جست‌وجوی استاد و پژوهشگر

Tiago Bresolinمشاهده پروفایل

استادیار

• Animal Genetics
• Livestock Genomics
• Precision Agriculture
• +۳ مورد دیگر

Tiago Bresolin is an Assistant Professor in Animal Sciences at the University of Illinois at Urbana-Champaign, affiliated with the Center for Digital Agriculture and the National Center for Supercomputing Applications (NCSA). He holds a PhD in Animal Breeding and Genetics. His primary research focuses on integrating genomics, computer vision, and machine learning to advance livestock management and animal health. Research interests include genomic selection for cattle traits, precision phenotyping via computer vision systems, and predictive modeling for disease detection in dairy and beef cattle. Notable projects involve analyzing Nellore cattle genetics for growth and reproductive traits, developing ultrasound-based mammary gland monitoring systems, and deploying cloud computing frameworks for metabolic disorder prediction. His work bridges computational methods with agricultural challenges, emphasizing automated livestock monitoring, non-invasive biomarker detection, and data-driven breeding strategies. Collaborations span international institutions, reflecting his commitment to advancing global livestock science through interdisciplinary innovation.

• Machine Learning
• Healthcare Informatics
• Explainable AI
• +۶ مورد دیگر

Kai-Chih Pai is an Associate Professor at China Medical University's College of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning over 14 years. His work bridges computer science and healthcare, focusing on practical AI applications that address critical medical challenges. Dr. Pai's research interests center around Machine Learning applications in healthcare , Explainable AI systems , Natural Language Processing for Chinese language , and Educational Technology . His work demonstrates a strong commitment to developing AI solutions that are not only technically sophisticated but also interpretable and clinically useful. His research trajectory shows a clear evolution from educational technology applications to increasingly sophisticated healthcare AI systems. His publication record reveals significant contributions to medical decision support systems , particularly in acute kidney injury prediction , pneumonia diagnosis , and mortality prediction in critical care settings. More recently, he has been exploring cutting-edge applications of large language models for industrial knowledge management. His work consistently emphasizes the importance of model interpretability in medical contexts. Federated machine learning approaches for multi-institutional medical research Privacy-preserving healthcare technologies using homomorphic encryption Adaptive learning systems for Chinese language education Predictive analytics for critical care medicine Dr. Pai has established a productive research program with consistent publication output in high-impact venues, demonstrating expertise that spans both theoretical AI development and practical healthcare applications. His collaborative work with medical professionals across Taiwan highlights the interdisciplinary nature of his research and its real-world impact.

• Video Coding
• Energy Efficiency
• HEVC
• +۴ مورد دیگر

Christian Herglotz is a researcher affiliated with the University of Erlangen-Nuremberg , Germany. His work focuses on energy efficiency in video coding and decoding systems, with a particular emphasis on HEVC and VVC standards. He has published extensively in IEEE journals and conferences like ICIP, ICASSP, and QoMEX, often collaborating with André Kaup and Matthias Kränzler. Key research themes: energy-aware video compression, decoding power optimization, rate-energy-distortion modeling. Co-edited special sections on deep learning-based video coding. Recent Publications (2022-2025): Explored power reduction in HDR video encoding, motion prediction for 360-degree video, and heterogeneous quantization for DNN accelerators. His studies integrate machine learning with traditional codec design to improve energy efficiency. Technical Contributions: Developed models for decoding energy estimation, analyzed carbon impact of streaming devices, and proposed methods for viewport-adaptive motion compensation. Collaborative work spans thermal imaging for power analysis and reliability-aware DNN hardware optimization.

• Computer Architecture
• High Performance Computing
• GPU Programming
• +۵ مورد دیگر

Josie Esteban Rodriguez Condia is a Fixed-term Assistant Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is a member of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and serves as an invited member of both the College of Electronic, Telecommunications, and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Her research focuses on computer architecture reliability, particularly in GPU and AI accelerator systems. Key areas include functional testing, general purpose graphics processing units (GPGPUs), hardware accelerators, hardware architecture, and parallel processing. Her work addresses critical challenges in reliability assessment of AI-based automotive systems, self-test libraries for tensor cores, and hardening techniques for neural networks on GPUs. Her recent publications demonstrate a strong trend toward reliability engineering for AI hardware, with particular emphasis on automotive applications and GPU-based neural network implementations. The research spans fault injection methodologies, error modeling, and architectural solutions to enhance system resilience against soft errors and permanent faults. Dr. Rodriguez Condia actively supervises PhD students including Gustavo Vilar De Farias, Giuseppe Esposito, and Robert Alexander Limas Sierra, all working on reliability evaluation and enhancement of neural networks. She is a member of the PNRR Research Group for the National Center for HPC, Big Data and Quantum Computing (2022-2025). She teaches multiple courses including GPU Programming and High Performance Computing for both Computer Engineering and Quantum Engineering programs. Her editorial work includes serving as Guest Editor for APPLIED SCIENCES in 2024.

Gjoreski Hristijanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Healthcare Technology
• +۳ مورد دیگر

Gjoreski Hristijan is an Assistant Professor at the Faculty of Electrical Engineering and Information Technologies (Ss. Cyril and Methodius University in Skopje), with expertise in Artificial Intelligence and Machine Learning. He leads research in healthcare technology, focusing on developing intelligent systems using wearable sensors for activity recognition, fall detection, stress monitoring, and elderly care applications. His work bridges theoretical machine learning with practical healthcare solutions. PhD (2015): Jožef Stefan International Postgraduate School, Slovenia MSc (2011): Jožef Stefan International Postgraduate School, Slovenia BSc (2010): Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Skopje Research interests include: Machine learning for healthcare Wearable sensor systems Activity recognition algorithms Federated learning for privacy-aware applications Smart glasses integration for health monitoring His awards highlight impactful contributions, including first-place wins in international competitions like ChallengeUP (2019) and EvAAL (2013), and the prestigious Best Young Scientist Award (2017) from the President of Macedonia. Recent work focuses on OCOsense smart glasses for facial expression monitoring and real-time health diagnostics. He actively collaborates with institutions like the University of Sussex (UK) and Jožef Stefan Institute (Slovenia), advancing interdisciplinary research in AI and healthcare.

Ricardo da Silva Torresمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Prof. Ricardo da Silva Torres is a Professor in Data Science and Artificial Intelligence at Wageningen University & Research, Netherlands. Previously, he held professorships at NTNU (2019-2024) and the University of Campinas, Brazil (2005-2019). He earned his B.Sc. and Ph.D. in Computer Science from the University of Campinas (2000 and 2004, respectively). His research focuses on eScience projects involving multimedia analysis, machine learning, databases, and visualization. Education: B.Sc. in Computer Engineering, University of Campinas, Brazil (2000) Ph.D. in Computer Science, University of Campinas (2004) Research Interests: Multidisciplinary eScience projects Machine Learning applications in sports science and agriculture Knowledge graphs and digital twins for urban and agricultural planning Computer vision for environmental monitoring and aquaculture Notable Projects: KD-BIO: Large-scale AI infrastructure for green life sciences IUMENTA: Animal digital twin framework NorDark-DT: Urban lighting digital twin He serves as Associate Editor for Pattern Recognition Letters and is an IEEE member. His work bridges computational methods with real-world challenges in agriculture, sports, and urban systems.

Yvan Petillotمشاهده پروفایل

استاد

• Marine Robotics
• Autonomous Systems
• Sensor Processing
• +۴ مورد دیگر

Yvan Petillot is a Professor of Robotics and Autonomous Systems at Heriot-Watt University, co-academic lead of the National Robotarium, and chair of Robotics at the Scottish Research Partnership in Engineering. His expertise spans marine robotics, autonomous systems, sensor fusion, and subsea domain control. He leads a £3M grant portfolio focused on marine robotics innovations, emphasizing multi-vehicle collaboration and AI-driven autonomy. Petillot co-founded SeeByte Ltd to translate academic research into commercial solutions and pioneered EU robotics competitions fostering talent. His research emphasizes underwater robotics, including navigation, perception, and intervention in hazardous environments. Notable achievements include developing advanced underwater imaging techniques, reinforcement learning platforms (e.g., MarineGym), and frameworks for digital twins in teleoperation. He has received the Royal Society Industry Fellowship (2013) and was part of the 2022 Research Team of the Year award. Key technical contributions include synthetic aperture sonar systems, underwater SLAM (Simultaneous Localization and Mapping), and control strategies for vehicle-manipulator systems. His work addresses challenges in autonomous exploration, environmental monitoring, and safe human-robot collaboration in marine and industrial settings.

Ladan Tahvildariمشاهده پروفایل

استاد

• Software Engineering
• Cloud Computing
• Self-Adaptive Systems
• +۵ مورد دیگر

Ladan Tahvildari is a Full-time Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Her research focuses on software engineering, self-adaptive systems, cloud computing, and test case prioritization. She leads the Software Technologies Applied Research Laboratory (STAR Lab), emphasizing hands-on industry collaboration and practical software solutions. Her work spans over two decades, with notable contributions to adaptive software systems, runtime adaptation frameworks (GRAF), and cloud modeling (StratusML/Adoop). She has pioneered techniques for flaky test detection (FlaKat), spatiotemporal auto-scaling (STaleX), and POMDP-based uncertainty management for security systems. Key research areas include: Self-protecting software systems Component-based software evolution Defect detection and prioritization Autonomic computing decision models Cloud infrastructure optimization Her recent work bridges academia and industry through hands-on learning approaches and frameworks like Semeru Cloud Compiler for performance enhancement. She has served as workshop chair for ACSOS 2023 and contributed to IBM tool integration in educational curricula. Her lab focuses on transforming theoretical concepts into practical tools like StarMX for self-managing systems and ReLACK for VoIP steganography. Current efforts emphasize adaptive machine learning frameworks and scalable microservice architectures.

Nawa Raj Pokhrelمشاهده پروفایل

استادیار

• Data Science
• Machine Learning
• Cybersecurity
• +۳ مورد دیگر

Nawa Raj Pokhrel is an Assistant Professor in the Department of Data Science at Xavier University of Louisiana. His research focuses on applying machine learning and deep learning techniques to solve real-world problems in environmental science, healthcare, finance, and cybersecurity. He specializes in predictive modeling for sequential data, vulnerability analysis in software systems, and developing frameworks for data-driven decision-making. His work spans interdisciplinary applications, including air quality prediction in Gulf Coast communities, stock market volatility modeling using ESG indices, and coronary artery disease diagnosis through machine learning. He has also explored the impact of news sentiment on stock prices and developed frameworks like LSTM-SDM and Deep-sdm for sequential data analysis. In cybersecurity, he has contributed predictive models for software vulnerabilities and network security risk assessment. His recent publications emphasize leveraging deep learning for environmental and financial forecasting while maintaining a strong focus on algorithmic innovation and practical implementation. No scientific awards or grants are explicitly listed in the provided information. His advising activities and lab affiliations are not detailed in the available texts.

Andrzej Kordeckiمشاهده پروفایل

استادیار

• Object classification using CNNs
• Autonomous robot navigation
• Image correction methods
• +۳ مورد دیگر

Andrzej Kordecki, Ph.D., is an Assistant Professor at the Warsaw University of Technology's Faculty of Power and Aeronautical Engineering, Department of Theory of Machines and Robots. His research focuses on computer vision applications for robotics, including object classification with convolutional neural networks, autonomous navigation systems using LIDAR and camera data, and advanced image correction techniques. He also specializes in color management systems and calibration of imaging devices. Teaching responsibilities include courses on neural networks, digital image processing, and sensors in robotics. His work has been published in journals like Journal of Intelligent & Robotic Systems and Signal, Image and Video Processing , with conference contributions at venues such as the International Conference on Machine Vision (ICMV). Research highlights include development of the OffRoadSynth dataset for autonomous ground vehicle navigation and pioneering work on vignetting correction in camera systems. His technical expertise spans both theoretical and applied aspects of computer vision and robotics engineering.

• Medical Imaging
• Nuclear Medicine
• Biomedical Engineering
• +۴ مورد دیگر

Tianyi Zeng is an Associate Research Scientist at the Yale PET Center and junior faculty in the Department of Radiology and Biomedical Imaging at Yale School of Medicine. He holds a Ph.D. in Biomedical Engineering from the University of Chinese Academy of Sciences (2021) and a B.E. from Xi'an Jiaotong University (2016). His research focuses on advancing PET imaging technologies, particularly motion correction and reconstruction methods for the NeuroEXPLORER brain PET scanner. Key areas include deep learning applications, MRI compatibility, and improving image quality. Research Interests PET image reconstruction and motion correction Development of high-resolution PET systems (e.g., NeuroEXPLORER, SIAT bPET) Deep learning for medical imaging Multi-modal imaging (PET/MRI integration) Recent Articles Recent work emphasizes motion correction algorithms, NeuroEXPLORER performance evaluation, and semi-supervised learning in medical image segmentation. His studies address challenges in clinical PET accuracy and hardware innovation. Awards 2024 IEEE NSS/MIC/RTSD Trainee Grant 2024 SNMMI Poster Award (Third Place) 2023 Best Paper Award (2nd Place) at Machine Learning in Clinical Neuroimaging workshop Grants & Collaborations Recipient of multiple IEEE and SNMMI grants. Collaborations include United Imaging Healthcare America for multi-tracer PET studies. Labs & Teams Primary affiliation: Yale PET Center. Active in the Department of Radiology and Biomedical Imaging, contributing to translational imaging research.

• Generative Models
• Computer Science
• Machine Learning
• +۳ مورد دیگر

Tuomas Kynkäänniemi is a Doctoral Researcher at the School of Science's Department of Computer Science, affiliated with Professor Jaakko Lehtinen's professorship. His research focuses on generative models, diffusion models, and evaluation metrics for machine learning systems. He has contributed to advancements in image generation, algorithm optimization, and feature space analysis. Education highlights include an MSc Thesis awarded the prestigious 'Best MSc Thesis Award' in 2020. His work bridges theoretical computer science with practical applications in image processing and time-series analysis. Key research areas include disentangling cyclic/random effects in time-lapse sequences, improving diffusion model performance through novel guidance techniques, and evaluating generative models using metrics like Fréchet Inception Distance. Collaborations span academic institutions, with recent work appearing in top conferences like NeurIPS and ICLR. Award-winning contributions include pioneering studies on ImageNet class impacts on evaluation metrics and scalable training methods for StyleGAN2. His research emphasizes both foundational machine learning theory and real-world applicability in domains like planetary science and environmental monitoring.

Zafer Doganمشاهده پروفایل

استادیار

• Machine Learning
• Data Analytics
• Signal Processing
• +۵ مورد دیگر

Zafer Dogan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) research group and serves as Deputy Director of the Koç University İşbank AI (KUIS AI) Research Center. He holds a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and a B.Sc. from Middle East Technical University (METU). Before joining Koç University, he worked as a postdoctoral researcher at Harvard University's John A. Paulson School of Engineering and Applied Sciences (2016–2019). His research bridges theoretical advancements in machine learning, optimization, and signal processing with practical applications in high-dimensional data mining, AI-driven imaging (e.g., biomedical imaging), and inverse problem solutions. Key areas include generative models, image super-resolution, neural network dynamics, and domain-specific interpolation techniques. His work emphasizes trustworthy AI, perceptual-quality image enhancement, and biomedical applications like neuroimaging analysis. Notable contributions include developing the MMSR framework for class-specific image super-resolution, wavelet-domain losses for artifact reduction, and leveraging vision-language models for trustworthy super-resolution. His articles span domains from theoretical ML to applied biomedical imaging solutions. Dr. Dogan’s academic journey includes postdoctoral training at Harvard and a strong focus on interdisciplinary research at the intersection of engineering and AI. He currently leads initiatives at KUIS AI, advancing applied AI research and innovation.

Marlous Hallمشاهده پروفایل

دانشیار

• Epidemiology
• Cardiovascular Disease
• Multimorbidity
• +۶ مورد دیگر

Dr. Marlous Hall is an Associate Professor in Epidemiology at the University of Leeds, affiliated with the Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM). She serves as Deputy Head of the Clinical and Population Sciences Department and co-leads the Leeds Institute for Data Analytics (LIDA) Health Community. Her expertise lies in advanced analytical methods applied to large clinical datasets, focusing on cardiovascular survivorship, multimorbidity, and causal inference. She holds a Sir Henry Wellcome Fellowship and has established the Survivorship and Multimorbidity Epidemiology Research Group. Educations: BSc Mathematics (University of Sheffield), MSc Statistics (University of Sheffield), PhD in Paediatric Cancer Epidemiology (University of Leeds) Professional Roles: Co-Director of the British Heart Foundation PhD Programme, Member of LICAMM Executive Committees, and Fellow of the Higher Education Academy Her research emphasizes multimorbidity patterns in cardiovascular patients, frailty management, and improving guideline adherence. Recent work includes analyzing 1.6 million cardiac surgery patients and 56 million myocardial infarction survivors. She supervises multiple PhD students exploring topics like anticoagulation in atrial fibrillation and blood pressure management in frail populations. Awards: Sir Henry Wellcome Fellowship (2014), University Academic Fellowship (2017) Labs/Teams: Survivorship and Multimorbidity Epidemiology Research Group, Leeds Institute for Data Analytics

Donna Ankerstمشاهده پروفایل

دانشیار

• Biostatistics
• Clinical Risk Prediction
• Prostate Cancer Research
• +۳ مورد دیگر

Professor Donna Ankerst holds an Associate Professorship in Biostatistics at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Mathematics. She also serves as a Research Professor at the University of Texas Health Sciences Center in San Antonio. Her work spans clinical risk prediction (e.g., prostate cancer risk calculators), deep learning for medical imaging (e.g., 3D mammograms), and climate change impacts on Bavarian forests and health. She has pioneered internationally validated tools like the prostate cancer risk calculator and collaborates with TUM's Rechts der Isar Hospital and School of Life Sciences. Education: PhD in Statistics (Carnegie Mellon University), postdoctoral training at Harvard University and the Fred Hutchinson Cancer Research Center. Awards include the 2018 TUM PhD Supervisor Award and the 2015 Prostate Cancer Dream Challenge win. She teaches courses on applied regression, statistical computing, and data analysis. Research focuses on integrating biostatistical methods with clinical and environmental data, including multi-center trials, electronic health records, and climate modeling. Key projects include predictive algorithms for cancer diagnosis and climate-driven health outcomes. Grants managed include collaborations on kidney/pancreas transplantation outcome tools and prostate cancer decision support systems. Labs/Teams: Part of TUM's Data Science initiatives and interdisciplinary groups like the TUM School of Life Sciences. She chairs the Data Safety Board for the WISDOM breast cancer trial and edits Biometrics reviews.