جست‌وجوی استاد و پژوهشگر

• Biomechanics
• Soft Tissue Repair
• Medical Devices
• +۳ مورد دیگر

Vito Burgio is a PhD student in Civil and Environmental Engineering at the Polytechnic University of Turin (38th cycle, 2022-2025) and an external lecturer/teaching assistant in the Department of Structural, Building and Geotechnical Engineering (DISEG). He teaches the Master's course Science of Bio and Nanoconstructions for Biomedical Engineering across multiple academic years (2023/24-2025/26). His research focuses on biomechanics and translational medical device development, specializing in soft tissue repair mechanisms, surgical instrumentation (e.g., staplers), and computational modeling of biological structures. Key interests include ligament/tendon biomechanics, animal-to-human tissue surrogate translation, finite element analysis of orthopedic devices, and bio-nano construction applications in regenerative medicine. This work bridges engineering principles with clinical orthopedics and sports medicine. Recent publications (2022-2024) reveal a cohesive research trajectory centered on soft tissue repair innovation, combining systematic reviews of commercial medical devices with experimental biomechanical studies. His work spans surgical tool analysis, ligament property characterization, global injury epidemiology, and patent evaluations, demonstrating strong interdisciplinary integration between mechanical engineering, materials science, and clinical practice. Burgio is supervised by Professors Cecilia Surace and Paola Antonaci for his doctoral research. No information is available regarding student supervision, research grants, or laboratory affiliations beyond his patent work on the T-SURE soft tissue repair device.

• Multiple Myeloma
• Hematology
• Stem Cell Transplantation
• +۴ مورد دیگر

Professor Graham Jackson is a distinguished Professor of Haematology at Newcastle University's Faculty of Medical Sciences, where he has established himself as a leading researcher in multiple myeloma and hematological malignancies. His work primarily focuses on genetic profiling, clinical trial design, and improving treatment outcomes for myeloma patients through large-scale studies including the landmark Myeloma XI trial. Professor Jackson's research interests center on multiple myeloma, with particular emphasis on genetic risk stratification , clinical trial design , and optimizing treatment protocols for both transplant-eligible and transplant-ineligible patients. His work has significantly contributed to understanding the role of cytogenetic abnormalities, minimal residual disease monitoring, and the development of risk prediction models such as the UK Myeloma Research Alliance Risk Profile (MRP). His research spans from basic science investigations into the molecular mechanisms of myeloma to large-scale clinical trials that directly impact patient care. Analysis of Professor Jackson's extensive publication record reveals a clear trend toward increasingly sophisticated genetic and molecular profiling of myeloma patients. His recent work demonstrates a shift toward personalized medicine approaches, incorporating genomic data to predict treatment response and optimize therapeutic strategies. He has been instrumental in major clinical trials including the Myeloma XI study, which has transformed treatment paradigms for multiple myeloma, particularly in areas like maintenance therapy, risk-adapted treatment, and management of high-risk disease. Professor Jackson has received significant research funding from organizations including the Medical Research Council, Cancer Research UK, Celgene, and Amgen. His work on the development and validation of the UK Myeloma Research Alliance Risk Profile (MRP) has provided clinicians with a valuable tool for predicting outcomes in transplant-ineligible myeloma patients. His contributions to understanding the impact of the COVID-19 pandemic on myeloma diagnosis and treatment have also been particularly timely and relevant. As a principal investigator on numerous clinical trials, Professor Jackson has mentored many researchers in the field of hematology. His collaborative approach is evident in the extensive list of co-authors across his publications, reflecting partnerships with leading institutions and researchers worldwide. His work continues to shape clinical practice guidelines and improve outcomes for patients with multiple myeloma.

• Medical Oncology
• Cancer Research
• Genomic Profiling
• +۵ مورد دیگر

Rimini Margherita is a fixed-term researcher (Researcher) in the Department of Medical Oncology at Vita-Salute San Raffaele University. She actively contributes to clinical research and teaching, with a focus on hepatocellular carcinoma and cholangiocarcinoma. Current Roles: Tutor for medical oncology residents, medicine students, and international MD program fellows (since 2022). Research Focus: Translational studies on genomic profiling in biliary tract cancers, and real-world investigations of tyrosine kinase inhibitors and immunotherapies in hepatocellular carcinoma. Key Research Areas: Medical Oncology, Cancer Biology, Precision Medicine, and Clinical Trials, with specific emphasis on biomarker discovery , targeted therapies , and prognostic modeling . Publication Trends: Recent work focuses on optimizing immunotherapy combinations (e.g., durvalumab, atezolizumab, bevacizumab), biomolecular profiling, and real-world treatment outcomes for biliary tract and liver cancers. Collaborative studies span multinational clinical networks. Training & Education: 2009-2015: Medical Degree (110/110 cum Laude), University of Modena and Reggio Emilia. 2016-2021: Residency in Medical Oncology (110/110 cum Laude), University of Modena and Reggio Emilia. 2021: Clinical Fellowship at Vall d’Hebron Institute of Oncology, Barcelona. Clinical Engagement: Active in the Multidisciplinary Disease Unit of Cholangiocarcinoma and Hepatocellular Carcinoma at IRCCS San Raffaele Hospital, applying translational research findings to clinical practice.

Richard H. Crawfordمشاهده پروفایل

استاد

• Computer-Aided Design
• Design Theory and Methodology
• Geometric Modeling
• +۳ مورد دیگر

Richard H. Crawford is a Professor of Mechanical Engineering at The University of Texas at Austin, holding the Earl N. & Margaret Brasfield Endowed Faculty Fellowship and serving as Director of the Design Projects Program. His educational background includes a BSME from Louisiana State University (1982), and MSME (1985) and Ph.D. (1989) from Purdue University. Dr. Crawford's research spans computer-aided mechanical design, design theory, and engineering education, with four primary focus areas: (1) computer representations for conceptual design and design retrieval; (2) geometric modeling for engineering design and manufacturing; (3) additive manufacturing including design tools and applications; and (4) pre-college engineering education. His industrial collaborations include faculty internships at Ford Motor Company, IBM, and Sandia National Laboratory. His recent publications demonstrate consistent emphasis on design optimization, metamodeling (particularly NURBS-based approaches), additive manufacturing applications, and biomedical engineering solutions for prosthetics and orthotics, reflecting integration of computational methods with practical engineering challenges. Dr. Crawford has received significant recognition: 1995 Fred Merryfield Design Award from the American Society for Engineering Education 2010 Ralph Coates Roe Award for exemplary Mechanical Engineering education 2011 University of Texas System Regents’ Outstanding Teaching Award He co-founded the Design Technology and Engineering for All Children (DTEACh) program providing K-12 teacher training, and partnered with UTeach to develop the "Engineer Your World" high school curriculum adopted by over 200 school districts, featuring a dual-credit option through UT System institutions.

Zhiguang Caoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Science
• Decision Making & Optimization
• +۳ مورد دیگر

Dr. Zhiguang Cao is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). With a PhD from Nanyang Technological University (2017), his work bridges Artificial Intelligence , Deep Learning , and Combinatorial Optimization to tackle complex logistics and urban mobility challenges. Research Advisor to PhD/Master's students: XIAO Xiangjie, YI Hang, ZHANG Ni Recipient of the World’s Top 2% Scientist recognition (2024), IEEE Transactions on Industrial Informatics Outstanding Paper Award (2022), and Nobert Wiener Review Award (2022) Completed degrees: PhD (NTU), MSc (NTU), BEng (GDUT, China) Research Interests focus on Learning to Optimize (L2Opt) and Neural Combinatorial Optimization , with applications in: Vehicle Routing Problems (TSP, CVRP, PDP) Job Shop Scheduling and Multimodal Optimization UAV Routing , Drone Delivery , and Stochastic Programming His 15 most recent publications (2024-2025) span top venues like NeurIPS , ICML , IJCAI , and IEEE Transactions journals, covering topics from multi-task vehicle routing to graph domain adaptation and learning with large language models . Scientific Awards : World’s Top 2% Scientist (Stanford, 2024) IEEE Transactions on Industrial Informatics Outstanding Paper (2022) Nobert Wiener Review Award (2022) BMW Summer School Excellent Research Work (2014) IEEE Singapore Chapter Tennis Prize (2012) Dr. Cao advises the L2Opt Research Group , which recruits PhD students and remote interns. He has served as Area Chair for ICLR, SIGKDD, ICML, and Senior PC for AAAI and IJCAI. His ongoing projects include Trustworthy Human-AI Optimization (AISG, 2025-2027) and Mobile Crowdsourcing Emergency Supplies (SMU-UofT, 2025-2026).

• Aerospace Engineering
• Computational Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Professor Andy Keane is a Professor of Computational Engineering at the University of Southampton , focusing on advanced computational methods for aerospace system design and optimization. He is affiliated with the Computational Engineering and Design Group and has led numerous EPSRC and EU-funded research projects, including DECODE , DYNAMO , and ICAReS . Research spans Aerospace Engineering , Artificial Intelligence , and Computer-Aided Design (CAD) Key projects include Novel Passive Vibration Isolators and Multi-fidelity Simulation for Aero Engine Seals Active in mentoring PhD students such as Gerico Vidanes and Petru-Cristian Cimpoesu Research Trends : Recent publications highlight applications of surrogate modeling , generative adversarial networks , and hybrid machine learning in aerospace and CAD. Themes include uncertainty estimation , meta-learning , and vibration isolation . Scientific Awards : Fellow of the Institution of Mechanical Engineers (1998) Fellow of The Royal Institution of Naval Architects (2011) Collaborations : Partnered with institutions across the UK and EU on projects like CRESCENDO and E-Break . Supervision of PhD students focuses on AI-driven design and optimization . Labs & Teams : Member of the Computational Engineering and Design Group , integrating analytical, computational, and experimental techniques for multidisciplinary engineering.

• Materials Science
• Mechanical Engineering
• Structural Optimization
• +۳ مورد دیگر

Fatma ABID is a Lecturer and researcher at CESI Lyon, affiliated with the Engineering and Numerical Tools Research team. She holds a PhD in Mechanics from INSA Rouen (2019) and an Electromechanical Engineering degree from ENIS, Tunisia (2016). Her research focuses on materials integration, uncertainty quantification, and reliability optimization of structures, particularly involving shape memory alloys (SMAs) and micro-actuators. Education: Doctor of Mechanics, INSA Rouen, 2019 Electromechanical Engineer, ENIS (Tunisia), 2016 Research Interests: Reliability-based design optimization Uncertainty analysis in structural systems Finite element modeling of materials Application of meta-modeling and surrogate methods Her publications emphasize robust design methodologies for SMA actuators and structures, leveraging polynomial chaos expansions and numerical modeling under perturbations. She has contributed to automotive suspension systems and MEMS technologies through advanced optimization frameworks. Grants & Awards: No awards listed. Advising: No formal advisees listed. Labs/Teams: Member of CESI's Engineering and Numerical Tools Research group.

Wendy E. Wardمشاهده پروفایل

استاد

• Nutrition
• Bone Health
• Oral Health
• +۳ مورد دیگر

Wendy E. Ward is a Professor in the Department of Kinesiology and cross-appointed in Health Sciences at Brock University, and serves as Interim Associate Dean (Research and Graduate Studies) in the Faculty of Applied Health Sciences. Her research focuses on dietary strategies to combat osteoporosis and enhance periodontal health, utilizing advanced imaging and biochemical analyses to study food components like flaxseed, tea polyphenols, and soy isoflavones. Funded by NSERC, CIHR, and CFI, her work also explores early-life nutritional programming of bone and muscle health. Ward holds a Canada Research Chair (Tier 2) and has received awards such as the Osteoporosis Canada Backbone Award (2021) and the Canadian Nutrition Society Volunteer Leadership Award (2020). She leads the Nutrition, Bone and Oral Health Research Group, emphasizing knowledge mobilization through media and professional education. Roles: Professor (Kinesiology/Health Sciences), Interim Associate Dean (Research & Graduate Studies), Senior Research Fellow Research Focus: Dietary interventions, bone health, periodontal healing, nutritional programming Affiliations: Editor-in-Chief, Applied Physiology, Nutrition, and Metabolism ; Chair, Osteoporosis Canada Dietary Recommendations Working Group Her research combines preclinical models with clinical studies, investigating sex-specific responses to dietary interventions. Key areas include tea polyphenols’ effects on osteoblast activity, probiotics in bone health, and maternal nutrition’s long-term skeletal impacts. Ward collaborates with dental professionals to link oral and systemic health, emphasizing translational research. Her awards highlight her leadership and contributions to osteoporosis prevention and nutrition science. Current grants include studies on dietary strategies for bone preservation and probiotic effects in rodent models. Ward mentors numerous graduate students and has published extensively in top journals, with a focus on bridging academic research and public health applications.

Raphaël Pestourieمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Inverse Design
• Electromagnetism
• +۷ مورد دیگر

Raphaël Pestourie is an Assistant Professor in the School of Computational Science and Engineering at Georgia Tech (2023–present). He holds a PhD in Applied Mathematics (Harvard University, 2020) and an MBA from ESSEC. His research focuses on scientific machine learning and inverse design in electromagnetism, combining AI with physics-based models for engineering optimization. Notable contributions include fast approximate PDE solvers, surrogate models for metasurface design, and end-to-end optimization frameworks. **Education**: PhD in Applied Mathematics, Harvard University (2020) Secondary field in Computational Science & Engineering, Harvard MBA, ESSEC Masters in Nanosciences (Paris Saclay), Engineering (École Centrale Paris), and Statistics (Harvard) **Research Interests**: His work bridges applied mathematics and AI to solve large-scale engineering problems. Key areas include: Inverse design of metasurfaces for optical applications Scientific machine learning for PDE-based optimization Hybrid models combining theory-driven and data-driven approaches End-to-end optimization of electromagnetic systems **Awards & Support**: Funded by MIT-IBM Watson AI Lab, DARPA, Simons Foundation, and others Arthur Sachs Fellow (French Fulbright Commission) Member of Harvard's Graduate School Leadership Institute **Advisees & Collaborations**: Advises PhD/MSc students at Georgia Tech and collaborates with MIT, Yale, and UC Berkeley researchers Current projects include metasurface design for chemical detection and imaging **Teaching**: Created new courses: CSE 8803 (Scientific Machine Learning) and CSE 8801 (Linear Algebra/Probability/Stats)

Keith R Abramsمشاهده پروفایل

استاد

• Health Technology Assessment (HTA)
• Health Data Science
• Clinical Trials
• +۵ مورد دیگر

Keith R Abrams is a Professor of Statistics & Data Science at the University of Warwick's Department of Statistics and an Adjunct Professor of Biostatistics in Warwick Medical School. He is also an NIHR Senior Investigator Emeritus and Honorary Professor in the Centre for Health Economics at the University of York. Previously, he held positions at the University of Leicester, London School of Hygiene & Tropical Medicine, and King's College London. His research focuses on statistical methods in Health Technology Assessment (HTA) and Health Data Science, including clinical trial design, evidence synthesis, disease modeling, and dynamic risk prediction using electronic health records. Current funded projects include the UK TAVI Trial, REDDIE (Real-world Evidence for Diabetes), and studies on hyperthyroidism outcomes. Key achievements include methodological contributions to meta-analysis, indirect comparisons, and handling observational data in HTA. Awards include NIHR Senior Investigator Emeritus status. His work spans 229+ publications, emphasizing Bayesian methods and real-world evidence applications in policy and healthcare decision-making. Grants: NIHR HTA Programme, MRC/NIHR Methods Research Programme, EU Horizon Collaborations: UK TAVI Trial, EGRET study, REDDIE project Labs/Teams: Warwick Statistics Group, Centre for Health Economics (York)

Minghui Chengمشاهده پروفایل

استادیار

• Digital Twin-enabled Smart Cities
• Life-cycle Management of Infrastructure
• Sustainability and Resilience
• +۳ مورد دیگر

Minghui Cheng is an Assistant Professor at the University of Miami, jointly appointed in the School of Architecture and the College of Engineering's Department of Civil & Architectural Engineering, with an affiliation to the Climate Resilience Academy. His research focuses on digital twin technologies for smart cities, life-cycle management of infrastructure systems, and climate resilience strategies. Prior to this role, he was a postdoctoral associate at Cornell University's Systems Engineering department and earned his PhD in Structural Engineering from Lehigh University in 2021. Education: PhD in Structural Engineering, Lehigh University, 2021 BS in Civil Engineering, Hunan University, China, 2016 Visiting Student, Arizona State University, 2015 His work bridges engineering and data science, emphasizing probabilistic modeling, uncertainty quantification, and stakeholder decision-making frameworks. Recent projects include developing automated knowledge graphs (AutoGraCS) for bridge network management and Bayesian network-based digital twin frameworks for infrastructure systems. His research addresses urban resilience to climate change and integrates multi-source data for life-cycle risk assessment. Advising & Grants: While no advisees are listed, his projects likely involve graduate students in systems engineering and civil engineering. He has contributed to grants focused on infrastructure resilience and digital twin applications. Labs & Teams: Affiliated with the Climate Resilience Academy, collaborating on interdisciplinary projects to enhance urban infrastructure sustainability and disaster preparedness.

• Machine Learning
• Evolutionary Computation
• Multiobjective Optimization
• +۱۰ مورد دیگر

Professor Xiaodong Li is a faculty member at RMIT University's School of Computing Technologies, serving as Assistant Associate Dean for Data Science & Artificial Intelligence. He holds a Ph.D. in Artificial Intelligence from the University of Otago, New Zealand. His research focuses on machine learning, evolutionary computation, swarm intelligence, and optimization techniques with applications in blockchain security, renewable energy, and logistics. He has received prestigious awards including the 2013 ACM SIGEVO Impact Award and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and is an IEEE Fellow. His academic contributions include editorial roles at IEEE Transactions on Evolutionary Computation and leadership in IEEE Task Forces on Swarm Intelligence and Multi-modal Optimization. Current research interests span automated code generation, quantum AI-driven logistics, and anomaly detection. Supervision projects highlight interdisciplinary applications in AI ethics, solar energy monitoring, and fraud detection. Education: Ph.D. in Artificial Intelligence, University of Otago, New Zealand Key Roles: IEEE Fellow, ARC College of Experts (2023–2025) Publications: Over 280 peer-reviewed articles, including works on niching methods and evolutionary algorithms. Research trends show strong emphasis on hybrid optimization techniques, blockchain security, and AI-driven solutions for sustainability challenges. Recent articles explore dynamic environments, quantum rerouting strategies, and explainable machine learning systems. Awards: ACM SIGEVO Impact Award, IEEE Fellow, ARC College Membership Grants/Projects: Multiple industry-collaborative grants in smart logistics and energy systems. He leads the Data Science & AI team at RMIT, fostering innovation in large-scale optimization and metaheuristics. Active in international conferences like GECCO and IEEE CEC, he promotes open-source benchmark datasets for algorithm testing.

Edgar Torresمشاهده پروفایل

پژوهشگر

• Physics-informed Neural Networks
• Neural Operators
• Transfer Learning
• +۷ مورد دیگر

Edgar Torres is a Researcher at the Institute for Artificial Intelligence, University of Stuttgart, specializing in data-efficient machine learning methods for scientific simulations. His work focuses on solving partial differential equations through physics-informed neural networks, neural operators, and adaptive learning techniques including transfer learning and meta-learning, with applications in computational fluid dynamics and solid mechanics. Research Focus: His primary research addresses computational bottlenecks in scientific simulations by developing adaptive ML models that minimize data requirements. Key interests include Physics-informed Neural Networks (PINNs), Neural Operators, Transfer Learning, Meta-learning, Few-shot Learning, Computational Fluid Dynamics (CFD), Solid Mechanics, Surrogate Models, and Neural Fields. His approach integrates sparse data observations with prior system knowledge to enhance model generalization across simulation tasks. Publications Insight: His 2025 survey publication analyzes adaptive techniques in physics-informed neural networks, revealing how transfer learning and meta-learning frameworks enable rapid adaptation to new PDE systems with minimal data. This work identifies critical pathways for improving computational efficiency in scientific machine learning, particularly for design optimization and real-time assessment scenarios where traditional simulations are prohibitively expensive. Academic Engagement: Torres serves as Teacher Assistant for the Deep Learning for the Sciences seminar (WS-2024/25) and actively supervises Bachelor's and Master's theses. Prospective students must submit CVs and Transcripts of Records; he encourages proposals aligned with his research areas when formal openings are unavailable. Research Context: As part of the Machine Learning for Simulation Science group, he contributes to the Institute's mission of bridging AI and scientific computing. His current PhD project develops adaptive surrogate models that overcome limitations of reduced-order modeling through meta-learning frameworks, targeting applications requiring rapid multi-configuration evaluations.

• Malaria
• Tropical Medicine
• Infectious Diseases
• +۲ مورد دیگر

Professor Kevin Baird serves as Head of Unit at OUCRU Indonesia, a partnership between the University of Oxford and the Eijkman Institute for Molecular Biology, and holds the title of Professor of Malariology in the Nuffield Department of Medicine at Oxford. Based in Jakarta, he leads research on tropical infectious diseases with a primary focus on Plasmodium vivax malaria. His research spans malaria diagnostics, therapeutics, and vector control, with particular emphasis on the challenges of treating vivax malaria in glucose-6-phosphate dehydrogenase (G6PD) deficient patients. He also investigates pharmacogenetic factors affecting drug metabolism and conducts clinical trials for malaria, tuberculosis meningitis, and cryptococcal meningitis. Recent publications highlight a strong trend in vivax malaria research, including studies on primaquine treatment in children, biomarker development for drug activity monitoring, and vector control strategies. His work increasingly integrates systematic reviews and meta-analyses to inform treatment guidelines, while expanding into COVID-19 vaccine response studies in Indonesian populations. OUCRU Indonesia, under Professor Baird's leadership, operates from the Eijkman Institute's facility in Jakarta and maintains extensive collaborations with Indonesian universities, the Ministry of Health, and the Indonesian Armed Forces. The unit has pioneered a unique soldier volunteer model for malaria relapse studies and actively engages in field research across Indonesia.

Lu Tianمشاهده پروفایل

استاد

• Survival Analysis
• Semiparametric Modeling
• Restricted Mean Survival Time
• +۴ مورد دیگر

Lu Tian is a Professor of Biomedical Data Science in the School of Medicine at Stanford University, with a courtesy appointment as Professor of Statistics. He has been at Stanford since 2008, progressing from Assistant Professor to his current full Professor position in the Department of Biomedical Data Science. Sc.D. in Biostatistics, Harvard University (1998-2002) M.S. in Mathematics, Nankai University, Tianjin, P.R. China (1995-1998) B.S. in Mathematics, Nankai University, Tianjin, P.R. China (1991-1995) Professor Tian's research spans several critical areas in biostatistics and data science. His work focuses on developing innovative statistical methodologies with direct clinical applications. He has made significant contributions to survival analysis, particularly in restricted mean survival time methods, which offer clinically interpretable alternatives to traditional hazard ratios in clinical trials. His research in meta-analysis addresses limitations of existing methods, especially for studies with sparse data or few studies. In personalized medicine, he develops statistical frameworks for identifying patient subgroups that benefit most from specific treatments. His recent publications demonstrate a clear trend toward developing statistically rigorous methods with direct clinical interpretability. Rather than focusing solely on theoretical advances, his work emphasizes practical applications in oncology and cardiology clinical trials, where he develops methods that provide clear, actionable insights for clinicians. His research bridges the gap between complex statistical theory and real-world clinical decision-making, with particular emphasis on survival analysis techniques that clinicians can readily interpret and apply. Wangkechang Scholarship, Nankai University (1991-1998) Howard Hughes Fellowship (1999-2002) Robert B. Reed Award for Excellence in Biostatistics, Harvard (2000) Distinction in Teaching Award, Harvard School of Public Health (2000-2002) Professor Tian has served as Associate Editor for several prominent journals including Biometrics, Statistics in Medicine, and Biostatistics and Epidemiology, and as Book Editor for Advanced Medical Statistics. He was a Board Director of the International Chinese Statistical Association (2015-2018). His methodological contributions have been widely adopted in clinical research, particularly his work on restricted mean survival time which has changed how treatment effects are reported in oncology trials. His development of exact inference methods for meta-analysis has provided more reliable approaches for combining evidence from small studies.