Cognition Lab for AR/VR integration in infrastructure evaluation.

Igor RudanView profile

Professor

• Genetics
• Genomics
• Genome-Wide Association Study
• +7 more

Igor Rudan is Professor of International Health and Molecular Medicine at the University of Edinburgh, serving as Co-Head of the Centre for Global Health within the Usher Institute, part of the College of Medicine and Veterinary Medicine. His academic position is described as a 'Personal Chair of International Health & Molecular Medicine' at the Deanery of Molecular, Genetic and Population Health Sciences. His research interests include: Genome-Wide Association Studies Genetics and Genomics Genetic Divergence Meta-Analysis Single Nucleotide Polymorphism research Systematic Reviews in global health Epidemiology of respiratory diseases Health inequalities and social determinants of health Professor Rudan's recent publications reveal a strong focus on the intersection of molecular medicine and population health. His work spans genetic epidemiology, pandemic response research, and health equity studies, with particular emphasis on understanding how genetic, social, and environmental factors interact to influence health outcomes across diverse populations. His research frequently employs large-scale genomic analyses combined with population health approaches to address global health challenges. His scientific recognition includes: NIHR Impact Prize (2025) for respiratory disease research RSE Mary Somerville Medal (2023) Professor Rudan leads and collaborates on numerous international research projects including EQUI-RESP-AFRICA (improving respiratory health outcomes in Africa), the NIHR Global Health Research Unit on Respiratory Health (RESPIRE-2), and documentation of the EQUIST tool's global impact. His work often involves large-scale data analysis and international collaborations across multiple continents, positioning him at the forefront of global health research that bridges molecular medicine with population-level health interventions.

Aidong ZhangView profile

Professor

• Machine Learning
• Data Mining
• Bioinformatics
• +3 more

Aidong Zhang is the Thomas M. Linville Professor of Computer Science at the University of Virginia, with joint appointments in Biomedical Engineering and the School of Data Science. Her research focuses on machine learning, interpretable AI, federated learning, and generative AI applications in healthcare and bioinformatics. She holds a Ph.D. in Computer Science from Purdue University. Dr. Zhang has been honored with prestigious awards including the ACM Fellow (2017), IEEE Fellow (2009), and the 2025 Distinguished Researcher Award from UVA. Her work bridges computational methods with biomedical challenges, emphasizing fairness, robustness, and explainability in AI systems. Key research areas include federated learning frameworks, concept-based models, and large language models for scientific hypothesis generation. Dr. Zhang leads a lab offering PhD positions in machine learning, bioinformatics, and health informatics. Notable grants include NSF projects on explainable AI platforms and hardware-software co-design for extreme-scale machine learning. Education: Ph.D., Computer Science, Purdue University Affiliations: School of Engineering and Applied Science, School of Data Science Grants: NSF-funded projects on federated learning, multimodal analysis, and biomedical AI Labs/Teams: Zhang's Research Group focusing on interpretable machine learning and healthcare applications