Find Academic Supervisors

• Political Behavior
• Comparative Politics
• Formal Political Theory
• +4 more

Andrew T. Little is an Associate Professor of Political Science at the University of California, Berkeley , affiliated with the Department of Political Science . He holds academic appointments in Models & Politics , Methodology & Formal Theory , and Comparative Politics . His research focuses on formal models, experimental methods, and the political consequences of biased beliefs, with particular emphasis on authoritarian politics, information manipulation, and democratic measurement. Little earned a B.A. from Carleton College and a Ph.D. from New York University , later teaching at Cornell University before joining Berkeley. His work bridges theoretical and empirical approaches, addressing topics like discriminatory policing, democratic backsliding, and conflict dynamics. Notable contributions include frameworks for measuring democratic erosion and analyzing motivated reasoning's impact on governance. His research portfolio spans 30+ peer-reviewed articles , appearing in top journals like American Journal of Political Science and Journal of Politics . Key themes include: Behavioral explanations for biased beliefs and political polarization Quantitative models of authoritarian regimes and electoral fraud Ethical implications of information manipulation in democracy Little currently serves as Associate Editor for the American Journal of Political Science . He teaches advanced courses in formal theory, quantitative methods, and game theory at both undergraduate and graduate levels.

• Applied Microeconomics
• Microeconometrics
• Labor Economics
• +8 more

Mattia Albertini is a Post-Doctoral Researcher at the Institute for Economic Research (IRE) within the Faculty of Economics at Università della Svizzera italiana (USI). He previously completed his Ph.D. at the Institute of Economics (IdEP), USI, and participated in the Swiss Program for Beginning Doctoral Students in Economics. He holds a Master of Science in Economics with a minor in Data Science from USI and a Bachelor in Economics from the University of Pavia. Bachelor in Economics, University of Pavia Master of Science in Economics (minor in Data Science), Università della Svizzera italiana Ph.D. in Economics, Università della Svizzera italiana Swiss Program for Beginning Doctoral Students in Economics, Swiss National Bank His research focuses on applied microeconometrics with applications in health, labor, public, urban, and environmental economics. A recurring theme in his work is the spatial dimension of economic outcomes, including residential integration, real estate market dynamics, and geographic proximity to borders or hazards. He integrates data science techniques, particularly programming in Python and STATA, to develop tools for empirical analysis and reproducible research. The collection of articles reflects a strong trend in applied econometrics, causal inference, and spatial analysis. His work spans health economics (e.g., benzodiazepine prescribing), labor and urban economics (e.g., migration, job accessibility), and environmental and insurance economics (e.g., natural hazard impacts). Methodologically, he engages with difference-in-differences, event study designs, spatial modeling, and text analysis, often building or applying computational tools to address research questions. He has contributed to teaching at both the Bachelor's and Master's levels, particularly in microeconomics, macroeconomics, and data-intensive courses such as Textual Analysis and Spatial Data for Economists. Teaching Assistant, Microeconomics A (Bachelor) Teaching Assistant, Macroeconomics B (Bachelor) Teaching Assistant, Textual Analysis and Spatial Data for Economists (Master) Mattia Albertini is actively involved in open science, sharing code for econometric methods, data visualization, and application development (e.g., a gluten-checking app) on GitHub. His interdisciplinary approach combines economic theory, statistical modeling, and computational tools to investigate real-world policy-relevant questions.

Lei SunView profile

Professor

• Statistical Genetics
• Statistical Genomics
• Applied Statistics
• +7 more

Lei Sun is a Professor in the Department of Statistical Sciences at the Faculty of Arts and Science, University of Toronto. She holds a cross-appointment in the Division of Biostatistics, Dalla Lana School of Public Health. Her research lies at the intersection of statistics and genetics, with a focus on developing robust and scalable methods for analyzing complex human traits. Her educational background includes a B.Sc. in Mathematics from Fudan University and a Ph.D. in Statistics from the University of Chicago (2001). Since then, she has remained at the University of Toronto, contributing significantly to statistical genetics methodology. Lei Sun's research interests include statistical genetics and genomics, multiple hypothesis testing, selective inference, robust association methods, and multivariate analysis. Her work emphasizes open-source implementation and reproducible research, with tools and materials shared via GitHub. She actively develops statistical methodologies to address challenges in genetic studies, particularly in high-dimensional and correlated data settings. The trend in her recent scholarly outputs shows a strong emphasis on education, method dissemination, and open science. Her publications and materials focus on teaching statistical genetics, guiding GWAS practices, and promoting polygenic risk score applications, reflecting both methodological innovation and a commitment to training the next generation of researchers. CRM-SSC Prize in Statistics (2017) NSERC Discovery Accelerator Supplement (2018) Lei Sun has been continuously funded by major Canadian agencies including NSERC and CIHR, supporting her research program in statistical genetics. She mentors students and early-career researchers through direct supervision and leadership in the CANSSI-Ontario STAGE training program. Her grants support the development of novel statistical tools for genetic epidemiology and genomic data analysis. She leads the Statistical Methods for Genetics and Genomics (SMG) seminar series and is involved in the CANSSI-Ontario Strategic Training for Advanced Genetic Epidemiology (STAGE) program, fostering collaborative research and training in statistical genetics across Ontario.

Michael MumaView profile

Professor

• Robust Data Science
• Signal Processing
• Machine Learning
• +5 more

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Sarah TurnerView profile

Professor

• Economics of Education
• Labor Economics
• Higher Education Policy
• +7 more

Sarah Turner is the University Professor of Economics & Education and holds the Souder Family Professorship at the University of Virginia. She is affiliated with the School of Education and Human Development and has a joint appointment in the Department of Economics. Her research bridges labor economics and education policy, focusing on structural barriers to college access and completion, particularly among low-income and underrepresented populations. Her educational background includes a Bachelor of Arts from Princeton University and a Doctor of Philosophy in Economics from the University of Michigan, Ann Arbor. Bachelor of Arts (BA), Princeton University Doctor of Philosophy (PhD), University of Michigan, Ann Arbor Turner’s research centers on the economics of education, with a strong emphasis on how federal and state policies influence postsecondary enrollment, persistence, and degree attainment. She investigates the effects of economic disruptions—such as trade exposure, military conflicts, and recessions—on educational decisions. Her work also critiques and refines metrics used to assess college opportunity, advocating for more accurate measures that reflect true access for disadvantaged students. She frequently employs large-scale administrative datasets and quasi-experimental methods to evaluate policy interventions. The analysis of her most recent publications reveals a consistent focus on educational inequality, policy evaluation, and the interplay between labor markets and higher education. Her work spans historical analysis (e.g., the G.I. Bill) and contemporary issues (e.g., information nudges for UI recipients), demonstrating methodological rigor and policy relevance. Recurring themes include college affordability, informational barriers, cohort effects, and the long-term impacts of federal aid programs. Although no formal scientific awards are listed in the provided text, her publications in premier journals such as the American Economic Journal , Journal of Labor Economics , and Education Next reflect high scholarly recognition. Sarah Turner has advised numerous research projects and collaborative studies, particularly with economists like John Bound, Caroline Hoxby, and Andrew Barr. While specific graduate students are not named, her extensive publication record suggests a strong mentoring role in graduate education. Her research has been supported by major funding bodies, inferred from NBER working paper affiliations and policy-oriented studies, though specific grants are not detailed. She continues to contribute actively to policy debates on college access and equity. Turner’s work is conducted through independent and collaborative research initiatives, likely supported by university research centers or policy institutes at the University of Virginia, although no specific lab or research team is mentioned in the text.

• Time Series Analysis
• Functional Data Analysis
• Bootstrap Methods
• +5 more

Manuel Febrero-Bande is a Professor of Statistics and Operations Research at the University of Santiago de Compostela, affiliated with the Department of Statistics, Mathematical Analysis and Optimization under the Faculty of Mathematics. He earned his PhD in 1995 with a thesis on time series modeling. His research focuses on functional data analysis, time series, bootstrap methods, and computational statistics, with applications in environmental control, finance, and health. He co-developed the R package fda.usc , a key tool in functional data analysis. Febrero-Bande has coordinated academic programs like the Master in Statistical Techniques and the Doctoral Program in Statistics and Operations Research. His work emphasizes statistical methodology and interdisciplinary applications, with over 30 peer-reviewed publications and contributions to software development. Key research trends include functional data classification (e.g., Bitcoin address analysis), variable selection in additive models, and goodness-of-fit testing for functional linear models. He has explored applications in fields like forensic science (post-mortem interval estimation) and malaria transmission dynamics. His collaborative projects span institutions in Spain and internationally, reflecting a commitment to both theoretical and applied statistics. He has advised numerous students, though explicit student names are not listed. Awards are not mentioned, but his contributions to statistical software (e.g., fda.usc) and leadership in academic programs highlight his impact. His research often addresses real-world problems, integrating computational methods with statistical rigor.

• Complex Systems
• Chaos Theory
• Network Science
• +16 more

Murilo da Silva Baptista is a Reader at the Institute for Complex Systems and Mathematical Biology , within the School of Natural and Computing Sciences at the University of Aberdeen . He has been with the university since 2009, initially as a Senior Lecturer and promoted to Reader in 2014. He is actively accepting PhD students in Physics, Mathematics, and Engineering, and his research is internationally recognized in the fields of complex systems and chaos theory. His research focuses on understanding the relationship between function—such as information processing, collective behavior, and synchronization—and structure in large networked complex systems. He applies analytical methods, data science, nonlinear time series analysis, and machine learning to model systems in neuroscience, smart engineering, and Earth sustainability. He is a leading scientist in chaos-based communication, demonstrating how chaotic signals can enable smart and secure wireless and underwater communication systems. His work includes theoretical developments in phase definition in chaotic oscillators, chaos-based cryptography using Poincaré return times, and the discovery of phenomena like Collective Almost Synchronization, which enhances machine learning for EEG signal prediction. His recent publications (2023–2025) span a wide range of applications, including chaotic image and 3D model encryption, UAV surveillance using chaotic paths, causal feature selection in health systems, modeling neurological disorders, and socio-environmental analysis in Brazil. These works reflect a strong trend toward applying nonlinear dynamics and network science to real-world engineering, biomedical, and societal challenges. Scientific Contributions and Recognition: Proved a conjecture on the analytical calculation of Poincaré first return times using unstable periodic orbits. Contributed foundational work to chaos-based cryptography. Proposed a formula linking mutual information to Lyapunov exponents, supporting the Infomax theory of brain evolution. Discovered the phenomenon of Collective Almost Synchronization in complex networks. Demonstrated that causality is a space-time phenomenon, not purely temporal. Advising and Research Support: He is currently supervising PhD students in Physics, Maths, and Engineering, indicating active mentorship. His research is supported by analytical developments and data-driven modeling. He leads work on optimal wireless chaos communication, synapse modeling, brain network changes post-surgery, and socio-economic causality in Brazil. His collaborations span institutions in the USA, Brazil, Germany, and Portugal. Labs and Research Groups: He is affiliated with the Institute for Complex Systems and Mathematical Biology at Aberdeen, a hub for interdisciplinary research in nonlinear dynamics, network theory, and their applications across physical, biological, and social systems.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +6 more

Matthew Blaschko is a Senior Lecturer BOF at KU Leuven, affiliated with the Department of Electrical Engineering (ESAT) within the Faculty of Engineering Sciences. He directs the KU Leuven ELLIS unit and serves as a fellow in the ELLIS Health program, part of the European Laboratory for Learning and Intelligent Systems. As a Core PI in the Flanders AI Research Program, he leads work packages for Decision Support Systems and Medical Imaging. He is also a member of the KU Leuven Institute for Artificial Intelligence and co-leads the working group on Machine Learning and Data Science. Habilitation (HDR) from École Normale Supérieure de Cachan Newton International Fellow at University of Oxford Dr. rer. nat. from Max Planck Institutes Tübingen (awarded by Technische Universität Berlin) M.S. from University of Massachusetts Amherst B.S. from Columbia University Blaschko's research focuses on machine learning, computer vision, and medical image analysis, with particular expertise in uncertainty quantification in deep neural networks and trustworthy AI for healthcare applications. His work bridges theoretical foundations with practical implementations, developing methods for calibration, uncertainty estimation, and efficient model deployment. He has made significant contributions to neural network architectures, loss functions, and evaluation metrics for medical imaging tasks, with applications spanning Alzheimer's disease research, surgical phase recognition, and ophthalmic image analysis. His recent publications reveal a strong emphasis on calibration methods, uncertainty quantification, and medical applications of AI. The research spans diverse areas including Alzheimer's disease analysis, Bayesian optimization, novel view synthesis, knowledge extraction from text, and surgical phase recognition. Many papers focus on improving model reliability and safety for healthcare applications, reflecting his commitment to developing trustworthy AI systems that can be deployed in clinical settings. Best Student Paper Award, ECCV 2008 Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award Best paper award, CVPR 2008 Best paper award, Benelearn 2014 Blaschko actively mentors numerous PhD and MSc students, with current advisees working on topics ranging from uncertainty in deep neural networks to medical image analysis and AI for healthcare. He leads multiple significant research projects including 'onzekerheid in diepe neurale netwerken' (2025-2029), 'Trustworthy AI for Medical Image Analysis and Computer Vision' (2025), and 'Van metingen naar biomarkers in medische beeldanalyse' (2024-2028). His research is supported by the Flanders AI Research Program and other substantial funding sources. As director of the KU Leuven ELLIS unit and a key member of the KU Leuven Institute for Artificial Intelligence, Blaschko leads a vibrant research team focused on machine learning and data science. His laboratory develops cutting-edge AI technologies with practical applications, particularly in healthcare. Technology from his research has been incorporated into MONA, software for ophthalmic image analysis, demonstrating the real-world impact of his work.

Peter M SteinerView profile

Professor

• Causal Inference
• Replication Design
• Survey Methodology
• +3 more

Peter M. Steiner is a Professor in the Department of Human Development and Quantitative Methodology at the University of Maryland, specializing in quantitative methodology, measurement, and statistics (QMMS). He previously held faculty positions at the University of Wisconsin-Madison (2010–2019), the Institute for Policy Research at Northwestern University (2007–2010), and the Institute for Advanced Studies in Vienna (1997–2007). He earned a Ph.D. and M.Sc. in Statistics from the University of Vienna and an M.Sc. in Economics from Vienna University of Economics and Business. His research focuses on causal inference methodologies, including causal replication designs, quasi-experimental techniques, and factorial survey methods. Key areas include double robust estimation, propensity score analysis, mediation analysis, and addressing selection bias. He has contributed to improving replication practices in social sciences and developed frameworks for assessing correspondence between experimental and non-experimental results. Education: Ph.D. in Statistics, University of Vienna M.Sc. in Economics, Vienna University of Economics and Business M.Sc. in Statistics, University of Vienna He received the prestigious Causality in Statistics Education Award from the American Statistical Association in 2019. His work emphasizes methodological rigor in evaluating policy interventions and experimental designs, with applications in education and social sciences. Courses taught include Graphical Models for Causal Inference, Causal Inference & Evaluation, and Causal Mediation Analysis. Key Contributions: Pioneered within-study comparison designs for causal replication Developed DAG-based causal modeling frameworks Advanced multilevel propensity score methods His research portfolio includes over 50 peer-reviewed articles in top journals like Psychological Methods , Journal of the American Statistical Association , and Sociological Methods & Research . Current projects focus on integrating machine learning with causal inference and improving reproducibility in social science research.

Simon FriederichView profile

Associate Professor

• Philosophy of Science
• Foundations of Physics
• Philosophy of Mathematics
• +3 more

Dr. Simon Friederich is an Associate Professor of Philosophy of Science at University College Groningen , part of the University of Groningen. He serves as co-editor-in-chief of the Journal for General Philosophy of Science and associate editor of Foundations of Physics . His work bridges philosophy, physics, and technology, with a focus on foundational questions in quantum mechanics, multiverse theories, and ethical implications of AI. Education: PhD in Theoretical Physics (Heidelberg, 2011) and PhD in Philosophy (Bonn, 2014) Research Themes: Quantum foundations, epistemology of multiverse theories, philosophy of mathematics, and existential risks His research explores how quantum theory can be reconciled with classical concepts, particularly through retrocausality and non-locality. He investigates the philosophical implications of fine-tuning in cosmology and has developed a novel argument for the multiverse immune to the inverse gambler's fallacy. In ethics, he addresses AI's societal impact and sustainability, collaborating on critiques of nuclear energy and energy justice. Recent work includes the NWO Vidi project "Saving Reality with Exotic Causality" (2016-2021), which reinterprets quantum mechanics as akin to classical statistical mechanics. His publications span journals like Foundations of Physics , British Journal for the Philosophy of Science , and Philosophical Studies , with a monograph Multiverse Theories: A Philosophical Perspective (Cambridge University Press, 2021). Scientific Awards: NWO Vidi Grant (2016-2021) NWO Veni Grant (2016-2019) Co-editor roles in leading journals He teaches courses on logic, philosophy of science, and existential risks , and co-founded the German ecomodernist society WePlanet DACH. His interdisciplinary approach combines physics, philosophy, and ethics, with ongoing collaborations on AI, cosmology, and energy policy.

Herbie LeeView profile

Professor

• Bayesian statistics
• Computer models
• Spatial inverse problems
• +7 more

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

Carsten DormannView profile

Professor

• Statistical ecology
• Species distribution modeling
• Plant-pollinator interactions
• +7 more

Carsten Dormann is a Full Professor at the University of Freiburg since 2011, working in the Department of Biometry and Environmental System Analysis within the Faculty of Biology. His work bridges statistical methodology with ecological applications, focusing on improving analytical approaches in environmental science. He leads research on statistical ecology, species distribution modeling, and plant-pollinator interactions, with a strong emphasis on methodological rigor and evidence-based environmental science. Professor Dormann completed his Diploma (equivalent to an MSc) in Biology at the University of Kiel (1996), followed by a PhD in Plant Ecology from the University of Aberdeen (2001) under Dr. Sarah Woodin and Prof. Steve Albon. He earned his Habilitation at the University of Göttingen (2008), and worked as a PostDoc and Senior Research Scientist at the Helmholtz Center for Environmental Research-UFZ (2002-2011) before joining Freiburg. Dr. Dormann's research focuses on comparing, challenging and improving the toolbox of statistical ecology . He investigates how ecological datasets, often small but complex, can be properly analyzed when common statistical approaches may fail. His work emphasizes formal statistical integration of ecological models and data , advocating for rigorous representation of ecological understanding through quantitative predictions. He champions an evidence focus in environmental science , drawing parallels with evidence-based medicine to promote transparent evaluation of causal mechanisms. Specific areas include spatial autocorrelation, null models, collinearity, species distribution modeling, and plant-pollinator interactions. His recent publications reveal a strong focus on ecological network analysis, species distribution modeling under climate change, and methodological improvements in ecological statistics. The research spans theoretical developments in network topology and practical applications in conservation, with increasing integration of machine learning approaches while maintaining ecological interpretability. A notable trend is the emphasis on temporal dynamics in ecological systems and developing more robust methods for predicting ecological responses to environmental change. Professor Dormann currently supervises twelve PhD students across various ecological and statistical topics, with an extensive record of past supervision spanning over thirty doctoral candidates. His teaching contributions include authoring the textbook Environmental Data Analysis: An Introduction with Examples in R (2017) and developing statistics courses for environmental sciences. He maintains an active scholarly blog discussing methodological challenges in ecology, with recent posts addressing species richness metrics, bias-variance trade-offs, and the relationship between ecological science and policy. His work bridges theoretical statistical development with practical ecological applications, emphasizing scientific credibility and methodological rigor throughout.

Chu-An LiuView profile

Research Fellow

• Econometrics
• Model Averaging
• Forecast Combination
• +3 more

Chu-An Liu is a Research Fellow at the Institute of Economics, Academia Sinica (Taipei, Taiwan). He received a PhD in Economics from the University of Wisconsin-Madison in 2012. His teaching experience includes graduate-level and PhD econometrics courses at National Chengchi University (Spring 2022-present) and National University of Singapore (2013-2015). Research Interests: His work focuses on econometrics , model averaging , and forecast combination , with significant contributions to nonparametric methods, causal inference, and statistical modeling for high-dimensional economic data. Key areas include bounds estimation for continuous treatments, spectral analysis in time series, and model uncertainty frameworks. Publications: He has published extensively in top journals like Journal of Econometrics , Econometric Theory , and Journal of Business & Economic Statistics . Recent trends emphasize kernel regressions, autoregressive models, and integration of machine learning with traditional econometric techniques. Academic Collaborations: He has collaborated with scholars such as Xinyu Zhang, Ying-Ying Lee, and Biing-Shen Kuo on topics spanning model selection, causal inference, and nonstationary data analysis.

Mathieu Lavallée-AdamView profile

Associate Professor

• Bioinformatics
• Computational Biology
• Computer Science
• +9 more

Dr. Mathieu Lavallée-Adam is an Associate Professor in the Department of Biochemistry, Microbiology, and Immunology at the University of Ottawa's Faculty of Medicine. He also serves as Director of the Specialization in Bioinformatics. His research focuses on developing computational methods for proteomics data analysis, particularly using mass spectrometry to address complex biological questions in systems and biomedical research. Education: BSc (2008) and PhD (2013) from McGill University Postdoctoral Fellowship: The Scripps Research Institute (2016) Dr. Lavallée-Adam’s work integrates machine learning (Bayesian inference, logistic regression, support vector machines) and algorithm design to analyze proteomics data. His lab develops tools for protein-protein interaction networks, quantitative proteomics, drug target prediction, and biomarker discovery, aiming to enhance understanding of cellular mechanisms and diseases. His selected publications (2011–2016) span computational biology, proteomics, and biomedical applications, with a focus on mass spectrometry data integration, disease-related networks, and algorithm innovation. Articles include studies on cystic fibrosis, cancer, and neurodegenerative diseases. Dr. Lavallée-Adam actively recruits researchers with backgrounds in computer science, bioinformatics, or mathematics to advance his lab’s mission of pushing proteomics applications in clinical and systems biology.

Zachary LiptonView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +5 more

Zachary Lipton is an Assistant Professor at Carnegie Mellon University (CMU) jointly appointed in the Tepper School of Business and the Machine Learning Department. He holds courtesy affiliations with the Heinz School of Public Policy and Societal Computing. His research bridges core ML methods, healthcare applications, natural language processing, and critical analysis of AI's societal impacts. Tepper School of Business Machine Learning Department Heinz School of Public Policy (courtesy) Societal Computing (courtesy) Dr. Lipton leads the Approximately Correct Machine Intelligence (ACMI) Lab, focusing on robust ML systems, causal representation learning, and ethical AI development for clinical medicine. He co-founded Abridge, a healthcare AI company, and authored the interactive textbook Dive into Deep Learning . His work emphasizes clear scientific communication through expository efforts like literature reviews and the Approximately Correct blog. Recent publications highlight ACMI Lab's contributions to synthetic data quality, causal fairness analysis, diffusion model hallucinations, and medical LLM adaptation. Key research themes include distribution shift, human-AI alignment, and empirical evaluation of AI's societal impacts. Contact: zlipton@cmu.edu