جست‌وجوی استاد و پژوهشگر

• Energy Systems Optimization
• Conceptual Process Design
• Life Cycle Assessment
• +۸ مورد دیگر

Prof. Dr. André Bardow is a Full Professor in Energy and Process Systems Engineering at ETH Zurich , leading research at the intersection of thermodynamics, machine learning, and sustainable energy systems. Previously, he held professorships at RWTH Aachen University (2010-2020) and TU Delft (2007-2010). He also served as part-time director at Forschungszentrum Jülich (2017-2022) and visiting professor at UC Santa Barbara (2015/16). His work focuses on energy systems optimization , computer-aided molecular design , and CO2 capture & utilization . PhD from RWTH Aachen University Current ETH Zurich affiliation Former roles at RWTH Aachen, TU Delft, Jülich Research Center His research integrates machine learning with thermodynamic modeling to optimize processes like crystallization and electrochemical cooling . Recent publications demonstrate advancements in solvent design, CO2 transport LCA, and ORC working fluid optimization. He chairs the VDI Technical Committee for Thermodynamics (2016-2024) and has received multiple awards including the Covestro Science Award and Arnold-Eucken-Award . Current projects address carbon circular economies , electrified chemical production , and AI-driven process optimization . His lab at ETH Zurich develops cutting-edge technologies like ML-CAMPD frameworks for sustainable separation processes and photoacid-based CO2 capture systems. Funding from the H2020 Systemic Expansion of Circular Ecosystems (grant 101036854) supports these initiatives. 2024 Clarivate Highly Cited Researcher 2022 Inaugural Lecture: "To sustainability and beyond: A computer-animated story on energy & chemicals" Recipient of multiple teaching and research excellence awards

Lourdes Pena-Castilloمشاهده پروفایل

استاد

• Bioinformatics
• Genomics
• Machine Learning
• +۵ مورد دیگر

Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.

Thomas Papeمشاهده پروفایل

استاد

• Systematics
• Taxonomy
• Phylogeny
• +۷ مورد دیگر

Thomas Pape is a Professor at The Natural History Museum of the University of Copenhagen, specializing in the systematics, taxonomy, phylogeny, biogeography and evolution of Diptera (two-winged insects), with special emphasis on Calyptratae families including Sarcophagidae, Oestridae, and Calliphoridae. He has been a professor since 2025 after serving as an Associate Professor from 2004-2024. PhD from Copenhagen University (1990) Docent at Stockholm University (1998) Associate Professor at Danish Bilharziasis Laboratory (1990-1992) Research Entomologist at Naturhistoriska Riksmuseet, Stockholm (1994-2004) His research spans taxonomy, systematics, phylogeny, biogeography and evolution of Diptera with special emphasis on calyptrate families. He collaborates on Systema Dipterorum, conducts comparative larval morphology studies, and investigates the phylogeny of Calyptrata. His work includes Diptera nomenclature and large-scale biodiversity inventories. Analysis of his recent publications reveals a strong trend toward molecular phylogenetics (using RAD-seq and anchored phylogenomics), forensic entomology applications, and biodiversity informatics. His research spans entomology, systematics, evolutionary biology, and increasingly intersects with bioinformatics and conservation science, with significant contributions to understanding flesh flies, bot flies, and blow flies. President of the International Council of Zoological Nomenclature (since 2016) Chair of the Council for the International Congresses of Dipterology (2010-2018) Member of editorial boards for Entomotropica, Studia dipterologica, Stuttgarter Beiträge zur Naturkunde, and Tijdschrift voor Entomologie Dr. Pape teaches courses in Entomology (structure, phylogeny and biology of terrestrial arthropods), Field Biology II - Zoology, and Ecology and Evolution of East Africa (a 2-week field course in Tanzania with Nikolaj Scharff). He has served on scientific panels for SYNTHESYS and as chair of the scientific advisory committee for Museum Koenig in Bonn (2006-2009). His research group employs both morphological and molecular approaches to address evolutionary questions in Diptera, with particular expertise in calyptrate families. The laboratory maintains active international collaborations across multiple continents, as evidenced by his extensive publication record and research network.

Changhuei Yangمشاهده پروفایل

استاد

• Biophotonics
• Microscopy
• Computational Imaging
• +۳ مورد دیگر

Changhuei Yang is the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering at California Institute of Technology, serving as Executive Officer for Electrical Engineering and Investigator at Heritage Medical Research Institute. He holds a Ph.D. and three master's degrees from MIT, with appointments at Caltech since 2003. Research focuses on: Advanced microscopy techniques including Fourier Ptychography Wavefront shaping for biological tissue imaging Optical phase conjugation for deep-tissue applications Compact medical devices for cerebral monitoring Publications demonstrate leadership in computational imaging, with recent advances in stain-free embryo analysis, portable cerebral blood flow monitors, and high-resolution volumetric imaging techniques using neural representations. Honored as National Academy of Inventors member. Research applications span deep-tissue biochemical imaging, incisionless surgery, and optogenetic activation systems.

• Neurology
• Movement Disorders
• Parkinson's Disease
• +۶ مورد دیگر

Professor Victor Fung is a leading academic in neurology and motor control at the Sydney Medical School , University of Sydney. He serves as Director of the Movement Disorders Unit at Westmead Hospital and holds prominent roles in the International Parkinson and Movement Disorder Society (President) and the Asia Pacific Affairs Committee of the Australian and New Zealand Association of Neurologists. His research focuses on Parkinson's disease , dystonia , tremor , and movement disorder genetics , with over 250 peer-reviewed publications and an h-index of 61. His work spans Clinical trials for Parkinson's treatments (e.g., foslevodopa/foscarbidopa infusion, incobotulinumtoxinA) Genetic studies on dystonia and Parkinsonism Neurophysiological assessments of tremor and balance Development of screening tools for dystonia and Parkinson's complications Key article trends highlight genetic diversity in dystonia, neuroinflammatory mechanisms in tremor, and behavioral neurology in Parkinson's disease. He contributes to editorial boards of npj Parkinson's Disease , Journal of Clinical Movement Disorders , and Faculty Opinions . While no explicit student lists are provided, his collaborations span global institutions. His clinical work at Westmead Hospital addresses device-assisted therapies and complex movement disorders.

Jundong Liمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Machine Learning
• +۱۲ مورد دیگر

Jundong Li is an Assistant Professor at the University of Virginia with primary appointment in the Department of Electrical and Computer Engineering and secondary appointments in Computer Science and the School of Data Science. He is affiliated with the School of Engineering and Applied Science and conducts research at the intersection of machine learning, data mining, and artificial intelligence. Education: Ph.D. in Computer Science, Arizona State University, 2019 M.Sc. in Computer Science, University of Alberta, 2014 B.Eng. in Software Engineering, Zhejiang University, 2012 His research focuses on graph machine learning , trustworthy and fair AI , and large language models . He investigates how to make deep learning models more interpretable, robust, and equitable, especially in graph-structured data and NLP applications. His work combines causal inference, feature selection, and model explanation techniques to build reliable AI systems. His recent publications (2024–2022) reveal a strong trend toward large language models , with topics including in-context learning, knowledge editing, and collaborative reasoning. Simultaneously, he continues pioneering research on fairness and interpretability in graph neural networks , addressing structural bias, adversarial attacks, and node attribution. His work is highly interdisciplinary, spanning computer science, data science, and social impact. Scientific Awards: SIGKDD Rising Star Award (2024) PAKDD Best Paper Award (2024) NSF CAREER Award (2022) SIGKDD Best Research Paper Award (2022) JP Morgan Faculty Research Award (2021, 2022) Cisco Faculty Research Award (2021) Stanford/Elsevier Top 2% Scientist (2024) Jundong Li actively advises graduate students, as seen in his co-authored papers with researchers like Song Wang, Yushun Dong, and Binchi Zhang. His research is generously funded by the National Science Foundation (NSF) through multiple programs including CAREER, III, SaTC, SAI, and S&CC, as well as by the Department of Energy (DOE) , Office of Naval Research (ONR) , Jefferson Lab , and industry partners including JP Morgan, Cisco, Netflix, and Snap . He leads a dynamic research group focused on advancing the frontiers of graph learning and trustworthy AI, with projects on causal inference, model unlearning, and explainable systems. His lab contributes to both theoretical foundations and real-world applications in public health, transportation, and network security.

Levent Burak Karaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Design
• +۱۲ مورد دیگر

Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He is a leading researcher in AI-driven computational design, additive manufacturing, and intelligent engineering systems, leading the Visual Design and Engineering Lab (VDEL) at CMU. Education: B.S., Mechanical Engineering, Middle East Technical University (1998) M.S., Mechanical Engineering, Carnegie Mellon University (2000) Ph.D., Mechanical Engineering, Carnegie Mellon University (2005) His research focuses on integrating machine learning, optimization, and geometric modeling to revolutionize engineering design and manufacturing. Key areas include topology optimization, CAD intelligence, digital twins, generative design, bioengineering, and electronic design automation. His work enables automation of traditionally labor-intensive design processes using deep learning and reinforcement learning. His recent publications reveal a strong trend toward physics-informed surrogate modeling, real-time simulation, manufacturability prediction, and AI-driven automation in mechanical, biomedical, and electronic systems. These works frequently appear in top journals such as Journal of Mechanical Design and Journal of Applied Mechanics , and at premier conferences like NeurIPS and DAC. Scientific Awards: National Science Foundation CAREER Award ASME Design Automation Society Young Investigator Award Google AI for Social Good Impact Scholar Kara advises several Ph.D. students and has secured significant funding from federal agencies such as the NSF and the U.S. Army Research Laboratory, as well as collaborations with industrial leaders including Cadence Design Systems and NVIDIA. His research is also supported by CMU’s NextManufacturing Center and the Critical Technology Initiative. He is actively involved in developing intelligent design systems that leverage AI to automate product design, optimize manufacturing processes, and improve medical diagnostics, particularly in oral cancer screening and organ preservation. His lab, VDEL, is a hub for innovation in AI-enabled engineering.

• Machine Learning
• Artificial Intelligence
• Large Language Models
• +۷ مورد دیگر

Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine. Education: PhD in Machine Learning, School of Computer Science, Carnegie Mellon University Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery. Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation. Scientific Awards: NIH MIRA Award (2025) NSF CAREER Award (2024) Best Graduate Teacher Award – UCSD ECE (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) UCSD Faculty Career Development Award (2022) Tencent Faculty Award (2021) Outstanding Reviewer – ICLR (2021) AMIA Doctoral Dissertation Award Finalist (2020) Amazon AWS Research Award (2020) Tencent AI-Lab Faculty Award (2020) Innovator Award – Pittsburgh Business Times (2018) Siebel Scholarship (2014) Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students. Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models . His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.

Sebastian Schuckمشاهده پروفایل

استاد

• Cell Biology
• Molecular Biology
• Membrane Biology
• +۴ مورد دیگر

Overview Sebastian Schuck is a Professor of Biochemistry and Molecular Cell Biology at Heidelberg University's Biochemistry Center (BZH). His research focuses on organelle homeostasis, particularly the endoplasmic reticulum (ER), with emphasis on ER membrane biogenesis, ER-phagy, and SHRED pathways. He leads an international team investigating how cells adapt ER structure and function under stress or disease conditions. Education & Career Since 2021: Professor at Heidelberg University BZH 2013–2021: Independent Group Leader at Heidelberg University's Center for Molecular Biology 2006–2013: Postdoc with Peter Walter at UCSF 2001–2006: PhD and Postdoc with Kai Simons at Dresden's Max Planck Institute 1995–2000: Biochemistry studies at Universities of Hannover and Tübingen Research Interests Dr. Schuck's lab explores molecular mechanisms underlying ER homeostasis, including: 1. ER expansion during stress via lipid synthesis 2. Microautophagy-mediated ER degradation via ESCRT machinery 3. SHRED pathway regulation of proteasomal degradation of misfolded proteins 4. Links between ER stress and neurodegenerative diseases/cancer Awards & Honors No specific awards listed, but recognized for pioneering contributions to understanding microautophagy and ER quality control mechanisms. Advising & Collaborations Advised over 20 PhD/Master's students and postdocs Collaborations with Carlos Bas-Orth (MPI Biochemistry), Liam Holt (NY), and others Labs & Teams Current lab includes 10+ members focusing on: - Human ER morphogenesis - Microautophagy dynamics - SHRED pathway mechanisms

• Neuroscience
• Synaptic Plasticity
• Neural Circuit Formation
• +۴ مورد دیگر

Colin J Akerman is Professor of Neuroscience and Group Leader in the Department of Pharmacology at the University of Oxford, concurrently serving as Corange Fellow and Medical Tutor at Corpus Christi College. His research investigates fundamental mechanisms of synaptic circuit formation and plasticity, with direct implications for epilepsy, dementia, and schizophrenia through multidisciplinary approaches integrating electrophysiology, optical imaging, and computational modeling. His primary research interests encompass Synaptic Plasticity, Neural Circuit Formation, and Excitatory-Inhibitory Balance, with specific focus on neuronal progenitor influences on connectivity, chloride dynamics in inhibitory transmission, and learning mechanisms in disease contexts. The lab employs custom-built equipment and molecular tools to probe synaptic function across in vivo , in vitro , and in silico platforms, emphasizing how activity-dependent processes shape neural networks during development and disease. Recent publications (2023-2025) reveal strong thematic convergence on intracellular chloride regulation in sleep-wake cycles, cortical circuit assembly from embryonic progenitors, and innovative optical tools for neural monitoring. This work bridges molecular neuroscience with systems-level understanding of synaptic plasticity, particularly regarding ionic mechanisms in epilepsy and sleep homeostasis. No scientific awards or fellowships are explicitly documented in the source materials. Professor Akerman currently mentors four PhD students (Vourvoukelis, Selfe, Wang, Gemayel) and multiple postdoctoral researchers, having previously trained scientists now leading independent groups in Toronto, Edinburgh, Cape Town, Oxford, and London. His research is funded by the European Research Council, Innovative Medicines Initiative, and Wellcome Trust, supporting investigations into synaptic mechanisms underlying neurological disorders. The Akerman Group, established in 2008, operates as an integrative neuroscience hub within Oxford's Pharmacology Department. The 10-member team combines expertise in patch-clamp electrophysiology, optogenetics, multiphoton imaging, and computational modeling, with current projects spanning neuronal progenitor biology, inhibitory synaptic plasticity, and learning rule implementation in neural networks. The lab emphasizes technical innovation, regularly developing custom instrumentation and molecular tools for neural observation and manipulation.

Joel Weadgeمشاهده پروفایل

دانشیار

• Bacterial Biofilms
• Bacterial Carbohydrates
• Protein Structure and Function
• +۷ مورد دیگر

Joel Weadge is an Associate Professor in the Biology Department at Wilfrid Laurier University , Waterloo, Ontario. His research focuses on bacterial biofilms, glycobiology, and protein structure-function relationships. Contact: jweadge@wlu.ca , Office: BA425 (Bricker Academic). Education: PhD in Microbiology (University of Guelph, 2006) BSc (Hons) in Microbiology (University of Guelph, 2000) Research Interests center on bacterial biofilms as virulence factors in pathogens like E. coli and Salmonella . Key areas include: Structural and functional characterization of biofilm proteins (cellulose, curli fimbriae) Enzymology of carbohydrate modifications (acetylation, phosphoethanolamine transfer) Developing therapeutics targeting biofilm synthesis Biopolymer applications for medical/industrial use Publications highlight studies on Pseudomonas and Salmonella biofilm mechanisms, glycosyltransferases, and carbohydrate-active enzymes, with methodologies spanning X-ray crystallography to high-throughput biofilm profiling. Labs and Teams: The Weadge Lab investigates biofilm roles in food/water security and oral health, utilizing enzymology, mass spectrometry, and structural biology. Current members include graduate students, technicians, and research assistants.

William V. Giannobileمشاهده پروفایل

استاد

• Periodontology
• Regenerative Medicine
• Tissue Engineering
• +۳ مورد دیگر

Dr. William V. Giannobile serves as the Dean and A. Lee Loomis, Jr. Professor of Oral Medicine, Infection, and Immunity at Harvard School of Dental Medicine (HSDM). He leads the institution as its chief academic and administrative officer while maintaining an active research program and clinical practice in periodontics and implant dentistry at the Harvard Dental Center in Cambridge, MA. Dr. Giannobile received his DDS and MS in oral biology from the University of Missouri, followed by a certificate in periodontology and Doctor of Medical Sciences (DMSc) in oral biology from Harvard School of Dental Medicine. He completed postdoctoral training in molecular biology at the Dana-Farber Cancer Institute and Harvard Medical School. With over 30 years of continuously NIH-funded research, Dr. Giannobile's work focuses on oral and periodontal regenerative medicine, tissue engineering, and precision medicine. He serves as principal investigator of the NIDCR U-Award Harvard Clinical Practice-based Research Program for Dental Students (H-CREDENT), which evaluates the genomics of oral pain in a multi-institutional academic setting. His research has produced over 300 manuscripts, textbook chapters, and patents focused on periodontology, regenerative medicine, and oral health research. He has edited or co-edited over 10 books in clinical and translational research, periodontology, and regenerative medicine. His publications demonstrate consistent focus on periodontal regeneration, biomaterials, tissue engineering, and the molecular mechanisms underlying oral diseases. ADA Gold Medal Award for Excellence in Dental Research (2024) International Association for Dental Research Distinguished Scientist Research in Periodontal Disease Award (2022) Norton M. Ross Clinical Research Award from the American Dental Association (2018) William J. Gies Award (2014) Jan Lindhe International Periodontology Research Award (2011) As an educator and mentor, Dr. Giannobile completed a 10-year term as editor-in-chief for the Journal of Dental Research, the official journal of the International Association for Dental Research. He has served as a consultant to the U.S. Food and Drug Administration for Dental Devices and as an associate editor for the NIDCR Report on Oral Health of the U.S. in 2021. His laboratory continues to advance research in periodontal regeneration and precision medicine approaches to oral health, with ongoing NIH funding supporting innovative translational research.

• Anthropological Genomics
• Paleogenomics
• Human Body Size Evolution
• +۷ مورد دیگر

George Perry is a Professor of Anthropology at Pennsylvania State University, with research intersections in Biology, Evolutionary Medicine, and Genomics. He is affiliated with the Huck Institutes' Center for Infectious Disease Dynamics, Ecology, Molecular Cellular and Integrative Biosciences, and Bioinformatics and Genomics programs. Perry directs the Anthropological Genomics Lab , focusing on paleogenomics and evolutionary adaptation. Research areas: anthropological genomics, parasite evolution, human body size transitions, and evolutionary medicine Key collaborations: international teams in Madagascar, Europe, and Africa Leadership: Bioinformatics and Genomics Chair (2019–2023) His 2025–2022 publications span evolutionary responses to invasive species, human migration health impacts, chemosensory gene adaptation, and primate genomic diversity. Notable methodological contributions include ancient DNA recovery and comparative paleogenomics. Perry advises graduate students like Vanessa Garcia and Annette Mercedes, with grants including NIH support for Cuban health disparity studies. Scientific leadership includes tenure-line promotions (2023) and NASA Space Grant collaborations.

• Statistical Methods in Biomedical Research
• Biomarker Analysis
• Clinical Trial Design
• +۷ مورد دیگر

Todd A. Alonzo is a Professor of Research in the Department of Preventive Medicine at the University of Southern California . As Group Statistician for the Children's Oncology Group , he focuses on statistical methods for biomarker analysis, medical diagnostic testing, and clinical trial design in pediatric acute myeloid leukemia (AML). Education: B.S. in Statistics, California State Polytechnic University (1994) MS and PhD in Biostatistics, University of Washington (1997, 2000) Research Interests include: Development of statistical frameworks for diagnostic accuracy Genomic and proteomic profiling in AML Pharmacogenomic score systems for chemotherapy response Non-inferiority trial design in low-event-rate settings Health disparities in pediatric oncology Scientific Awards : Fellow, American Statistical Association (2018) Outstanding Teacher Award, International Society for Magnetic Resonance in Medicine (2017) NIH Predoctoral Cardiovascular Biostatistics Training Grant (1995) ENAR Biometrics Society Distinguished Student Paper Award (1999) WNAR Biometrics Society Best Student Oral Presentation (1999) Leadership & Service includes editorial board memberships (Biometrics, Pediatric Blood & Cancer, Biometrical Journal), reviewer for 30+ scientific journals, and roles on multiple Data Safety and Monitoring Boards. He served as President of the International Biometric Society Western Northern America Region (WNAR) in 2009.

• Multiscale Modeling
• Multiphysics Modeling
• Biophysics
• +۷ مورد دیگر

Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.