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Mayank GoswamiView profile

Associate Professor

• Algorithms
• Computational Geometry
• Theoretical Machine Learning
• +12 more

Mayank Goswami is an Associate Professor at the Department of Computer Science, City University of New York (CUNY) Queens College and Graduate Center. He earned his Ph.D. in Applied Mathematics and Statistics from Stony Brook University, advised by Joe Mitchell and David Gu, followed by a post at the Max-Planck Institute for Informatics in Germany. His research spans Algorithms , Computational Geometry , Theoretical Machine Learning , and Conformal Geometry , with a focus on geometric optimization, data structures, and adversarial robustness in AI. His work addresses problems like diverse solution design, Teichmuller maps, and noise-tolerant learning models. Publications highlight trends in Geometric algorithms for NP-hard problems (FCT 2025, ICALP 2025) Theoretical ML for manifold topology and adversarial robustness (TMLR 2025, AISTATS 2022) Efficient data structures for similarity search and sorting (WADS 2024, APPROX 2024) He has received a Best Paper Award at FCT 2025 and NSF grants for projects on geometric solution diversity (CCF-1751847) and dynamic optimality conjectures (CCF-1910873). His students include Paul Cesaretti and GiBeom Park, and he organizes the Queens College CS Colloquium and Fall Workshop on Computational Geometry (FWCG 2018, 2024–2025).

Ulrike Pestel-SchillerView profile

Researcher

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +2 more

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

• Visual Perception
• Color Vision
• Individual Differences
• +7 more

Jenny Bosten is an Associate Professor in Psychology at the School of Psychology, University of Sussex . Her research focuses on visual perception, particularly color vision and individual differences, using neuroimaging (fMRI, EEG) and psychophysics. ERC-funded project COLOURCODE (2020-2025) on cortical color representation Studied genetics of visual trait variation and anomalous trichromacy Collaborated with institutions including UC San Diego and University of Cambridge Her work explores how color perception is shaped by natural scene statistics and genetic factors. Publications address cortical encoding mechanisms, visual enhancement technologies, and ecological approaches to perception. Current teaching includes advanced neuroscience courses and supervision of MSc research projects. Professional roles include membership in the International Colour Vision Society and former Secretary of the Colour Group (Great Britain).

Shen-Shyang HoView profile

Professor

• Machine Learning
• Data Mining
• Artificial Intelligence
• +7 more

Shen-Shyang Ho is a Full Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His work spans machine learning, data mining, and edge computing with applications in urban mobility, precision agriculture, and data privacy. He leads NSF-funded research projects on dynamic graph analysis and spatiotemporal anomaly detection. Ph.D. in Computer Science, George Mason University Post-Doctoral Associate, Caltech & NASA JPL B.S. in Mathematics with Computational Science, National University of Singapore Research expertise includes graph-based machine learning, conformal prediction, cooperative inference, and privacy-preserving ML. Current work focuses on federated learning for edge devices and anomaly detection in evolving systems. He has developed tools like SplitTracer for cooperative inference evaluation and ParkGauge for urban mobility monitoring. Recent publications highlight his contributions to 2026 Pattern Recognition journal (martingale-based graph analysis), 2025 IEEE ICAIC conference (blockchain gas optimization), and 2024 ACM SAC symposium (shared mobility systems). His work integrates machine learning with real-world constraints across energy grids, transportation, and agricultural technology. NSF Grant (2022) for Dynamic Graph Anomaly Detection NSF Grant (2018) for Spatiotemporal Analysis Google Scholar Classic Paper Recognition (2017) for 2006 Radar Micro-Doppler Study Professional memberships include the Association for Computing Machinery (ACM). His teaching portfolio ranges from introductory programming to advanced ML courses. He previously held positions at Nanyang Technological University before joining Rowan in 2016.

Eran HalperinView profile

Professor

• Computational Biology
• Genomics
• Machine Learning in Medicine
• +4 more

Dr. Eran Halperin is a Professor at the University of California, Los Angeles, affiliated with the School of Engineering (Computer Science Department) and the School of Medicine (Human Genetics, Computational Medicine, Anesthesiology). His research bridges computational biology, genomics, and machine learning, with a focus on developing statistical methods to analyze big medical data for disease prediction and treatment. Developed open-source software tools like FEAST, ReFACTor, and Bisque Recipients of prestigious awards including Rothschild Fellowship and ISCB Fellow (2021) Research Interests: Computational Genomics: Applying machine learning to genomic data (methylation, RNA expression) for disease understanding. Machine Learning in Medicine: Creating deep learning architectures for ophthalmology, anesthesiology, and acute care applications. Medical Data Science: Integrating electronic health records with genomic datasets for predictive modeling. Scientific Recognition: Rothschild Fellowship Technion-Juludan Research Prize Krill Prize in Science Elected ISCB Fellow (2021) Dr. Halperin's lab collaborates across disciplines, utilizing software platforms such as GLINT (methylation analysis) and MTV-LMM (microbiome prediction). His work has received funding from NIH, NSF, and international foundations.

• computational neuroscience
• theoretical neuroscience
• sensory systems
• +3 more

Wiktor Młynarski is a Professor and Research Group Leader at the Faculty of Biology, Ludwig-Maximilians-University Munich, where he directs computational neuroscience research focused on adaptive neural computations in sensory systems. His work bridges theoretical frameworks with experimental validation to uncover fundamental principles of biological information processing. His research centers on computational and theoretical neuroscience, leveraging information theory, statistics, and probabilistic machine learning to model how neural systems efficiently represent dynamic natural environments. Key interests include sensory coding adaptation across timescales, statistical structure of natural stimuli, and the development of normative frameworks applicable from synaptic to behavioral levels. This inherently collaborative approach integrates theoretical predictions with experimental neuroscience to identify universal processing rules. Analysis of Młynarski's publication trajectory (2014-2025) reveals consistent exploration of efficient coding principles across auditory and visual domains, with recent work emphasizing anticipatory processing during locomotion, time-constrained decision-making, and panoramic visual statistics. His research demonstrates how environmental dynamics shape neural representations, moving beyond static models to capture the brain's adaptive capabilities in real-world contexts. Professor Młynarski leads the Computational Neurobiology Research Group (https://compneurobio.org), fostering interdisciplinary collaborations to confront theoretical models with experimental data. The group's work aims to establish general rules for biological information processing by examining how sensory systems dynamically optimize computations in response to environmental regularities and changes.

• Artificial Intelligence
• Physics
• Computer Technology
• +4 more

Pedro Lind is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, where he serves in the Department of Information Technology with a focus on Artificial Intelligence. His academic appointments include active participation in research groups for Applied Artificial Intelligence and Mathematical Modeling. Dr. Lind's research spans interdisciplinary domains including: Biomedical AI applications (EEG classification, ECG analysis, eye tracking) Stochastic processes and complex systems modeling Trustworthy machine learning for security/privacy Physics-inspired computational methods Renewable energy statistics and modeling His recent publications demonstrate strong focus on developing novel AI methodologies for medical diagnostics (2024-2025), particularly using generative models and interpretable AI approaches for physiological data analysis. He leads significant research initiatives including the AI-Mind project developing diagnostic tools for dementia. Additional projects include international technology transfer collaborations with Czech Republic institutions. Dr. Lind maintains active research teams and labs focused on computational neuroscience and applied AI.

René VidalView profile

Professor

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +3 more

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .

• Data Science
• Veterinary Epidemiology
• Precision Livestock Farming
• +7 more

Dr. Miel Hostens is an Assistant Professor in Farm Animal Health at Utrecht University's Faculty of Veterinary Medicine, with additional affiliation at Ghent University. His work bridges dairy science and data science, focusing on precision livestock farming to monitor sustainable food production systems from a global perspective. He holds a 0.9 FTE position at Utrecht University and a 0.1 FTE at Ghent University, coordinating courses on data-driven precision agriculture at both institutions. Hostens received his MSc in Veterinary Medicine from Ghent University in 2006 and completed his PhD in 2012 on 'Health and Fertility Challenges in High Yielding Dairy Cows during the Transition Period and the Use of Dietary Fatty Acids as an Optimization Strategy.' His academic journey includes extensive training in epidemiology, statistics, and data science, with over 15 post-graduate educations completed. He has attended more than 50 national and international trainings between 2007 and 2020, demonstrating deep commitment to continuous learning in his interdisciplinary field. His research interests focus on applying data science methodologies to veterinary medicine, particularly in dairy cow health monitoring. He specializes in precision livestock farming, big data analytics, and herd health management. His work integrates sensor technology, machine learning, and traditional veterinary approaches to develop predictive models for health monitoring. He has established a new research focus area at the intersection of dairy and data science, creating innovative methodologies for monitoring sustainable food production systems. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning to dairy farming challenges. His work spans predictive modeling for transition period success, hypocalcaemia prediction, behavioral pattern analysis, and milk yield monitoring. The research increasingly integrates multi-source data streams including sensor data, milk composition, and behavioral observations to develop comprehensive health monitoring systems for dairy cows. Microsoft Research Grant (€150,000) Microsoft Research Grant (€25,000) Ambassador of the Faculty of Veterinary Medicine within Utrecht University's Applied Data Science focus area Member of the Dairy Cattle Milk Recording Working Group of the International Committee for Animal Recording Hostens has guided over 70 MSc students in Veterinary Medicine and currently supervises multiple PhD candidates. His research is supported by significant funding including a €9,000,000 European gpluse.eu consortium (FP7) project, a €1,300,000 VLAIO project, and a ZonMW research grant. He leads the 'Sense of Sensors' research-industry consortium focused on prediction of health indicators using common sensor technology in the Netherlands. His work has practical applications with industry partners including Delaval.com and DairyDataWarehouse.com. Hostens established a research group that has grown through attracting MSc and PhD students, resulting in the completion of Jenne de Koster's PhD in 2016 and supervision of five ongoing PhD projects. His team frequently expands with visiting researchers, enhancing international collaboration. He serves as an advisor for 'De Tolakker' research farm, supporting Utrecht University's investment to transform it into a research innovation center for circular agriculture. His work connects with the Utrecht Platform for Applied Data Science and the Future Food Utrecht initiative.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +3 more

Simone Bianco is an Associate Professor at the Department of Informatics, Systems and Communication (DISCo) of the University of Milano-Bicocca, Italy. His academic and research contributions span computer vision, artificial intelligence, machine learning, and optimization algorithms applied to multimodal and multimedia systems. His educational background includes a PhD in Computer Science (2010) and BSc/MSc degrees in Mathematics (2003/2006), both from the University of Milano-Bicocca. Bianco’s research focuses on color constancy, deep learning for video restoration, neural architecture search, and computational color imaging, with a strong emphasis on practical applications like biometric recognition, medical imaging, and environmental monitoring. The 15 most recent articles (2025–2020) highlight trends in computer vision, including uncertainty estimation in color constancy, portable material appearance modeling, temporal consistency in low-light videos, and advanced deep learning architectures for image and video processing. His work often integrates photogrammetry, sensor technology, and multimodal data analysis. Scientific accolades include recognition on Stanford University’s World Ranking Scientists List for achievements in artificial intelligence and image processing. Bianco also serves as R&D Manager for the University of Milano-Bicocca spin-off Imaging and Vision Solutions and contributes to international conferences and workshops.

Nicolas DuchateauView profile

Associate Professor

• Medical Imaging
• Cardiac Function Analysis
• Statistical Atlases
• +5 more

Nicolas Duchateau is an Associate Professor at Université Lyon 1 and researcher at the CREATIS lab in Lyon, France. He is also a Junior member of the prestigious Institut Universitaire de France (IUF) and serves as Associate Editor for the Neurocomputing journal. His academic career includes positions at Universitat Pompeu Fabra in Barcelona and INRIA Epione in Sophia-Antipolis, with his current role at Polytech Lyon's Biomedical Engineering department since 2016. His research focuses on characterizing diseases from medical imaging populations, with methodological development centered on statistical atlases and machine learning approaches to represent populations. On the applicative side, he concentrates on cardiac function and imaging modalities such as echocardiography and magnetic resonance. His work spans computational anatomy, pattern statistics, representation learning, manifold learning, auto-encoders, information fusion, cardiac imaging, shape and deformation analysis, risk stratification, and image synthesis. Duchateau's recent publications reveal strong trends in multimodal data fusion, particularly combining echocardiography with clinical records for patient stratification. He has made significant contributions to representation learning for cardiac population analysis, with increasing emphasis on diffusion models, uncertainty estimation, and domain adaptation techniques. His work bridges deep learning methodologies with clinical cardiology applications, focusing on interpretable AI solutions for cardiac function assessment. Junior Member of Institut Universitaire de France (2021) PhD prize for knowledge transfer from Universitat Pompeu Fabra (2014) Young Investigator Award at Euroecho conference (2010) Duchateau actively supervises numerous PhD and Master's students, including Anita Salvador, Thierry Judge, Pierre-Elliott Thiboud, and Romain Deleat-Besson. He has secured major research funding including a €75k grant from the Institut Universitaire de France (2021-2026), a €251k French ANR Young Researchers grant for the "MIC-MAC" project (2019-2024), and a €139k grant from the Fédération Française de Cardiologie for the "MI-MIX" project (2020-2023). He leads a vibrant research team at CREATIS lab focused on medical image analysis, where his group develops computational approaches to characterize cardiac diseases through population analysis. The team works at the intersection of machine learning, medical physics, and clinical cardiology, with ongoing projects spanning echocardiography analysis, MRI processing, synthetic data generation, and clinical decision support systems.

Sven KlaaßenView profile

Research Fellow

• Machine Learning
• Causal Inference
• Deep Learning
• +1 more

Dr. Sven Klaaßen serves as a Research Fellow at the University of Hamburg's Hamburg Business School within the Professorship for Statistics with Application in Business Administration, collaborating closely with Prof. Dr. Martin Spindler since 2021. His research focuses on developing advanced statistical methodologies for complex data environments. His academic credentials include: Ph.D. in Statistics from Hamburg Business School (2020) Visiting Scholar at MIT Department of Economics (2022) M.Sc. in Business Mathematics from University of Hamburg (2016) BSc in Business Mathematics from University of Hamburg (2014) Dr. Klaaßen's research program centers on Machine Learning, Causal Inference, Deep Learning, and High-Dimensional Statistics, with particular emphasis on developing robust inference techniques for modern data challenges. His work bridges theoretical statistics with practical applications in business analytics and econometrics, often addressing the complexities of high-dimensional datasets where traditional methods fail. Analysis of his recent publications reveals a clear trajectory toward integrating machine learning with causal inference frameworks, exemplified by his leadership in the DoubleML software ecosystem. His research increasingly tackles multimodal data challenges while maintaining rigorous statistical foundations, with applications spanning economics, operations research, and business decision systems. As an active member of Prof. Spindler's research group, Dr. Klaaßen contributes to collaborative projects developing open-source statistical tools and advancing methodological frontiers in causal machine learning. The team maintains strong industry and academic partnerships focused on translating theoretical innovations into practical analytical solutions.

Satu-Pia ReinikainenView profile

Professor

• Multivariate Statistical Analysis
• Process Control
• Spectroscopy
• +3 more

Satu-Pia Reinikainen is a tenured Professor in Computational Engineering at the Lappeenranta-Lahti University of Technology (LUT) School of Engineering Sciences . With expertise in chemometrics and multivariate analysis, her work bridges statistical modeling, spectroscopy, and environmental monitoring. Research Focus Development of advanced kernel-based methods for process control Application of hyperspectral imaging in material and environmental analysis Microplastic pollution dynamics in aquatic systems Integration of spectroscopic techniques for real-time monitoring Conservation of geological and heritage materials through data-driven approaches Her research combines chemometric algorithms, environmental data analysis, and industrial process monitoring to solve complex analytical challenges.

• Forensic Science
• DNA Profiling
• Y-STR Analysis
• +3 more

Dr. Roberto Puch-Solis is a Principal Investigator at the Leverhulme Research Centre for Forensic Science , affiliated with the University of Dundee . His work focuses on probabilistic decision support systems, forensic statistics, and computational methods in forensic analysis. Expertise: Forensic genetics, DNA profiling, gas chromatography-mass spectrometry (GCMS), convolutional neural networks (CNNs), and Y-STR mutation modeling. Key Contributions: Development of open-access software ( MUCalc ), segmentation datasets for firearm analysis, and ground truth datasets for drug profiling. Collaborations: Active in interdisciplinary networks, with partnerships in digital forensics, analytical chemistry, and machine learning. Research Trends: Recent work integrates deep learning for forensic image analysis (e.g., shoeprint matching, cartridge case segmentation) and statistical frameworks for DNA evidence interpretation. Applications span firearms identification, drug quantification, and crime scene reconstruction. Activities: Delivered invited talks on probabilistic systems, served as an external examiner, and participated in neural network training workshops.

• Fuzzy Logic Systems
• Artificial Intelligence
• Model-Based Predictive Control
• +6 more

Professor Mahdi Mahfouf holds the Chair in Intelligent Systems at the University of Sheffield's School of Electrical and Electronic Engineering . He obtained his MPhil (1988) and PhD (1991) in Control Systems from the same institution. After postdoctoral research (1992-1996) on Leverhulme-funded projects in Model-Predictive Control and Fuzzy Logic, he progressed through academic ranks at Sheffield to Full Professor (2005). Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award (for ICU Decision Support Systems) Over 370 publications, including 130+ journal papers Head of the Intelligent Systems Research Laboratory Research Themes His work spans fundamental research in Fuzzy Logic (modelling, control), Neural-Fuzzy Systems, Self-Organising Control, and Evolutionary Optimization, alongside applied domains in pharmaceutical manufacturing, aerospace systems, biomedical engineering (ICU monitoring), and intelligent transportation. Recent publications focus on hybrid AI for pharmaceutical processes , type-2 fuzzy control systems , and machine learning in manufacturing metrology . Lab initiatives include multistage process monitoring and human-machine interaction systems for stress management.