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Enes MakalicView profile

Professor

• Machine Learning
• Bayesian Statistics
• Information Theory
• +5 more

Enes Makalic is a Professor of Machine Learning at the Department of Data Science & AI, Faculty of Information Technology, Monash University, Melbourne, Australia. With over 15 years of experience in Bayesian inference, information theoretic statistics, and digital health, his academic career demonstrates significant interdisciplinary impact across computer science, statistics, and medical research. Faculty of Information Technology, Monash University (Current) Department of Data Science & AI, Monash University (Current) Professor Makalic completed his PhD in Machine Learning at Monash University in 2007, following a Bachelor of Computer Science (Honours) from the same institution in 2002. His research expertise spans theoretical and applied statistics, with particular focus on model selection for ultra-high dimensional statistical models, minimum message length principles of inductive inference, and applications of information theoretic statistics to epidemiology and medical imaging. His current research activities prominently feature image processing and risk prediction, with special emphasis on digital mammography and breast cancer, as well as statistical genetics and genomic prediction models for rare, polygenic diseases and traits. Professor Makalic has developed and coordinated subjects in computer science, biostatistics, survival analysis, and machine learning, and has supervised Honours, Masters, and PhD students to completion. Analysis of his recent publication trends reveals a strong focus on breast cancer risk prediction through mammographic analysis, statistical genetics, and applications of minimum message length principles. His work integrates machine learning with medical imaging to develop automated measures for breast cancer risk assessment, with significant translational potential for clinical implementation. Active reviewer and program committee member for numerous journals and conferences Recipient of research funding from NHMRC and Cancer Council Victoria Professor Makalic's research contributes to UN Sustainable Development Goals related to good health and well-being through his work in digital health and medical applications of machine learning. His projects demonstrate strong collaborative partnerships across disciplines, particularly with medical researchers focused on cancer prevention and early detection.

Daniel SchmidtView profile

Senior Lecturer

• Bayesian Inference
• Information Theory
• Statistical Genomics
• +7 more

Daniel Schmidt is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in Bayesian inference, information theory, and statistical genomics. He holds a PhD in Computer Science (2008) and a Bachelor of Digital Systems (Honours) from Monash University (2003). His research focuses on high-dimensional Bayesian regression, Minimum Message Length (MML) principles, and applying machine learning to medical risk prediction, particularly in breast cancer via mammography analysis. He leads projects on scalable time series forecasting, quantum computing applications, and epileptic seizure prediction. Teaching commitments include developing and lecturing in units like FIT2086 (Modelling for Data Analysis) and FIT3154 (Advanced Data Analysis). He collaborates with institutions like the University of Melbourne (Adjunct Senior Research Fellow) and has contributed to open-source tools like the BayesReg package and GitHub repositories for statistical methods. His work contributes to UN Sustainable Development Goals related to health and innovation. Key research projects include efficient time series classification (MONSTER repository), seizure forecasting using EEG data, and quantum computing applications. He has authored over 119 publications, with recent work addressing adversarial attacks on time series models and Bayesian shrinkage priors for regression. Notable collaborations involve international teams in cancer genomics, statistical epidemiology, and materials science. His GitHub contributions include tools for random correlation matrix generation and fast AR model estimation.

Houying ZhuView profile

Lecturer

• High-dimensional learning
• Statistical computing
• Modelling and visualisation
• +4 more

Dr. Houying Zhu is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. Her research focuses on high-dimensional learning, statistical computing, and modelling/visualization, with an emphasis on developing efficient methodologies for modern data analysis. She holds a PhD in Applied Mathematics from the University of New South Wales and has held research roles at City University of Hong Kong and the University of Melbourne as a Maurice Belz Research Fellow in Statistics. Dr. Zhu is Co-Director of the Statistical Modelling Research Group, providing mentorship to early-career researchers and HDR students through regular supervision and collaborative projects. She actively engages in academic leadership, serving on the Board of Directors for the IASC-ARS (2023-2027) and the SSA NSW branch council (2021-2023). Her teaching spans undergraduate and postgraduate units, including Bayesian Data Analysis, Generalized Linear Models, and Statistical Graphics. Research Highlights: Innovations in quasi-Monte Carlo methods, feature selection algorithms, and medical imaging applications. Awards: Includes the President’s Award for Leadership in Statistics (2022) and multiple funding grants (MATRIX Family Funding, SSA Fellowship). Community Engagement: Organized the MATRIX Computational Mathematics for High-Dimensional Data program (Feb 2023) and contributes to statistical education initiatives. Her interdisciplinary work bridges computational statistics and real-world applications, with recent projects addressing wildfire risk modelling, protein engineering, and 6G communication systems.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

Anna ScampicchioView profile

Researcher

• Intelligent Control Systems
• Model Predictive Control
• Machine Learning
• +3 more

Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.

• Machine Learning
• Signal Processing
• Quantum Computing
• +4 more

Andreas Spanias is a Professor at Arizona State University specializing in Machine Learning , Signal Processing , and Quantum Computing with applications in renewable energy, healthcare, and wireless networks. His research includes Photovoltaic Fault Detection , Quantum Machine Learning , and Real-Time Energy Monitoring . Key research areas: Machine Learning, Quantum Computing, Renewable Energy Systems, Medical Imaging, Wireless Sensor Networks. Notable collaborations: Glen S. Uehara, Cihan Tepedelenlioglu, Sunil Rao, Jayaraman J. Thiagarajan. Recent Publications (2025–2024) focus on quantum machine learning for photovoltaic fault classification, Bayesian optimization in circuit design, and real-time solar array monitoring. Trends include quantum algorithms for signal processing, energy-efficient ML, and educational innovations in quantum computing. Educational Initiatives include REU programs in Quantum Machine Learning and integrating ML into signals and systems courses. He leads international collaborations like the ASU-DCU Sensors and ML Workforce Development Program.

• Radio Astronomy
• Medical Imaging
• Compressed Sensing
• +3 more

Yves Wiaux is a Professor at Heriot-Watt University's School of Engineering & Physical Sciences, affiliated with the Institute of Sensors, Signals & Systems. He specializes in advanced signal processing techniques with applications in radio astronomy and medical imaging. His research focuses on compressed sensing, optimization algorithms, and imaging methods for radio interferometry and MRI. His work integrates mathematical frameworks like wavelet transforms and sparse regularization to address challenges in high-dimensional data reconstruction. Notable contributions include the development of algorithms like SARA (Sparsity Averaging Reweighted Analysis) and PURIFY for radio interferometric imaging, as well as applications in medical imaging such as magnetic resonance fingerprinting. Wiaux collaborates extensively in international projects, including the LOFAR Two Meter Sky Survey. He advises PhD students through the BASP (Biomedical and Astronomical Signal Processing) group and leads research initiatives in scalable imaging techniques for large datasets.

Joshua LukemireView profile

Research Professor

• Imaging Bayesian Analysis
• High-Dimensional Imaging Data Analysis
• Brain Network Analysis
• +3 more

Joshua Lukemire is an Assistant Research Professor at Emory University's Department of Biostatistics and Bioinformatics. His research focuses on developing statistical methods for analyzing high-dimensional imaging data, particularly in pediatric applications like near-infrared spectroscopy for monitoring red blood cell transfusion efficacy in preterm infants. He also explores brain network differences between clinical groups using Bayesian techniques and hierarchical modeling. Education: B.S. from University of Georgia; M.S. and Ph.D. from Emory University (Department of Biostatistics). Research interests include Imaging Bayesian Analysis, longitudinal brain network connectivity studies, and statistical methods for healthcare applications. His work bridges neuroimaging analysis, pediatric medicine, and advanced computational statistics. Recent publications highlight innovations in functional brain network generation with graph neural networks (e.g., Fbnetgen and Braingb tools), as well as clinical studies on transfusion efficacy in preterm infants and electroconvulsive therapy outcomes. His methodological contributions include sparse Bayesian modeling, optimal experimental designs, and nonparametric vector autoregression approaches. No advising or grant details are listed in the provided text. His work is supported by collaborations across disciplines, though specific lab affiliations are not mentioned.

Yasser RoudiView profile

Professor

• Theoretical Neuroscience
• Neural Networks
• Information Processing
• +3 more

Professor Yasser Roudi holds the position of Professor of Disordered Systems at King’s College London’s Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. He earned his PhD from SISSA (International School for Advanced Study) in 2005 and a physics degree from Sharif University of Technology in Tehran. Prior to joining King’s, he worked at prestigious institutions like the Kavli Institute for Systems Neuroscience in Norway and NORDITA. His research focuses on theoretical neuroscience, neural networks, and the physics of disordered systems, employing mathematical tools to model information processing in brains and machines. He has been recognized with awards including the Eric Kandel Young Neuroscientist Prize (2015) and membership in the Royal Norwegian Society of Sciences and Letters. His work explores topics such as grid cells’ toroidal topology, restricted Boltzmann machines, and maximum entropy models for cortical populations. Recent publications highlight advancements in understanding neural oscillations, stochastic processes, and reinforcement learning algorithms. Roudi leads the Disordered Systems research group at King’s, contributing to the study of complex systems and statistical mechanics. Education: PhD, SISSA (2005); BSc, Sharif University of Technology (Physics). Affiliations: King’s College London, Kavli Institute for Systems Neuroscience, NORDITA, UCL, and the Institute for Advanced Study. Roudi’s research bridges theoretical physics and neuroscience, addressing fundamental questions about neural information processing and system dynamics. His awards underscore his impactful contributions to the field, while his interdisciplinary approach continues to push boundaries in understanding complex biological and artificial systems.

• Robust Methods
• Statistical Genetics
• Multivariate Analysis
• +7 more

Asuman Turkmen is a Professor of Statistics at The Ohio State University, affiliated with the Translational Data Analytics (TDA) initiative and based at the Newark Campus. She holds a PhD from Auburn University (2008) and specializes in robust statistical methods for multivariate analysis and statistical genetics. Her research focuses on outlier detection, rare variant associations in complex diseases, and addressing missing heritability through approaches like structural variations, epigenetics, and gene-environment interactions. Her professional service includes roles such as Newark Campus Ombudsman (2022–present), judge for student research forums, and leadership in university committees (e.g., JEDI Committee, Undergraduate Committee). She co-directs the TDAI Data Science for Women Summer Camp and mentors in doctoral programs. Notably, she received the Ohio State University Newark Scholarly Achievement Award in 2015. Dr. Turkmen’s recent work emphasizes Bayesian methods for population stratification correction, X-linked variant effects, and robust statistical techniques. Her publications span Genetic Epidemiology , Annals of Human Genetics , and other top journals, reflecting her contributions to both theoretical and applied statistical genetics.

Sarat DassView profile

Professor

• Statistics
• Bayesian Statistics
• Data Science
• +7 more

Sarat Dass is a Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. He is actively engaged in research, teaching, and supervising PhD students. His primary research interests span Statistics, Bayesian Statistics, Data Science, and their applications in Epidemiology and Spatio-temporal processes. He has published extensively, with 136+ research outputs since 2006, focusing on topics like ionospheric TEC forecasting, disease modeling (e.g., chikungunya, COVID-19), and statistical computing methods. Education & Teaching: Teaches courses such as F79BI: Bayesian Inference and Computational Methods, emphasizing advanced statistical methodologies and computational techniques. Research Interests: His work integrates machine learning (e.g., LSTM, neural networks) with statistical modeling for environmental and health applications. Key areas include predicting ionospheric disturbances during solar flares/earthquakes, analyzing disease spread dynamics, and optimizing hydrocarbon exploration using Gaussian processes. Collaborations: Collaborates internationally, particularly in Malaysia, Indonesia, and Pakistan, on projects involving epidemiology, space weather, and geophysical modeling. His research contributes to Sustainable Development Goals related to health, climate action, and innovation. Advising & Grants: Accepts PhD students and has secured grants for projects on disease modeling and environmental data analysis. His work bridges theoretical statistics with practical applications in public health and geophysics.

Luis LehnerView profile

Research Professor

• Strong Gravity
• Gravitational Wave Astronomy
• General Relativity
• +7 more

Luis Lehner holds the Carlo Fidani Rainer Weiss Chair in Theoretical Physics at the Perimeter Institute for Theoretical Physics and is an Associate Professor in the Department of Physics at the University of Guelph. He also holds adjunct roles at the University of Wisconsin-Milwaukee and Louisiana State University. His research focuses on strong gravity regimes, testing alternative theories of gravity using gravitational wave detectors, and exploring higher-dimensional scenarios and AdS/CFT correspondence. He is a prominent figure in gravitational wave astronomy, working on multimessenger observations and developing frameworks to probe General Relativity's validity. Lehner has held leadership positions including Deputy Faculty Chair at Perimeter Institute and advisory roles at KITP and ICTP-SAIFR. His work bridges theoretical physics with observational astrophysics, emphasizing black hole dynamics, relativistic hydrodynamics, and turbulence. Recent publications highlight advancements in gravitational wave signal analysis, black hole mimicker studies, and nonlinear gravity effects. Education : Not explicitly listed in text, but his roles imply PhD-level training in Physics. His awards include membership in the National Academy of Sciences of Argentina, the Buchalter Cosmology Prize, and fellowships from APS and the Institute of Physics. He actively participates in international collaborations like the Gravitational Wave International Committee and contributes to detector networks like LIGO-Virgo.

Matthias SchonlauView profile

Professor

• Survey Methodology
• Natural Language Processing
• Data Visualization
• +3 more

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.

Julien ChiquetView profile

Part-Time Lecturer

• Statistics
• Machine Learning
• Sparse Methods
• +10 more

Julien Chiquet is a researcher and part-time lecturer at École Polytechnique (2020-2024), and the head of the UMR MIA Paris-Saclay research unit. He is affiliated with AgroParisTech on the Paris-Saclay campus and leads the SOLsTIS team. He serves as the chief editor of the journal Computo , focusing on computational statistics and data science. Education: Habilitation (2015) Research Interests: His work bridges statistical methodology and life sciences, with a focus on developing and applying advanced statistical methods such as sparse models, multivariate analysis, and optimization techniques. Key areas include dimension reduction in high-dimensional count data, probabilistic graphical models, and applications in ecology, genomics, and metagenomics. He emphasizes reproducible research practices, recently developing a Quarto template for HCERES reports integrating LaTeX and Pandoc. Recent Contributions: Recent work includes advancements in the Poisson lognormal model, stability selection via the sharp R package, and Python tools like pyPLNmodels for analyzing multivariate count data. His research often addresses challenges in ecological network inference, scRNA-seq analysis, and genomic prediction. Labs & Teams: Based at UMR MIA Paris-Saclay, he contributes to the SOLsTIS team, advancing computational methods for biological data analysis. The lab relocated to the new Palaiseau campus in 2022, fostering interdisciplinary collaborations.

Jianfeng LuView profile

Professor

• Mathematical Analysis
• Algorithm Development
• Computational Physics
• +5 more

Jianfeng Lu is the James B. Duke Distinguished Professor of Mathematics at Duke University, with affiliations in the Department of Mathematics, Chemistry, and Physics within the Trinity College of Arts & Sciences. He holds a Ph.D. from Princeton University (2009). His research focuses on mathematical analysis, algorithm development for computational physics, theoretical chemistry, materials science, and machine learning, particularly in high-dimensional PDEs, quantum dynamics, and multiscale modeling. Notable achievements include the 2024 James B. Duke Distinguished Professorship. His work spans interdisciplinary collaborations, with grants from the National Science Foundation (NSF) and Department of Energy (DOE), addressing challenges in quantum computing, electronic structure theory, and AI-driven material design. Research interests include quantum Markov processes, reinforcement learning theory, and numerical methods for PDEs. He advises numerous graduate students and postdocs, mentoring in applied mathematics and computational physics. His labs and teams contribute to projects like the Duke Materials Initiative and the ELSI collaboration for electronic structure theory.