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Kangjie ZhouView profile

Research Fellow

• High-dimensional statistics
• Non-convex optimization
• Probability theory
• +3 more

Kangjie Zhou is a Founder's Postdoctoral Fellow in the Department of Statistics at Columbia University. He completed his PhD in Statistics at Stanford University under the advisement of Professor Andrea Montanari, with additional collaborative work with Professor Tselil Schramm. He holds a Bachelor's degree in Mathematics from Peking University's School of Mathematical Sciences. His research focuses on high-dimensional statistics, non-convex optimization, probability theory, and deep learning. Notable contributions include work on computational-to-statistical gaps, theoretical aspects of deep learning, and high-dimensional probability. He has been recognized with the 2024 Department Dissertation Award. Key research themes include analysis of tensor decompositions, optimization landscapes in neural networks, and the interplay between overparametrization and computational tractability. His work bridges statistical theory with algorithmic development, often addressing foundational questions in machine learning and data science.

Bart De MoorView profile

Professor

• Numerical Linear Algebra
• Optimization
• Algebraic Geometry
• +11 more

Bart De Moor is a Full Professor at the Department of Electrical Engineering, KU Leuven, Belgium, and a guest professor at the University of Siena. He leads the STADIUS research group and has supervised 85 PhD students. His roles include chairman of Health House (2016–present), member of the Board of VIB (Biotech Institute), and former Vice-Rector for International Policy (2009–2013). Education: Master Degree in Electrical Engineering (1983), KU Leuven PhD in Engineering (1988), KU Leuven Research Interests: His work spans numerical linear algebra, optimization, algebraic geometry, systems and control theory, data-driven AI, machine learning, and applications in process industry and biomedical big data. He has contributed to subspace identification, tensor decomposition, bioinformatics, and quantum computing. Publications Trends: His publications highlight subspace identification methods, tensor decomposition, bioinformatics, and biomedical data analysis. These reflect interdisciplinary advancements in control theory, quantum physics, and mathematical engineering, with applications in industrial and healthcare domains. Scientific Awards and Honors: Leslie Fox Prize (1989) Laureate of the Belgian Royal Academy of Sciences (1992) Bi-annual Siemens Award (1994) Fellow of IEEE (since 2004) Member of the Royal Academy of Belgium for Science and Arts (since 2000) Fellow of IFAC (since 2022) Commander in the Order of King Leopold I (2020) Fellow of SIAM (since 2017) FWO Excellence Award (2010) Advising and Grants: He has led a research group of 20 PhD students and postdocs, co-founded 8 spinoff companies, and secured the ERC Advanced Grant ‘Back to the roots’ (2020–2025). He also co-holds the KU Leuven Chair on healthcare systems (2018–present). Labs and Organizations: Active in the STADIUS research group (KU Leuven), he has served on boards of the Flemish Interuniversity Institute for Biotechnology (VIB), the Alamire Foundation, and the Health Tech Experience Center Health House. His spinoffs include Trendminer, Cartagenia, and Ugentec.

• Polyhedral combinatorics
• Extended formulations
• Combinatorial optimization
• +2 more

Samuel Fiorini is Associate Professor in the Department of Mathematics at the Université libre de Bruxelles (ULB) , member of the Algebra and Combinatorics group (CP 216). His research centres on polyhedral combinatorics, extended formulations, combinatorial optimisation and approximation algorithms , with frequent overlap into structural graph theory. Research in depth: Fiorini’s work explores how high-dimensional polytopes can sometimes be expressed compactly through extended formulations, proving exponential lower bounds when they cannot. He has contributed new approximation algorithms for classical problems such as vertex cover, clique transversal and odd-cycle packing, and has advanced the understanding of sorting and entropy in partially ordered sets. His papers often combine tools from graph minors, communication complexity and polyhedral theory. Scientific recognition: Best Paper Award, 44th ACM Symposium on Theory of Computing (STOC 2012) Programme committees: FOCS, IPCO, APPROX, STACS, WAOA Organiser, Sixth Cargese Workshop on Combinatorial Optimization Advising & grants: He currently supervises PhD students Carole Muller and Matthew Drescher and has mentored six completed PhDs as well as more than a dozen post-doctoral researchers. His group has been supported by an ERC starting grant and other national and international projects focusing on polyhedral approaches to hard optimisation problems. Lab & team: Fiorini leads a vibrant team within the Algebra and Combinatorics cluster at ULB, maintaining active collaborations with researchers worldwide and hosting frequent visitors working on discrete optimisation and polyhedral combinatorics.

Bastien BerretView profile

Professor

• Neuroscience
• Biomechanics
• Robotics
• +5 more

Bastien Berret is a Full Professor at the University of Paris-Saclay, where he leads the MHAPS research team within the Faculty of Sports Sciences. His academic career has progressed from Assistant Professor at Univ. Paris-Sud (2012-present) to Associate Professor with Habilitation à Diriger des Recherches (HDR) in 2017, and finally to Full Professor in September 2020. His research focuses on understanding how the central nervous system controls voluntary movements through a multidisciplinary approach combining neuroscience, biomechanics, and robotics. From a fundamental perspective, he identifies computational principles to explain human behavior in various motor tasks, with practical applications in human-exoskeleton interactions. His work employs computational tools including stochastic optimal control, numerical optimization, and machine learning methods, alongside experimental tools such as motion capture devices, force platforms, and electromyography systems. Berret's publication record shows consistent output since 2008, with particular productivity in recent years (2020-2023). His research trends indicate a strong focus on motor control theory, gravity effects on movement, human-exoskeleton interaction, and computational modeling of neural processes. Recent publications demonstrate increasing interdisciplinary collaboration across neuroscience, robotics, and biomechanics. Notable scientific achievements: Junior member of the prestigious Institut Universitaire de France (IUF) from 2017-2022 Coordinator of the ANR project "EXOMAN" (2020-2024) Extensive publication record in high-impact journals including Science Advances, PLoS Computational Biology, and Journal of Neurophysiology Berret maintains active research collaborations with institutions including INSERM, Istituto Italiano di Tecnologia, and various neuroscience laboratories. His work on stochastic optimal control and muscle activity decomposition has contributed significantly to computational neuroscience methodology. As team leader of MHAPS, he directs research on motor control principles and their applications in rehabilitation and robotics.

Shahana IbrahimView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +5 more

Shahana Ibrahim is a tenure-track Assistant Professor at the University of Central Florida under the AI Initiative, holding a joint appointment in the Department of Electrical and Computer Engineering and Computer Science. Her research develops provable methods for robust machine learning systems with applications in critical real-world scenarios. Education: Ph.D. in Electrical and Computer Engineering, Oregon State University (advised by Dr. Xiao Fu) Prior industry experience: System Validation Engineer at Texas Instruments (2012-2017) and NVIDIA GPU intern (2018) Her research spans machine learning, signal processing, and optimization with core expertise in weakly supervised learning, tensor decomposition, and stochastic algorithms. She focuses on enhancing AI reliability through theoretical guarantees for noisy data environments, particularly addressing label noise, incomplete annotations, and structured factorization challenges. Her work bridges signal processing theory with modern AI to solve practical problems in data quality and system robustness. Recent publications (2023-2025) reveal strong thematic consistency in handling imperfect supervision. Key trends include crowdsourced label modeling, instance-dependent noise characterization, and tensor/matrix completion techniques. Her approach uniquely integrates signal processing perspectives with deep learning, emphasizing identifiability conditions and geometric regularization to extract reliable patterns from corrupted data. Scientific Awards: Outstanding PhD Dissertation Award from EECS, Oregon State University (2024) Dr. Ibrahim actively mentors graduate researchers including Faizul and Grey, who co-authored her ICIP 2025 and IEEE CAMSAP 2023 publications. She secured the AI-BTO DARPA grant (December 2024) for physics-informed machine learning research on intrinsically disordered proteins. Current funding supports multiple RA/TA positions for PhD students in her lab. She serves on program committees for AISTATS, AAAI AI for Social Impact, and WiML at NeurIPS. Her research group develops end-to-end learning frameworks for noisy data environments, with active projects funded by DARPA focusing on biomedical applications and robust AI validation. The lab maintains strong industry connections through NVIDIA and Texas Instruments collaborations.

Pauli MiettinenView profile

Professor

• Data Science
• Data Mining
• Matrix and Tensor Decompositions
• +4 more

Pauli Miettinen is a Professor of Data Science at the University of Eastern Finland, affiliated with the School of Computing within the Faculty of Science, Forestry and Technology. His research focuses on data science methodologies, including matrix and tensor decompositions, redescription mining, and social network analysis. Key applications span ecological niche modeling, health data analysis, and parliamentary candidate opinion analysis. He leads the Algorithmic Data Analysis research group and contributed to the Neuro-Innovation project (2021–2026). Recent work includes advancements in differentially private redescription mining and hyperbolic community graph generation. His publications emphasize efficient algorithms for data mining tasks like biclustering and non-negative matrix factorization. Selected achievements include developing the HyGen graph generator and pioneering techniques for interpretable data representation. His research bridges theoretical method development with practical applications in diverse domains.

Salar FattahiView profile

Assistant Professor

• Optimization
• Machine Learning
• Robust Matrix Recovery
• +4 more

Salar Fattahi is an Assistant Professor at the University of Michigan, affiliated with the College of Engineering’s Department of Industrial and Operations Engineering. He holds additional appointments with the Michigan Institute for Computational Discovery and Engineering (MICDE), Michigan Institute for Data Science (MIDAS), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). PhD in Industrial Engineering and Operations Research from UC Berkeley M.Sc. in Electrical Engineering from Columbia University B.Sc. in Electrical Engineering from Sharif University of Technology Research Focus: Developing scalable computational methods for structured optimization and machine learning problems by exploiting sparsity, low-rankness, and benign landscape properties. Applications span gene regulatory networks, power systems, and brain connectivity modeling. 2025: Parametric algorithms for MIQPs over trees 2024: Triple Component Matrix Factorization for global/local/noise separation 2023: Robust subspace recovery and dictionary learning Scientific Recognition: NSF CAREER Award (2023) INFORMS Best Paper Awards (2023, 2024) Dean’s MLK Spirit Award (2024) MICDE Catalyst Grant (2021) Academic Service: Associate Editor for INFORMS Journal on Data Science; Area Chair for NeurIPS, ICML, and ICLR. Mentored students including Jianhao Ma (now Tsinghua University), Geyu Liang (Amazon), and Aaresh Bhathena. Research supported by NSF, ONR, MICDE, MIDAS, START, and DEI Faculty grants.

• Expressive Speech Processing
• Speech Synthesis
• Emotion Recognition
• +3 more

Marie Tahon is a Professor at Le Mans University and Director of the LST (Langage, Signal et Texte) team at LIUM (Laboratoire d'Informatique de l'Université du Maine). Her research spans expressive speech processing with applications in speech synthesis, emotion recognition, and speaker identification, complemented by expertise in musical acoustics for automatic song analysis and organology. Education : Engineering degree from École Centrale de Lyon (2007), M.S. in Acoustics from École Centrale & INSA Lyon (2007), and Ph.D. in Computer Science from University of Paris-Sud (Orsay, 2012). Postdoctoral positions at LIMSI-CNRS (affective computing), LMSSC CNAM (acoustics), and IRISA (Expression team). Research Focus : Tahon's work centers on developing interpretable systems for expressive speech processing. Key contributions include the ALLIES corpus for speech segmentation/diarization and AlloSat for call-center emotion analysis. Her recent publications demonstrate strong emphasis on low-resource speech translation (e.g., Kurdish), speaker verification after resynthesis, and turn-taking analysis in French media using explainable AI techniques. She integrates acoustic and linguistic features for continuous emotion prediction and develops noise-robust models for digital holography. Collaborations & Infrastructure : Leads the COMMUTE and ESPERANTO projects while directing LIUM's LST team. Her work leverages specialized resources like the ALLIES corpus (segmentation, diarization, recognition) and AlloSat (satisfaction/frustration analysis). Current efforts focus on lifelong learning for MOS prediction, multilingual speech translation, and perceptual evaluation of turn-taking phenomena in broadcast media.

• Hyperspectral Imaging
• Machine Learning
• Low-rank Matrix Approximation
• +2 more

Kinan Abbas is a researcher at the University of Strasbourg's College of Science and Engineering, Department of Computer Science, specializing in hyperspectral imaging and machine learning. His work focuses on spectral image processing techniques including unmixing, demosaicing, and low-rank matrix approximation. His research interests center on hyperspectral imaging and machine learning applications, particularly developing novel methods for snapshot spectral image processing. Key contributions include locally-rank-one-based joint unmixing frameworks, diffusion models for texture synthesis, and entropy-weighted spectral deconvolution techniques. His work bridges theoretical signal processing with practical applications in remote sensing and computational photography. Analysis of his publication trend (2021-2025) shows evolution from foundational spectral unmixing techniques toward advanced generative models, with increasing focus on diffusion-based synthesis and multifractal analysis. His research consistently addresses computational challenges in spectral image reconstruction. Abbas actively collaborates with researchers from ICube laboratory (Strasbourg), including Matthieu Puigt, Gilles Delmaire, and Gilles Roussel, evidenced by consistent co-authorship across 14 publications. His work appears in IEEE Transactions, ICASSP, and French GRETSI conferences, indicating strong institutional support for his research program.

Syed Murtuza BakerView profile

Research Fellow

• Bioinformatics
• Single Cell RNA-seq
• Spatial Transcriptomics
• +2 more

Syed Murtuza Baker is a Research Fellow in the Division of Informatics, Imaging & Data Sciences at the University of Manchester. He earned his doctorate from Martin-Luther University Halle Wittenberg with a focus on kinetic models of biological systems. His expertise includes single-cell genomics, spatial transcriptomics, and bioinformatics. His research investigates gene regulatory networks, immune cell coordination in inflammatory diseases, and computational methods for analyzing spatial transcriptomic data. He contributes to UN Sustainable Development Goals through his work in health and biomedical research. Baker's publications demonstrate consistent focus on computational biology approaches to disease mechanisms, particularly in liver disease, leukemia, and immune disorders. His recent work develops novel algorithms for spatial transcriptomics data analysis. None reported No information is available regarding student advising or research grants. He collaborates extensively with international teams through the Sustainable Futures and Digital Futures research platforms.

• Machine Learning
• Statistical Learning
• Bayesian Methods
• +5 more

Pierre ALQUIER is a Professor at ESSEC Business School (Singapore) since 2023, specializing in statistical learning and machine learning. Previously, he held professorships at ENSAE Paris (2014–2019) and the University of Dublin (2012–2014). He earned his PhD in Mathematical Statistics from Pierre and Marie Curie University in 2006, with a focus on advanced statistical methodologies. His research centers on Bayesian methods, PAC-Bayes bounds, high-dimensional data analysis, and robust estimation, with applications in quantum computing and time series. He has authored over 60 peer-reviewed articles, including influential works on kernel mean embeddings and meta-learning. Alquier has received the 2019 Best Paper Award at the Asian Conference on Machine Learning. He actively contributes to academic leadership, serving as an associate editor for leading journals like the Journal of Machine Learning Research and organizing international workshops. His educational contributions include co-supervising multiple doctoral theses on topics like robust Bayesian inference and non-negative matrix factorization. Education: PhD in Mathematical Statistics (2006), Pierre and Marie Curie University MSc in Probability Theory and Statistics (2003), Pierre and Marie Curie University Diploma in Statistician-Economist (2003), ENSAE Research Focus: Machine learning theory, PAC-Bayes bounds, Bayesian computation, high-dimensional statistics, quantum tomography, and time series forecasting. Grants & Activities: Member of key academic societies (IMS, SFdS), reviewer for top conferences (NeurIPS, ICML), and organizer of workshops on approximate Bayesian inference and high-dimensional data analysis. His recent work emphasizes robust regression, meta-learning, and the theoretical foundations of deep learning, often addressing challenges in dependent data and model misspecification. He has developed R packages like regMMD for robust statistical estimation.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +7 more

Florence d’Alché-Buc is a full professor at Télécom Paris (Institut Polytechnique de Paris) since 2014, leading research in machine learning and AI. She heads the Image, Data, Signal (IDS) Department and holds the Data Science and Artificial Intelligence for Digitalized Industry and Services (DSAIDIS) Chair. Her work focuses on kernel methods, structured prediction, reliable AI, and bioinformatics applications. She co-leads the Master’s in Data Science at Université Paris-Saclay and pioneered the PASCAL Network’s Challenges program. Key roles include Scientific Director of Digiscome Labex and ELLIS Fellow. Research emphasizes frugal AI, reducing environmental impact, and enhancing model explainability. Recent contributions include work on metabolite identification via kernel-based methods and AI sustainability. She collaborates with institutions like Columbia University and co-organized the MLSE 2020 conference. Awards include the Simons Chair (2025) and leadership roles in AI ethics and regulation initiatives.

• Statistical Learning
• High-Dimensional Data Analysis
• Network Statistics
• +7 more

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

• Leukemia
• Biostatistics
• Computational Biology
• +6 more

Tim Triche, Jr., Ph.D., is an Associate Professor at the Van Andel Institute in the Department of Epigenetics . He earned his A.B. in chemistry from Cornell University , followed by an M.S. in biostatistics and a Ph.D. in statistical genetics from the University of Southern California . Before joining Van Andel in 2017, he was a postdoctoral fellow at USC's Norris Comprehensive Cancer Center focusing on cellular senescence in blood disorders. As a key member of The Cancer Genome Atlas Research Network since 2011 with over a dozen high-impact publications in Nature , Cell , and NEJM , Dr. Triche specializes in epigenetics , biostatistics , and computational biology . His lab develops innovative approaches for pediatric AML research, integrating next-generation sequencing with clinical trial design to improve patient outcomes. His work emphasizes statistical learning for patient stratification, molecular profiling of hematological cancers, and interpretable machine learning in biomedical contexts. He leads the Bioinformatics and Biostatistics Core as faculty advisor and maintains active collaborations across institutions. 2025 Nature Cancer study on developmental heterogeneity in cancer susceptibility 2024 NAR methods paper on BISCUIT multi-omics tools 2023 PLOS One validation of MAX regulation in pituitary adenomas 2022 Nature Metabolism obesity subtyping analysis Scientific contributions include: Chan Zuckerberg Initiative grant (2022) for biomedical computing NCI SPORE grant (2021) as co-recipient Key role in Pediatric AML molecular mapping (2017)

• Information Processing over Networks
• Adaptive Signal Processing & Learning
• Statistical Learning
• +4 more

Berberidis Kostas is a Professor at the University of Patras, Department of Computer Engineering and Informatics. His academic work focuses on Information Processing over Networks , Adaptive Signal Processing , and Wireless Communications . He is affiliated with the Division of Hardware and Computer Architecture. Specialized in Statistical Learning and Distributed Information Processing Contributed to advancements in Signal Processing for Communications and Array Signal Processing Notable research areas include: Adaptive and distributed learning algorithms Wireless channel equalization and relaying Hyperspectral and biomedical image processing Resource allocation with security constraints Recent publications cover topics like blind hyperspectral unmixing , secure resource allocation , and FIR filter optimization , reflecting his interdisciplinary focus on signal processing, communications, and computational imaging.