جستجوی استاد راهنمای دانشگاهی

• Data Science
• Machine Learning
• Harmonic Analysis
• +۷ مورد دیگر

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

Zixiang Xiongمشاهده پروفایل

استاد

• Machine Learning
• Image/Video Processing
• Network Information Theory
• +۷ مورد دیگر

Zixiang Xiong is a Professor and Associate Department Head in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Robert M. Kennedy '26 Endowed Professorship II. He earned his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1996. His career includes roles at Princeton University, University of Hawaii, and Texas A&M since 1999. Education: Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign, 1996 Visiting Research Associate, Princeton University, 1995–1997 University of Hawaii, 1997–1999 Research Interests: Focuses on machine learning, image/video processing, federated learning, network information theory, biomedical engineering, and communications. His work spans distributed source coding, genomic signal processing, and energy-efficient systems. Publications & Awards: Over 200 publications, including seminal works on distributed video coding and network information theory. Notable awards include the NSF Career Award (1999), ONR Young Investigator Award (2001), IEEE Fellow (2006), and the ECE Outstanding Faculty Award (2024). His research has led to patents in video compression and multimedia systems. Grants & Advising: Active in NSF-funded projects on coding theory and energy-delay tradeoffs. Advises numerous PhD and MS students, with over 50 alumni in academia and industry. Collaborates on biomedical imaging, remote sensing, and federated learning initiatives. Labs & Teams: Leads a dynamic research group at Texas A&M, focusing on cutting-edge projects in signal processing and machine learning applications. Collaborates with industry and governmental agencies on applied research.

Janie McClurkin Mooreمشاهده پروفایل

استادیار

• Post-harvest treatment technologies
• Bioprocess engineering
• Biomass valorization
• +۸ مورد دیگر

Dr. Janie McClurkin Moore is an Assistant Professor in the Department of Biological and Agricultural Engineering at Texas A&M University, part of the College of Agriculture & Life Sciences. Her research focuses on post-harvest technologies, food safety, and innovative engineering education strategies. She holds a PhD in Agricultural and Biological Engineering from Purdue University and has extensive expertise in applying cold plasma technology to agricultural challenges. Education: B.S., Bio-Environmental Engineering, North Carolina A&T State University (2006) M.S.A.B.E., Agricultural and Biological Engineering, Purdue University (2009) Ph.D., Agricultural and Biological Engineering, Purdue University (2015) Research interests span post-harvest treatment innovations, biomass valorization, and educational methodologies. Her work emphasizes non-thermal plasma applications for food safety, mycotoxin mitigation, and sustainable agriculture practices. Publications reveal a strong focus on plasma technology for pest control, food preservation, and hydroponic systems. Her engineering education research explores inclusive pedagogy and curriculum development in diverse educational settings. Advising and grants include contributions to the NSF Convergence Accelerator project addressing food security in extreme environments. She is affiliated with Texas A&M AgriLife Research, advancing applied agricultural solutions through interdisciplinary collaboration.

• Machine Learning
• Computer Vision
• Biomedical Engineering
• +۶ مورد دیگر

Prof. Huiyu Zhou is a Professor of Machine Learning at the School of Computing and Mathematical Sciences, University of Leicester . He leads the Biomedical Image Processing Lab (BIPL) and serves as Deputy Director of the Research Centre for Artificial Intelligence, Data Analytics and Modelling (AIDAM) , and a member of the Apollo-Leicester Centre for Digital Health and Precision Medicine (CDHPM) . Previously, he held roles as Reader at the University of Leicester and Lecturer at Queen’s University Belfast. Education: BEng in Radio Technology, Huazhong University of Science and Technology, China MSc in Biomedical Engineering, University of Dundee, UK PhD in Computer Vision, Heriot-Watt University, UK His research focuses on AI applications in biomedical imaging , robotics , and health informatics . Current projects include advancing medical image analysis, secure learning systems, and interdisciplinary collaborations in precision medicine. He chairs editorial boards for journals like IEEE Transactions on Human-Machine Systems and Pattern Recognition , and serves as Associate Editor for ICRA and Area Chair for BMVC and IJCAI. Prof. Zhou holds administrative roles such as PGR Director of CMS (2022–2025) , MSc Programme Director (2018–2019) , and coordinates MSc Distance Learning in Informatics . He reviews grants for global bodies including Horizon Europe, UKRI, and the Wellcome Trust, and assesses faculty promotions in the UK, China/HK, Israel, and others. He also acts as an external examiner for institutions across the UK, Hong Kong, Australia, India, and China. Affiliations include the National Academy of Artificial Intelligence (NAAI) and collaborations in interdisciplinary teams for digital health and AI-driven biomedical research.

Mitra Taheriمشاهده پروفایل

استاد

• Materials Science
• Electron Microscopy
• Additive Manufacturing
• +۶ مورد دیگر

Mitra Taheri is a Professor in the Department of Materials Science and Engineering at Johns Hopkins University, serving as Director of the Materials Characterization and Processing (MCP) facility and a member of the Hopkins Extreme Materials Institute. She holds affiliations with the Pacific Northwest National Laboratory and the Ralph O’Connor Sustainable Energy Institute. Her research focuses on electron microscopy, particularly in-situ and operando techniques, combined with artificial intelligence to study materials under extreme conditions (e.g., high temperatures, radiation, and oxidation). She aims to accelerate materials discovery by integrating AI with microscopy for real-time analysis. Dr. Taheri earned her BS, MSE, and PhD in Materials Science and Engineering from Carnegie Mellon University. Her work spans corrosion-resistant alloys, additive manufacturing, quantum materials, and biomaterials. Research sponsors include PNNL, JHU, NSF, ARPA-E, and ONR. She leads the Dynamic Characterization Group (DCG), which develops autonomous platforms for materials analysis and explores applications in energy, aerospace, and medical systems. Key research areas include: Design of corrosion-resistant multi-principal element alloys AI-driven microscopy for real-time material behavior insights Additive manufacturing of soft magnetic composites for electric vehicles Biomedical hydrogels for tissue engineering Her team develops novel materials and tools to probe structural, functional, and biological systems across scales, with an emphasis on sustainability and extreme environment applications.

Pankaj Mehtaمشاهده پروفایل

استاد

• Statistical Physics
• Systems Biology
• Ecological Dynamics
• +۳ مورد دیگر

Pankaj Mehta is a Professor in the Department of Physics at Boston University, with additional affiliations in the Department of Biomedical Engineering. His research bridges statistical physics, theoretical biology, and interdisciplinary systems approaches. Key areas include ecological dynamics, synthetic biology, and the application of machine learning principles to biological systems. His work focuses on understanding emergent phenomena in biological systems, such as cell fate decisions, ecosystem stability, and signal processing in cellular networks. He has pioneered methods combining physics-based modeling with computational tools to study complex systems, including gene circuits, microbial communities, and cancer dynamics. Recent contributions highlight the use of order parameters for interpreting cellular states, geometric frameworks for ecological niches, and machine learning analogies to ecological principles. His interdisciplinary approach integrates experimental data with theoretical models to address questions in biomedicine, environmental science, and fundamental physics.

• Soft Matter Physics
• Colloid & Interface Science
• Polymer Chemistry
• +۳ مورد دیگر

Prof. Walter Richtering is a Universitätsprofessor at RWTH Aachen University, affiliated with JARA-SOFT and the Institute of Physical Chemistry (IPC). His research focuses on soft matter physics, colloids, and polymer chemistry with emphasis on microgels, nanogels, and their applications in biomaterials and materials science. He leads the 'Physical Chemistry of Solids' group and contributes to the CRC 985 (Functional Microgels and Microgel Systems). Key interests include quantifying softness in colloids, interfacial phenomena, and developing educational tools like AFM-based microgel experiments for undergraduate labs. Position: Professor of Physical Chemistry Affiliations: JARA-SOFT, IPC RWTH Aachen, CRC 985 Research Groups: Physical Chemistry of Solids, Polymers and Colloids His work explores structure-property relationships in soft materials, including phase behavior under non-equilibrium conditions, thermoresponsive systems, and catalytic microgel applications. Recent studies address microgel mechanics, anisotropic architectures, and filtration technologies.

Sanjana Mudduluruمشاهده پروفایل

استادیار

• Machine Learning
• Medical Image Processing
• Data Analytics
• +۳ مورد دیگر

Sanjana Mudduluru is an Assistant Professor in the School of Computer Science at the University of Oklahoma (OU). She holds a BS from Jawaharlal Nehru Technological University (India), an MS in Data Science & Analytics, and a PhD in Computer Science, all from OU. Her research focuses on applying computer vision and machine learning to biomedical imaging, particularly in cancer research and medical diagnostics. She has extensive experience in software development and programming. Education: Ph.D., Computer Science, University of Oklahoma M.S., Data Science & Analytics, University of Oklahoma B.Tech, Computer Science, Jawaharlal Nehru Technological University Her research interests span machine learning, medical image processing, data analytics, and computer science education. She explores innovative deep learning models for medical image segmentation, classification, and synthetic data generation to enhance AI efficacy in healthcare. Recent work includes hybrid models for computer-aided diagnosis and self-supervised learning for rock image analysis. Awards: Dissertation Excellence Award (2023) Tomorrows Engineer Scholarship (2021–2022) CS Alumni Graduate Fellowship (2021–2022) Dr. Mudduluru has no listed advisees but has contributed to grants related to biomedical imaging research. She is affiliated with OU’s Devon Energy Hall and actively publishes in interdisciplinary fields blending computer science with healthcare applications.

Lukas Engelmannمشاهده پروفایل

مدرس ارشد

• History of Epidemiology
• History of Science and Medicine
• Digital Health
• +۷ مورد دیگر

Lukas Engelmann is a Senior Lecturer at the University of Edinburgh , specifically within the Science, Technology and Innovation Studies department under the School of Social and Political Science . His research focuses on the history and sociology of biomedicine , with particular interest in epidemiological reasoning , visual cultures of disease , digital epidemiology , and decolonial approaches to medical history . The Epidemy Lab , which he founded, explores the historical development of epidemiology and its contemporary influence on data-driven public health and pandemic policy-making . Engelmann's work has been funded by prestigious grants including an ERC Starting Grant (2021-2025) for his research on the history of epidemiological reasoning, and support from the Wellcome Trust for projects examining the social dimensions of digital health . His book 'Mapping AIDS' (2018) established him as a leading scholar in medical visualization , while 'Sulphuric Utopias' (2020) with Christos Lynteris explores the technological history of maritime sanitation and its political implications. Recent publications emphasize the visual and data practices that have shaped epidemiology, including works on epidemic modeling during the COVID-19 pandemic , the history of plague mapping , and the ethical implications of digital phenotyping . He has also contributed to interdisciplinary discussions on syndemics , co-infection epistemology , and the commercialization of bacteriology in the early 20th century. His scientific contributions have earned recognition through editorial roles in journals like Big Data and Society , and collaborative projects such as 'Working with Diagrams' (2022) which investigates the epistemological role of visual tools in medical knowledge production. Scientific Awards and Funding: ERC Starting Grant (2021-2025) Wellcome Trust Institutional Support Fund British Academy/Leverhulme Small Research Grant Chancellor's Fellowship (University of Edinburgh) 'Sulphuric Utopias' listed in The Guardian's 30 Books to Understand the World (2020)

Lin Yangمشاهده پروفایل

استادیار

• Reinforcement Learning Theory
• Learning for Control
• Non-convex Optimization
• +۱ مورد دیگر

Lin Yang is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA). His research focuses on reinforcement learning theory and applications, learning for control, non-convex optimization, and streaming algorithms. Education: PhD in Computer Science and PhD in Physics & Astronomy - Johns Hopkins University (simultaneously) Bachelor's degree in Math and Physics - Tsinghua University Research Interests: His work spans reinforcement learning theory and its applications, particularly in learning for control systems. He also investigates non-convex optimization techniques and streaming algorithms for efficient data processing. Scientific Awards: Dean Robert H. Roy Fellowship - Johns Hopkins University Previous Positions: Before joining UCLA, he was a postdoc at Princeton University working with Professor Mengdi Wang.

Doron Betelمشاهده پروفایل

استادیار

• Computational Genomics
• Bioinformatics
• Single-cell RNA-seq Analysis
• +۳ مورد دیگر

Doron Betel serves as an Assistant Professor at Weill Cornell Medicine's Graduate School of Medical Sciences, with affiliations in both the Physiology, Biophysics & Systems Biology and Computational Biology programs. He directs the Applied Bioinformatics Core (ABC), a central service group providing specialized computational and analytical support for biomedical research across multiple institutions. Dr. Betel's research focuses on developing computational genomic tools for studying human diseases and cellular development, with emphasis on integrative analyses of genomic and epigenomic data from high-throughput assays. His work addresses specific questions related to disease progression, treatment response, stem cell differentiation, and neurological processes through two closely interacting research groups: the Applied Bioinformatics Core and his independent research lab. The analysis of his recent publications reveals a strong emphasis on single-cell and spatial genomics, cross-species data integration, and machine learning applications in cancer immunology and neurodegenerative disease modeling. His research spans multiple high-impact areas including cancer immunotherapy, stem cell biology, diabetes research, and cardiovascular regeneration, with numerous publications in top journals like Nature, Cell, and Nature Immunology. Through the Applied Bioinformatics Core, Dr. Betel provides extensive analytical support across various genomic platforms including single-cell RNA-seq, spatial transcriptomics, ChIP-seq, ATAC-seq, and variant calling. The Core serves as a vital resource for researchers at Weill Cornell Medicine and the broader Tri-Institutional network, offering specialized analysis, computational pipelines, and training services. Dr. Betel maintains extensive collaborations with leading researchers including Lorenz Studer at MSKCC for stem cell and neurodegenerative disease research, Tuomas Tammela for cancer genomics, and multiple immunology researchers studying T cell function in autoimmunity and cancer. His work bridges computational methodology development with direct biomedical applications across multiple disease areas.

• Nano-Engineering
• Opto-Electronics
• Photonics
• +۱۸ مورد دیگر

Dr. Anna Baldycheva is a Senior Lecturer in Electronic Engineering at the University of Exeter, within the College of Engineering, Mathematics and Physical Sciences. She leads the interdisciplinary STEMM Laboratory, focusing on applied R&D in smart materials, photonics, AI, and IoT. With prior research experience at MIT, Trinity College Dublin, and Tyndall National Institute, she has established herself as an internationally recognized innovator and entrepreneur in emerging technologies. PhD in Electronic and Electrical Engineering, Trinity College Dublin (2008–2012) BSc (Hons) in Physics, St. Petersburg State University (2003–2008) Postgraduate Certificate in Academic Practice, University of Exeter (2016–2017) Postgraduate Certificate in Technology Management, Smurfit Business School (2009–2010) Her research spans Nano-Engineering, Opto-Electronics, Photonics, AI, and IoT , with a strong emphasis on real-world applications. She pioneers work in fluid opto-electronics , graphene nanocoatings , and AI-driven emotion recognition and early cancer detection . Her lab develops smart composite materials for flexible electronics, e-textiles, and structural applications, integrating machine learning into healthcare, education, and communications systems. The recent publications highlight a strong trend toward applied interdisciplinary innovation , combining materials science with AI and photonics for healthcare diagnostics, energy-efficient computing, and educational technology. Her work frequently bridges fundamental physics with commercialization potential, as seen in spin-out technologies like GSurf and the Electronic-Nose for lung cancer detection. Fellow, Royal Microscopical Society (RMS) Fellow, Higher Education Academy (FHEA) Expert, Future and Emerging Technologies, European Commission Featured in Forbes and Forbes Tech Council Editor-in-Chief, InSTEMM Journal Associate Editor, Nature Scientific Reports and Discover Nano Trustee, Royal Microscopical Society Founder, STEMM Global Scientific Society Founder, It’s Her! Women in STEMM Initiative Dr. Baldycheva actively supervises PhD students and has secured industrial collaborations with organizations such as Qinetiq and Lumentum. She leads multiple outreach initiatives, including STEMM Junior for underprivileged children, and serves on the committee for the Jocelyn Bell Brunel PhD Scholarship. She has raised significant research funding through national and international grants, though specific grant names are not listed. She leads the STEMM Laboratory , a multidisciplinary research group with divisions in Smart Composite Materials, Machine Learning & AI, and Opto-Electronics & Photonics. The lab emphasizes industry collaboration and technology transfer, having produced a university spin-out (GSurf) and multiple media-highlighted innovations.

Xin Luمشاهده پروفایل

دانشیار

• Cancer Biology
• Tumor Microenvironment
• Tumor Immunology and Immunotherapy
• +۱۲ مورد دیگر

Xin Lu is the John M. and Mary Jo Boler Collegiate Associate Professor in the Department of Biological Sciences at the University of Notre Dame. She is a full member of the Harper Cancer Research Institute (HCRI), the Boler-Parseghian Center for Rare and Neglected Diseases (CRND), and the Tumor Microenvironment and Metastasis Program at the Indiana University Simon Comprehensive Cancer Center. Her research spans tumor immunology, immunotherapy, metastasis, and multi-omics, with a focus on prostate, breast, and rare cancers. Ph.D. in Molecular Biology, Princeton University (2004–2010) B.S. in Biological Sciences, Tsinghua University, China (2000–2004) Postdoctoral Fellow, Dana-Farber Cancer Institute and M.D. Anderson Cancer Center (2010–2016) Assistant to Associate Professor, University of Notre Dame (2017–Present) Dr. Lu's research investigates the molecular and cellular mechanisms of tumor-immune crosstalk, particularly the role of myeloid-derived suppressor cells (MDSCs) and neutrophils in promoting immunotherapy resistance. Her lab uses genetically engineered mouse models, functional genomics, single-cell and spatial transcriptomics, and high-throughput screening to uncover novel therapeutic targets. A major focus is on how cancer-cell-intrinsic oncogenic signaling shapes the immunosuppressive tumor microenvironment. Her recent publications reveal mechanisms such as Acod1-mediated ferroptosis resistance in neutrophils, Pygo2-driven immunosuppression in prostate cancer, and the efficacy of ketogenic diets in overcoming checkpoint blockade resistance. These studies span multiple disciplines including cancer biology, immunology, metabolism, epigenetics, and bioengineering, reflecting a highly integrative approach to immuno-oncology. Jane Coffin Childs Postdoctoral Fellow John M. and Mary Jo Boler Faculty Appointment Cluster Chair, Cellular & Molecular Biology, IBMS PhD Program Junior Chair, Boler-Parseghian Center for Rare and Neglected Diseases Dr. Lu mentors a diverse team of graduate students, postdoctoral fellows, and undergraduates. Her lab is actively recruiting and has secured funding from federal agencies and private foundations. She collaborates with chemists, bioengineers, and bioinformaticians to develop novel therapeutics, including antibody-drug conjugates, CAR-NK cells, and small-molecule inhibitors. Her lab also develops innovative platforms like mini-tumor chips for immunotherapy evaluation. Her lab maintains affiliations with multiple interdisciplinary centers including the Warren Family Center for Drug Discovery, Eck Institute for Global Health, and Berthiaume Institute for Precision Health, underscoring her collaborative and translational research vision.

Sri Kurniawanمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Assistive Technology
• Virtual Reality Applications
• +۴ مورد دیگر

Sri Kurniawan is a researcher at the Computational Media Department within Baskin Engineering, University of California Santa Cruz . With a focus on Human-Computer Interaction , their work spans assistive technology , virtual reality applications , and accessibility design for aging populations and people with disabilities. Key research areas: Accessibility , Virtual Reality , Human-Computer Interaction Recent work explores immersive systems for emergency preparedness and Mixed Reality in biomedical visualization Publications from 2000-2025 demonstrate sustained engagement in mobile health and inclusive game design . Collaborations with institutions like University of Manchester and University of California systems highlight cross-continental research impact.

• Bioinformatics
• Systems Biology
• Disease Module Identification
• +۶ مورد دیگر

Zelmina Lubovac is a Senior Lecturer in BioInformatics at the School of Bioscience, University of Skövde. She serves as both a Course Coordinator for multiple undergraduate and graduate courses in bioinformatics and a Programme Coordinator for Master's level programs. Her academic work focuses on the intersection of computational methods and biological applications, particularly in disease analysis and biomarker discovery. Dr. Lubovac's research spans several key areas in bioinformatics and systems biology: Disease module identification in complex biological networks Multi-omics integration (genomics, proteomics, metabolomics) for biomarker discovery Machine learning applications in RNA-seq and other high-throughput biological data Development of bioinformatics software tools for network analysis miRNA analysis in cancer and neurological disorders Her recent publications (2022-2024) demonstrate a strong focus on applying computational approaches to understand disease mechanisms, particularly in pancreatic cancer and multiple sclerosis. She has developed several widely-used bioinformatics tools including MODalyseR, MODifieR, and TFTenricher that facilitate disease module analysis and gene network interpretation. Her work often involves collaborative research with clinical teams to translate computational findings into potential diagnostic applications. Dr. Lubovac has been involved in significant research projects including: BIO-AID (Biomedical AI-driven data analytics): Oct 2020 - Sep 2024 Systems Biology DMDPipe: Mar 2018 - Feb 2021 She actively contributes to both undergraduate and graduate education at the University of Skövde, coordinating multiple courses and programs in bioinformatics and bioscience, with a clear emphasis on preparing students for careers at the intersection of biology and computational science.