جست‌وجوی استاد و پژوهشگر

• Computational Chemistry
• Molecular Modeling
• Machine Learning in Biochemistry
• +۲ مورد دیگر

Bettina Lier is a researcher at the Institute of Molecular Modeling and Simulation within the Department of Natural Sciences and Sustainable Resources at the University of Natural Resources and Life Sciences, Vienna (BOKU). Her work focuses on computational approaches to biomolecular systems, particularly metalloprotein simulations using advanced machine learning techniques. Her research interests span computational chemistry, molecular modeling, and machine learning applications in biochemistry. She specializes in developing neural network approaches for polarizable embedding simulations and studying enzyme mechanisms in metalloproteins. Her work bridges theoretical computational methods with practical biochemical applications, particularly in understanding protein structure-function relationships. Analysis of her publications reveals a strong trend toward integrating machine learning with traditional molecular mechanics methods. Her research increasingly focuses on improving simulation accuracy for metalloproteins while developing educational resources for biomolecular simulation software. The work demonstrates consistent methodological innovation in computational biochemistry with applications spanning enzyme kinetics, protein engineering, and drug design methodologies. Bettina Lier has presented her research at numerous international conferences including the Biophysical Society Annual Meeting, German Conference on Cheminformatics, and various specialized symposia on biomolecular simulation. Her project Neural networks for metalloprotein simulations (2020-2023), funded by the Austrian Academy of Sciences, represents a significant contribution to the field of computational biochemistry. She is actively involved in developing the GROMOS biomolecular simulation software suite and has contributed to educational resources that support computational molecular science. Her work connects theoretical modeling with experimental biochemistry, particularly in understanding metalloenzyme mechanisms and developing more accurate simulation methodologies for complex biological systems.

Venkat Venkatasubramanianمشاهده پروفایل

استاد

• Risk Analysis
• Process Systems Engineering
• Big Data Analytics
• +۴ مورد دیگر

Venkat Venkatasubramanian serves as the Samuel Ruben-Peter G. Viele Professor of Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science. He maintains cross-disciplinary affiliations with the Foundations of Data Science Committee, Financial and Business Analytics group, and Education initiatives. His research focuses on three interconnected domains: (1) risk analysis in complex engineered systems using real-time hybrid intelligent frameworks for industrial safety; (2) cyberinfrastructure development for molecular design through Discovery Informatics and Ontological Informatics paradigms; and (3) complex adaptive teleological systems exploring emergent behavior in networked environments via statistical mechanics and artificial life models. These efforts integrate artificial intelligence, informatics, and mathematical programming to address challenges from process safety to pharmaceutical manufacturing. His 2004-2011 publications reveal consistent interdisciplinary innovation across risk management, molecular design, and complex systems theory. Key trends include systemic risk analysis in chemical processes, supply chain resilience under disruption, protein library pattern recognition, and fairness metrics in labor markets – demonstrating seamless integration of chemical engineering, computer science, and complex systems science. Professor Venkatasubramanian founded the Complex Resilient Intelligent Systems Laboratory (CRIS Lab) at Columbia, which serves as the operational hub for his research group. The lab develops real-time risk control methodologies, molecular discovery frameworks, and complex network analysis tools while mentoring graduate students in these cutting-edge interdisciplinary domains.

• Plant Metabolism
• Metabolomics
• Plant Stress Physiology
• +۳ مورد دیگر

Prof. Dr. Alisdair Fernie serves as a Research Group Leader within the Department of Root Biology and Symbiosis at the Max Planck Institute of Molecular Plant Physiology in Potsdam, Germany. His position as a full Professor reflects his leadership in plant metabolic research, where he directs the Central Metabolism research group investigating fundamental biochemical processes in plants. His research spans Plant Metabolism, Metabolomics, Plant Stress Physiology, Molecular Plant Biology, Plant Biochemistry, and Photosynthesis. Fernie employs advanced omics technologies to dissect metabolic networks, particularly focusing on how plants reconfigure their biochemistry in response to environmental stresses and developmental cues. His work integrates systems biology approaches to understand metabolic fluxes and regulatory mechanisms across diverse plant species. Analysis of his 2024-2025 publications reveals dominant themes in plant metabolomics, stress adaptation mechanisms, and biotechnological applications for crop improvement. His research frequently utilizes model systems like Arabidopsis alongside economically important crops including tomato, rice, maize, and citrus, with particular emphasis on postharvest physiology, nutrient signaling, and the molecular basis of specialized metabolism. Prof. Fernie leads the Central Metabolism research group, which investigates core metabolic pathways and their regulation through innovative combinations of biochemical, genetic, and computational approaches. His team's work on organelle interactions, lipid metabolism, and stress-responsive metabolic reprogramming has positioned them at the forefront of plant systems biology research.

Nadia Elghobashi-Meinhardtمشاهده پروفایل

استادیار

• Computational Chemistry
• Theoretical Chemistry
• Molecular Dynamics
• +۹ مورد دیگر

Nadia Elghobashi-Meinhardt is a Lecturer/Assistant Professor at the School of Chemistry, University College Dublin, holding an Ad Astra Fellowship. She earned her BSc in Chemistry from Stanford University (1999), followed by an MSc (2001) and PhD (2005) in Theoretical Chemistry at the Free University Berlin. Her postdoctoral work includes theoretical chemistry at the Fritz Haber Institute (2006) and computational biophysics at Heidelberg University (2007-2009). She later led a junior research group at the Free University Berlin (2011) and studied metalloenzyme catalysis at Technical University Berlin (2019). Her research focuses on computational biophysics , combining quantum mechanics (QM), molecular mechanics (MM), hybrid QM/MM, and machine learning to decipher structure-function relationships in biological systems. Key areas include biocatalysis in metalloenzymes, lysosomal protein dynamics in sterol homeostasis, and applications of computational methods to diseases like Niemann-Pick Type C (NPC). Her work bridges structural biology and quantum chemistry, with implications for clean energy and therapeutic development. Recent studies examine TRPML2 ion channels (2025), phosphatidylserine synthase regulation of SREBP pathways (2025), and transaminase-driven cascades for chiral N-heterocycles (2025). She also investigates sialin transporters (2024) and vitamin-K-dependent enzymes (2024), contributing to understanding lysosomal and metabolic disorders. Scientific Awards Ad Astra Fellowship (2023) Member of the Royal Society of Chemistry (2024) At UCD, she teaches Symmetry and Computational Chemistry (CHEM30230), Applied Introductory and Physical Chemistry (CHEM10130), and Scientific Enquiry (SCI10010), while coordinating Advanced Topics in Physical Chemistry (CHEM41430). She supervises PhD students in projects involving lysosomal proteins, nitrogenase mechanisms, and computational approaches to disease-related pathways.

Eun Hyun Ahnمشاهده پروفایل

دانشیار

• Genomics
• Systems Biology
• Translational Cell and Tissue Engineering
• +۸ مورد دیگر

Eun Hyun Ahn is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University, with affiliations in the Whiting School of Engineering and School of Medicine. Her research integrates genomics, systems biology, and translational cell/tissue engineering to study cancer and stem cell dynamics. Education: B.S. in Chemistry (Chung-Ang University), M.S. and Ph.D. in Biochemical Nutrition and Environmental Toxicology (Michigan State University), followed by postdoctoral training at Columbia University and University of Pennsylvania. Research Interests: Detection of low-frequency mutations via Duplex Sequencing; regulation of cell function by ECM-biomimetic nanotopography; genomic changes in breast cancer and glioblastoma; anti-migratory chemotherapeutic agent screening. Key Trends in Publications: Recent work spans nanotopographic cancer modeling, microgravity-induced tissue dysfunction, subclonal mutational diversity in colorectal cancer, and ECM-driven chemoresistance in breast cancer. Machine learning applications and microphysiological systems are recurring themes. Labs & Teams: The Ahn Lab focuses on biomarker discovery, tumor microenvironment modeling, and preclinical drug development using advanced sequencing and nanotopographic platforms. Contact: ahn@jhu.edu | Lab Website

Ernesto William De Lucaمشاهده پروفایل

استاد

• Human-Centred Artificial Intelligence
• Information Retrieval
• Computational Linguistics
• +۵ مورد دیگر

Ernesto William De Luca is a Professor at the Faculty of Computer Science , Otto von Guericke University Magdeburg , and Head of the Digital Information and Research Infrastructures (MTB) department. He also leads the Human-centred artificial intelligence research group. Education: Studied Computational Linguistics at Bielefeld University PhD in Computer Science from Otto von Guericke University Magdeburg His research spans Human-Centred Artificial Intelligence , Information Retrieval , and Natural Language Processing . Recent work explores ethical AI, bias mitigation in legal systems, and wearable technologies for sports medicine. Publications include 179 research outputs and 8 projects since 2025. Key article trends focus on semantic technologies, explainable AI, and interdisciplinary applications in education and law. He has organized major conferences like the International Conference on Metadata and Semantics Research and workshops on adaptive user interfaces.

• Theoretical Physics
• Statistical Mechanics
• Computational Biology
• +۲ مورد دیگر

Anna Paola Muntoni is a Fixed-term Assistant Professor at the Department of Applied Science and Technology (DISAT) within Politecnico di Torino. She is affiliated with the Institute of Condensed Matter Physics and Complex Systems (DISAT) and contributes to the College of Electronic, Telecommunications, and Physics Engineering. Her research bridges theoretical physics, computational biology, and machine learning. Her research focuses on applying statistical mechanics and computational methods to biological sequences, epidemic modeling, metabolic strategies, and complex systems. Key projects include epidemic contact tracing, co-evolutionary sequence alignment, and Boltzmann machine applications in bioinformatics. Selected publications highlight her interdisciplinary work across 2024: Probabilistic contact tracing in epidemic containment 2023: Biological sequence alignment and metabolic optimization 2021: Boltzmann machine modeling of protein sequences 2019: Statistical mechanics in optimization problems

• Powder Technology
• Molecular Dynamics Modeling
• Multiscale Modeling
• +۵ مورد دیگر

Bodhisattwa Chaudhuri is a faculty member in the Department of Pharmaceutical Sciences at the University of Connecticut's School of Pharmacy. His research bridges computational modeling and pharmaceutical manufacturing, focusing on multiscale approaches to materials science, drug delivery, and process optimization. Education: B.E. in Chemical Engineering (Jadavpur University), M.S. in Chemical Engineering (Indian Institute of Science), Ph.D. in Mechanical Engineering (New Jersey Institute of Technology), Postdoctoral Training in Chemical and Biochemical Engineering (Rutgers University) His work integrates Molecular Dynamics (MD) and Discrete Element Method (DEM) simulations with Computational Fluid Dynamics (CFD) to address challenges in pharmaceutical systems. Key areas include: Aggregation behavior of viral vectors like AAV8 AI/ML applications for biopharmaceutical process prediction 3D printing of tablets and freeze-thaw stability of biologics Tribotechnical challenges in powder systems The 15 most recent publications highlight his focus on computational modeling (MD, CFD, DEM) for pharmaceutical processes, machine learning applications in stability prediction, and multiscale studies of drug delivery systems like lipid nanoparticles and polymeric micelles. His work spans both fundamental research and industrial translation. Contact: bodhi.chaudhuri@uconn.edu | Personal Website

• Renewable Energy
• Biomass and Biofuels
• Waste Management
• +۳ مورد دیگر

Anamarija Peter is an Assistant Professor at the University of Zagreb Faculty of Agriculture , specifically within the Department of Sustainable Technologies and Renewable Energy Sources. Her work focuses on renewable energy systems and sustainable resource management. PhD in Sustainable Technologies and Renewable Energy (2019–2024) Master of Science in Agroecology (2014–2017) M.A. in Environmental Health Engineering (2014–2017) Her research spans renewable energy , biomass utilization , and invasive species management . She investigates biofuel properties of underutilized plant material and applies machine learning to predict biomass energy value. Recent publications highlight trends in invasive plant valorization , artificial neural networks for energy crop modeling, and waste management strategies. Her work connects Croatian ecosystems with global sustainability challenges. Projects include: LIFE OrnamentalIAS – Partner in invasive species management Erasmus+ Zero Food Waste – Coordinator for EU youth education

Eric Harshfieldمشاهده پروفایل

پژوهشگر ارشد

• Stroke
• Dementia
• Metabolomics
• +۷ مورد دیگر

Dr. Eric Harshfield is a Senior Research Associate and Research Fellow at the University of Cambridge's Department of Clinical Neurosciences, School of Clinical Medicine. Affiliated with St John's College and the UK Dementia Research Institute, his research is funded by the Alzheimer’s Society. He leads projects focused on stroke and dementia prediction using multi-omics and AI, with dual emphasis on identifying therapeutic targets and enhancing risk stratification models. His research integrates: Multi-omics approaches (metabolomics, proteomics, genomics) to map causal pathways in cerebrovascular disease Advanced neuroimaging (MRI, diffusion tensor imaging) for biomarker discovery Machine learning models for dementia prediction in small vessel disease Mendelian randomization to validate therapeutic targets Recent publications demonstrate strong focus on: AI-driven dementia biomarker discovery (2023) Metabolomic signatures of stroke/dementia (2023) Clinical trials targeting blood-brain barrier dysfunction (2024) Genetic determinants of cardiometabolic-cerebrovascular links (2021-2025) He mentors PhD candidates and collaborates internationally through the Stroke Research Group and Deep Dementia Phenotyping Network. Current projects include MINERVA—a phase II trial testing minocycline for small vessel disease—and UK Biobank genetic analyses.

• Agricultural Engineering
• Sustainable Agriculture
• Soil Management
• +۴ مورد دیگر

Joachim Müller is a Professor at the University of Hohenheim and leads the Subject Area Agricultural Engineering in the Tropics and Subtropics (440e) within the Institute for Tropical Agricultural Sciences (Hans-Ruthenberg-Institute) . He also serves as a leader in the International Graduate College 'Adaptation of Maize-based Agricultural Production Systems to Limited Phosphate Resources' (IRTG 2366/2) and contributes to the IRTG 2366 project. Research Focus: Sustainable agricultural systems, phosphorus recycling, biogas optimization, and postharvest processing technologies, particularly for cassava and tropical crops. Projects: Current work includes adaptation of maize systems to phosphate scarcity (IRTG 2366/2) and developing sustainable banana value chains through circular bioeconomy approaches. Publications: His recent work spans phosphorus recovery from digestate, soil management in Southeast Asia, cassava leaf drying, mycotoxin contamination, and innovative biogas solutions. Technological Contributions: Innovations in solar drying systems, UAV-based crop monitoring, and mechanical stimulation for plant growth regulation.

Mohammad Taherzadehمشاهده پروفایل

استاد

• Bioprocess Technology
• Waste-to-resource conversion
• Circular economy
• +۷ مورد دیگر

Mohammad Taherzadeh is a Professor in Bioprocess Technology at the University of Borås, specifically within the Academy of Textiles, Technology and Economics, Department of Resource Recycling and Community Development. With a background in Chemical Engineering and Bioscience, he leads research focused on developing circular economy solutions and biorefineries to transform waste and residuals into valuable products. His educational background includes a Ph.D. in Bioscience from Chalmers University of Technology (1999), M.Sc. in Chemical Engineering from Sharif University of Technology (1991), and B.Sc. in Chemical Engineering from Isfahan University of Technology (1989). His academic journey began with early research in high school chemistry experiments, leading to postgraduate studies in biotechnology. Professor Taherzadeh's research centers on waste valorization, with a vision that "waste" is actually a resource limited only by our current knowledge to transform it into valuable products. His work focuses on two main biorefinery approaches: Anaerobic Digestion Biorefinery for producing volatile fatty acids from various wastes, and Filamentous Fungi Biorefinery for converting residuals into ethanol, pigments, biopolymers, feed, and food using edible fungi. His research group is actively commercializing these methods through initiatives like Millow. His extensive publication record shows a strong focus on sustainable resource recovery, with numerous recent publications (2023-2025) addressing food waste management, volatile fatty acid production, fungal biomass cultivation, and circular bioeconomy applications. The research trends demonstrate a progression from fundamental bioprocess development toward practical applications in food systems, animal nutrition, and sustainable manufacturing. Among his notable scientific achievements is an impressive Google Scholar profile with over 42,983 citations, an h-index of 104, and an i10-index of 421, reflecting significant impact in the fields of bioprocess technology and sustainable resource recovery. Professor Taherzadeh serves as the main supervisor for several doctoral students including Mahtab Mohammadi, Clarisse Uwineza, Alvaro Dos Santos Neto, and Ricky Wang. His research has secured substantial funding for projects advancing biorefinery technologies and waste-to-resource conversion processes, with applications spanning food systems, animal nutrition, and sustainable manufacturing. His work is organized around the Swedish Centre for Resource Recovery, where he leads efforts to develop commercially viable technologies that transform waste streams into valuable products while addressing critical environmental challenges including greenhouse gas emissions, resource depletion, and waste management.

Clemens Kaminskiمشاهده پروفایل

استاد

• Biochemical Engineering
• Applied Optics
• Microscopy
• +۸ مورد دیگر

Professor Clemens Kaminski is affiliated with the University of Cambridge as Professor of Chemical Physics in the Department of Chemical Engineering and Biotechnology . He serves as the Head of Department and directs the EPSRC Centre for Doctoral Training in Sensor Technologies and Applications . His research focuses on advanced imaging techniques to study biological processes at molecular scales, particularly super-resolution microscopy , fluorescence lifetime imaging , and machine learning-assisted imaging . Collaborations span medics, biologists, physicists, and engineers , targeting mechanisms of neurodegenerative diseases like Alzheimer's and Parkinson's. Key technologies developed in his group include Laser Analytics tools for in vivo imaging, self-supervised spike sorting algorithms for neuronal activity analysis, and portable microscopy platforms . Publications emphasize high-resolution imaging , pathogen detection , and cellular thermogenesis in disease contexts. He leads the Laser Analytics Group and maintains active collaborations with Professor Gabriele Kaminski Schierle (Molecular Neuroscience Group) and other interdisciplinary teams. His work integrates optical engineering , biomolecular analysis , and computational methods to address challenges in neurodegeneration and pathogen imaging .

Alexei Lapkinمشاهده پروفایل

استاد

• Sustainable Reaction Engineering
• Flow Chemistry
• Heterogeneous Catalysis
• +۷ مورد دیگر

Professor Alexei Lapkin serves as Professor of Sustainable Reaction Engineering at the Department of Chemical Engineering and Biotechnology, University of Cambridge. He leads the Sustainable Reaction Engineering (SRE) Group, which focuses on developing cleaner manufacturing processes within chemical and chemistry-using industries, with particular emphasis on liquid- and multi-phase catalytic and biochemical processes. Professor Lapkin's research program centers on sustainable chemical engineering approaches, with expertise spanning flow chemistry, heterogeneous catalysis, and the integration of machine learning with chemical process development. His group pioneers methods of machine learning for automated process optimization and explores Big Data applications to chemical reaction networks. Current research focuses on developing innovative digital technologies to address sustainability challenges in the chemical industries, with specific projects on CO 2 electro-reduction, circular economy solutions, and sustainable manufacturing of molecules and materials. His publication record demonstrates a clear trajectory toward integrating AI and machine learning with traditional chemical engineering principles to solve sustainability challenges. His recent work spans reaction network optimization, digital twin development for chemical processes, sustainable pharmaceutical manufacturing, and electrochemical CO 2 conversion technologies. This research often involves interdisciplinary collaborations across chemistry, computer science, and engineering disciplines. Professor Lapkin actively supervises PhD students and has mentored researchers who have established successful spin-out companies including Chemical Data Intelligence (CDI), AM Ltd, and Reactwise (founded by former PhD students Alexander Pomberger and Daniel Wigh). His research is supported by diverse funding sources including UKRI (EPSRC), National Research Foundation (Singapore), and Pharma Innovation Programme Singapore (PIPS). He is deeply involved in several Cambridge research centers including the SynTech CDT, Innovation Centre in Digital Molecular Technologies (iDMT), and the Cambridge Centre for Advanced Research and Education in Singapore. His group leads the 'Sustainable Manufacture of Molecules and Materials' (SM3) project and plays a key role in the Pharma Innovation Programme Singapore, focusing on digital solutions for sustainable chemical manufacturing.

Yunha Hwangمشاهده پروفایل

استادیار

• Machine Learning
• Microbial Biochemistry
• Evolutionary Biology
• +۳ مورد دیگر

Yunha Hwang is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Biology, the Department of Electrical Engineering and Computer Science (EECS), and the Schwarzman College of Computing. They combine machine learning with experimental approaches to study microbial systems' biochemistry, ecology, and evolution. PhD in Evolutionary and Organismic Biology (2024), Harvard University MS in Earth Systems (2018), Stanford University B.Sc in Computer Science (2018), Stanford University Their research focuses on developing machine learning models to discover and design novel microbial functions, leveraging computational methods alongside evolutionary, ecological, and biochemical expertise. The Hwang Lab is located in MIT’s Koch Biology Building and actively recruits new members. Contact: Office 68-370A, Phone 617-253-9395, Assistant Jessica Klees (617-253-9395).