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Ahmed AbdulkadirView profile

Researcher

• Neuroimaging
• Machine Learning
• Medical Imaging Analysis
• +5 more

Dr. Ahmed Abdulkadir is a Researcher at the University Clinics UPD, part of the University of Bern, specializing in neuroimaging and machine learning applications in medical diagnostics. He holds a PhD in Vision Science from the University of Freiburg and has extensive experience in roles such as Senior Researcher at Lausanne University Hospital and Visiting Researcher at the University of Pennsylvania. His work focuses on automated MRI analysis for Alzheimer’s disease, schizophrenia, and other neurological disorders, leveraging deep learning techniques to improve diagnostic accuracy and clinical workflows. He has contributed to advancements in volumetric segmentation (e.g., 3D U-Net), brain aging studies, and cross-disciplinary applications of AI in cardiology and thyroid cancer diagnostics. His research emphasizes bridging clinical needs with computational solutions, including tools like Atri-U for cardiovascular imaging. Education: PhD in Vision Lab, University of Freiburg (2012-2016) MSc in Life Science, EPFL Lausanne (2010-2012) BSc in Life Science, EPFL Lausanne (2007-2010) BSc in Systems Engineering, Fachhochschule NTB Buchs SG (2001-2004) Research Interests: Machine learning-driven neuroimaging analysis Automated diagnosis of neurodegenerative diseases Cross-modal data harmonization Deep learning applications in medical imaging Brain aging patterns and genetic correlates Articles Highlight: His work spans Alzheimer’s classification via structural MRI, schizophrenia-related brain network studies, and deep learning models for microbleed detection. Recent focus includes AI tools for thyroid cancer cytology and cardiovascular MRI. Grants/Teams: Collaborates with institutions globally, including Zurich University of Applied Sciences (AI Center), and contributes to large-scale studies like the iSTAGING project on brain aging patterns. No explicit awards listed, but contributions to high-impact clinical tools reflect recognition. Labs/Teams: Active in interdisciplinary teams at University Clinics UPD, Lausanne University Hospital, and the Vision Lab, focusing on translational AI applications in healthcare.

Sam SilvaView profile

Assistant Professor

• Atmospheric Chemistry
• Computational Modeling
• Air Quality
• +3 more

Sam Silva is Assistant Professor in Earth Sciences, Civil and Environmental Engineering, and Population and Public Health Sciences at the University of Southern California. He leads the Atmospheric Composition and Earth Data Science group, investigating atmospheric chemistry's role in air quality and climate change through computational modeling and machine learning. Silva holds a PhD in Environmental Engineering and Computation from MIT and a BSc in Physics and MSc in Atmospheric Science from the University of Arizona. Previously, he was a Linus Pauling Distinguished Postdoctoral Fellow and Research Data Scientist at Pacific Northwest National Laboratory. Research focuses on atmospheric chemical composition using computer models, data science, and machine learning to address global environmental challenges. Work emphasizes aerosol-cloud interactions, chemical cycling dynamics, climate-health relationships, and equitable modeling approaches. Publications demonstrate strong trends in developing machine learning frameworks for atmospheric and climate science, with applications in chemical transport modeling, health impact assessments, and biogeochemical cycling. Recent work advances interpretable AI methods while addressing urban air quality-climate interactions.

Arthur M. de JongView profile

Assistant Professor

• Single-Molecule Sensing
• Biosensing Technology
• Colloidal Functionalization
• +3 more

Arthur M. de Jong is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the joint research group Molecular Biosensing for Medical Diagnostics, combining Applied Physics and Biomedical Engineering. He leads efforts in developing single-molecule biosensors for continuous patient monitoring in healthcare, food, and environmental applications. His work focuses on colloidal particle functionalization, non-fouling surfaces, and microfluidics integration. He holds an MSc in Chemical Engineering (1989) and a PhD in Catalysis (1994), with postdoctoral experience in surface science. He joined TU/e’s Applied Physics department in 2000 and later transitioned to biosensing in 2005. He is a core member of the Institute for Complex Molecular Systems, fostering interdisciplinary collaboration. His courses include Advanced Computational Skills, Optical Microscopy, and Experimental Physics. He contributes to UN Sustainable Development Goals via biosensor innovations for societal benefit. Education: MSc Chemical Engineering, TU/e (1989) PhD in Catalysis, TU/e (1994) Research Interests: Single-molecule biosensing technologies, colloidal particle engineering, antifouling surfaces, and integration of biosensors into clinical and industrial systems. His work bridges physics, chemistry, and biomedical engineering to enable real-time monitoring solutions. Projects: Leading the 'Real-time Biomolecular Sensing for Smart Food Industry' project (2021–2026), advancing biosensor applications in food safety and quality control. Labs/Teams: Molecular Biosensing Group and the Institute for Complex Molecular Systems at TU/e.

• Neural Fields
• Nonlinear Dynamics
• Numerical Methods
• +3 more

Daniele Avitabile is a Full Professor of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam, and holds affiliations with Amsterdam Neuroscience (Cellular & Molecular Mechanisms and Systems & Network Neuroscience). His research focuses on neural field equations, nonlinear dynamics, numerical methods, and mathematical biology. He investigates phenomena such as cortical spreading depression, working memory, and auxin transport in plants, leveraging interdisciplinary approaches that bridge mathematics and neuroscience. His recent work emphasizes computational neuroscience, including studies on neural network architectures, noise-induced patterns, and stochastic processes in disordered networks. He also explores mathematical models of urban development and interdisciplinary applications of dynamical systems theory. Teaching responsibilities include courses on nonlinear dynamical systems and numerical methods. Dr. Avitabile collaborates globally, contributing to both theoretical advancements (e.g., well-posedness of neural field solutions) and applied research (e.g., experimental validation of neural models in mouse cortex studies). His research often integrates analytical techniques like bifurcation analysis with numerical methods such as projection schemes for partial differential equations.

• Machine Learning
• Signal Processing
• Robotics
• +3 more

Shimon Schwartz is an Adjunct Assistant Professor affiliated with the National Research Council Canada (NRC), as indicated by his email domain. His research focuses on machine learning, signal processing, and robotics, with a particular emphasis on compressive sensing techniques and their applications in diverse fields such as medical imaging, e-commerce forecasting, and economic analysis. His work spans theoretical advancements in sparse signal reconstruction, optimized sampling strategies, and the integration of deep learning models for predictive analytics. Notable contributions include saliency-guided approaches for compressive fluorescence microscopy and energy-efficient optical coherence tomography (OCT) systems. Shimon's research trends reflect a blend of algorithmic innovation and practical problem-solving, addressing challenges in data efficiency, imaging technology, and business intelligence. While no formal awards or grants are explicitly mentioned, his publications highlight interdisciplinary collaboration across engineering, computer science, and biomedical domains. No specific information is provided about his educational background, advising roles, or laboratory affiliations in the available texts.

• Statistical Modeling
• Machine Learning
• Bioinformatics
• +6 more

Farhad Shokoohi is an Assistant Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His research focuses on statistical modeling, machine learning, and their applications in healthcare analytics, bioinformatics, and financial forecasting. He has contributed to advancements in Bayesian methods, small area estimation, and computational statistics, with particular emphasis on cancer genomics and epigenetic analysis. Key research interests include hybrid statistical models (e.g., SP-RF-ARIMA for energy load forecasting), hidden Markov models for genomic data analysis, and sparse estimation techniques in high-dimensional data. His work bridges theoretical statistics with practical applications in finance, healthcare, and policy evaluation. He has developed R packages like fmrs for finite mixture models, emphasizing computational tools for statistical inference. His articles reflect a blend of methodological innovation and domain-specific solutions, spanning topics such as differential methylation in colorectal cancer, dual-input deep learning for forex prediction, and trans-dimensional MCMC for genomic studies. While no awards or grants are explicitly listed in the provided texts, his interdisciplinary approach underscores contributions to both computational and applied statistical research.

• Synthetic Biology
• Biomedical Engineering
• Systems Biology
• +7 more

Professor Richard Ian Kitney is Professor of Biomedical Systems Engineering in the Department of Bioengineering within Imperial College London's Faculty of Engineering. He serves as Chairman of the Institute of Systems and Synthetic Biology and Co-Director of the EPSRC National Centre for Synthetic Biology and Innovation. His institutional affiliations include the Centre for Synthetic Biology, Centre for Translational Nutrition and Food Research, and the Industrial Biotechnology Hub CsynBI. His academic qualifications include a DSc (Eng) from the University of London and a PhD in Electrical Engineering from Imperial College London. He holds professional designations as Chartered Engineer (CEng), Fellow of the Institution of Electrical Engineers (FIEE), and Chartered Scientist (CSci). Kitney's research focuses on synthetic biology, systems biology, and biomedical engineering, with emphasis on mathematical modeling, medical imaging, and engineering biology principles. He pioneered the integration of automation and standardization in biological design, establishing Imperial College as a global leader in synthetic biology through the development of the Imperial College Hub for Synthetic Biology and SynbiCITE, the UK's national industrial translation center. His work bridges fundamental research with industrial applications in biomanufacturing and sustainable solutions. Analysis of his recent publications reveals a decisive shift toward engineering biology as a systematic discipline, characterized by automation-driven workflows, biofoundry infrastructure development, and applications addressing global challenges including pandemic response and sustainable vaccine production. Key trends include standardization of biological parts, computational design tools, and policy frameworks enabling the advanced bioeconomy. His distinguished honors include: Order of the British Empire (OBE) for services to Information Technology in Biomedicine (2001) Fellowship in the World Technology Network (1999) Academician of the International Academy of BioMedical Engineering (2003) Honorary Fellowships of The Royal College of Physicians and Surgeons (2006) Fellowships of The Royal Academy of Engineering and Royal Society of Edinburgh Kitney has secured transformative funding through leadership of national initiatives including the EPSRC National Centre for Synthetic Biology and Innovation and SynbiCITE. He chaired The Royal Academy of Engineering Inquiry into Synthetic Biology and co-authored the UK Roadmap for Synthetic Biology, establishing critical policy frameworks. His grants portfolio emphasizes industrial translation, biofoundry development, and building the UK's engineering biology capability. He directs the Imperial College Synthetic Biology Hub (CsynBI) and The Foundry DNA synthesis facility, which form the core of Imperial's engineering biology infrastructure. These centers operate as integrated platforms connecting academic research with industrial applications through standardized workflows, automated design-build-test cycles, and collaborative projects with pharmaceutical and biotechnology companies.

• Computer Architecture
• Memory Systems
• Reconfigurable Computing
• +6 more

Ronald Dreslinski is a Professor in the Department of Computer Science and Engineering at the University of Michigan, College of Engineering. His work focuses on computer architecture, memory systems, and reconfigurable computing, with applications in machine learning, wireless communication, and autonomous systems. Research spans hybrid memory systems, fault-tolerant networks, and low-power processors Key technologies include PIM (Processing-in-Memory), systolic arrays, and Galois field accelerators His publications highlight trends in adapting reconfigurable architectures for sparse data, optimizing wireless communication decoders, and integrating neural networks with traditional circuit design. He mentors PhD students through weekly 1-on-1 meetings, project-specific sessions, and group presentations. The department emphasizes broadening participation in computing and student support through community initiatives.

Joydeep GhoshView profile

Professor

• Machine Learning
• Data Mining
• Web Mining
• +6 more

Joydeep Ghosh is the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at The University of Texas at Austin, leading the IDEAL Lab and UT-MINDS. He holds courtesy faculty positions in Biomedical Engineering, Population Health, and McCombs School of Business. Dr. Ghosh earned his B.Tech from IIT Kanpur (1983) and PhD from the University of Southern California (1988). His research focuses on machine learning, data/web mining, scalable analytics, and ethical AI, with applications in healthcare and industry. He has published over 500 papers, co-edited 20 books, and secured funding from NSF, Google, IBM, and others. Notable awards include the 2020 ICDM Research Contribution Award and 2015 IEEE Technical Achievement Award. He advises startups like CognitiveScale and teaches advanced courses in machine learning and data science, consistently recognized as 'Best Professor' by students. His lab collaborates with industry via sponsored research, addressing real-world challenges in AI and data analytics. Education: B.Tech, Electrical Engineering, IIT Kanpur (1983) Ph.D., Electrical Engineering, University of Southern California (1988) Research Interests: Predictive and Prescriptive Analytics Trustworthy AI/Explainable Machine Learning Federated Learning & Privacy-Preserving Techniques Health Informatics & Computational Phenotyping Scalable Algorithms for Big Data Grants & Industry Collaboration: Sponsored by NSF, Google, IBM, ONR, and others Co-founder/Advisor to Accordion Health, CognitiveScale, and Neonyoyo IDEAL Lab partnerships with industry for problem-driven research Labs & Teams: IDEAL Lab: Focuses on intelligent data analysis across domains UT-MINDS: Engineering Data Science and Machine Learning initiatives

Liam PaninskiView profile

Professor

• Neuroscience
• Statistics
• Computational Neuroscience
• +3 more

Liam Paninski is a Professor of Statistics and Neuroscience at Columbia University, co-director of the Grossman Center for the Statistics of Mind, and a member of the Center for Theoretical Neuroscience, Kavli Institute for Brain Science, and Doctoral Program in Neurobiology and Behavior. His research focuses on statistical analysis of neural data, computational neuroscience, and developing advanced methods for analyzing large-scale neural recordings. His work spans topics such as spike sorting, calcium imaging analysis, behavioral video analysis, and decoding methods. He has received multiple grants and awards, including NSF/NIH funding, Simons Collaboration grants, and fellowships like the Alfred P. Sloan Research Fellowship. His lab collaborates on projects involving neural data science, aiming to bridge experimental neurotechnology with analytical methods. Paninski teaches advanced courses on statistical analysis of neural data and computational statistics, emphasizing applications in neuroscience. His research integrates statistical theory with practical challenges in neuroscience, addressing questions in neural coding, network connectivity, and high-dimensional data analysis.

Bruno SudretView profile

Professor

• Uncertainty Quantification
• Reliability Analysis
• Sensitivity Analysis
• +3 more

Prof. Bruno Sudret is a Full Professor of Risk, Safety, and Uncertainty Quantification at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. His research focuses on computational methods for uncertainty quantification, reliability analysis, and Bayesian approaches for model calibration. He has contributed to the development of the UQLab software framework and the UQWorld community platform. Before joining ETH Zurich in 2012, Sudret held roles at the University of Berkeley (postdoc), EDF R&D (researcher and group head), and Phimeca Engineering (Director of Research and Strategy). Education: Master's in Science from Ecole Polytechnique (1993), Master's and PhD in Civil Engineering from Ecole Nationale des Ponts et Chaussées (1996, 1999). His work emphasizes stochastic simulators, surrogate modeling, and reliability-based design optimization. He serves on editorial boards for journals like Reliability Engineering and Systems Safety and Structural Safety .

Victor BoussangeView profile

Researcher

• Ecology and evolution
• Scientific machine learning
• Complex dynamical systems
• +3 more

Victor Boussange is a Postdoctoral Researcher at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and the Department of Environmental Systems Science (D-USYS) at ETH Zurich. His research focuses on integrating scientific machine learning with ecological and evolutionary modeling to understand complex systems dynamics. He holds a PhD in Environmental Sciences from ETH Zurich (2022) and an MSc in Energy and Environmental Sciences from INSA Lyon (2018). Primary affiliation: Dynamic Macroecology Group at WSL Secondary affiliation: D-USYS, ETH Zurich Research Interests: Development of hybrid mechanistic-machine learning frameworks to model ecosystem responses to disturbances, ecological connectivity analysis, and eco-evolutionary dynamics. His work emphasizes interpretable models and scalable computational methods. Publications highlight contributions to high-dimensional PDE solvers (HighDimPDE.jl), inverse modeling frameworks (PiecewiseInference.jl), and ecological connectivity prioritization (JAXScape). His work bridges theoretical ecology with computational tools for real-world applications like biodiversity conservation and climate change impact assessment.

Shaoning HanView profile

Assistant Professor

• Mathematical Optimization
• Mixed-Integer Nonlinear Programming
• Nonsmooth Optimization
• +3 more

Shaoning Han is a Presidential Young Professor (Assistant Professor) in the Department of Mathematics and the Institute of Operations Research and Analytics (IORA) at the National University of Singapore (NUS). He previously held a postdoctoral position at the University of Southern California (USC), working under Prof. Jong-Shi Pang. His research focuses on mathematical optimization, including mixed-integer nonlinear programming, nonsmooth optimization, and their applications in operations research and data science. Han earned his Ph.D. in Industrial Engineering from USC under Prof. Andrés Gómez and a B.S. in Mathematics from the University of Science and Technology of China (USTC). His work emphasizes advancing optimization methodologies, particularly for problems involving complementarity constraints, indicator variables, and sparsity. Recent contributions include progressive MIP methods for indefinite QPs, real-time solutions for banded matrix problems, and convexification techniques for nonconvex optimization. Han is actively recruiting PhD students interested in mathematical optimization. Research Highlights: His articles span theoretical advances (e.g., epi-stationarity analysis, parametric variational inequalities) and applied frameworks (e.g., robust SVMs, binarized neural networks). Key themes include polynomial-time solvability of MRFs, convex relaxations for low-rank functions, and sparse signal estimation. Awards: Third Place, INFORMS JFIG Paper Competition (2023) Journal of Global Optimization Best Paper Honorable Mention (2023) Teaching: Courses include MA5243 (Advanced Mathematical Programming) and MA3252 (Linear and Network Optimization). Labs/Teams: Active member of IORA, NUS’s Smart Nation Research Cluster.

Long ChenView profile

Lecturer

• Digital Twin Technology
• Systems Engineering
• Design Change Management
• +2 more

Dr. Long Chen is a Lecturer at the School of Architecture, Building and Civil Engineering, Loughborough University. He holds a PhD and BEng, with professional affiliations including MCIOB and A.M.ASCE. Previously, he was a Postdoctoral Research Associate at Imperial College London's Centre for Systems Engineering and Innovation (CSEI), where he contributed to the CDBB-funded project 'Analysing Systems Interdependencies using a Digital Twin' and the Alan Turing Institute/Lloyd’s Register Foundation's 'Grand Challenge III: Data-Driven Design Under Uncertainty'. His research focuses on digital twin-driven approaches for design change management in complex systems and image-based digital twinning for as-built buildings. Key research areas include Systems Engineering, Data-Centric Engineering, and Building Information Modeling. Dr. Chen's publications primarily explore machine learning fairness, Bayesian computation, and computer vision techniques, with recent works addressing algorithmic bias mitigation, transformer calibration, and semantic segmentation robustness. His research demonstrates consistent applications in probabilistic modeling and adaptive systems. He maintains active collaborations with Imperial College London and the Alan Turing Institute, leveraging his expertise in systems interdependencies and digital twin implementations.

John SabatiniView profile

Research Professor

• Cognition
• Instruction and Learning Sciences
• Reading, Text, and Discourse Research
• +3 more

John Sabatini is a Distinguished Research Professor in the Department of Psychology and an affiliate of the Institute for Intelligent Systems at the University of Memphis. He holds a B.A. from the University of Chicago and a Ph.D. from the University of Delaware. His research focuses on cognition, instruction design, reading comprehension, educational assessment, and adult literacy. He has held significant roles, including Managing Principal Research Scientist at ETS, and has contributed to major initiatives like the NAEP Oral Reading Fluency Study and PIRLS. Sabatini’s work emphasizes scenario-based assessments and the integration of AI in education, with patents on virtual reading companion technology. He serves on advisory boards for organizations like the National Center for Family Learning and chairs conferences on text and discourse. His research addresses challenges in adult literacy, cognitive processing deficits, and the application of technology to enhance learning. Collaborations include projects funded by IES, NIH, and the Department of Education, focusing on interventions like the Strategic Adolescent Reading Intervention. His patents and publications highlight innovations in adaptive assessments and tools for language learners. Sabatini’s lab at the University of Memphis explores 21st-century literacy challenges, emphasizing AI-driven solutions and evidence-based practices.