جست‌وجوی استاد و پژوهشگر

Donald C Beitzمشاهده پروفایل

استاد

• Animal Nutrition
• Dairy Science
• Veterinary Medicine
• +۳ مورد دیگر

Dr. Donald C Beitz is a Distinguished Professor at Iowa State University. His research focuses on animal nutrition, dairy science, and metabolic disorders in livestock. Education: PhD, Michigan State University (1967); MS and BS, University of Illinois (1963, 1962). Research Interests: Mineral and vitamin supplementation in livestock Fatty liver disease in dairy cattle Immune responses to pathogens (e.g., Mycobacterium avium ) Impact of dietary interventions (e.g., CLA, DDGS) on animal health Metabolic adaptations in dairy and swine production systems Recent work emphasizes in vitro disease modeling, microbiome analysis, and non-coding RNA functions in milk. He pioneered methods to detect viable pathogens in dairy products and explored vitamin D’s role in intestinal immunity. Advancing feed efficiency and reducing metabolic disorders remain central to his research agenda.

Don Beitzمشاهده پروفایل

استاد

• Lipid Metabolism
• Animal Nutrition
• Livestock Health Management
• +۳ مورد دیگر

Don Beitz is the Charles F. Curtiss Distinguished Professor at Iowa State University's Department of Animal Science, within the College of Agriculture and Life Sciences. His research focuses on lipid metabolism, nutritional and genetic control of animal-derived food quality, and etiology/prevention of bovine metabolic disorders like fatty liver disease. Collaborative projects include work with the National Animal Disease Center on nutritional immunology and Johne’s disease prevention. Education: B.S. (Agricultural Science, University of Illinois, 1962), M.S. (Dairy Science, University of Illinois, 1963), Ph.D. (Biochemistry and Dairy Nutrition, Michigan State University, 1967). Research Interests: His work spans dairy cow nutrition, fatty acid synthesis, greenhouse gas mitigation in livestock, and vitamin D's role in intestinal health. Key projects include glucagon therapy for ketosis, distillers grains effects on milk quality, and genetic regulation of milk/meat composition. Collaborations & Grants: Active collaborations with USDA and industry partners on metabolic disease prevention, feed efficiency, and food quality. Extensive grant-funded research on lipid metabolism and environmental impacts. Labs/Teams: Leads research teams at Iowa State's Department of Animal Science and collaborates with the National Animal Disease Center on immunological studies.

• Veterinary Medicine
• Toxicology
• Bioinformatics
• +۴ مورد دیگر

Jiazhang Cai is a Professor in the Department of Precision One Health at the College of Veterinary Medicine, Ohio University. His research focuses on interdisciplinary areas including veterinary toxicology, bioinformatics, microbiome analysis, and machine learning applications in veterinary medicine. Key research interests include: Canine toxicology and emergency care protocols Biomarker discovery for neurodegenerative and environmental health disorders Development of spatial transcriptomics and ensemble algorithms in genomics Machine learning applications for bioinformatics and animal behavior analysis Recent studies explore topics like hypernatremia management in dogs, neuromuscular blockade effects in veterinary critical care, and multi-center clinical trials. His work bridges computational methods with clinical veterinary practice. Cai's publications span 2023–2025, demonstrating a focus on precision health approaches across animal and environmental systems. His research has implications for advancing diagnostic tools, therapeutic strategies, and understanding host-microbiome interactions.

• AI for Law Enforcement
• Community Safety
• Digital Forensics
• +۴ مورد دیگر

Campbell Wilson is an Associate Professor in the Department of Data Science & AI at Monash University, serving as founding co-director of the AiLECS Research Centre. His research bridges technical and socio-technical aspects of AI应用于 law enforcement and community safety, with a focus on digital forensics, machine learning, and ethical AI frameworks. He has held leadership roles including Deputy Head of the Caulfield School of IT and Associate Dean (International). Education: PhD in Computer Science (Monash University, 2003), Master of Computing (1996), and Bachelor of Science in Applied Mathematics (1993), all from Monash University. Research interests include AI applications for law enforcement, cybersecurity, bioinformatics, and information retrieval. He leads projects like 'Improving Legal Frameworks for CSAM Prosecutions' and 'Detecting Threats in Temporal Networks', collaborating with agencies like Victoria Police and the British Asian Trust. Teaching commitments include chief examining roles for units such as FIT2100 Operating Systems and FIT5166 Information Retrieval Systems. He actively supervises PhD students focusing on AI for law enforcement and community safety. Labs/Teams: AiLECS Research Centre focuses on AI-driven solutions for law enforcement challenges. Projects emphasize ethical AI deployment and forensic data analysis.

Lan Duمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing (NLP)
• Active Learning
• +۴ مورد دیگر

Dr. Lan Du is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of IT. His research focuses on cross-disciplinary applications of machine learning and AI, particularly in text analytics, uncertainty estimation, knowledge distillation, and multi-modal learning. He leads projects addressing real-world challenges in public health, marketing, and clinical decision-making. Key collaborations include work with Victoria Police, Monash Health, and the National Health and Medical Research Council (NHMRC). Education: PhD in Computer Science (ANU, 2012), Bachelor of Information Technology (ANU, 2007), and B.Communication & IT (Flinders University, 2006). Research Interests: Machine/deep learning for NLP, active learning strategies, uncertainty quantification, and AI-driven solutions for healthcare and business analytics. His work bridges theoretical advancements with practical implementation, emphasizing translational research. Recent Projects (2021–2026): Includes AI models for predicting fracture outcomes (NHMRC-funded PRAISE study), risk prediction tools for pregnancy complications, and medical surveillance systems. He also collaborates on business insights derived from unstructured customer data. Teaching Commitment: Served as Chief Examiner and Lecturer for courses like FIT5149 (Applied Data Analysis) and FIT5196 (Data Wrangling). Lab/Team Involvement: Leads initiatives in AI for healthcare analytics and cross-modal learning, with active participation in Monash's research networks.

Joe A Bradleyمشاهده پروفایل

استادیار بالینی

• Technology Management
• Product Development
• Entrepreneurship
• +۷ مورد دیگر

Joe A Bradley is a Clinical Assistant Professor in Bioengineering at the Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, and serves as Director of Engineering Education and Entrepreneurship at the Siebel Center for Design. His academic roles also include Teaching Assistant Professor in Biomedical and Translational Sciences. Bradley’s research focuses on technology management, product development, entrepreneurship, and engineering design education, particularly examining knowledge brokering in STEM ventures and education systems. He has contributed to curricular innovations in interdisciplinary bioengineering capstone design and K-12 STEM outreach programs. Bradley’s work bridges academia and industry, emphasizing practical skill development and ethical decision-making in engineering education. His research interests span product development processes, human-centered design, and technology value chain analysis. Notable contributions include frameworks for adaptive scaffolding in team dynamics and domain adaptation of AI models for enterprise applications. Bradley has been recognized with the Excellence in Instruction Award for his educational contributions. Bradley collaborates widely across disciplines, including electrical engineering and computational social networks. His laboratories and teams at the Siebel Center for Design focus on translating theoretical concepts into practical solutions through design thinking and entrepreneurial methodologies. Recent work explores the economic trade-offs of large language models and ethical challenges in engineering education.

Maurice Pagnuccoمشاهده پروفایل

استاد

• Knowledge Representation
• Cognitive Robotics
• Artificial Intelligence

Professor Maurice Pagnucco is Deputy Dean (Education) of the Faculty of Engineering and former Head of School of Computer Science and Engineering at University of New South Wales. His research specializes in knowledge representation, cognitive robotics, and artificial intelligence, with applications in automated reasoning and machine ethics. He holds BSc (Hons I) and PhD degrees from University of Sydney. Academic appointments include positions at University of Sydney, Macquarie University, University of Toronto, and UNSW. Leadership roles include Presidency of Australian Council of Deans of ICT and committee positions with Data61. Research integrates symbolic and sub-symbolic AI approaches, with current projects in cognitive robotics disassembly, machine ethics formalization, and visual question answering. Recent publications address domain adaptation in medical imaging, trajectory prediction, and neural-symbolic integration.

• Machine Learning
• Control Systems
• Computer Vision
• +۳ مورد دیگر

Hansjörg Gisler is affiliated with the Department of Integrated Systems at ETH Zürich, contributing to the Professorship for Digital Integrated Circuits and Systems. His research focuses on interdisciplinary applications of machine learning, control systems, and medical informatics. Key areas include 3D object detection using advanced neural networks, optimization algorithms for convex-concave problems, and IoT-driven medical diagnostic systems. He has collaborated extensively with researchers on projects involving deep learning frameworks for adverse condition detection, semantic segmentation, and adaptive control systems. Publications span topics like parameter-separable optimization methods, proximal Lagrangian techniques, and weakly-supervised learning for 3D point cloud processing. His work bridges theoretical advancements in optimization with practical applications in healthcare monitoring and autonomous systems. Gisler's contributions to real-time sleep apnea diagnosis and muscle fatigue detection systems highlight his commitment to biomedical engineering innovations. Collaborations with institutions like ETH Zürich's Institute for Integrated Systems underscore his role in fostering cross-disciplinary research.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Kaushik Roy is an academic researcher affiliated with multiple institutions including North Carolina State University, Purdue University, and North Carolina A&T State University. His primary focus is on computer science, cybersecurity, and AI-driven solutions for real-world challenges. He has extensive experience in machine learning applications for intrusion detection, deepfake detection, biometrics, and federated learning frameworks. His work often bridges theoretical advancements with practical implementations in domains like IoT security, healthcare analytics, and cyber-physical systems. Affiliations: North Carolina State University, Purdue University, North Carolina A&T State University, and others Research Interests: Cybersecurity, Deep Learning, Biometric Authentication, Federated Learning, and AI Ethics He has co-authored over 105 publications, focusing on topics like anomaly-based intrusion detection, malware traffic classification, and privacy-preserving techniques. His recent work emphasizes federated learning architectures for healthcare and transportation IoT systems. Roy collaborates with industry partners to translate research into deployable solutions, particularly in edge computing and sensor networks.

• Computer Vision
• Machine Learning
• Biometrics
• +۳ مورد دیگر

Ali Hassani is a researcher specializing in computer vision and machine learning with a focus on facial recognition, cybersecurity, and synthetic data augmentation. His work often involves collaboration with co-authors such as Zaid A. El-Shair, Hafiz Malik, and Jon Diedrich. Key contributions include advancing presentation-attack detection systems and developing compact spatio-temporal representations for event-based vision. Research areas: Biometrics, Cybersecurity, Deep Learning Notable publications: 5 articles between 2019-2023 in journals like Sensors and IEEE Access

• Bioinformatics
• Computational Biology
• Machine Learning
• +۳ مورد دیگر

Hong Qin is a Professor affiliated with the University of Tennessee at Chattanooga, USA, and previously with Tuskegee University's Department of Agricultural and Environmental Sciences. His research spans computational biology, machine learning, environmental informatics, and healthcare data science. He has co-authored over 20 papers since 2007, focusing on topics like viral fitness modeling, optimization algorithms, and AI applications in healthcare diagnostics. Research interests emphasize interdisciplinary approaches, including bioinformatics for viral mutation analysis, swarm intelligence for optimization problems, and explainable AI for medical imaging. Recent work includes federated learning for healthcare privacy, blockchain interoperability, and environmental monitoring via satellite imagery. His publications reflect a strong focus on AI-driven solutions for global health challenges, such as SARS-CoV-2 variant analysis and equitable AI frameworks for public health. He has contributed as an editor for journals like IEEE ACM Transactions on Computational Biology and Bioinformatics, highlighting his role in shaping bioinformatics research.

• Biomedical Informatics
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Yi-Jia Zhang is a Professor at the School of Computer Science and Technology, Dalian University of Technology. They hold affiliations with multiple institutions, including Zhejiang Sci-Tech University and Jilin University. Their research focuses on biomedical informatics, machine learning, and healthcare applications, with significant contributions to medical knowledge fusion, drug recommendation systems, and multimodal analysis. Over 140 publications since 2011 reflect expertise in areas like graph neural networks, natural language processing, and medical image analysis. Key collaborations include work with Hongfei Lin, Mingyu Lu, and Jian Wang on projects such as drug-repositioning models and radiology report generation frameworks. A notable emphasis is placed on applying AI techniques to solve biomedical challenges, including ICD code classification and sentiment analysis in healthcare contexts. No formal awards are listed, but their extensive publication record underscores academic impact.

• Machine Learning
• Computer Vision
• Anomaly Detection
• +۴ مورد دیگر

Simon THOMINE is affiliated with the University of Technology of Compiègne (UTT), listed in their Research Directory under the academic discipline of Doctorat. His research focuses on anomaly detection in industrial and textile applications using advanced machine learning techniques such as transfer learning, knowledge distillation, and convolutional neural networks. He has contributed to projects like FABLE (Fabric Anomaly Detection Automation Process) and developed frameworks for expedited training times in transfer learning contexts. His work emphasizes practical applications in manufacturing quality control and texture analysis. Current contact information includes the email simon.thomine@utt.fr, with profile details last updated in July 2021.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Shwai He is a PhD student and Affiliate Assistant Professor at the University of Maryland, advised by Ang Li. Their research focuses on advancing AI systems through innovative approaches in large language models (LLMs), fairness in machine learning, and efficient neural network architectures. Key areas include multi-agent systems, causal modeling for bias mitigation, and optimization techniques for mixture-of-experts models. Research interests span artificial intelligence, machine learning, and natural language processing with emphasis on practical applications like healthcare diagnostics and social pairing systems. Notable work includes developing GNWT-based multi-agent digital twins for social platforms and improving LLM transparency through token analysis. Publications highlight contributions to counterfactual fairness, dynamic-depth transformers, and parameter-efficient methods. Current efforts explore computational efficiency in vision-language models and bio-inspired antibody prediction systems. No scientific awards have been mentioned. Advising and grants: Currently a PhD student under Ang Li's supervision. Research involves collaborations across computer science and bioinformatics domains.

Daniel Beanمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Electronic Health Records
• Medical Informatics
• +۳ مورد دیگر

Daniel Bean is a researcher specializing in Natural Language Processing (NLP) and Electronic Health Records (EHRs) , with a focus on Medical Informatics and Clinical Data Modeling . His work intersects Artificial Intelligence and Healthcare Innovation , particularly in extracting value from large-scale patient data. Research Themes Transformer-based generative models for patient timeline prediction Scalable NLP systems for hospital-wide EHR summarization Knowledge distillation frameworks for hospitalization outcome prediction Statistical methodologies in trial emulation for medical research Project Leadership : Bean leads research initiatives funded by BHF (British Heart Foundation, 2023-2025) for AI in heart failure detection MRC (Medical Research Council, 2 scholarly years) on knowledge graph learning EPSRC (short-term 2021 grant) for patient outcome modeling Academic Impact : With over 1000 citations , his work appears in top journals like The Lancet Digital Health , PLOS Digital Health , and IEEE Journal of Biomedical and Health Informatics . His research has been blogged , retweeted by 60+ social media users , and discussed in 32 news outlets .