جستجوی استاد راهنمای دانشگاهی

Jiangwen Sunمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Medicine
• +۶ مورد دیگر

Jiangwen Sun is an Assistant Professor in the Department of Computer Science at Old Dominion University (ODU), within the College of Science. He directs the ODU Computational Systems Medicine Lab and focuses on machine learning approaches for analyzing multi-dimensional biological data (phenome, genome, transcriptome, etc.) to advance precision medicine and its automation. His research is supported by ODU and federal agencies like NIH and NSF. Education: Ph.D., University of Connecticut M.E., Nanjing University, China B.M., Secondary Military Medical University, China Research Interests: Machine learning/data mining for medicine, health, drug discovery, and bioinformatics Multi-view bi-clustering and integrative analysis of genomic/phenotypic data Phenotype refinement and genetic association studies for complex diseases Applications in addiction medicine, cardiovascular biology, and bovine development Publications: Over 40 peer-reviewed articles in top venues like NIPS, ICML, Bioinformatics, and BMC Genomics. Recent work focuses on cryo-EM protein structure analysis, single-cell multiomics, and epigenetic modeling. Awards/Grants: NIH/NSF funding pending; previously supported by UConn's Health Informatics Lab and collaborations with University of Pennsylvania. Teaching: Courses include Machine Learning (CS722/822), Data Structures (CS361), and Deep Learning in Medicine (CS795/895). Labs/Teams: Leads the ODU Computational Systems Medicine Lab and collaborates with UConn Health Informatics Lab, Penn Medicine, and others.

• Computer-aided drug discovery
• Docking
• Virtual screening
• +۸ مورد دیگر

Garrett M. Morris is an Associate Professor in Systems Approaches to Biomedicine at the University of Oxford, affiliated with the Department of Statistics and Green Templeton College. He holds roles as Deputy Director of Graduate Studies, Co-Director of the SABS R³ Centre for Doctoral Training, and Research Fellow at Green Templeton College. His research focuses on computational chemistry, drug discovery, and AI integration in biomedicine. He earned his DPhil from Oxford under Prof. W. Graham Richards, with subsequent work at The Scripps Research Institute and Oxford spinouts like InhibOx and Crysalin. Research interests include protein-ligand docking, virtual screening, and machine learning applications in cheminformatics. Notable contributions include the AutoDock software and the FightAIDS@Home project. He co-organizes conferences like the Royal Society of Chemistry’s 'AI in Chemistry' and founded Comp Chem Kitchen. His lab, Oxford Protein Informatics Group (OPIG), develops novel methods for drug discovery and evaluates AI-based docking methods' validity (e.g., PoseBusters). Recent work critiques AI docking methods' physical plausibility and generalizability. He advises numerous graduate students in statistics and drug discovery, with alumni in academia, pharma, and venture capital. Publications span molecular generation, scoring functions, and computational tools for drug design. Collaborations emphasize reproducibility, responsible research, and cloud computing in biomedicine.

Xuesen Dongمشاهده پروفایل

دانشیار

• Prostate Cancer
• Cancer Therapy Resistance
• Cancer Progression and Metastasis
• +۷ مورد دیگر

Dr. Xuesen Dong is an Associate Professor in the Department of Urologic Sciences at the University of British Columbia and a senior scientist at the Vancouver Prostate Centre. His research focuses on prostate cancer therapy resistance mechanisms and developing novel therapeutics. His educational background: Medical training in China MSc in Molecular and Cellular Biology, Laval University PhD in Physiology, University of Toronto Postdoctoral training: Samuel Lunenfeld Research Institute (Mount Sinai Hospital, University of Toronto) and Vancouver Prostate Centre Research interests include prostate cancer, therapy resistance, RNA splicing, gene regulation, drug development, hormone-dependent cancers, endocrine regulation, and biomarker discovery. His lab uses RNA sequencing and computer-aided drug design to identify new targets and diagnostics for prostate cancer. Scientific awards and funding: Rising Star Award from Prostate Cancer Canada Continuously funded by Canadian Institutes of Health Research (CIHR) grants since 2005 Dr. Dong supervises graduate students in UBC's Cell and Developmental Biology, Experimental Medicine, and Interdisciplinary Oncology programs. His laboratory at the Vancouver Prostate Centre is dedicated to overcoming treatment resistance in prostate cancer.

Shadi Daliliمشاهده پروفایل

دانشیار

• Biologically Active Isatin Derivatives
• Hole Transport Materials (HTMs) for Perovskite Solar Cells
• Augmented Reality in Chemistry Education
• +۳ مورد دیگر

Shadi Dalili is an Associate Professor, Teaching Stream at the University of Toronto's Scarborough (UTSC) campus. He specializes in Chemistry Education and Organic Chemistry research. His roles include teaching undergraduate courses like Organic Chemistry I and mentoring students in directed research projects. Education: BSc, University of Tehran, Iran (1999) MSc, University of Toronto (2001) PhD, University of Toronto (2005) Research Interests: His work focuses on synthesizing biologically active isatin derivatives, developing hole transport materials (HTMs) for Perovskite solar cells, and advancing chemistry education through technologies like augmented reality (AR). Collaborations with Dr. Kagan Kerman and Dr. Meissam Norouzifar address renewable energy materials, while his work with Dr. Maryam Abdinejad led to the ARchemy app, supported by grants from the Centre for Teaching and Learning and ITIF. Publications: Key contributions span AR-based educational tools, organic synthesis methodologies, and drug discovery. Recent work highlights include studies on student perceptions of AR in chemistry and lab skills seminars. Grants & Collaboration: Received $20,000 in grants for ARchemy development and partnered with UofT’s MADlab. Research emphasizes bridging technology and education, with applications in molecular visualization and renewable energy materials. Labs/Teams: Lead the ARchemy project team and collaborates on Perovskite solar cell research, fostering interdisciplinary innovation.

• HIV Research
• Virology
• Immunology
• +۳ مورد دیگر

Yulong Wei is a Researcher in the Department of Microbial Pathogenesis at Yale School of Medicine. His work focuses on understanding viral persistence mechanisms, particularly in HIV-1 and SARS-CoV-2, using cutting-edge genomic and immunological approaches. He explores how host cellular environments influence viral integration, reservoir formation, and immune evasion. Research interests include: HIV reservoir dynamics and latency mechanisms Host-pathogen interactions in viral persistence Single-cell multiomics analysis of viral infections Antiviral drug discovery and repurposing Ribosomal adaptation and translation mechanisms in bacteria Recent work highlights his contributions to understanding how interferon signaling and chromatin structure affect HIV integration sites, as well as computational studies of griseofulvin's potential in combating SARS-CoV-2. He has also investigated evolutionary genomic signatures in microbes related to translation efficiency and environmental adaptation. His lab is part of the Yale School of Medicine's broader efforts in microbial pathogenesis and infectious disease research, with a focus on translational applications for persistent viral infections.

Xiaobo Liمشاهده پروفایل

استاد

• Neuroscience
• Medical Imaging
• Machine Learning Applications in Neurology
• +۳ مورد دیگر

Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.

• Pharmacokinetics
• Pharmacodynamics
• Population Pharmacokinetics
• +۷ مورد دیگر

Leon Aarons is a Professor in the Manchester Pharmacy School at the University of Manchester, UK. He joined the institution in 1976 after a postdoctoral fellowship at the School of Chemistry, University of Leeds. His research focuses on pharmacokinetics, with specialties in population pharmacokinetics and pharmacodynamics (PK/PD), data analysis, and drug development. He collaborates widely in academia and industry, particularly in Europe, and contributes to initiatives like CAPKR (Computer Aided Pharmacokinetic Research) and the COST B25 management committee. Aarons holds editorial roles for journals including the Journal of Pharmacokinetics and Pharmacodynamics (European Editor) and British Journal of Clinical Pharmacology (Executive Editor). His work addresses variability in drug response across populations, with applications in malaria, pediatric care, anti-inflammatories, and oncology. He leads the Statistical Modelling Research Group and oversees collaborations with researchers like Kayode Ogungbenro and Hitesh Mistry. Education: PhD in Pharmacy (1973), University of Manchester MSc in Pharmacy (1971), University of Calgary BSc in Pharmacy (1968), University of Sydney Research Interests: Population PK/PD modeling using Maximum Likelihood and Bayesian techniques Optimal design for clinical trials and PK/PD studies Computer-aided trial design (CATD) via stochastic simulation Drug-drug interaction analysis in diverse patient populations Impact & Collaboration: Aarons' research contributes to UN Sustainable Development Goals, particularly in advancing drug development practices for precision dosing and addressing global health challenges. His work has influenced regulatory policies and industrial practices in pharmacokinetics. Labs & Teams: Active in the Statistical Modelling Research Group, CAPKR collaboration, and affiliated with the Digital Futures Institute and Christabel Pankhurst Institute at the University of Manchester.

Iwan de Eschمشاهده پروفایل

استاد

• Drug Design
• Fragment-Based Drug Design (FBDD)
• Computer-Aided Drug Design (CADD)
• +۵ مورد دیگر

Prof. Iwan de Esch is a Full Professor in the Department of Chemistry and Pharmaceutical Sciences at VU University Amsterdam, leading the Drug Design & Synthesis division and serving as Head of Department. He holds dual appointments in the Faculty of Science and AIMMS (Amsterdam Institute for Molecular and Life Sciences). His research focuses on understanding molecular interactions for drug discovery, employing CADD and FBDD approaches. He co-founded three biotech companies (De Novo Pharmaceuticals, IOTA Pharmaceuticals, Griffin Discoveries) and coordinates the EU-funded FRAGNET network. PhD from VU University (1998) Postdoc at University of Cambridge (Drug Design Group) Recipient of the 2011 Galenus Research Prize Received grants from EU-FP7, IMI, and Dutch STW Research interests span fragment-based drug design, receptor-ligand interactions, and pre-clinical drug candidate development. His work impacts GPCRs, PDEs, kinases, and protein-protein interactions. He teaches courses on drug discovery methodologies and computational tools. Over 200 peer-reviewed publications and 8 datasets/software contributions highlight his impactful career.

Franziska Mathis-Ullrichمشاهده پروفایل

استاد

• Surgical Robotics
• Medical Robotics
• Artificial Intelligence in Medicine
• +۳ مورد دیگر

Prof. Dr. Franziska Mathis-Ullrich is a Professor at Friedrich-Alexander-University Erlangen-Nuremberg (FAU) leading the Surgical Planning and Robotic Cognition Lab (SPARC) in the Department of Artificial Intelligence in Biomedical Engineering. Previously, she was an Assistant Professor at Karlsruhe Institute of Technology (KIT) from 2019 to 2023. Her research focuses on minimally invasive robotic systems, soft robotics, and embedded machine learning for surgical applications. She holds a PhD in Microrobotics from ETH Zurich (2017), with earlier degrees from the same institution. Education: B.Sc. and M.Sc. in Mechanical Engineering and Robotics (ETH Zurich, 2009–2012) Ph.D. in Microrobotics (ETH Zurich, 2017) Research Interests: Minimally invasive medical robotics, soft robotic systems, AI-driven surgical assistance, microrobotics, and robot-assisted surgery. Her work emphasizes translating robotics innovations into clinical applications through interdisciplinary collaboration. Key Awards: IEEE ICRA Best Paper Award in Medical Robotics (2014) IEEE BioRob Best Student Paper Award (2016) ICRA Microassembly Challenge First Prize (2014 & 2015) Forbes 30 under 30 (2017) Grants & Projects: Leading a Bavarian State Ministry-funded project on endometriosis diagnostics (€3M). Active in multidisciplinary collaborations with Erlangen University Hospital. Serves as Vice-President of the German Society for Computer- and Robot-assisted Surgery (CURAC). Labs & Teams: Directs the SPARC Lab, which develops cognitive robotic systems for surgical planning and execution. Collaborates with institutions like Max Planck, Fraunhofer, and Helmholtz.

Xiang Wangمشاهده پروفایل

دانشیار

• Computer-Aided Drug Design
• Machine Learning
• Artificial Intelligence
• +۸ مورد دیگر

Xiang Wang is an Associate Professor in the Department of Pharmaceutical Sciences at Howard University College of Pharmacy, where he also directs the Artificial Intelligence and Drug Discovery Core Laboratory for the District of Columbia Center for AIDS Research (DC CFAR). With approximately 20 years of experience, his work bridges computational methods and pharmaceutical innovation. Dr. Wang's research focuses on advancing drug discovery through cutting-edge technologies. His expertise includes computer-aided drug design (CADD) , machine learning , structure-based drug design (SBDD) , high-throughput screening (HTS) , and biomolecular simulation . He has been actively integrating virtual reality (VR) , augmented reality (AR) , and artificial intelligence (AI) into both pharmacy education and research, reflecting a forward-looking interdisciplinary approach. He has secured over 20 grants from federal, state, university, and international funding bodies and has published around 50 peer-reviewed articles, reviews, book chapters, and patents, along with 190 conference abstracts and invited lectures. His scholarly impact is further amplified by his service as a reviewer for more than 45 major journals, including the Journal of Medicinal Chemistry and Journal of Chemical Information and Modeling . National Institutes of Health (NIH) National Aeronautics and Space Administration (NASA) American Association of Colleges of Pharmacy (AACP) Alzheimer’s Drug Discovery Foundation (ADDF) Dr. Wang has served on review panels for prominent funding agencies such as the NIH, NASA, AACP, and ADDF, contributing to the evaluation and advancement of scientific research in his fields. His leadership in the AI and Drug Discovery Core underscores his role in building research infrastructure and mentoring in a high-impact domain. While specific advisees are not listed, his extensive grant activity and lab direction suggest active student and postdoctoral involvement. His work in developing immersive technologies for professional pharmacy education highlights a commitment to innovation in teaching. The convergence of AI, VR, and AR in his research program points to a vision of next-generation drug discovery and pedagogy.

Niklas Mattsson-Carlgrenمشاهده پروفایل

دانشیار

• Alzheimer's Disease
• Neurodegenerative Diseases
• Tau Pathology
• +۷ مورد دیگر

Niklas Mattsson-Carlgren serves as an Associate Professor and Senior Lecturer at Lund University's Faculty of Medicine, Department of Clinical Sciences. He holds multiple significant roles including Deputy Research Team Manager and Principal Investigator for several major research projects. His affiliations extend to the Wallenberg Centre for Molecular Medicine (WCMM), LU Profile Area: Proactive Ageing, and MultiPark: Multidisciplinary Research Focused on Parkinson's Disease. Dr. Mattsson-Carlgren's research primarily focuses on Alzheimer's disease and other neurodegenerative conditions, with additional work on acute brain injuries such as those following cardiac arrest. His expertise spans neurochemistry, including the regulation of pain and sleep. He employs biochemical measurements, neuroimaging, and cognitive testing to study disease processes in vivo across the spectrum from preclinical disease to advanced dementia. His work aims to improve diagnostic and prognostic methods in clinical practice, enhance clinical trial design for novel therapies, and deepen understanding of disease mechanisms. His recent publications demonstrate a strong emphasis on plasma biomarkers for Alzheimer's disease detection and monitoring, with particular focus on tau and amyloid pathology. The research shows increasing integration of machine learning approaches with traditional biomarker analysis, reflecting a trend toward more sophisticated diagnostic tools that combine multiple data sources for improved accuracy in predicting disease progression. As Principal Investigator for multiple significant projects including 'Longitudinal plasma biomarkers, cognitive data and amyloid PET to improve early prevention of Alzheimer's disease' and 'Validation of an MTBR tau immunoassay for CSF and plasma,' he leads substantial research efforts funded by organizations including Familjen Rönströms stiftelse, Eli Lilly and Company, and the Knut and Alice Wallenberg Foundation. He also serves as supervisor for PhD students, notably for 'Studies of induced neuronal cells in Alzheimer's disease.' Dr. Mattsson-Carlgren is actively involved in the neuroscience community, serving on the program committee for Neuroscience Day 2025. His research contributes to UN Sustainable Development Goals related to health and wellbeing. His work has gained significant attention, with multiple papers being picked up by news outlets and referenced across social media platforms including X (Twitter) and Bluesky.

Jens Carlssonمشاهده پروفایل

استاد

• G protein-coupled receptors (GPCRs)
• Structure-based drug design
• Molecular docking
• +۷ مورد دیگر

Jens Carlsson is a Professor at Uppsala University, affiliated with the Department of Cell and Molecular Biology and the Science for Life Laboratory (SciLifeLab) . His research focuses on computational biochemistry , particularly G protein-coupled receptors (GPCRs) , using physics-based modeling to advance structure-based drug design . Academic rank: Full Professor (since 2022) Key methodologies: Molecular dynamics simulations, docking, free energy calculations Research themes: GPCR ligand interactions, virtual chemical space screening, allosteric modulators Recent work (2025) highlights AI-driven discovery of brain disease therapeutics and DNA repair inhibitors , while 2024 projects explore AlphaFold applications in TAAR1 agonist design . His group has received Swedish Research Council grants (3.6M SEK, 6M SEK) and industry collaborations . Scientific awards include the Göran Gustafsson Prize (2016), Excellence Prize in Molecular Design (2022), and Ingvar Carlsson Award (2012). Key students include PhD candidates Mariama Jaiteh and Pierre Matricon , with postdocs like Nicolas Panel and Duy Duc Vo .

Amanda Watsonمشاهده پروفایل

استادیار

• Wearable Technology
• Biomedical Engineering
• Cyber-Physical Systems
• +۲ مورد دیگر

Amanda Watson is an Assistant Professor in Electrical and Computer Engineering at the University of Virginia, with joint appointments in Computer Science. She leads the Watson Research Lab within the UVA Link Lab, a multidisciplinary center for Cyber-Physical Systems (CPS) and Internet of Medical Things (IoMT) research. Her work bridges wearable technology with healthcare and athletic performance applications, focusing on noninvasive monitoring, physiological signal analysis, and safety-critical medical devices. She is also the cofounder and CEO of Luminosity Wearables, commercializing a noninvasive continuous glucose monitor. Education: PhD in Computer Science (2020) - College of William & Mary MSc in Computer Science (2016) - College of William & Mary Bachelors in Computer Science and Mathematics (2014) - Drury University Her research spans multiple domains including: Wearable spectroscopy for nutrition and skin health Machine learning for drug overdose and fall risk detection Biomechanical monitoring in sports medicine Wearable support for visual and neurological impairments IoMT device integration and analytics Recent publications (2024-2025) show strong emphasis on calibration-free physiological monitoring systems, with technical contributions in spectral analysis , multi-wavelength sensing , and rapid prototyping for healthcare wearables. Applications range from maternal health to gerontological social isolation detection. Lab and Team: The Watson Research Lab at UVA develops wearable solutions for clinical and athletic contexts, with ongoing collaborations in the PRECISE Center at University of Pennsylvania and LENS lab at William & Mary alumni network. She works with multidisciplinary teams including engineers, clinicians, and data scientists.

• Computational Chemistry
• Artificial Intelligence in Chemistry
• Molecular Modeling
• +۶ مورد دیگر

Professor Neil Berry is a distinguished academic and researcher in the Department of Chemistry at the University of Liverpool's School of Physical Sciences. He currently serves as Head of Department (2018-2024) and has held various significant administrative roles including Director of Post Graduate Research for the School of Physical Sciences (2015-2018) and Departmental PGR Lead. His academic journey began at Exeter College, University of Oxford where he earned his MChem in Chemistry (1999) followed by a DPhil (2002) under the supervision of Professor Paul Beer. Professor Berry's research expertise spans computational chemistry with a focus on applying AI, machine learning, and molecular modeling to solve complex problems in chemistry. His work primarily centers on four key areas: medicinal chemistry (particularly antimalarial and antiparasitic drug discovery), materials chemistry (including metal-organic frameworks and supramolecular gels), reaction mechanisms, and chemoinformatics. His group adopts a rational approach to research through molecular and material design, integrating computational modeling with experimental validation through close collaborations with the Liverpool School of Tropical Medicine, University of Washington, and industry partners including GSK, Bayer, and Unilever. His publication record demonstrates a strong focus on applying computational methods to drug discovery for neglected tropical diseases and advanced materials science. Recent work shows increasing integration of machine learning techniques across all research areas, with significant contributions to antimalarial drug development, snakebite treatment, and materials science applications. His research consistently bridges computational prediction with experimental validation, demonstrating the practical impact of his work. Treasurer - Royal Society of Chemistry Chemical Information and Computer Applications Group (2021 - present) Invited committee member - Royal Society of Chemistry Chemical Information and Computer Applications Group (2015 - present) Fellow of Higher Education Academy (2012 - present) Member of Royal Society of Chemistry (2005 - present) Referee for journals including Nature Communications, Journal of Medicinal Chemistry, and Journal of Computer Aided Molecular Design As an educator, Professor Berry has supervised numerous PhD students and postdoctoral researchers, with a particular emphasis on integrating computational approaches with experimental chemistry. His teaching innovations include Chemtube3D (web-based interactive 3D simulations of organic reactions), lecture recording systems, and ChemPreLab (a pre-lab interactive tutoring system). His research group operates at the intersection of chemistry, biology, and computer science, securing substantial funding from EPSRC, MRC, Wellcome Trust, and industry partners.

Sophia Banoمشاهده پروفایل

استادیار

• Computer Vision
• Surgical Robotics
• Medical Imaging
• +۳ مورد دیگر

Sophia Bano is an Assistant Professor in Robotics and Artificial Intelligence at the Department of Computer Science, University College London (UCL), since November 2022. She is affiliated with the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Surgical Robot Vision group, UCL Robotics Institute, and the Centre for Artificial Intelligence. Previously, she was a Senior Research Fellow at WEISS, contributing to the GIFT-Surg project. Her research focuses on AI-driven techniques for context awareness, navigation, and surgical robotics in minimally invasive procedures, including endoscopic workflow analysis, 3D reconstruction, and surgical vision systems. Education: BEng in Mechatronics Engineering (NUST, Pakistan), MSc in Electrical Engineering (NUST), MSc in Computer Vision and Robotics (Erasmus Mundus VIBOT), PhD from Queen Mary University of London and Technical University of Catalonia (Erasmus Mundus Fellowship). Post-doctoral work at University of Dundee (EPSRC ACE-LP project) and Imperial College London (ERC STING project). Research interests include computer vision for surgery, medical imaging, surgical robotics, and AI in healthcare. She leads the UKRI EPSRC-funded 'AI-enabled Decision Support in Pituitary Surgery' project and contributed to the GIFT-Surg and CARES projects. Publications span 3D reconstruction in fetoscopy, surgical workflow recognition, and AI models for medical imaging. Awards include the IJCARS-MICCAI Best Paper Award (2020) and the Best Innovation Award at the 2022 Surgical Robot Challenge. She organizes conferences like EndoVis and serves as a reviewer for journals like IEEE Transactions on Medical Imaging and MICCAI.