جست‌وجوی استاد و پژوهشگر

Marina S. Leiteمشاهده پروفایل

استاد

• Materials Science
• Renewable Energy Materials
• Optical Materials
• +۳ مورد دیگر

Marina S. Leite is a Professor in the Department of Materials Science and Engineering at the University of California, Davis. Her research focuses on novel materials for renewable energy, optical devices, and materials under extreme environments. She leads the Leite Lab, pioneering work in perovskite photovoltaics, thermophotovoltaic emitters, and transient photonics using machine learning for accelerated materials discovery. Her group combines advanced characterization techniques with computational methods to address challenges in energy harvesting and optical material performance. PhD: Not explicitly listed in provided text Her research interests include: Machine learning-driven materials discovery Halide perovskites for stable solar cells High-temperature optical materials Transient photonics using magnesium-based systems Thermophotovoltaic emitter design Key research trends from recent articles emphasize AI integration for predicting material behaviors, environmental stressor impacts on optoelectronics, and alloy systems for dynamic optical properties. Her lab has developed methods for automated experimentation and spectral selectivity in emitters. 2025 Optica Fellow 2025 SPIE Fellow Advising: Supervises students like Hannah Darr. Active in DARPA cross-disciplinary projects and editorial roles in energy journals. Leads grants focused on machine learning in materials science and photonic device development. The Leite Lab collaborates on projects involving transient materials and high-temperature photonics. Future work includes scaling superabsorber technologies, developing eco-friendly Pb-free perovskites, and advancing AI tools for material property prediction.

Shaomeng Wangمشاهده پروفایل

استاد

• Cancer Biology
• Pharmacology
• Drug Development
• +۳ مورد دیگر

Shaomeng Wang is a Warner-Lambert/Parke-Davis Professor of Medicine, Medical Director, and holds professorships in Internal Medicine, Pharmacology, and Medicinal Chemistry at the University of Michigan's Medical School and College of Pharmacy. He specializes in cancer biology, pharmacology, and drug development with a focus on molecular degraders like PROTACs. His research includes developing therapies targeting MDM2, STAT proteins, and androgen receptors for cancers such as breast, prostate, and pancreatic cancers. He leads projects at the Rogel Cancer Center and Center for Computational Medicine and Bioinformatics. Recent work includes novel degraders for IKZF2, SMARCA2, and CBP/p300, demonstrating efficacy in tumor regression and immunotherapy. His publications emphasize translational research bridging basic science and clinical applications. Education: PhD in relevant field (not explicitly stated in text) Affiliations: Medical School, College of Pharmacy, Rogel Cancer Center, Center for Computational Medicine and Bioinformatics (CCMB) Grants/Projects: Multiple NIH-funded projects on molecular degraders (implied via publication outputs) Labs/Teams: Leads drug discovery teams focusing on cancer immunotherapy and targeted therapies Research interests emphasize molecular mechanisms of cancer progression, development of targeted therapies, and PROTAC-based degraders to overcome drug resistance. Recent studies highlight tumor microenvironment modulation and immune checkpoint strategies.

Lucy Gloverمشاهده پروفایل

پژوهشگر

• Molecular Parasitology
• DNA Repair
• Antigenic Variation
• +۷ مورد دیگر

Lucy Glover is a Researcher and Group Leader of the Trypanosome Molecular Biology laboratory at the Department of Parasites and Insect Vectors, Institut Pasteur, Paris, France. She joined the institute in March 2016 as a G5 group leader, focusing on the molecular mechanisms of antigenic variation and DNA repair in Trypanosoma brucei , the causative agent of Human African Trypanosomiasis. Her research integrates molecular, biochemical, and high-throughput approaches to study DNA recombination and immune evasion. Institut Pasteur, Paris (2016–Present) PhD, London School of Hygiene and Tropical Medicine Her research interests center on antigenic variation , DNA repair , and homologous recombination in trypanosomes. She investigates how DNA double-strand breaks influence variant surface glycoprotein (VSG) switching, a critical process for parasite survival in the host. Her lab uses advanced techniques such as RIT-seq, phosphoproteomics, and CRISPR-based tools to dissect these mechanisms at the molecular level. The recent publications of Lucy Glover span molecular parasitology, DNA damage response, and diagnostic development. Key themes include the role of the MRN complex in DNA repair, phosphoproteomic profiling of DNA damage responses, and the design of CRISPR-based diagnostics for HAT. Her work reveals how chromosomal context influences repair pathways and antigenic variation, contributing to a deeper understanding of parasite biology. While no formal scientific awards are listed in the provided text, her contributions are evident through high-impact publications and leadership in collaborative research projects. Lucy Glover advises several PhD and postdoctoral researchers, including Emilia MacLaughlin, Nuria Sima Teruel, and Alix Thivolle. Her lab is supported by institutional initiatives such as LabEx IBEID and transversal drug discovery programs. She collaborates with teams working on host-pathogen interactions, genomics, and infectious disease epidemiology. Her laboratory is part of the Department of Parasites and Insect Vectors and participates in departmental retreats and seminars. The team includes post-docs, research engineers, and administrative staff, reflecting a collaborative and interdisciplinary research environment focused on combating neglected tropical diseases.

Md Arifuzzamanمشاهده پروفایل

استادیار

• High-Performance Networking
• Quantum Networking
• Distributed Systems
• +۳ مورد دیگر

Dr. Md Arifuzzaman is an Assistant Professor in the Department of Computer Science at Missouri University of Science and Technology (Missouri S&T). He specializes in High-Performance Systems, Quantum Networking, and Distributed Systems, focusing on optimizing large-scale system performance and scalability. His work addresses challenges in next-generation networks and storage systems, with publications in top venues like IEEE TPDS and ACM Supercomputing. Education: Ph.D. in Computer Science and Engineering, University of Nevada, Reno (2023) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2016) Research Interests: Dr. Arifuzzaman's work spans cutting-edge topics including quantum entanglement routing, reinforcement learning for network optimization, and high-speed file transfer protocols. His research emphasizes practical solutions for emerging technologies like terabit networks and quantum communication systems. Publications: Recent work focuses on quantum network protocols, machine learning-driven network probing, and storage reliability. His articles highlight advancements in both theoretical frameworks and real-world system implementations. Awards: No specific awards mentioned in the provided information. Advising/Grants: Details regarding student advising and grant activities are not explicitly stated in the text.

Christoph T. Kochمشاهده پروفایل

استاد

• Electron Microscopy
• Materials Science
• Condensed Matter Physics
• +۳ مورد دیگر

Christoph T. Koch is a Professor of Physics at Humboldt-Universität zu Berlin, where he has held the W3 Chair since 2015. Previously, he held a similar position at Ulm University (2011–2015), supported by the Carl Zeiss Foundation. His research focuses on advanced electron microscopy techniques, including quantitative transmission electron microscopy (TEM), electron holography, and strain mapping. He leads the AG Strukturforschung/Elektronenmikroskopie group, advancing materials science through innovations in imaging and spectroscopy. Education: B.Sc./M.Sc. in Physics at Heidelberg University (1996–1998), followed by an exchange at Arizona State University (1997–1998). PhD in Physics from Arizona State University (2002, advisor: Prof. John C.H. Spence). Postdoctoral research at the Max Planck Institute for Metals Research, Stuttgart (2002–2011). Research interests include: Electron diffraction and phase retrieval Nanometer-scale strain and defect analysis Electron energy-loss spectroscopy (EELS) for plasmonics and bandgap mapping Development of FAIR data infrastructure for materials science Leadership: Managed the Department of Physics at Humboldt University (2020–2024). Collaborates widely, with key co-authors including P.A. van Aken, W. Sigle, and C. Felser. His work bridges experimental microscopy and computational modeling, addressing challenges in semiconductors, ceramics, and 2D materials. Notable contributions include pioneering methods for 3D reconstruction via electron ptychography, dynamic electron diffraction analysis, and strain mapping in advanced CMOS technologies. Current efforts emphasize real-time imaging and AI-driven data analysis in materials research.

• Soft Materials
• Quantum Mechanics
• Machine Learning
• +۳ مورد دیگر

Professor Nicholas E. Jackson is an Assistant Professor of Chemistry at the University of Illinois, affiliated with the College of Liberal Arts & Sciences and the Beckman Institute for Advanced Science and Technology. He holds the Lincoln Excellence for Assistant Professors Scholar distinction. His research focuses on soft materials, quantum mechanics, and machine learning, particularly in developing coarse-grained electronic structure methods and sustainable polymer design. Education: B.A. in Physics from Wesleyan University (2011), Ph.D. in Chemistry from Northwestern University (2016). Postdoctoral work as a Named Fellow and Assistant Scientist at Argonne National Laboratory (2016–2021). Joined the University of Illinois faculty in 2021. Research interests include conjugated materials theory, electronic structure predictions, and interdisciplinary applications of machine learning in materials science. His work bridges computational chemistry, materials engineering, and AI, addressing challenges in renewable energy and sustainable materials. Recent recognition: 2025 Kavli Foundation Emerging Leader in Chemistry Award for innovative research in young scientists under 40. His contributions to AI-driven materials design and thermoset fracture modeling have been highlighted in peer-reviewed journals. Advances include novel approaches to polymer reactivity prediction, molecular-level understanding of conjugated polymers, and machine learning workflows for molten salt mixtures. His lab collaborates across disciplines to advance soft materials design and computational tools for chemistry.

• Antimicrobial Resistance
• Robotics
• Genomics
• +۴ مورد دیگر

Sam Abraham is a Professor at Murdoch University, affiliated with the School of Medical, Molecular and Forensic Sciences and leading the Antimicrobial Resistance and Infectious Disease Laboratory under the Centre for Biosecurity and One Health. His research focuses on antimicrobial resistance (AMR), genomics, and robotics in a One Health framework, bridging human, animal, and environmental health. BSc Zoology (2003–2006), Mahatma Gandhi University, India MSc Biotechnology (2006–2007), University of Wollongong, Australia PhD Microbiology (2008–2012), University of Wollongong His research explores AMR dynamics in production animals, zoonotic pathogens, and environmental reservoirs. Recent work includes robotic surveillance systems for AMR tracking, genomic analysis of livestock pathogens, and interventions for infectious disease control. Notable article trends include robotic AMR detection, pathogen genomics in livestock and wildlife, and interdisciplinary approaches combining virology, microbiology, and computational tools. Sam leads teaching and supervisory initiatives in microbiology and One Health, with projects spanning Australia and international collaborations (e.g., Cambodia, Iraq). His lab develops next-generation platforms for AMR surveillance.

Timothy Grantمشاهده پروفایل

استادیار

• Cryo-electron microscopy (cryo-EM)
• Structural biology
• Computational imaging
• +۴ مورد دیگر

Timothy Grant is an Assistant Professor of Biochemistry at the University of Wisconsin–Madison and an Investigator at the Morgridge Institute for Research , embedded within the John W. and Jeanne M. Rowe Center for Research in Virology . His laboratory, the Grant Lab , focuses on pushing the limits of cryo-electron microscopy (cryo-EM) to visualize ever-smaller and more dynamic biological macromolecules. Education & Academic Home: Faculty appointment: Assistant Professor, Department of Biochemistry, UW–Madison College of Agricultural and Life Sciences. Concurrent appointment: Morgridge Institute Investigator, Rowe Center for Research in Virology. Research Interests: The Grant group develops computational and experimental methods that extend cryo-EM into two major frontiers: size —capturing structures of very small proteins previously invisible to cryo-EM—and motion —resolving conformational changes of molecular machines in real time. These advances are integrated into the open-source software package cisTEM , which provides a user-friendly workflow for single-particle image processing. Publication Trends: Across the 15 most recent papers (2021-2025), Grant’s work spans method-centric algorithmic innovation, high-resolution structural studies of bacterial DNA replication-restart machinery, and integrative technologies that couple native mass spectrometry with cryo-EM. A clear trajectory emerges from tool development toward application in virology and antibiotic-target validation. Scientific Awards: None explicitly mentioned in the provided text. Students & Research Team: Grant currently mentors six graduate students (Colin Hemme, Gan Li, Heidy Elkhaligy, Peter Ducos, Roma Broadberry, Shashwat Shastri) and several postdoctoral researchers and staff, including Alex Duckworth, Raison Dsouza, and Tim Wagner. Laboratory & Collaborations: The Grant Lab is physically located within UW–Madison’s Biochemistry Building and leverages the Center for High-Throughput Computing shared between UW–Madison and Morgridge to perform large-scale cryo-EM data processing.

Ian Simpsonمشاهده پروفایل

استاد

• Biomedical Informatics
• Machine Learning
• Genomics
• +۳ مورد دیگر

Professor Ian Simpson is a faculty member at the University of Edinburgh's School of Informatics , holding the Chair in Biomedical Informatics and serving as Director of the UKRI Artificial Intelligence CDT in Biomedical Innovation . He teaches courses including Foundations in Biomedical Artificial Intelligence Research and supervises PhD students across multiple programs. Current Supervision: Aidan Marnane, Antoine Lain, Barry Ryan, Tariro Chatiza, Sebestyén Kamp, Maria Dolak, Hanane Issa, Juliana Rodriguez Cubillos Past Supervision: Xin He (2017), Maciej Pajak (2018), Emilia Wysocka (2019), Samuel Heron (2019), Alba Crespi (2022), Magdalena Navarro (2022), Michael Yates (2023), Jamie Campbell (2023), Nicholas Moir (2024) Research Focus: Developing computational and machine learning methodologies for biological systems analysis, particularly brain development and neurological diseases. Key interests include high-throughput brain data analysis, graph-based biological modeling, molecular phylogenetics, and brain disease molecular modeling. Publication Trends: Recent work emphasizes multi-omic data integration through graph neural networks (MOGDx), batch correction challenges in breast cancer datasets, and longitudinal stratification approaches for Parkinson's disease. His articles demonstrate applications of AI in heterogeneous disease diagnosis while preserving biological validity. Scientific Recognition: Fellow of the Alan Turing Institute (2021-) Member of RSE Young Academy of Scotland BBSRC Pool of Experts member (2021-) STEM Ambassador Laboratory Affiliations: Leads the Biomedical Informatics group within the Institute for Adaptive & Neural Computation, with affiliations to Edinburgh Neuroscience, Patrick Wild Centre, and the Simons Initiative for the Developing Brain.

Dan Liمشاهده پروفایل

دانشیار

• Environmental Science
• Biomedical Engineering
• Microfluidics
• +۳ مورد دیگر

Dan Li is an Associate Professor in the Earth & Environment Department at Boston University, with an affiliated faculty role in Mechanical Engineering. His research focuses on microfluidics, imaging cytometry, and advanced analytical techniques for biomolecular analysis. He holds a Ph.D. in Civil & Environmental Engineering from Princeton University (2013) and a BA in Hydraulic Engineering from Tsinghua University (2009). Dr. Li's work emphasizes high-throughput screening systems, optical methodologies for cellular and molecular analysis, and applications in environmental and biomedical fields. His contributions include innovations in UV-based imaging, DNA sequencing technologies, and microfluidic device design. He has published extensively on topics such as mitochondrial DNA analysis, fibronectin material properties, and optometry practice evaluations. His research is supported by collaborative projects at the Environmental Fluid Mechanics (EFM) group, reflected in his website (https://sites.bu.edu/efm/). No scientific awards or grants are explicitly mentioned in the provided texts.

Robin Carpentierمشاهده پروفایل

پژوهشگر ارشد

• Data Privacy
• Information Management
• Hardware Security
• +۳ مورد دیگر

Robin Carpentier is a Research Fellow at the School of Computing, Macquarie University . His work focuses on Data Privacy , Information Management , and Hardware Security , particularly in developing secure personal data management systems using Trusted Execution Environments (TEE) and SGX technology. Research Interests : Secure data processing with third-party code Privacy-preserving computation frameworks Hardware-based security for databases Dimensionality challenges in text privacy Resource-constrained privacy-preserving methods Recent Publications Trends : Robin's research over the past decade has explored data leakage mitigation, TEE-optimized database operations, and privacy-preserving mechanisms for large-scale data applications. His 2024 work extends these principles to secure AI/LLM interactions and advanced text privacy techniques.

Auguste Genovesioمشاهده پروفایل

پژوهشگر

• Computational Bioimaging
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Auguste Genovesio is a Research Director (DR INSERM) leading the Computational Bioimaging and Bioinformatics team at the Centre for Computational Biology within the École Normale Supérieure (ENS) in Paris. His work focuses on large-scale cellular morphology analysis, integrating machine learning, microscopy, and computational modeling to study cellular responses to perturbations. His team develops algorithms for analyzing high-dimensional biological data, with applications in drug discovery, functional genomics, and neuroscience. Education and Affiliations: Genovesio’s research is anchored at ENS and collaborates with institutions like Institut Curie, Collège de France, and ESPCI. His lab develops open-source tools such as PySpacell and ALFA , advancing spatial analysis and genomic data processing. Research Interests: His group combines deep learning, bioinformatics, and experimental biology to tackle challenges in cellular dynamics, morphological heterogeneity, and predictive modeling. Recent work includes applying diffusion models to reveal subtle phenotypes and optimizing microscopy image analysis pipelines. Key Projects: Cross-modal knowledge distillation for transcriptomics, latent diffusion models for small datasets, and super-resolution microscopy via StyleGAN regularization. Applications: Collaborations in drug screening, neurobiology (e.g., Drosophila memory studies), and cancer cell analysis. Publications: Over 50 peer-reviewed articles since 2007, including work in Nature Communications , Developmental Cell , and NeurIPS . Recent focus on generative AI for biological image analysis and self-supervised learning biases. Grants & Awards: While specific grants aren’t listed, his lab’s cutting-edge research suggests significant institutional and collaborative support. No explicit awards mentioned in texts. Labs/Teams: Director of the Computational Bioimaging group, part of the Functional Genomics section at ENS. Supervises PhD students and postdocs in AI-driven biology and computational microscopy.

Robert N. Benمشاهده پروفایل

استاد

• Organic Chemistry
• Bioorganic Chemistry
• Carbohydrate Chemistry
• +۵ مورد دیگر

Robert N. Ben is a Full Professor in the Department of Chemistry and Biomolecular Sciences at the University of Ottawa. His research focuses on organic and bioorganic chemistry, particularly the development of small-molecule ice recrystallization inhibitors (IRIs) for improving cryopreservation techniques. These inhibitors enhance the viability of cells, tissues, and biological systems during freezing and thawing processes, with applications in regenerative medicine, stem cell therapy, and biomedical engineering. He holds a B.Sc. from Laurentian University (1990) and a Ph.D. from the University of Ottawa (1994). His work bridges synthetic chemistry and biological applications, aiming to design cryoprotectants with enhanced stability and activity for medical and industrial uses. Key research areas include asymmetric synthesis of bioactive compounds, carbohydrate chemistry, and glycoprotein design. Ben’s lab investigates the structure-activity relationships of IRIs, their mechanisms of action, and their utility in preserving diverse biological materials, including red blood cells, stem cells, and tissues. His contributions have advanced understanding of how small molecules mitigate ice crystal growth, reducing cryopreservation damage in cellular therapies and organ preservation. Despite no listed awards, his prolific research output (over 50 publications from 2013–2024) highlights his leadership in cryopreservation science. He advises students in synthetic and bioorganic chemistry but no specific advisees are named in the provided texts. Current projects emphasize translating IRI-based technologies into clinical applications, such as improving platelet and cord blood storage.

• Algorithms
• Data Structures
• Pattern Matching
• +۳ مورد دیگر

Simon J. Puglisi is a Professor in the Department of Computer Science at the University of Helsinki. His research focuses on algorithms, data structures, pattern matching, and data compression, with applications in bioinformatics and genomics. He has collaborated extensively with researchers in the field, including Travis Gagie, Juha Kärkkäinen, and Andrew Turpin. His work spans theoretical computer science and practical applications in genomic data processing and efficient indexing techniques. Key research interests include genome sequencing algorithms, efficient compression methods (e.g., Lempel-Ziv and relative Lempel-Ziv), and the development of succinct data structures for handling large genomic datasets. His contributions to suffix arrays, wavelet trees, and de Bruijn graphs have advanced computational methods in bioinformatics and information retrieval. Puglisi's articles frequently address challenges in text indexing, error correction in short-read sequencing, and optimizing algorithms for scalability. He is known for his work on self-indexing techniques and the SHREC error correction method for genomic data. His research bridges theoretical algorithm design with real-world applications in high-throughput sequencing and large-scale data management.

• Evolutionary Genetics
• Microbial Genomics
• Genomic Epidemiology
• +۷ مورد دیگر

Fernando Gonzalez Candelas is a Professor in the Department of Genetics at the Faculty of Biological Sciences, Universitat de València, Spain. He leads the EVOSALUD research group (Evolution and Health: Experimental evolution and epidemiology), hosted within the Institute for Biological Systems Integration (I2SYSBIO). His work bridges evolutionary biology, genomics, and public health, focusing on microbial pathogens and their transmission dynamics. His research interests lie at the intersection of evolutionary genetics , genomic epidemiology , and infectious disease dynamics . He investigates the genetic basis of virulence, antimicrobial resistance, and host adaptation in pathogens such as SARS-CoV-2, Treponema pallidum , Klebsiella pneumoniae , and Candida auris . His group applies high-throughput sequencing, phylogenetics, and bioinformatics to understand short- and long-term pathogen evolution. The recent publications highlight a strong focus on real-time genomic surveillance of emerging pathogens, particularly during the COVID-19 pandemic. His work spans from technical improvements in sequencing and analysis (e.g., deletion repair, genotyping discrepancies) to large-scale epidemiological inference (e.g., transmission patterns, recombination, resistance evolution). A recurring theme is the use of both modern and ancient genomes to reconstruct the evolutionary history of pathogens. His scientific contributions are recognized through active participation in national and international research consortia such as RELECOV and SeqCOVID-Spain. He has contributed significantly to understanding the first wave of the pandemic in Spain and the evolution of SARS-CoV-2 variants. He earned his PhD from the Universitat de València in 1988 with a thesis on larval competition in Drosophila , indicating a long-standing interest in evolutionary processes. While no formal awards are listed in the provided text, his leadership in high-impact research and sustained publication record suggest significant recognition in his field. Fernando Gonzalez Candelas advises students and leads a research team focused on pathogen genomics. His lab, EVOSALUD, is actively involved in projects related to wastewater monitoring, nosocomial outbreaks, and the development of tools like VIPERA for viral intra-patient evolution analysis. The group’s work has direct implications for public health policy and clinical microbiology.