Find Academic Supervisors

Adeel RaziView profile

Professor

• Computational Neuroscience
• Machine Learning
• Neuroimaging
• +6 more

Adeel Razi is a Professor of Computational Neuroscience at the Turner Institute for Brain and Mental Health , School of Psychological Sciences, Monash University, Australia. He directs the Computational Neuroscience Laboratory , focusing on multiscale Bayesian methods and active inference frameworks to decode brain dynamics in health and disease. Education : B.E. in Electrical Engineering (Gold Medal, N.E.D. University, Pakistan); M.Sc. in Communications Engineering (RWTH Aachen, Germany); Ph.D. in Electrical Engineering (University of New South Wales, Australia, 2012) Postdoc : Wellcome Centre for Human Neuroimaging, University College London (2012-2018) Research Themes span three pillars: (1) Dynamic Causal Modeling (DCM) of brain pathologies using fMRI/dMRI/EEG; (2) Designing active inference-based artificial neural networks; (3) Psychedelic-assisted therapies for psychiatric disorders via neuroimaging. His work bridges NeuroAI , Neurodegenerative Disease Modeling , and Consciousness Studies . Recent Publications emphasize context-dependent brain activity under psychedelics, neuroimaging tool development (e.g., Network Correspondence Toolbox), and Bayesian frameworks for spiking networks. Collaborations span UNSW , UCL , CIFAR , and clinical partners like Wellcome Leap . Scientific Awards : ARC Future Fellowship, NHMRC Investigator (Emerging Leadership), CIFAR Azrieli Global Scholar Supervision : Seeks interdisciplinary students in Quantitative Sciences (Engineering/CS/Physics) or Neuroscience/Medicine for PhD projects Labs : Computational Neuroscience Laboratory at Monash, international partnerships with neuroimaging centers

Wenhai SunView profile

Assistant Professor

• Privacy-enhancing Technologies
• Blockchain
• AI/ML for Security
• +5 more

Wenhai Sun is an Assistant Professor in the Department of Computer and Information Technology at Purdue University, affiliated with the Applied AI Research Center (AARC), CERIAS, and the Institute of Control, Optimization and Networks (ICON). He holds dual PhDs in Computer Science from Virginia Tech and Telecommunications Engineering from Xidian University. His research focuses on secure and privacy-preserving networked systems, leveraging AI/ML, cryptography, and trusted hardware. He has published widely in top venues like USENIX Security, NeurIPS, and IEEE journals. Dr. Sun's honors include the NSF CAREER Award (2023) and the Distinguished Paper Award at ACM ASIACCS 2013. He serves as an Associate Editor for IEEE Transactions on Dependable and Secure Computing and IEEE Transactions on Information Forensics & Security. He is a Senior Member of IEEE and ACM. His research grants include projects on local differential privacy frameworks and blockchain implementations. He explores privacy-enhancing technologies, blockchain applications, and AI-driven security solutions. His work addresses challenges in data privacy, adversarial machine learning, and secure distributed systems. Recent research directions include secure IoT data markets, resilient blockchain consensus mechanisms, and explainable AI for reinforcement learning systems. His work emphasizes real-world applicability, such as smart grid privacy and device authentication using respiratory patterns.

Tolulope FadinaView profile

Assistant Professor

• Financial Mathematics
• Risk Management
• Operational Research
• +3 more

Tolulope Fadina is an Assistant Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on financial mathematics, risk management, stochastic processes, and quantitative finance, with emphasis on uncertainty modeling and actuarial applications. His work addresses topics such as optimal reinsurance strategies under multivariate risks, parametric variability in risk measures, and axiomatization of quantiles. He explores frameworks for risk assessment under ambiguity, contributing to both theoretical and applied aspects of financial engineering. Notable publications include studies on reinsurance optimization with dependence uncertainty, risk measures under parameter uncertainty, and hyperfinite constructions in stochastic calculus. His research has appeared in journals like European Journal of Operational Research , Finance and Stochastics , and SIAM Journal on Financial Mathematics . No scientific awards are explicitly listed in the provided information. He advises no formally listed students or mentees. His office is located at 273 Altgeld Hall, Urbana, IL.

Todd KuffnerView profile

Associate Professor

• Statistical Theory
• Bayesian Asymptotics
• Higher-Order Asymptotics
• +7 more

Todd Kuffner is an Associate Professor in the Department of Mathematics and Statistics at Washington University in St. Louis, within the College of Arts & Sciences. His research focuses on statistical theory, foundations, and methodology, particularly Bayesian asymptotics, higher-order asymptotics, post-selection inference, and bootstrap methods. He holds a PhD in Mathematics from Imperial College London and has organized major workshops like WHOA-PSI and BFF conferences. Education: PhD in Mathematics (Imperial College London), M.Sc. in Econometrics and Mathematical Economics (London School of Economics), M.Sc. in Economics (London School of Economics), B.A. in Economics (University of Michigan). Research Interests: Kuffner explores validity, accuracy, and power of statistical inference procedures, with a focus on neo-Fisherian, Bayesian, and frequentist paradigms. Current projects include post-selection inference, bootstrap methods, and prediction after model selection. He actively engages in interdisciplinary applications across neuroscience, environmental science, and high-energy physics. Grants & Advising: Principal Investigator on NSF grants including DMS-1811936 (2018-2021) and DMS-1812088 (2018). Advised PhD students like Qi Wang and Qiyiwen Zhang. Organized over 20 conferences/workshops, including the 5th WHOA-PSI in 2020. Labs/Teams: Leads the Workshop on Higher-Order Asymptotics and Post-Selection Inference (WHOA-PSI), co-edits journals like Harvard Data Science Review and Journal of the American Statistical Association. Hosted numerous visiting scholars and collaborators.

• Machine Learning
• Causal Inference
• High-Dimensional Statistics
• +2 more

Malte Kurz is a researcher affiliated with the University of Hamburg Business School, specifically within the Department of Statistics. His primary role was as a Researcher under the Professorship of Statistics with Applications in Business Administration. He holds a PhD in Statistics from Ludwig-Maximilians-Universität München (2018), an M.Sc. in Statistics from LMU Munich (2013), and a B.Sc. in Mathematical Finance from Universität Konstanz (2011). His research interests focus on Machine Learning , Causal Inference , High-Dimensional Statistics , Financial Econometrics , and Dependence Modelling & Copulas . He has contributed to advancements in vine copula theory, distributed machine learning frameworks, and state space models. Key publications include works on vine copula simplifications (2019), low-dimensional Kalman smoothers (2018), and distributed double machine learning architectures (2021). He has developed influential software tools like the pacotest R package for copula hypothesis testing and the DoubleML framework for Python/R. Kurz has also contributed to open-source projects such as the VineCopulaMatlab toolbox and the SSMwLS MATLAB package for state space modeling. His work bridges statistical theory and computational implementation, with applications in finance and econometrics.

Dafne ZorzettoView profile

Researcher

• Bayesian Nonparametrics
• Causal Inference
• Environmental Epidemiology
• +3 more

Dafne Zorzetto is a Postdoctoral Research Associate in the Data Science Institute at Brown University, collaborating with Roberta De Vito. She holds a PhD in Statistics from the University of Padova and has conducted research at Harvard University's Department of Biostatistics under Francesca Dominici. Her work focuses on Bayesian Nonparametric methods and Causal Inference, with applications in environmental epidemiology and public health. Education: PhD in Statistics, University of Padova (2020–2023) MSc in Statistical Sciences, University of Padova (2018–2020) BSc in Statistics for Economics and Business, University of Padova (2015–2018) Her research interests include Bayesian Nonparametric models, causal inference methodologies, and their applications in environmental health. She has developed novel approaches for addressing unmeasured confounding using negative controls and characterizing heterogeneous causal effects through dependent Dirichlet mixtures. She has presented her work at conferences such as the ISBA World Meeting and the New England Statistics Symposium. Her contributions include organizing academic events like 'Explain like I’m an Undergrad' to foster statistical communication among early-career researchers. Awards: Young researcher travel award (2022 ISBA World Meeting) Zorzetto mentors students across institutions, including Harvard College and Bocconi University, focusing on statistical research and methodology.

Jian LiView profile

Professor

• Quantitative Statistical Methods
• Hierarchical Linear Modeling
• Structural Equation Modeling
• +4 more

Dr. Jian Li is a Professor in the Department of Research, Measurement and Statistics at Kent State University's College of Education, Health, and Human Services. She holds a Ph.D. in Quantitative Research, Evaluation, and Measurement from The Ohio State University. Her research focuses on methodological issues in quantitative statistics, including hierarchical linear modeling, structural equation modeling, and experimental designs applied to health behavior and rehabilitation contexts. Research Interests: Dr. Li specializes in developing and applying advanced statistical methods to understand how physical and psychological health influence behavior, particularly in vulnerable populations. Her work integrates quantitative techniques with substantive research in rehabilitation counseling and educational outcomes. Teaching: She teaches doctoral courses in quantitative methods including Hierarchical Linear Modeling, Structural Equation Modeling, and Program Evaluation. Her teaching focuses on developing research competence in graduate students, with three advisees receiving national dissertation awards. Awards: Outstanding Research Award (2021) President’s Faculty Excellence Award (2018) Multiple best paper awards in rehabilitation research Grants & Projects: Dr. Li has served as Co-PI on projects funded by National Institutes, including studies on multiple sclerosis and disability employment retention. She leads initiatives integrating statistical methodology with rehabilitation science.

Rob MaloufView profile

Professor

• Computational Linguistics
• Morphological Theory
• Statistical NLP
• +3 more

Professor and Chair of Linguistics at San Diego State University with adjunct appointment in Big Data Analytics. Research specializes in computational morphology, statistical NLP, and machine learning applications in linguistics. Developed models for paradigm-based morphology using information theory and neural networks. Education: Ph.D. from Stanford University (1998). Recipient of multiple teaching and research awards including SDSU Faculty of the Year (2014). Supervised over 20 graduate students in linguistics and computational methods. Research Areas: Computational morphology, statistical language processing, machine learning, corpus linguistics Teaching: Computational linguistics, statistical methods, machine learning seminars Tools: Developed Toolkit for Advanced Discriminative Modeling, Rescorla-Wagner learning models

Lujia BaiView profile

Researcher

• Non-stationary Time Series
• Time-Varying Networks
• Functional Time Series
• +4 more

Dr. Lujia Bai is a Researcher in the Department of Mathematics at Ruhr University Bochum, affiliated with Holger Dette's research group. She holds a PhD from Tsinghua University (2024) and a B.Sc. from Shanghai University of Finance and Economics (2020). Her research focuses on non-stationary time series, time-varying networks, and functional time series analysis, with expertise in long-range dependence and complex data structures. Education: Ph.D., Statistics and Data Science, Tsinghua University (2024) B.Sc., Statistics and Management, Shanghai University of Finance and Economics (2020) Research Interests: Non-stationary time series analysis Time-varying correlation networks Functional time series modeling Long-range dependence detection Key Awards: 2025 Danish Data Science Academy Visit Grant 2024 Young Researcher Scholarship (Bernoulli-IMS World Congress) 2024 ICSA Junior Researcher Award Teaching Experience: Mathematical Foundations of Data Science (Ruhr University Bochum, 2024) Elementary Probability Theory (Tsinghua University, 2020) Financial Statistics (Tsinghua University, 2021–2024) Academic Contributions: Project leader in TRR391 (German Collaborative Research Center) Peer reviewer for journals like Stat , Journal of the Royal Statistical Society Series B , and NeurIPS

Ning XieView profile

Professor

• Algorithms
• Computational Complexity
• Property Testing
• +3 more

Ning Xie is a Professor in the Department of Computer Science at Florida International University's School of Computing and Information Sciences. He has been actively teaching graduate and undergraduate algorithm courses since at least 2013, including COT-6405 Analysis of Algorithms, COP-4534 Algorithm Techniques, and COT-6446 Randomized Algorithms. Dr. Xie's research focuses on theoretical computer science, particularly in the areas of sub-linear algorithms, property testing, local computation algorithms, and Fourier analysis of Boolean functions. He was advised by Professor Ronitt Rubinfeld during his doctoral studies. His work bridges theoretical foundations with practical algorithmic applications, contributing significantly to our understanding of computational complexity and efficient algorithm design. His research has resulted in numerous publications in top theoretical computer science venues including FOCS, STOC, SODA, and various prestigious journals. Dr. Xie mentors several PhD students, currently supervising Yekun Xu and Xiaolong Zhu, and has successfully graduated students including Shokoufeh Mokhtari (now at Microsoft) and Shuai Xu (now at Case Western Reserve University). His teaching portfolio demonstrates a strong commitment to algorithm education at both undergraduate and graduate levels, with detailed course materials covering dynamic programming, graph algorithms, NP-completeness, and other advanced topics.

Jeffrey StevensView profile

Professor

• Canine cognition
• Human-animal interaction
• Animal decision making
• +5 more

Jeffrey Stevens is a Susan J. Rosowski Professor in the Department of Psychology and the Center for Brain, Biology & Behavior at the University of Nebraska-Lincoln. He directs the Canine Cognition and Human Interaction Lab and serves as a Data Science Mentor at Posit Academy. His academic journey includes a Ph.D. in Ecology from the University of Minnesota, a postdoctoral fellowship in Psychology at Harvard University, and research scientist position at the Max Planck Institute for Human Development in Berlin. Dr. Stevens' research program integrates perspectives from biology, psychology, computer science, neuroscience, and economics to understand decision making in humans and other animals. His work spans canine cognition, human-animal interactions, intertemporal choice, and quantitative methods in behavioral research. He has worked with over 15 different species of fish, birds, and mammals, with particular focus on dogs and corvids. His research employs data science approaches and emphasizes open science practices to ensure transparency and reproducibility. The analysis of his recent publications reveals a strong trend toward big team science approaches in comparative cognition, particularly through the ManyDogs Project which involves multi-lab collaborations to investigate canine behavior and cognition. His work increasingly combines traditional behavioral methods with computational approaches, including machine learning algorithms for similarity judgments and decision making. There is also a clear trajectory toward understanding the intersection of biological mechanisms and behavioral outcomes, particularly in the domains of impulsivity, quantity discrimination, and social cognition. Susan J. Rosowski Professor (named professorship) Co-director of the ManyDogs Project Active participant in Big Team Science initiatives Dr. Stevens actively mentors undergraduate and graduate students, having trained hundreds of students in data science methods. He is currently seeking motivated undergraduate students to join his Canine Cognition and Human Interaction Lab. His research has been supported by multiple grants including from the National Science Foundation, University of Nebraska Food for Health Collaboration Initiative, Nebraska EPSCoR, and the Alexander von Humboldt Foundation. He teaches courses in Evolution, Behavior, and Society; Controversial Issues in Psychology; Animal Learning and Cognition; and Psychology of Decision Making. Dr. Stevens leads the Canine Cognition and Human Interaction Lab, which conducts research on dog behavior, cognition, and the human-dog bond. The lab is part of the ManyDogs Project, a large-scale collaborative effort investigating canine cognition across multiple laboratories worldwide. His work emphasizes methodological rigor, open science practices, and interdisciplinary approaches to understanding decision making across species.

• Bioinformatics
• Radiation Biology
• Biodosimetry
• +3 more

Sonja Strunz is a researcher at the Department of Systems Biology and Bioinformatics, affiliated with the Faculty of Computer Science and Electrical Engineering at the University of Rostock. Her work focuses on radiation biodosimetry and biomarker discovery through transcriptomic analysis of radiation-exposed human peripheral blood lymphocytes. Doctorate in Computer Science (2007-2014), University of Rostock Masters in Bioinformatics (2000-2007), University of Tübingen (inferred) Her research investigates radiation-induced transcriptional changes, dose-dependent gene expression patterns, and development of computational frameworks for radiation dose prediction. She has contributed to understanding radiation's effects on apoptosis, DNA damage, and cell-cycle regulation through bioinformatic analysis. Analysis of her publications reveals expertise in radiation biology, transcriptomics, and systems modeling. Her work combines p-value/fold-change methods with network approaches to identify robust gene signatures for dose estimation and develops computational workflows for molecular biodosimetry applications. She maintains collaborations with institutions including Forschungszentrum Jülich and University Medical Center Hamburg-Eppendorf, supported by funding from Germany's Federal Ministry for Education and Research (BMBF) and Federal Ministry for the Environment.

Ameet SoniView profile

Associate Professor

• Machine Learning
• Computational Biology
• Computational Medicine
• +2 more

Ameet Soni is an Associate Professor in the Computer Science Department at Swarthmore College, currently on leave (2021–2024) serving as Associate Dean of the Faculty for Diversity, Recruitment, and Retention. He holds a Ph.D. in Computer Science from the University of Wisconsin (2011), where he was advised by Professor Jude Shavlik. His research integrates machine learning with computational biology and medicine, focusing on probabilistic graphical models, deep learning, and applications in genomics, medical diagnosis, and biomedical data analysis. His core research interests span: Machine Learning : Probabilistic inference, statistical relational learning, and deep neural networks. Biomedical Applications : Protein-structure prediction, gene modeling, Alzheimer's/Parkinson's diagnosis, and biomedical text/image analysis. His publications emphasize machine learning applications in bioinformatics and medicine, with trends including deep learning for neurodegenerative disease diagnosis (e.g., Alzheimer’s, Parkinson’s), ethical AI education, protein-structure determination, and biomedical data mining. Recent work (2020) also explores AI ethics pedagogy. Awards : Best Paper Award, ACM International Conference on Bioinformatics and Computational Biology (2010) Advising & Labs : He has supervised 25+ students (22 alumni, 3 current) in machine learning and computational biology projects. Alumni pursue careers in academia (e.g., Michigan State University), industry (Google, Amazon), and medicine. His lab investigates transcription factor binding, brain MRI analysis for Alzheimer’s, and statistical relational learning.

Sebastian SchmidlView profile

Researcher

• Distributed Computing
• Data Profiling
• Time Series Anomaly Detection
• +3 more

Sebastian Schmidl is a Ph.D. researcher at the Information Systems Group (University of Potsdam, Digital Engineering Faculty ). He is also affiliated with the Big Data Analytics Group at Philipps-Universität Marburg . Research Interests : Distributed computing, time series anomaly detection, data profiling, and scalable reactive systems using actor programming. Academic Roles : Supervised multiple master's and bachelor's theses, delivered guest lectures on reproducibility and order dependencies, and contributed to academic reviewing for conferences like SIGMOD and journals like SoftwareX. His recent publications focus on time series anomaly detection , data profiling query languages , and distributed algorithm design . He has collaborated extensively on benchmarking frameworks and optimization techniques for univariate and multivariate time series data.

Yogendra P. ChaubeyView profile

Professor

• Sampling
• Distribution Theory
• Survival Analysis
• +3 more

Dr. Yogendra P. Chaubey is a Professor in the Department of Mathematics and Statistics at Concordia University, Montréal, Canada. Holding a Ph.D. from the University of Rochester (1977), his research focuses on statistical methodology with emphasis on sampling theory, distribution modeling, and survival analysis. Education: Ph.D., University of Rochester (1977) His work spans nonparametric density estimation for circular and non-negative data, wavelet-based noise reduction in biological imaging, entropy estimation, left-censored spatial modeling (e.g., arsenic contamination), and inverse Gaussian distributions. Recent collaborations include applications in protein classification and public health. Dr. Chaubey has published extensively on statistical theory and methodology, including corrections and extensions to classical estimators. His teaching includes advanced courses in sample survey theory and applications.