جستجوی استاد راهنمای دانشگاهی

Zhandong Liu is an Associate Professor at Baylor College of Medicine with joint appointments in the Department of Pediatrics and Department of Neurology . He serves as Chief of Computational Sciences at Texas Children's Hospital and co-directs the Quantitative & Computational Biosciences Graduate Program at Baylor. Education: B.S. in Computer Science, Nankai University (2001) M.S. in Computer Science, Wayne State University (2003) Ph.D. in Genomics and Computational Biology, University of Pennsylvania (2010) Dr. Liu's research integrates genomics , machine learning , and bioinformatics to advance understanding of neurological diseases. His work focuses on: Multi-omics data integration for disease mechanism discovery Development of cloud-based CRISPR analysis tools like CRISPRcloud Augmented reality platforms for biomedical data visualization Identification of disease genes through computational models Alternative splicing analysis in cancer and neurodegeneration Single-cell and spatial transcriptomics algorithms His recent publications emphasize Alzheimer's disease , MECP2 syndromes , and computational therapy prediction across multiple domains. Scientific awards include the 2018 Outstanding Service Award from the International Association for Intelligent Biology and Medicine. He has secured major grants from NIH, CPRIT, and NSF for projects including: NSF grant #199977 (2018-2020): Augmented reality therapy platforms CPRIT grant #RP170387 (2016-2019): Network-guided cancer analysis NIH #1R01AG057339 (2017-2022): Alzheimer's disease networks As head of the Liu Lab , he leads teams developing tools like: MARRVEL : Human-model organism gene variant integration CRISPRcloud : Secure CRISPR screen analysis platform CrypSplice : Cryptic splicing detection algorithm

• Bioinformatics
• Big Data analysis
• Machine Learning
• +۵ مورد دیگر

Professor Rafal Bogacz is a leading academic at the University of Oxford, affiliated with St Edmund Hall and the MRC Brain Network Dynamics Unit . He teaches computational neuroscience and statistics at both undergraduate and postgraduate levels, including the MSc in Neuroscience and BSc in Biomedical Science programs. MSc: Wroclaw University of Technology PhD: University of Bristol Postdoctoral Researcher: Princeton University His research focuses on computational neuroscience , particularly modeling brain networks involved in action selection , decision making , and Parkinson's disease pathophysiology. Key themes include: Developing predictive coding models of cortical computations Understanding basal ganglia neural circuits in healthy and diseased states Designing closed-loop deep brain stimulation paradigms Recent publications highlight work in neural plasticity , dopamine signaling , and computational psychiatry , with a notable Wellcome Discovery Award supporting research on learning in neurons . The Bogacz Group maintains strong collaborations with experimental neuroscientists and shares open datasets through the MRC BNDU Data Sharing Platform . Wellcome Discovery Award (2025): For learning in neurons Europe PMC Open Access (multiple): For numerous PLoS, Nat Neurosci, and J Neural Eng publications As a computational neuroscientist, Professor Bogacz supervises D.Phil. students and leads research programs that bridge theoretical neuroscience with clinical applications . The group actively participates in MRC BNDU training initiatives and public engagement activities like Schools Open Day demonstrations.

• Computational Neuroscience
• Neural Networks
• Parkinson's Disease
• +۵ مورد دیگر

Pixu Shi is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University's School of Medicine. Previously, they served as a Visiting Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison (2018-2020) and as a Postdoctoral Researcher in the Department of Biostatistics at the University of Wisconsin-Madison (2016-2018). Dr. Shi earned their PhD in Biostatistics from the University of Pennsylvania in 2016 under advisor Hongzhe Li. They also hold an MS in Biostatistics from the University of Pennsylvania (2015), an MS in Statistics from Rutgers University (2012), and a BS in Statistics from Peking University (2010). Dr. Shi's research focuses on developing statistical methods for Microbiome Research, Longitudinal/Temporal Omic Data analysis, Integration of Omic Data, Spatial Omics, and High-dimensional Statistical Inference. Their work bridges statistical theory with practical applications in biomedical research, particularly in microbiome studies where they've made significant contributions with the TEMPTED (TEMPoral TEnsor Decomposition) method. The article trends show a strong focus on microbiome analysis, statistical methodology development, and applications in obesity, infectious disease, and cancer research. Their most recent work (2024-2025) demonstrates expertise in tensor decomposition methods, longitudinal data analysis, and integrating microbiome data with clinical outcomes across diverse areas including adolescent obesity, viral infections, and cancer metastases. Dr. Shi has secured multiple substantial research grants from major institutions including the National Institutes of Health (NIMH, NIAID, NCI, NIDDK, NIA), totaling over a decade of continuous funding for projects related to microbiome research, HIV/AIDS, cancer biomarkers, and metabolic studies. They actively contribute to education through teaching courses such as BIOSTAT 905: Linear Models and Inference at Duke University and previously taught statistics courses at the University of Wisconsin-Madison. Dr. Shi has also organized specialized workshop series including Quantitative Methods for HIV/AIDS, Microbiome, Immunology, and Cancer Bioinformatics.

• Microbiome Research
• Longitudinal/Temporal Omic Data
• Integration of Omic Data
• +۲ مورد دیگر

Anru Zhang is the tenured Eugene Anson Stead, Jr. M.D. Associate Professor with joint appointments in Biostatistics & Bioinformatics, Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. He holds a Ph.D. from the University of Pennsylvania (2015, advised by T. Tony Cai) and a B.S. in Mathematics from Peking University (2010). Current roles: Associate Professor at Duke (2024–present), previously Assistant Professor at UW-Madison (2018–2021) Research focus: Tensor learning, high-dimensional statistics, EHR analysis, and healthcare applications Mentorship: Supervises active research team including postdocs (Jianbin Tan, Qiuyi Wu) and PhD students (Runshi Tang, Yinrui Sun) Research Trends : His recent publications emphasize tensor methods in biomedical data (EHR, microbiome, Alzheimer’s), Riemannian optimization for high-dimensional problems, and hybrid statistical-computational approaches. Key themes include healthcare AI, EHR analysis, and non-convex optimization. Scientific Awards : COPSS Emerging Leader Award (2024) IMS Tweedie New Researcher Award (2022) ASA Gottfried E. Noether Junior Award (2021) NSF CAREER Award (2020) AMIA Data Science Outstanding Paper Award (2023) Advising & Grants : Mentored 16+ students/postdocs, including Yuetian Luo (IMS Lawrence D. Brown Award) and Yuchen Zhou (IMS Hannan Travel Award). Current grants include NIH-funded projects on sepsis detection, mental health AI, precision genetic testing, and telehealth interventions, plus NSF CAREER funding for statistical inference in high-dimensional structures. Labs & Teams : Leads a research group at Duke focusing on tensor learning, statistical theory, and healthcare AI applications. Collaborates with Duke’s AI Health initiative and serves as Associate Editor for leading journals like Annals of Statistics and JASA.

• Tensor Learning
• High-Dimensional Statistics
• Machine Learning
• +۳ مورد دیگر

Amy K Johnson, PhD, serves as Associate Professor in Pediatrics (Adolescent and Young Adult Medicine) at Northwestern University's Feinberg School of Medicine, with clinical affiliation at Ann & Robert H. Lurie Children's Hospital of Chicago. She holds key roles in the Institute for Public Health and Medicine (IPHAM) - Center for Dissemination and Implementation Science, Northwestern University Clinical and Translational Sciences Institute (NUCATS), and Robert J. Havey, MD Institute for Global Health (IGH). Dr. Johnson earned her PhD from the University of Illinois, Chicago in 2016. Her research centers on adolescent sexual and reproductive health with dual emphases on HIV/STI intervention implementation and ethical dimensions of community participation in cure research. She develops prevention packages targeting adolescents—particularly young women—and addresses surveillance gaps in sexually transmitted diseases through implementation science frameworks. Analysis of her 2024-2025 publications reveals a dominant focus on PrEP implementation across diverse populations and geographies. Her work consistently addresses health disparities through mixed-methods approaches, examining structural barriers among Black women in Chicago, migrant communities in France, and youth in Nigeria. Common threads include innovative delivery strategies (text messaging, peer navigation), cultural adaptation of interventions, and ethical considerations in community engagement—demonstrating her commitment to translating research into equitable prevention solutions. Her scientific contributions have been recognized with the Excellence in Scholarship award from Lurie Children's Hospital (2023) and Best Poster Award from the American Sexually Transmitted Disease Association (2016). Excellence in Scholarship, Lurie Children's Hospital (2023) Best Poster Award, American Sexually Transmitted Disease Association (2016) Dr. Johnson actively shapes public health practice through leadership in the Illinois PrEP Working Group, Getting to Zero Illinois, and as Vice Chair of Epidemiology for the Illinois Public Health Association. Her editorial role at Frontiers in Reproductive Health and memberships in the American College of Epidemiology and American Public Health Association reflect her influence in advancing methodological rigor and equity-focused HIV prevention science. Current projects like iCARE Nigeria and POWER Up demonstrate her commitment to scalable interventions addressing the HIV care continuum.

• Adolescent Sexual and Reproductive Health
• Sexually Transmitted Diseases
• HIV Prevention
• +۳ مورد دیگر

Helder Carvalho is an Associate Professor at the University of Minho's School of Engineering, Campus de Azurém, where he also serves as Director of the Department of Textile Engineering and Director of the Master's program in Textile and Accessories Product Design and Innovation. His academic career spans over three decades, with a focus on textile engineering and its intersection with electronics and automation systems. His educational background includes: PhD in Textile Engineering (2004) from University of Minho, School of Engineering MSc in Textile Engineering (1998) from University of Minho, School of Engineering BSc in Electrotechnical and Computer Engineering (1992) from University of Porto, Faculty of Engineering Professor Carvalho's research primarily focuses on smart textiles, textile sensors, and instrumentation systems for industrial sewing machines. His work bridges the gap between traditional textile manufacturing and modern electronics, creating innovative solutions for interactive textiles and wearable technology. He has particular expertise in developing flexible sensors that can be integrated into fabrics for applications ranging from sports performance monitoring to healthcare. His recent publications demonstrate a strong trend toward sports applications of smart textiles, with numerous papers on fencing apparel, karate body protectors, and general athletic performance monitoring. The research spans material science, sensor development, and user experience design, showing a comprehensive approach to creating functional and attractive smart textile products. Professor Carvalho has received recognition through research funding from major institutions: BE@T Bioeconomy Textile and Clothing (current) Greenauto - Green Innovation for the Automotive Industry (current) Factor ST+ (2021-2023) FAMEST (2017-2020) TSSIPRO (2016-2019) He has directed multiple academic programs including the Master's in Textile and Accessories Product Design and Innovation, and coordinated educational initiatives like the CET 'Fashion Commerce' program. His work at the Textile Science and Technology Centre demonstrates a commitment to translating research into practical applications across sports, healthcare, and industrial manufacturing sectors.

• Textile Engineering
• Smart Textiles
• Textile Sensors
• +۴ مورد دیگر

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +۳ مورد دیگر

FURUZUKI, Takayuki is a Professor at Waseda University's Faculty of Science and Engineering, Graduate School of Information, Production, and Systems. He holds a PhD from Kyushu Institute of Technology and maintains the NCLab research group (http://nclab.w.waseda.jp/nclab/). His professional memberships include the Institute of Electronics, Information and Communication Engineers, Institute of Electrical Engineers of Japan, Society of Instrument and Control Engineers, and IEEE. His educational background includes a PhD in Information Science from Kyushu Institute of Technology (1994-1997), a Master's degree in Electronics Engineering from Sun Yat-Sen University (1983-1986), and a Bachelor's degree in Electrical & Electronic Systems Engineering from Sun Yat-Sen University (1979-1983). Professor FURUZUKI's research spans neural networks, genetic algorithms, system identification and control, complex systems, bioinformatics, and combinatorial optimization. His work integrates soft computing techniques with medical informatics and control systems engineering, developing innovative approaches for problems ranging from brain tumor segmentation to music information retrieval. His research methodology often combines theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His recent publication record shows a strong focus on sequential recommendation systems, graph neural networks for medical imaging, music information retrieval, and power systems analysis. The consistent thread across his work is the application of advanced machine learning techniques to solve complex, real-world problems across diverse domains, with particular emphasis on handling missing data, improving model efficiency, and enhancing prediction accuracy. His scientific achievements have been recognized with several prestigious awards: ISCIIA2008 Excellent Paper Award (2008) Institute of Electrical Engineers of Japan Excellent Paper Award (2001) Guangdong Province Higher Education Science and Technology Progress Second Prize (1991) Guangdong Province Higher Education Science and Technology Progress Second Prize (1989) Chinese Ministry of Education Scientific and Technological Achievements Second Prize (1986) Professor FURUZUKI has maintained continuous research activity since joining Waseda University in 2003, with significant contributions to multiple research projects spanning neural computation, soft computing, and system engineering. His work demonstrates strong international collaboration, with publications involving researchers from various institutions worldwide. His research output includes over 410 papers with 3,623 citations and an h-index of 23 according to Scopus data. The NCLab research group, which he leads, focuses on neural computation and related areas, providing a platform for interdisciplinary research that bridges theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His laboratory environment fosters innovation in machine learning algorithms and their application to real-world problems, with particular emphasis on developing efficient, robust solutions for complex data analysis tasks.

• Neural Networks
• Genetic Algorithms
• System Identification and Control
• +۷ مورد دیگر

Professor Kiyotaka Iwasaki at Waseda University's Faculty of Science and Engineering is a leading figure in biomedical engineering with a focus on cardiovascular device development , tissue engineering , and regulatory science . His career spans over two decades at Waseda University, including roles as Associate Professor (2006-2014) and positions at Harvard Medical School's Laboratory for Tissue Engineering. Holding a Doctor of Engineering from Waseda, he serves on numerous international regulatory committees and has contributed to ISO/TC194 standards for medical devices. 1993-2002: Waseda University Education in Mechanical Engineering 2001-2004: Research Associate at Waseda University 2004: Research Scientist at Harvard Medical School 2018-Present: Professor at Waseda University His research interests include Non-clinical testing methodologies for medical devices Regulatory science frameworks Tissue engineering for ligament and cardiac applications Cardiovascular biomedical engineering His scientific contributions reveal through Development of decellularized tissue grafts for orthopaedic surgery Innovations in 3D cardiac tissue engineering using fibrin-based cell sheet stacking Pioneering bioresorbable stent technology with magnesium alloys Creation of biomechanical simulators for valvular disease modeling His awards span from the 2021 Japanese Ministerial Science Commendation 2020 JSME Standards Award 2018 ARIA Innovation Award 2001 ASAIO Fellowship While his publications demonstrate expertise in Vascular and cardiac device testing Bioresorbable stent evaluation Machine learning in medical device regulation Decellularized tissue applications

• Biomaterials
• Biomedical Engineering
• Non-clinical testing methodology for Medical Devices
• +۳ مورد دیگر

Mark Churchland is an Associate Professor of Neuroscience at Columbia University's Mortimer B Zuckerman Mind Behavior Institute. His research focuses on understanding neural dynamics in motor cortex, particularly how these dynamics generate movement patterns and contribute to neural prosthetics development. He is also a Co-director of the Grossman Center for the Statistics of Mind and a member of the Kavli Institute for Brain Science. Co-director, Grossman Center for the Statistics of Mind Member, Kavli Institute for Brain Science Member, Behavior Research Churchland's research investigates the computational properties of neural circuits in motor cortex, emphasizing how self-generated neural activity underpins movement. His work bridges theoretical neuroscience with practical applications in brain-machine interfaces (BMIs), translating basic science into technologies that restore movement capabilities by bypassing spinal cord injuries. Key themes include neural population dynamics, movement variability, and cortical preparation mechanisms. His publications reveal consistent exploration of neural dynamics in motor systems, focusing on population coding, variability suppression, and BMI algorithm design. Research spans both fundamental questions about cortical computation and translational efforts to enhance prosthetic devices through improved understanding of preparatory neural activity. McKnight Scholar Award Sloan Research Fellow NIH Director's New Innovator Award Program Searle Scholars Program Burroughs Wellcome Fund Career Award in the Basic Biomedical Sciences Churchland has secured NIH grants for projects on dynamical systems in neuroscience (2012-2017). His collaborative work with Krishna Shenoy and colleagues at Stanford has produced foundational studies on cortical activity patterns and motor control. He actively participates in academic committees and research networks focused on computational neuroscience and mind-brain relationships.

• Cognitive Neuroscience
• Systems Neuroscience
• Motor Systems
• +۳ مورد دیگر

Jiefeng Sun serves as Assistant Professor in the Department of Aerospace and Mechanical Engineering within Arizona State University's School for Engineering of Matter, Transport and Energy. His research program centers on designing artificial-muscle-driven robots that replicate biological adaptivity through advanced modeling and control systems. His academic credentials include: Ph.D. in Robotics and Control from Colorado State University (2022) M.S. in Mechanical Engineering from Dalian University of Technology (2017) B.S. in Mechanical Engineering from Lanzhou University of Technology (2014) Dr. Sun's research integrates soft robotics, artificial muscles, and adaptive control to create morphologically intelligent systems. His work spans aerial robotics, wearable exoskeletons, and biomimetic locomotion, with emphasis on shape-changing mechanisms and energy-efficient actuation that enables robots to operate in unstructured environments. Analysis of his recent publications reveals dominant themes in twisted-and-coiled actuators, tensegrity structures, and physics-informed control methods. Key trends include variable-stiffness systems for wearable devices, data-efficient simulation techniques using Koopman operators, and bistable mechanisms for aerial grasping applications. His research excellence has been recognized through: Finalist for Best Student Paper Award at IEEE/RSJ IROS 2018 Reviewer of the Year 2021 for Smart Materials and Structures Journal 2022 DARPA Riser designation Dr. Sun actively recruits graduate students for robotics research and has secured significant funding including DARPA support. He teaches core courses including System Dynamics and Control I (MAE 318) while supervising thesis research and applied projects through MAE 599 and MAE 792. He directs the Sun Robotics Lab (https://sunroboticslab.github.io), which collaborates across biomechanics, materials science, and control theory to develop next-generation adaptive robotic systems with applications in healthcare, exploration, and human augmentation.

• Soft Robotics
• Artificial Muscles
• Robotics
• +۳ مورد دیگر

Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.

• Computer Vision
• Artificial Intelligence
• Human Behavior Analysis
• +۷ مورد دیگر

Chao Cheng serves as an Adjunct Associate Professor in the Department of Biomedical Data Science at the Geisel School of Medicine, Dartmouth College. His academic appointment contributes to one of the nation's leading medical research institutions, focusing on data-driven approaches to biomedical challenges. Dr. Cheng's research spans Biomedical Data Science, Health Data Science, Implementation Science, Mental Health Informatics, and Clinical Informatics. His work emphasizes leveraging computational methods to improve healthcare delivery systems, particularly through mental health applications and evidence-based implementation strategies. Recent institutional developments include Dartmouth's $12 million NIH COBRE grant supporting Implementation Science research, reflecting the department's strategic focus areas. The Department of Biomedical Data Science operates within Dartmouth's Geisel School of Medicine ecosystem, which recently launched initiatives like mental health games for autistic youth through the play2Prevent Lab. While specific advising activities aren't documented, the department actively participates in interdisciplinary research programs including MS in Health Data Science and Implementation Science.

• Biomedical Data Science
• Health Data Science
• Implementation Science
• +۲ مورد دیگر

Prof. Dr. sc. techn. ETH Oliver Staadt is Full Professor of Computer Science and Chair of Visual Computing at the University of Rostock , Germany. Since 2023 he also serves as Director of the Institute for Visual and Analytic Computing within the Faculty of Computer Science and Electrical Engineering . Previously he was Dean (2016–2018) and Vice Dean (2010–2016) of the same faculty. Education Ph.D. in Computer Science, ETH Zürich (2001) M.Sc. in Computer Science, TU Darmstadt (1994) Research Interests Prof. Staadt’s research spans virtual and augmented reality , computer graphics , visualization , telepresence , immersive analytics , and human–computer interaction . A particular focus lies on real-time rendering and display technologies for large high-resolution display systems, depth-image enhancement for RGB-D sensors, and interaction techniques that leverage spatial cognition and eye-tracking. His work is frequently applied to collaborative settings and microgravity environments, including experiments aboard parabolic flights and the International Space Station. Recent Publication Trends Between 2019 and 2021 his output centers on foveated rendering , AR viewpoint guidance , collaborative analytics on wall-sized displays , and embodied interaction metaphors . Earlier work addressed bandwidth-efficient telepresence, depth-image filtering, and physically-based animation. The corpus reveals a steady evolution from fundamental graphics algorithms toward applied immersive systems. Scientific Awards & Honors Fellow of the Eurographics Association Associate Editor, IEEE Transactions on Visualization and Computer Graphics (past) Associate Editor, Computers & Graphics (past) Associate Editor, Computer Animation and Virtual Worlds (past) Associate Editor, Frontiers in Virtual Reality (current) Chair, Expert Group on Virtual & Augmented Reality, German Informatics Society (2013–2020) Advising & Funding He has successfully supervised more than ten PhD graduates whose dissertations range from collision detection and physically-based animation to 3D interaction in microgravity and predictive user modeling. Current PhD researchers include Bipul Mohanto, Mana Takhsha, and Sven Kluge. His projects are supported by national and EU programs such as EVOCATION, SMOOTH, ARGuide, 3DPick, DIVA, and Telepresence. Labs & Teams Prof. Staadt leads the Visual Computing Group at Rostock, operating state-of-the-art facilities including large tiled display walls, VR/AR laboratories, and motion-capture systems. The institute hosts interdisciplinary collaborations with partners in visualization, computer vision, psychology, and aerospace engineering.

• Virtual Reality
• Augmented Reality
• Computer Graphics
• +۷ مورد دیگر

Ming-Jun Lai is a Professor in the Department of Mathematics at the University of Georgia. His career spans decades, focusing on multivariate splines, sparse solutions of linear systems, wavelet theory, and their applications in numerical analysis and machine learning. Education: Lai received his Ph.D. from Texas A&M University and completed postdoctoral training at the University of Utah. He has supervised 22 Ph.D. students and two current Ph.D. candidates. Multivariate Splines: Applied to scattered data fitting, numerical PDE solutions, image enhancement, and surface design. Sparse Solutions: Used in compressed sensing, low-rank matrix recovery, and graph clustering. Wavelet Theory: Construction of biorthogonal and tight wavelet frames for image edge detection. Optimal Transport: Numerical solutions for Monge-Ampère equations. Research Trends (2025–2023): Recent work includes interpolating space curves with geometric continuity, spherical spline smoothing, and applications in machine learning, particularly graph clustering and optimal control in biological systems. Scientific Awards: UGA Research Medal (2002) McCay Award (2013) Advisees: Lai has mentored 24 Ph.D. students, including Zhaiming Shen (2024), Jinsil Lee (2023), and current students Valerio Palamra and Ye Tian. Laboratory & Collaborations: He collaborates with institutions like Georgia Tech, UCLA, and Zhejiang University, applying splines in aerospace engineering and biomedical imaging.

• Multivariate Splines
• Sparse Solutions of Underdetermined Linear Systems
• Wavelet Theory
• +۸ مورد دیگر