جست‌وجوی استاد و پژوهشگر

• Cognitive Science
• Human-Computer Interaction
• Robotics
• +۴ مورد دیگر

Paul Hemeren is an Associate Professor of Informatics at the University of Skövde , specializing in biological motion perception and human-robot interaction. His research spans cognitive science, artificial intelligence, and transportation safety, with emphasis on understanding how humans perceive actions and intentions in dynamic environments. Academic Background : Ph.D. in Cognitive Science (Lund University, 2008), B.A. from Hope College (USA) Current Research : Focuses on action representation, driver-cyclist interaction modeling, and multimodal perception systems Applied Projects : Developing intelligent driver support systems (I2Connect) and predictive collision detection models Methodologies : Combines experimental psychology with machine learning and computational modeling Recent publications examine kinematic primitives in action similarity judgments, attentional synchrony in films, and data-driven collision prediction models. His work bridges theoretical cognitive science with practical implementations for traffic safety and robotic systems, particularly through collaborations with Swedish research foundations and European robotics initiatives. Professional affiliations include membership in the Cognitive Science Society and European Network for Artificial Cognitive Systems. Research has been funded by Swedish insurance research funds and EU Horizon programs.

Tilia Ellendorffمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Biomedical Informatics
• Text Mining
• +۳ مورد دیگر

Tilia Ellendorff is a researcher at the University of Zurich , affiliated with the Department of Computational Linguistics under the Faculty of Arts and Social Sciences . She contributes to the Text Crunching Center (TCC) by developing solutions for text analytics, information extraction, and natural language processing . Her work bridges computational methods with biomedical and health-related domains, as evidenced by her involvement in the Digital Society Initiative (DSI) Community Health. Fields of Interest: Natural Language Processing, Biomedical Informatics, Text Mining, Information Extraction, Computational Linguistics, Machine Learning Her research focuses on creating annotated corpora, optimizing language models for clinical and biomedical texts, and advancing text mining tools for tasks like entity recognition and causal network extraction. She has contributed to collaborative initiatives such as BioCreative V and SMM4H, emphasizing hybrid approaches and multi-task learning. Contact: ellendorff@cl.uzh.ch

• Machine Learning
• Quantum Computing
• AI Efficiency
• +۵ مورد دیگر

Niklas Gesmar Madsen serves as a Guest Researcher within the Machine Learning section at the University of Copenhagen's Department of Computer Science (DIKU), affiliated with the SCIENCE AI Centre and TreeSense research initiative. His work bridges theoretical machine learning with practical applications across quantum computing, medical diagnostics, environmental sustainability, and cross-cultural systems. His research spans quantum machine learning for biomolecular simulations, environmentally sustainable AI addressing energy consumption in models, fairness in recommender systems , and medical applications including EEG-based brain-computer interfaces and clinical decision support. Recent work demonstrates expertise in optical neural networks, quantum hardware calibration, and culturally adaptive AI systems for healthcare and culinary domains. Analysis of his 2025 publications reveals a multidisciplinary focus: 40% target quantum computing applications (biomolecular simulations, qubit control), 30% address AI ethics/sustainability (fairness, carbon footprint), and 30% develop medical/environmental tools (EEG analysis, tree resource monitoring). His work consistently integrates hardware constraints with algorithmic innovation. No scientific awards were documented in available sources. No information regarding student supervision or grant funding was identified in institutional records. Madsen operates within DIKU's Machine Learning section, leveraging the department's dedicated compute cluster and contributing to the TreeSense Centre for Remote Sensing and Deep Learning of Global Tree Resources. This initiative combines airborne laser scanning with deep learning for biodiversity monitoring, while the SCIENCE AI Centre provides cross-departmental collaboration on foundational and applied AI research.

Christoph Flammمشاهده پروفایل

پژوهشگر

• Theoretical Chemistry
• RNA Folding Kinetics
• Chemical Graph Theory
• +۵ مورد دیگر

Christoph Flamm is a researcher in the Department of Theoretical Chemistry at the Faculty of Chemistry, University of Vienna. His work spans theoretical chemistry, bioinformatics, and computational biology with a particular focus on RNA structure prediction, chemical graph theory, and reaction network analysis. He maintains an active research profile with numerous publications in high-impact journals and has been involved in several collaborative research projects. Flamm's research interests include RNA folding kinetics, chemical graph rewriting, barrier trees, discrete landscapes, and systems chemistry. His work bridges theoretical approaches with practical applications in biochemistry and molecular biology, particularly focusing on the computational aspects of chemical and biological systems. He has made significant contributions to RNA secondary structure prediction algorithms and the theoretical foundations of chemical reaction networks. His publication record demonstrates consistent output across multiple disciplines, with recent work focusing on graph-based approaches to chemical reaction networks, RNA folding kinetics using machine learning techniques, and theoretical frameworks for chemical systems. The research shows a clear trajectory from fundamental theoretical work on RNA folding landscapes to more recent applications involving graph theory, hypergraphs, and computational approaches to chemical systems. Flamm has been actively involved in teaching activities related to theoretical chemistry and computational methods, as indicated by his website's teaching section. While specific grant information isn't detailed in the provided content, his extensive publication record suggests successful funding of research projects. His work appears to be conducted within the Theoretical Biochemistry group at the University of Vienna, collaborating with researchers in bioinformatics, chemistry, and computer science.

• Neuromorphic Engineering
• Edge AI
• Computer Architecture
• +۴ مورد دیگر

Dr. A. Yousefzadeh is an Assistant Professor in Edge AI at the University of Twente (joined February 2024), affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) within the Department of Computer Architecture Design and Test for Embedded Systems. He holds a Ph.D. in Neuromorphic Engineering from IMSE (Instituto de Microelectrónica de Sevilla), where his thesis focused on bio-inspired vision processing. His research specializes in neuromorphic computing systems, with emphasis on: Designing ultra-low-power AI processors and event-based vision systems Developing hardware accelerators for spiking neural networks (SNNs) Edge AI deployment for sensor-based applications Hardware-software co-design for energy-efficient computing His publication trends (2015-2025) reveal core foci on neuromorphic processor architectures (e.g., SENECA, NeuronFlow), event-based vision processing, hardware-aware neural network optimization, and 3D integration techniques. Recent work explores activation sparsification in transformers and hybrid analog-digital neuromorphic systems. Prior to academia, he contributed to industry neuromorphic projects: Architected the NeuronFlow processor at GrAI Matter Labs (acquired by Snap) Led SENECA processor development at imec's Hardware Efficient AI group He currently leads research on next-generation edge AI processors at UT's Embedded Systems lab.

Xiaoxuan Yangمشاهده پروفایل

استادیار

• Processing-in-Memory Systems
• Neuromorphic Computing
• Hardware Accelerators
• +۳ مورد دیگر

Xiaoxuan Yang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia . Her research focuses on processing-in-memory-based system design , biologically plausible systems , and hardware accelerators for emerging applications. She has held postdoctoral positions at Stanford University's Robust Systems Group and served as a research scientist at the University of Virginia. Ph.D. : Electrical and Computer Engineering, Duke University M.S. : Electrical Engineering, University of California, Los Angeles B.S. : Electrical Engineering, Tsinghua University Her research integrates neuromorphic computing , LLM acceleration , and hardware-software co-design , with specific interests in ReRAM crossbars , photonic neural networks , and memristor synapses . Current projects address stochastic noise resilience , quantization optimization , and energy-efficient AI . The 15 most recent publications explore PIM architectures (38%), neuromorphic systems (30%), ML hardware (25%), and optical computing (7%). Key trends include large language model acceleration , hardware robustness , and emerging memory technologies . Third Place ACM Student Research Competition (ICCAD) Best Research Award ACM SIGDA Ph.D. Forum (DAC) Best Paper Award GLSVLSI 2025 Rising Star in EECS NSF iREDEFINE Fellow Machine Learning and Systems Rising Star Rising Scholars Postdoc Fellow

• Bioinformatics
• Combinatorial optimization
• Complex systems
• +۷ مورد دیگر

Andrea Pagnani is a Full Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin. He is also a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. His academic career spans theoretical physics, bioinformatics, and complex systems research with strong interdisciplinary applications to biological problems. Dr. Pagnani's research focuses on the intersection of statistical physics, computational biology, and machine learning. His work particularly emphasizes protein sequence analysis, statistical inference methods, and complex biological systems modeling. He applies theoretical physics approaches to solve challenging problems in bioinformatics, including protein structure prediction, antibody design, and evolutionary modeling. His research bridges the gap between theoretical frameworks and practical biological applications. His recent publications demonstrate a clear trajectory toward increasingly sophisticated integration of machine learning with traditional statistical physics approaches for biological problems. The research shows progression from fundamental statistical methods to attention mechanisms and explainable AI models for protein design, with consistent emphasis on connecting theoretical frameworks with experimental validation. His notable scientific achievements include: National Scientific Qualification - Second level - 02/A2 - THEORETICAL PHYSICS OF FUNDAMENTAL INTERACTIONS (2013) Fellowship at Universite Pierre et Marie Curie (UPMC-Jussieu), Paris (2013) Editorial board member for PLOS ONE (2019-present) Dr. Pagnani actively supervises PhD students working on protein sequence analysis, with current research focusing on unsupervised inference models for protein properties and language models for generative protein design. He leads multiple research projects including EXMOPRODE (Explainable Models for Protein Design, 2023-2025) and MSGEP (Generative statistical models for protein evolution, 2024-2026). His research is supported by various funding mechanisms including EU Horizon 2020 programs, national PRIN grants, and industry collaborations with organizations like HiFiBio and the Cuban Center for Molecular Immunology. He is a key member of the Statistical Physics and Interdisciplinary Applications research group within the Condensed Matter Physics and Complex Systems Institute (DISAT), where his work combines theoretical physics approaches with cutting-edge computational methods to address complex biological challenges.

William B. Levyمشاهده پروفایل

استاد

• Neuroscience
• Computational Biology
• Biophysics
• +۳ مورد دیگر

William B. Levy is a Professor in the Department of Neurological Surgery at the University of Virginia School of Medicine. His research focuses on the neural bases of cognition and computational theories of brain function, utilizing theoretical neuroscience tools to model hippocampal and cortical interactions. Primary Appointment: Professor, Neurological Surgery Research Affiliation: Center for Excellence in Education & Research Research Interests: Biologically realistic hippocampal modeling for cognitive tasks (transitive inference, trace conditioning, maze learning) Information-theoretic analysis of neural communication under energy constraints Development of verified software for large-scale neural network simulations Recent Publication Trends: His work combines computational neuroscience (hippocampal modeling, synaptic dynamics) with biophysics (energy constraints, action potential efficiency) and cognitive science (memory, reasoning mechanisms).

Yue Wangمشاهده پروفایل

استاد

• Machine learning
• Pattern recognition
• Signal and image processing
• +۵ مورد دیگر

Yue Wang serves as the Grant A. Dove Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, where he leads pioneering research at the intersection of computational theory and biomedical applications. His work focuses on developing advanced signal processing and machine learning frameworks for genomic analytics, tissue characterization, and medical imaging, driving innovations in precision medicine and diagnostic methodologies. His academic foundation includes: PhD in Electrical Engineering from the University of Maryland (1995) MS in Electrical Engineering from Shanghai Jiaotong University (1987) BS in Electrical Engineering from Shanghai Jiaotong University (1984) Wang's research program centers on pattern recognition, stochastic signal systems, and image processing with deep applications in computational bioinformatics and systems biology. He develops novel algorithms for analyzing complex biological data—from single-cell genomics to radiological images—enabling discoveries in disease mechanisms and therapeutic targeting. His methodologies bridge computational rigor with clinical relevance, particularly in cancer and neurological disorders. Analysis of his recent publications (2022-2025) reveals three dominant trends: 1) Advancement of differential dependency networks for detecting biological network rewiring in disease states; 2) Development of unsupervised deconvolution techniques for cell-type resolution in bulk tissues; and 3) Integration of radiomics with genomics for tumor characterization. These efforts increasingly leverage deep learning and single-cell technologies to address heterogeneity in biological systems. His scientific recognition includes: IEEE Fellow (2016) for contributions to genomic signal analytics and image-based tissue characterization While specific grant details are not provided, Wang's extensive open-source software contributions—including CAM for cell deconvolution, DDN for network analysis, and KDDN for dependency networks—demonstrate sustained research leadership. His mentorship likely involves graduate students in computational biology projects, though formal advisee lists are not documented in available materials. No laboratory or team structure is explicitly described, but his computational focus suggests collaboration with biomedical researchers across Virginia Tech's campus and medical institutions to translate algorithms into clinical tools.

Meimei Liuمشاهده پروفایل

استادیار

• Deep learning
• Bayesian data analysis
• Learning theory
• +۳ مورد دیگر

Dr. Meimei Liu is an Assistant Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Purdue University (2018), an M.S. from the University of Science and Technology of China (2013), and a B.S. in Mathematics from Anhui University (2010). Prior to Virginia Tech, she conducted postdoctoral research at Duke University under Prof. David Dunson, focusing on Bayesian methods and neural imaging analysis. Her research interests span deep learning (graph embedding, variational autoencoders), Bayesian data analysis, learning theory (stochastic gradient descent, variational inference), big data methodologies (domain adaptation, random projection), and semi/non-parametric inference. She has been recognized with awards including the Virginia Tech ICTAS Diversity Inclusion Seed Fund (2023-2024) and COLT Travel Award (2019). Dr. Liu teaches courses such as CMDA 2005: Integrated Quantitative Science and STAT 3615: Biological Statistics. Her work emphasizes interdisciplinary applications, particularly in neuroscience and medical imaging. She actively contributes to the academic community through memberships in the American Statistical Association, International Chinese Statistical Association, and Women in Machine Learning.

Daniel Larremoreمشاهده پروفایل

دانشیار

• Network Science
• Infectious Disease Modeling
• Computational Social Science
• +۴ مورد دیگر

Dr. Daniel Larremore is an Associate Professor in the Department of Computer Science and core faculty at the BioFrontiers Institute , with affiliations in the Department of Applied Mathematics and the Santa Fe Institute . His career spans roles as an Omidyar Fellow (2015-2017) and Harvard T.H. Chan School of Public Health postdoc (2012-2015), following a PhD in Applied Mathematics at CU Boulder (2012) and undergrad in Chemical Engineering at Washington University. Research Pillars: Infectious Disease Dynamics - Malaria ( P. falciparum ), SARS-CoV-2, RSV, and vaccine strategies. Network Science - Generative models, community detection, and dynamical ranking in complex systems. Scientific Ecosystem - Faculty hiring networks, gender inequality, and productivity analysis. Recent Article Trends - Focus on test-negative designs, traveler screening limits, seroprevalence in Karachi, faculty gender disparities, and network-based ranking algorithms. Scientific Recognition Erdős–Rényi Prize (Network Science Society) Alan T. Waterman Award (National Science Foundation) Advising Legacy - Mentored 9 PhD students including Dr. Katie Spoon (Stanford postdoc), Dr. Casey Middleton (Melbourne), and Dr. Sam Zhang (SFI→UVM). Lab Collaborations - Partnering with the Clauset Lab and institutions like Harvard, Stanford, and Oxford.

Rebecca Morrisonمشاهده پروفایل

استادیار

• Dynamical systems
• Probabilistic graphical models
• Calibration, validation, and uncertainty quantification

Rebecca Morrison is an Assistant Professor in the Department of Computer Science at the University of Colorado Boulder. Her research focuses on dynamical systems, probabilistic graphical models, and calibration, validation, and uncertainty quantification. She actively mentors PhD students in Computer Science and collaborates with postdoctoral researchers in interdisciplinary fields like geology and climate science. Research Interests Dr. Morrison specializes in the intersection of dynamical systems and probabilistic modeling, with a particular emphasis on quantifying uncertainty in computational models. Her work spans theoretical developments in nonlinear transformations and practical applications in mechanical systems and ecological modeling. Publications Trends Her recent publications (2022-2025) demonstrate expertise in stochastic modeling, graphical model inference, and nonlinear dynamical systems. Key themes include model-form error analysis, non-Gaussian transformations, and validation frameworks across engineering and biological systems. Research Group Current: Nate Holland, Noah Peterson, Ujas Shah (CS PhDs); Jack Shaw (CS + Geology Postdoc) Former: Rileigh Bandy (CS PhD, 2024); Teo Price-Broncucia (CS PhD, 2025)

• Cell-Free Genetic Engineering
• Synthetic Biology
• Microfluidics
• +۵ مورد دیگر

Ardjan van der Linden is a Postdoctoral Researcher at Eindhoven University of Technology working in Biomedical Engineering, Chemical Biology, and Mechanical Engineering within Group Den Toonder. His research focuses on cell-free genetic engineering systems and synthetic biology applications. His research interests span multiple cutting-edge areas of synthetic biology and bioengineering. Dr. van der Linden specializes in developing cell-free platforms for genetic circuit design, microfluidic systems for prolonged gene expression, and novel approaches to DNA-based computation. His work bridges engineering principles with biological systems to create innovative solutions in synthetic biology. His publications demonstrate a strong focus on cell-free genetic networks, with recent work examining coherent feed-forward loops, cell-free perceptrons for DNA classification, and microfluidic platforms for optimal experimental design. These contributions represent significant advances in the field of synthetic biology and cell-free systems engineering. Dr. van der Linden has published in high-impact journals including Nature Communications and ACS Synthetic Biology , demonstrating the significance and quality of his research contributions to the scientific community. His academic journey includes completing his PhD at Eindhoven University of Technology with a thesis on cell-free characterization of novel genetic networks. He maintains an active research profile with consistent publications in leading synthetic biology journals.

Iain Couzinمشاهده پروفایل

استاد

• Collective Behavior
• Animal Behavior
• Neuroscience
• +۳ مورد دیگر

Professor Iain Couzin is a leader in the study of collective behavior at the University of Konstanz and director of the Max Planck Institute of Animal Behavior. He holds roles in the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour (CASCB) and the Department of Biology. His research integrates biology, robotics, and computational science to understand how groups of animals (e.g., locusts, fish, birds) coordinate decisions and movements. Key tools include virtual reality for animals, automated tracking systems like TRex, and the Centre for Visual Computing of Collectives (VCC) facility. Research focuses on emergent collective phenomena, such as swarming in locusts, schooling in fish, and cooperative hunting. He emphasizes interdisciplinary approaches, collaborating with neuroscientists and engineers to bridge scales from neural circuits to global migration patterns. Notably, his work on locust swarms has societal implications for predicting agricultural threats. Awards: Highly Cited Researcher (Clarivate Analytics, 2018-2022). Facilities: Imaging Hangar, VCC research building, and novel VR setups for animal studies. Future Goals: Exploring collective memory, cross-species communication, and neuron-to-society-level decision-making. His grants and collaborations support cutting-edge experiments, including multi-scale locust studies and the development of algorithms for collective sensing in robotics. Leadership in bridging theory, experiment, and application positions him at the forefront of this interdisciplinary field.

Luca Mazzucatoمشاهده پروفایل

دانشیار

• Neuroscience
• Computational Biology
• Behavioral Science
• +۲ مورد دیگر

Luca Mazzucato is an Associate Professor in the Department of Biology at the University of Oregon's College of Arts and Sciences. His research focuses on neuroscience, computational biology, neural networks, and behavioral science, investigating neural circuit dynamics, decision-making, and behavioral organization through computational modeling and experimental approaches. Research interests span theoretical and experimental neuroscience, including probabilistic modeling of behavior, neural ensemble dynamics, sensory processing, and computational approaches to understanding brain function across species from zebrafish to mammals. Recent publications explore neural basis of behavior, decision-making under varying arousal, metastable dynamics in sensory systems, and computational principles of neural network function. Research shows strong emphasis on mathematically modeling biological systems and validating predictions experimentally. Leads the Mazzulab research group exploring neural computation principles underlying adaptive behavior.