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Yihao LiuView profile

Assistant Professor

• Medical Image Analysis
• Image Registration
• Deep Learning
• +3 more

Yihao Liu serves as Research Assistant Professor in Vanderbilt University's School of Engineering, Department of Electrical and Computer Engineering, focusing on clinically relevant image analysis for personalized treatment strategies and large-scale medical data interpretation. His work bridges engineering and clinical applications through advanced computational methodologies. Education: PhD in Electrical and Computer Engineering, Johns Hopkins University (May 2024) Dr. Liu's research spans medical image analysis, deformable registration, deep learning, and computer vision with applications in CT, MRI, and OCT imaging. He develops AI-driven tools for pulmonary nodule diagnosis, MS lesion analysis, body composition assessment, and dermatological imaging, emphasizing clinical translation and multi-modal data fusion. His 2024-2025 publications reveal concentrated innovation in diffusion models for field-of-view extension, bi-directional lesion synthesis, and unsupervised registration techniques, demonstrating leadership in foundational registration models and longitudinal analysis for precision medicine. Dr. Liu directs the Vanderbilt Lab for Immersive AI Translation (VALIANT) and collaborates with the Medical-image Analysis and Statistical Interpretation Lab (MASI) under VISE Affiliate Bennett Landman, PhD. He holds the Stevenson Chair in Electrical and Computer Engineering and participates in NIH grant writing initiatives through Vanderbilt's medical partnerships.

Yi PanView profile

Professor

• Parallel and Cloud Computing
• Wireless Networks
• Bioinformatics
• +3 more

Dr. Yi Pan is a Distinguished University Professor and Chair of the Department of Computer Science at Georgia State University. He holds a B.Eng. and M.Eng. from Tsinghua University (China) and a Ph.D. from the University of Pittsburgh (USA). His research focuses on parallel computing, wireless networks, bioinformatics, and machine learning, with over 330 publications and 39 authored/edited books. Dr. Pan has organized international conferences and delivered over 40 keynote speeches globally. Education: B.Eng., Computer Engineering, Tsinghua University, 1982 M.Eng., Computer Engineering, Tsinghua University, 1984 Ph.D., Computer Science, University of Pittsburgh, 1991 Research Interests: Dr. Pan’s work spans parallel and cloud computing, wireless networks, and bioinformatics. His contributions include scalable algorithms for functional module discovery in protein networks, self-defense mechanisms for cyber threats, and efficient routing protocols for sensor networks. Recent efforts integrate AI into healthcare, such as lesion localization in OCT imaging and autism spectrum disorder diagnosis via EEG analysis. Awards & Recognition: IEEE Distinguished Speaker (2000–2002) Yamacraw Distinguished Speaker (2002) Listed in Who’s Who in America and Who’s Who in Computational Science Awards from NSF, AFOSR, IBM, and Mellon Foundation Advising & Leadership: As department chair, Dr. Pan oversees interdisciplinary collaborations between Computer Science and Computer Information Systems. He has advised numerous students and holds editorial roles in 15 journals, including several IEEE Transactions. His grants focus on advancing AI-driven healthcare, cybersecurity, and computational biology. Labs & Teams: Leads research initiatives in bioinformatics, medical AI, and parallel computing, collaborating with industry partners like IBM and Shell Oil. Active in organizing conferences such as the IEEE International Conference on Bioinformatics and Biomedicine.

• Machine Learning
• Computer Vision
• Signal Processing
• +6 more

Giacomo Boracchi is a researcher at the Polytechnic University of Milan , focusing on machine learning, computer vision, and signal processing. His work spans anomaly detection, change detection in data streams, 3D imaging, and biomedical applications. Key Collaborations : Diego Carrera, Luca Magri, Cesare Alippi Industries : Embedded systems, medical imaging, environmental monitoring Recent research explores adaptive Kalman filtering for battery estimation, zero-shot anomaly detection, and explainable AI for vision-language models. Publications highlight applications in waste sorting, histological data generation, and cardiac monitoring. His technical focus includes convolutional networks, ensemble learning, and domain adaptation. Boracchi's work bridges theoretical advancements with real-world systems in fraud detection, semiconductor manufacturing, and wearable health devices.

Shahram PayandehView profile

Professor

• Robotics
• Geometrical Robotics
• Networked Dynamical Systems
• +4 more

Shahram Payandeh is a Professor of Engineering Science at Simon Fraser University (SFU), affiliated with the School of Engineering Science within the Faculty of Applied Sciences. He holds a Ph.D. from the University of Toronto (1990), M.A.Sc. (1986), and B.Sc. (1981). His research focuses on robotics, geometrical robotics, networked dynamical systems, and medical robotics, with emphasis on haptic devices, multi-view tracking, and multi-modal interaction in serious games. Education: Ph.D., University of Toronto, Canada, 1990 M.A.Sc., University of Toronto, Canada, 1986 B.Sc., University of Akron, United States, 1981 Research Interests: Robotics and medical robotics applications Geometrical robotics and sensor networks Multi-view tracking and haptic rendering Networked dynamical systems and decision support His work spans visual tracking algorithms, sensor calibration, and teleoperation systems, with practical applications in healthcare and elderly care technology. Teaching: ENSC 482: Introduction to Decision Making in Engineering ENSC 890: Advanced Robotics: Mechanics and Control Labs & Facilities: Associated with the Engineering Science Research Lab (erl.ensc.sfu.ca) Office: ASB 10829

• Monte Carlo Methods
• Parallel Computing
• Statistical Analysis
• +3 more

Kari Heine is a Lecturer in the Department of Mathematical Sciences at the University of Bath. Their research focuses on computational methods, parallel algorithms, and statistical analysis, contributing to the UN Sustainable Development Goals. Key affiliations include the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) and the Centre for Mathematics and Algorithms for Data (MAD). Research interests include Monte Carlo methods, parallel computing, and algorithm development for data science applications. Recent work involves particle filtering techniques, multi-modal likelihood inference, and environmental noise analysis using machine learning. Heine leads the active project 'On-line drill system parameter estimation and hazardous event detection' funded by UK industry. Their research outputs span statistical computing, computational biology, and environmental signal processing.

• Statistical Learning
• Molecular Medicine
• Bioinformatics
• +3 more

Zhi Zhao is a Researcher at the University of Oslo's Faculty of Medicine, affiliated with the department of Statistical learning in molecular medicine. His work focuses on statistical and computational methods applied to molecular medicine, including omics data analysis, pharmacogenomics, and cancer genomics. Research Interests Statistical learning for multi-omics data integration Bayesian variable selection in drug sensitivity studies Biomarker discovery for immune-related diseases Survival analysis modeling in clinical applications Key Contributions Developed R packages like BayesSUR for multivariate Bayesian regression and EnrichIntersect for set enrichment analysis. His recent work includes studies on immune checkpoint inhibitor toxicity biomarkers and pan-cancer drug response prediction. Awards No scientific awards explicitly mentioned in the provided text. Labs/Teams Core member of the Statistical learning in molecular medicine research group.

• Bioinformatics
• Health Informatics
• Affective Computing
• +3 more

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, and GPU computing. He holds a BSc, MSc, and PhD in Computer Science from the University of Athens and the Athens University of Economics and Business. Before Durham, he was a Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016–2020). His work spans biometric identification via EEG signals, medical image analysis, and deep learning applications in healthcare. Education: BSc (Hons.) in Informatics and Telecommunications from National and Kapodistrian University of Athens MSc in Computer Science from Athens University of Economics and Business PhD in Computer Science from National and Kapodistrian University of Athens Research interests include bioinformatics (e.g., microarray analysis), health informatics (e.g., ECG-based emotion modeling), and GPU computing for real-time applications. He has pioneered studies on EEG-based biometrics and affect recognition in human-horse interaction. Recent articles explore chest X-ray classification using deep learning , AI-generated content detection , and trajectory prediction with graph networks . His research emphasizes interdisciplinary applications of machine learning in healthcare and education. He advises six postgraduate students and collaborates on UK parliamentary AI governance reports. Notable contributions include the BED EEG dataset and the DREAMER emotion recognition database.

• Machine Learning
• Graph Neural Networks
• Algorithmic Reasoning
• +3 more

Dobrik Georgiev is a Lecturer in the Department of Computer Science and Technology within the School of Technology at the University of Cambridge. His research centers on bridging algorithmic reasoning with neural architectures, focusing on how neural networks can execute and generalize algorithmic processes. His primary research interests include: Neural algorithmic reasoning and its applications to combinatorial problems Graph neural networks and hypergraph learning systems Explainable AI through concept-based interpretability Deep equilibrium models for algorithmic execution Biological data analysis using neural architectures Georgiev's publication record demonstrates consistent innovation in neural execution models, with recent work exploring bottlenecks in algorithmic reasoning (2025), multi-solution reasoning frameworks (2024), and generalization beyond synthetic graph models (2023). His research shows strong interdisciplinary connections between theoretical computer science, machine learning, and computational biology. While no formal awards are documented in available sources, his work has established significant contributions to neural algorithmic reasoning frameworks. Georgiev maintains active research collaborations through the Department of Computer Science and Technology's initiatives, particularly in the areas of machine learning and neural architectures. His technical leadership is evident in software contributions like the LENs library for logic-explained networks.

• Information Theory
• Optimization
• Wireless Communications
• +1 more

Dr. Teng-Hui Huang is a Postdoctoral Research Associate at the School of Electrical and Information Engineering , University of Sydney. His academic journey includes a Ph.D. in Electrical and Computer Engineering from Purdue University (2022), and prior degrees from National Taiwan University (B.S. 2015, M.S. 2017). Research Interests: Information Theory, Optimization, Wireless Communications, and Machine Learning. Current Projects: Information-Theoretic Multi-Modal Machine Learning, Machine Learning for Next-Generation Wireless Communications, Non-convex Optimization Algorithms for Information Theoretic Privacy. Publications highlight his work on information bottleneck methods, optimization algorithms, and wireless communication systems. He advises research student Abd Al-rahman ODEH on unsupervised multimodal learning.

• Machine Learning
• Computer Vision
• Medical Imaging Analysis
• +5 more

Almabrok Essa is an Assistant Professor in the Department of Mathematics, Computer Science, and Data Science at John Carroll University. His research focuses on advanced machine learning techniques applied to medical imaging, computer vision, and signal processing. He holds a faculty position at Dolan Science Center E207 and can be reached via email at aessa@jcu.edu. His work bridges theoretical computer science with practical applications in healthcare, environmental monitoring, and security systems. Research interests include deep learning architectures for medical diagnostics (e.g., chest X-ray analysis and skin lesion segmentation), steganography for secure image transmission, and remote sensing technologies for geospatial analysis. He has pioneered methods like Multibit LSB matching for data hiding and developed frameworks like COVID-CLNet for pandemic response. His contributions span over 20 peer-reviewed articles from 2015 to 2025, showcasing innovations in face recognition algorithms, brain signal analysis, and environmental pattern recognition. Notable applied projects include real-time heart/respiration monitoring systems and automated machinery threat detection for infrastructure protection. His research demonstrates strong interdisciplinary potential, combining computational methods with domains like biomedical engineering, environmental science, and cybersecurity.

Lichao SunView profile

Assistant Professor

• Artificial Intelligence Security
• AI Privacy
• Machine Learning
• +7 more

Lichao Sun is an Assistant Professor of Computer Science and Engineering at Lehigh University. He holds a Ph.D. in Computer Science from the University of Illinois at Chicago (2020), and bachelor's and master's degrees in Computer Science and Engineering from the University of Nebraska-Lincoln. His research focuses on AI security and privacy, addressing vulnerabilities in AI systems, particularly in large language models and multimodal systems. He also explores data mining, federated learning, and reinforcement learning applications. His educational background includes: Ph.D., Computer Science, University of Illinois at Chicago (2020) M.S., Computer Science, University of Nebraska-Lincoln B.A., Computer Science and Mathematics, University of Nebraska-Lincoln Research interests emphasize mitigating security threats in AI, including adversarial attacks (backdoors, token-level vulnerabilities), robust watermarking, and privacy-preserving techniques in federated learning. His work integrates vision-language models, multimodal reasoning, and ethical AI safety. Recent studies highlight innovations in secure embedding aggregation, generative AI defenses, and strategic reasoning in LLMs. Publications (2025) reflect a focus on AI security, multimodal systems, and reinforcement learning applications, with contributions to conferences like CCS, NeurIPS, and AAAI. His research bridges theoretical advancements with practical cybersecurity solutions for modern AI architectures. No scientific awards explicitly mentioned in the provided text. His advising and grants details are not detailed here, though his prolific publication record suggests active research funding. No lab/teams explicitly referenced in the input data.

Laura FerrariView profile

Assistant Professor

• Multimodal Machine Learning
• Deep Learning on Physiological Signals
• Epidermal Electronics
• +3 more

Laura Ferrari is an Assistant Professor at Scuola Superiore Sant'Anna, affiliated with the LAMPSE group. Her research focuses on translating innovative technologies into practical applications, particularly in neuroscience and wearable devices. She specializes in developing seamless tattoo-based wearables for biomonitoring and advancing multimodal emotion recognition through collaborations with INRIA (France). Key projects include stress identification systems for autism support and wearable devices analyzing physiological signals combined with video data. Her work bridges data science, materials science, and engineering to enhance quality of life through technology. Research interests span multimodal machine learning, deep learning on physiological signals, epidermal electronics, and tattoo technology. Notable collaborations involve INRIA for emotion analysis and interdisciplinary projects combining wearable sensors with clinical applications. Her contributions address challenges in autism care, stress management, and biomarker detection through novel device designs and algorithm development. Publications emphasize innovations in wearable sensor fabrication, material science for biocompatibility, and multimodal datasets for stress and emotion recognition. These works highlight advancements in both theoretical frameworks (e.g., EEG analysis) and applied technologies like inkjet-printed conductive polymers.

Charlotte SenkpielView profile

Researcher

• Energy System Modelling
• Social Science Integration in Energy Modelling
• Technology Diffusion Modelling
• +3 more

Dr. Charlotte Senkpiel is a Senior Scientist in Energy System Analysis at the Fraunhofer Institute for Solar Energy Systems (ISE), Division Power Solutions. Since 2023, she holds dual roles as Deputy Secretary General and Project Lead at Germany's Expert Council on Climate Change (ERK). Her career includes membership in the German Council of Experts on Climate Change (ERK) since 2019, and prior roles as a PhD candidate and researcher at Fraunhofer ISE (2012–2023). She earned her PhD from the University of Freiburg in 2023, focusing on integrating social science with energy system modeling. Research & Projects: Her work centers on energy system modeling, technology diffusion, and policy analysis. Major projects include SOUVERÄN (sustainable supply chains), AutGrid (grid autarky risks/benefits), and Sozio-E2S (socio-cultural influences on energy transitions). Her expertise spans renewable energy integration, decarbonization pathways, and socio-technical systems analysis. Publications: Over 40 peer-reviewed articles and reports since 2013, focusing on energy policy scenarios, decarbonization strategies, and agent-based modeling of technology adoption. Recent work addresses Germany’s 1.5°C carbon budget alignment and industrial electrification pathways. Labs/Teams: Leads energy transition modeling teams at Fraunhofer ISE, collaborating on cross-sectoral decarbonization projects. Active in developing open-source tools for energy system analysis and policy impact assessment.

Yangming LiView profile

Research Fellow

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +7 more

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Ali AbdollahzadehView profile

Research Fellow

• Medical Image Analysis
• Biophysics
• Diffusion MRI
• +6 more

Ali Abdollahzadeh is an Academy Research Fellow at the A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, within the Faculty of Health Sciences. His research focuses on medical image analysis and biophysics, particularly in neuroimaging techniques like diffusion MRI and electron microscopy. He develops advanced computational tools for analyzing white matter ultrastructures and neurovascular interactions. Key projects include studying the role of meningeal lymphatics in neuroinflammation and migraine mechanisms, as well as creating open-source software like SproutAngio and gACSON for bioimage informatics. His work bridges biophysics, computational neuroscience, and clinical applications. Recent studies explore axonal remodeling under stress, neurovascular dynamics, and deep learning applications in neurological disease analysis. He collaborates with the Computational Neuroanatomy Lab and Multiscale Imaging Group, advancing interdisciplinary research in brain imaging and neurodegenerative disorders. Publications highlight innovations in segmentation algorithms, 3D imaging techniques, and translational studies linking microscopic findings to clinical outcomes. His research aims to enhance understanding of CNS structure-function relationships and improve diagnostic tools through advanced imaging methodologies.