جست‌وجوی استاد و پژوهشگر

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Vikramaditya G. Yadavمشاهده پروفایل

دانشیار

• Biocatalysis
• Bioremediation
• Bioprocess engineering
• +۱۵ مورد دیگر

Vikramaditya G. Yadav is an Associate Professor at the University of British Columbia (UBC) in the Department of Chemical and Biological Engineering, Faculty of Applied Science. He directs the Master of Engineering Leadership (MEL) Program in Sustainable Process Engineering and leads the BioFoundry research group. Education: B.A.Sc., University of Waterloo (2007) Ph.D., Massachusetts Institute of Technology (2013) Postdoctoral Associate, Harvard University (2014) His research spans sustainable chemical manufacturing, metabolic engineering, and biotechnology. Key areas include: Designing biosynthetic enzymes for biomass valorization Developing bioremediation strategies for industrial water quality Creating innovative drug delivery systems and tissue engineering solutions Advancing synthetic biology for pharmaceutical and bioenergy applications His recent work focuses on ocular drug delivery, cannabinoid biosynthesis in E. coli, lignin-based nanoparticles for cancer therapy, and computational analysis of plant secondary metabolites. Collaborations with start-ups, industry, and medical labs drive innovation in Canada's bioeconomy. Professional Leadership: Chair, Biotechnology Division of the Chemical Institute of Canada Associate Editor, The Canadian Journal of Chemical Engineering He is affiliated with UBC's BioProducts Institute and contributes to project-based learning pedagogy.

Aberham Hailu Feyissaمشاهده پروفایل

دانشیار

• Food Process Engineering
• Heat and Mass Transfer
• Transport Phenomena in Porous Media
• +۷ مورد دیگر

Aberham Hailu Feyissa is an Associate Professor at the National Food Institute of the Technical University of Denmark , specializing in process modeling and sustainable food engineering. His research bridges complex transport phenomena with practical food manufacturing challenges. Ph.D. in Food Process Engineering, Technical University of Denmark MSc in Food Science, K.U.Leuven and Universiteit Gent BSc in Chemical Engineering, Bahir Dar University His work focuses on coupled mass and heat transfer during solid food processing, aiming to develop robust first-principles models for predictive process optimization. Key applications include Ohmic heating , digital twin technology , and bioactive ingredient extraction . Recent publications highlight his contributions to sustainable food processes , including insect-based feed modeling , seaweed bread kinetics , and clean-label cheese formulation . His methodological innovations span FTIR spectroscopy , computational fluid dynamics , and kinetic modeling . Supervisor for PhD students S. S. Turgut , M. E. Jabali , and Dahal S. Principal Investigator for projects like Modelling and Digitalisation of Food Processes and Sustainable Extraction of Bioactive Insect Fractions Active in conference presentations and peer review , he drives advancements in food process understanding through mechanistic modeling and mathematical simulation .

Charlotte Jacobsenمشاهده پروفایل

استاد

• Antioxidant Chemistry
• Lipid Oxidation
• Omega-3 Fatty Acids
• +۷ مورد دیگر

Charlotte Jacobsen is a Professor and Head of the Research Group for Bioactives – Analysis and Application at the National Food Institute, Technical University of Denmark (DTU). Her research focuses on lipid oxidation, antioxidants, and sustainable utilization of marine resources. She leads multiple interdisciplinary projects and supervises PhD students in food science and technology. Research Interests: Antioxidant chemistry in food systems Oxidative stability of omega-3 fatty acids Valorization of fish and seafood by-products Microalgae as sustainable sources of bioactives Functional foods and nutraceuticals Green extraction technologies Recent Research Trends: Her recent publications (2021–2025) reflect a strong focus on sustainable food systems, including the recovery of bioactive compounds from fish side-streams, stabilization of omega-3 lipids, development of anti-obesity peptides from seaweed, and cultivation of microalgae using industrial waste streams. The work spans food chemistry, marine biotechnology, and green processing, with applications in functional foods and nutrition. Scientific Awards: Danisco Award, 2003 Edwin Frankel Best Paper Award, 2010 and 2011 La Médaille Chevreul, 2010 Marcuse Lecturer grant, 1999 Advising and Grants: Professor Jacobsen actively supervises PhD students and leads multiple funded research projects, including 'Utilization of fish side-streams for production of novel food ingredients' and 'Sustainable Production of Microalgae Proteins'. She is involved in national and international collaborations, securing research funding for projects on omega-3 extraction, microalgae cultivation, and seafood quality. Labs and Teams: She leads the Research Group for Bioactives – Analysis and Application at DTU, which is part of the DTU Microbes Initiative. The group specializes in analytical methods for bioactive compounds, lipid oxidation analysis, and development of sustainable food ingredients.

Rex Yingمشاهده پروفایل

استادیار

• Graph Neural Networks
• Geometric Representation Learning
• Explainable AI
• +۳ مورد دیگر

Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. He leads research in graph neural networks, geometric representation learning, and explainable AI, with applications spanning physical simulations, biology, knowledge graphs, and recommender systems. His lab actively recruits PhD students interested in geometric deep learning, graph neural networks, and trustworthy AI. Dr. Ying received his PhD in Computer Science from Stanford University under Jure Leskovec, with a thesis titled "Towards Expressive and Scalable Deep Representation Learning for Graphs." Prior to that, he graduated from Duke University in 2016 with highest distinction, majoring in Computer Science and Mathematics. His research focuses on three interconnected areas: advancing graph neural network architectures for improved expressiveness, scalability, and interpretability; innovating in geometric representation learning for data with diverse characteristics; and developing real-world applications across scientific domains. He has pioneered influential algorithms including GraphSAGE, PinSAGE, and GNNExplainer, and developed the first billion-scale graph embedding services at Pinterest as well as graph-based anomaly detection algorithms at Amazon. His recent publication trends show a strong focus on hyperbolic geometry for foundation models, non-Euclidean representation learning, and multimodal applications in computational biology. The research demonstrates increasing integration of geometric deep learning with large language models and foundation model architectures. KDD 2022 Dissertation Award 2019 Baidu Scholarship in Artificial Intelligence Dr. Ying actively serves the research community as a committee member for major conferences including AAAI, ICML, NeurIPS, ICLR, KDD, and WebConf for over seven years, and as area chair for LoG 2022. He co-leads the open-source PyTorch Geometric project and has organized numerous workshops on graph learning. His industry collaborations include Pinterest, Amazon, Facebook AI Research, DeepMind, Siemens, SLAC National Accelerator Laboratory, and Saudi Aramco. He teaches "Deep Learning for Graph-Structured Data" at Yale and mentors students in developing cutting-edge graph learning algorithms. His research lab collaborates with both academic institutions and industry partners to advance the state-of-the-art in graph representation learning, with particular emphasis on geometric deep learning and its applications to scientific discovery and real-world systems.

Martin Burkeمشاهده پروفایل

استاد

• Organic Chemistry
• Small-Molecule Synthesis
• Molecular Prosthetics
• +۷ مورد دیگر

Martin Burke is the May and Ving Lee Professor for Chemical Innovation and Professor of Chemistry at the University of Illinois Urbana-Champaign , with additional appointments in Biochemistry, Biomedical & Translational Sciences, and multiple campus institutes including the Beckman Institute and the Carl R. Woese Institute for Genomic Biology. Education B.S. Johns Hopkins University , 1998 Ph.D. Harvard University , 2003 M.D. Harvard Medical School , 2003 Research Interests Burke’s program centers on molecular prosthetics : the design, synthesis and application of small molecules that replicate or replace missing or dysfunctional proteins. His group pioneered iterative cross-coupling (ICC) using MIDA-protected haloboronic acids to automate the construction of complex natural products and function-oriented small molecules. Current projects target ion-channel replacement in cystic fibrosis, iron-transport restoration in anemia, and non-toxic antifungals that overcome drug resistance. Scientific Awards & Honors National Academy of Medicine (2021) AAAS Fellow (2021) ASCI Member (2021) iCON Award (2019) Mukaiyama Award, Japan (2019) ACS Nobel Laureate Award for Graduate Education (2017) Thieme-IUPAC Prize in Synthetic Organic Chemistry (2014) Elias J. Corey Award (2013) Arthur C. Cope Scholar Award (2011) Research Output & Impact Burke has authored >120 peer-reviewed articles, >30 patents, and his work has been cited >20,000 times. High-impact publications in Nature , Science , and Angewandte Chemie have advanced automated synthesis, molecular prosthetics, and cystic fibrosis therapeutics. Laboratory & Training The Burke Laboratories house a multidisciplinary team of graduate students, post-doctoral researchers, and physician-scientists developing next-generation molecular prosthetics. The group is supported by NIH, NSF, private foundations, and industry partnerships aimed at democratizing molecular innovation.

• Robotics
• Mechatronics
• Ferrofluids
• +۳ مورد دیگر

Zoran Cenev holds a Tenure Track Assistant Professor position within the Mechatronics and Dynamics section of the Department of Mechanical and Production Engineering at the School of Engineering, Aarhus University. His primary institutional affiliation is with AU Engineering, and contact details include email zoran.cenev@mpe.au.dk and telephone +45 20 64 75 44, with office location Aarhus N, 5128-140. Research interests focus on interdisciplinary applications of magnetic and robotic systems: Robotic micromanipulation via electromagnetic needles Ferrofluid-based biofabrication for skeletal muscle engineering Laser-induced photothermal droplet control Theoretical modeling of particle dynamics at fluid interfaces Surface engineering for underwater metallic stability Nanostructure formation through ion bombardment His recent publications (2023-2025) reveal a dominant trend in adapting ferrofluids for biomedical automation, particularly 3D bioprinting of magnetically responsive tissues and droplet manipulation on engineered surfaces. This work bridges mechanical engineering with regenerative medicine, emphasizing practical implementations of theoretical models for microscale precision. Scientific awards are not documented in the provided information. As a faculty member, Dr. Cenev likely mentors graduate students and pursues research grants, though specific advisees or funding details are absent. Departmental laboratories and workshops support his experimental work in mechatronics, with emphasis on magnetic manipulation systems and surface characterization.

Hongtu Zhuمشاهده پروفایل

استاد

• Biostatistics
• Medical Image Analysis
• Precision Medicine
• +۴ مورد دیگر

Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Gillings School of Global Public Health and leads the Biostatistics and Imaging Genomics Analysis Lab. His expertise spans statistical learning, medical imaging, AI, and big data integration, with a focus on precision medicine and biomedicine. Dr. Zhu earned his PhD in Statistics from The Chinese University of Hong Kong (2000) and has held prior roles including DiDi Fellow/Chief Scientist (2018-2020) and Bao-Shan Jing Endowed Professor at MD Anderson Cancer Center (2016-2018). He has published over 345 peer-reviewed articles in top-tier journals like Nature, Science, and JASA, and actively contributes to editorial roles including Coordinating Editor of JASA. His research interests include neuroimaging analysis, knowledge graphs, and AI applications in healthcare. Notable awards include the COPSS Snedecor Award (2025), IEEE Fellowship (2025), and IMS Medallion (2027). He has mentored over 80 PhD students/postdoctoral fellows and serves on NIH grant review panels and professional organizations like the ASA's Section on Statistics in Imaging. Key Contributions: Imaging genomics, brain connectivity studies, ridesharing market optimization, medical AI frameworks Lab Innovations: Brain Imaging Genetics Knowledge Portal, Biomedical Knowledge Graph Interface Teaching: Advanced biostatistics courses (Generalized Linear Models, Deep Learning in Biomedicine) Recent work explores causal inference in healthcare, X chromosome's role in neurobiology, and AI ethics in medical vision-language models. His interdisciplinary projects bridge statistics, computer science, and clinical practice to address complex biomedical challenges.

• alternative proteins
• food biotechnology
• plant-based hybrid foods
• +۵ مورد دیگر

Mehdi Abdollahi is an Associate Professor at Chalmers University of Technology's Department of Food and Nutrition Science. His research focuses on alternative proteins, food biotechnology, and plant-based hybrid foods, with emphasis on sustainable utilization of food side streams, legumes, cereals, and microalgae as future protein sources. Develops pH-shift technology and ultrasound-assisted methods for protein extraction Specializes in hybrid food engineering via fermentation and biorefinery Works on 3D food printing and high-moisture extrusion for food analogs Researches collagen and biobased food packaging materials His work includes >100 peer-reviewed publications, 4 book chapters, and 3 patents. Current projects involve collaborations with Arla Foods, Lantmännen, and Nordic Seafarm, addressing sustainable seafood systems and plant-protein hybridization. 2024 Bertebos Prize recipient 2025: Recognized among Sweden's Top 101 Sustainability Figures Mentors 7 PhD students and 3 postdocs while collaborating with European institutions like Kristianstad University and Ankara University. His group explores biorefinery strategies, hybrid food functionality, and innovative packaging solutions.

Markus Heinonenمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +۵ مورد دیگر

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

Lawrence Goodridgeمشاهده پروفایل

استاد

• Food Safety
• Antibiotic Resistance
• Foodborne Pathogens
• +۶ مورد دیگر

Lawrence Goodridge is a Professor and Director of the Canadian Research Institute for Food Safety (CRIFS) at the University of Guelph's Ontario Agricultural College. He holds the Leung Family Professorship in Food Safety and leads research at the intersection of food safety, antibiotic resistance, and One Health principles. His work focuses on applying genomic technologies to study foodborne pathogens (E. coli, Salmonella, Listeria, Cronobacter) and leveraging wastewater surveillance for infectious disease outbreak prediction. Academic History: BSc Microbiology (University of Guelph, 1995), MSc Food Microbiology (2003), PhD Food Microbiology (2002), followed by post-doctoral training in Food Safety at the University of Georgia (2002). Joined CRIFS in 2003. Research Interests: Genomic analysis of pathogen emergence, wastewater-based epidemiology, bacteriophage applications, and consumer education strategies for food safety. His lab develops innovative methods for rapid pathogen detection in food systems and environmental samples. Articles Trends: Over 100 peer-reviewed publications emphasize genomic surveillance of foodborne pathogens and SARS-CoV-2, with a focus on wastewater sampling innovations. Recent work explores multi-modal data integration for public health forecasting and ethical data protection frameworks for surveillance programs. Awards: While no specific prizes are listed, his $50M+ research funding from Canadian/international sources underscores recognition of his impactful work. Grants support projects like phage-based sanitization and antimicrobial resistance tracking. Advising & Labs: Leads CRIFS laboratory operations and collaborates globally on food safety initiatives. His research has informed food industry guidelines and policy frameworks for mitigating pathogen risks in agricultural and environmental systems.

Mohammad Peydayeshمشاهده پروفایل

مدرس

• Soft Matter
• Self-Assembly
• Amyloid Fibrils
• +۸ مورد دیگر

Mohammad Peydayesh is a Lecturer and Senior Researcher at the Department of Health Sciences and Technology , ETH Zürich. He works in the Food and Soft Material Laboratory , focusing on sustainable materials derived from food waste and agri-food byproducts. PhD in Chemical Engineering (2018, Iran University of Science and Technology) Postdoctoral Fellow at ETH Zürich under Prof. Dr. Mezzenga Senior Assistant at ETH Zürich since 2021 His research spans soft matter , self-assembly phenomena , and amyloid fibril applications in: Environmental engineering (water purification, heavy metal removal) CO2 conversion and storage Smart packaging and bioplastics development Biorefinery concepts and circular economy Nanomaterials for waste valorization Recent publications highlight trends in amyloid-based hybrid materials for: Metal recovery from e-waste and contaminated water CO2 capture and conversion Bioplastic production from agricultural waste Antiviral and detoxification applications Water desalination and purification Photonic and catalytic materials He contributes to the Laboratory of Food & Soft Materials , advancing sustainable solutions through interdisciplinary material science.

Berat Z. Haznedaroğluمشاهده پروفایل

استادیار

• Environmental Microbiology
• Environmental Biotechnology
• Systems Biology
• +۷ مورد دیگر

Berat Z. Haznedaroğlu is an Assistant Professor at the Institute of Environmental Sciences, Bogazici University, Istanbul, where he has been serving since 2014. He previously held an Assistant Professor position at the Department of Civil, Structural, and Environmental Engineering, University at Buffalo, New York (2012–2014), and completed postdoctoral training at Yale University. His research is highly interdisciplinary, bridging environmental engineering, microbiology, and biotechnology. Education: Postdoctoral, Chemical and Environmental Engineering, Yale University, USA (2012) PhD, Chemical and Environmental Engineering, University of California-Riverside, USA (2010) MS, Civil and Environmental Engineering, Villanova University, USA (2005) BS, Biology, Middle East Technical University, Turkey (2003) His research interests include Environmental Microbiology , Microalgae Functional Genomics , Systems Biology , and the Fate and Transport of Pathogens . He is particularly known for his work on microalgae-based solutions for wastewater treatment, biofuel production, and space life support systems. His lab explores the integration of algae-bacteria consortia for sustainable biorefineries and environmental remediation. The recent publications reflect a strong trend toward applied environmental biotechnology, with emphasis on microalgae applications in wastewater treatment, high-value product recovery (e.g., phycocyanin), and thermal processing effects on algal nutritional quality. The work spans from fundamental metabolic pathway analysis to real-world implementation in biorefineries and space missions. Scientific Awards and Grants: INDEPENDENT Project (€6.9M, 2019–2024) – Integrated Biorefinery for Bioeconomy MICRO-JET Project ($448K, 2020–2023) – Algae-Based Bio-Jet Fuel VirALGin Project ($185K, 2022–2024) – Algae-Derived SARS-CoV-2 Inhibitors UzMAn Project ($65K, 2023–2024) – Microalgal Life Support for Space Renew-IST Project ($521K, 2022–2023) – Renewable Energy Center in Istanbul Dr. Haznedaroglu leads a dynamic research group with multiple funded projects and collaborative networks across institutions in Turkey and the U.S. He advises graduate students and has co-authored several book chapters and journal articles with junior researchers. His work is supported by major national and international funding bodies including TUBITAK, TUBITAK-Uzay, and the Ministry of Industry and Technology. Future research directions include closed-loop life support systems for space habitats and commercial-scale algal biorefineries. Laboratories and Research Platforms: His team operates within the CBE Institute at Bogazici University, utilizing facilities for microbial culturing, genomics, and environmental analysis. The INDEPENDENT and UzMAn projects involve advanced bioreactor systems and space-simulated environments.

Thomas Demeesterمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +۷ مورد دیگر

Thomas Demeester is an Associate Professor at the Internet Technology and Data Science Lab (IDLab), Ghent University - imec, Belgium. Appointed as Assistant Professor in 2019, he leads an AI research group focused on health applications and drug design, co-directing the Text-to-Knowledge research cluster with Prof. Chris Develder. His educational background includes: M.Sc. in Electrical Engineering from Ghent University (2005), completed with thesis work at ETH Zurich Ph.D. in Computational Electromagnetics from Ghent University (2009), funded by Research Foundation - Flanders (FWO) Demeester's research spans artificial intelligence with emphasis on deep learning and neuro-symbolic methods. Current tracks include energy-based models (Hopfield Networks, Deep Equilibrium Models), diffusion models for drug design, and clinical reasoning systems. His work bridges NLP, healthcare informatics, and generative AI with strong industry partnerships. Recent publications (2023-2025) reveal strategic expansion from NLP into health-centric AI: BioLORD biomedical encoders (2023), synthetic medical data frameworks (UAI/NeurIPS 2024), and novel diffusion model guidance (ICLR 2025). This evolution demonstrates convergence of generative modeling, clinical data analysis, and protein design. He actively mentors 24 PhD students across diverse AI domains: Current Research: Conversational agents, emotion analysis, clinical reasoning, antibody design, and diffusion model optimization Recent Graduates: Interpretable language models, biomedical semantics, task-oriented dialogue, and social media knowledge extraction Research is supported by imec funding and collaborations with Flemish biotech companies, building on his post-doctoral experience securing media-sector projects. Within IDLab, he co-leads the Text-to-Knowledge cluster driving NLP innovations for healthcare, legal, and economic applications.

Simone D. Castellarinمشاهده پروفایل

استاد

• Viticulture
• Berry Crop Physiology
• Plant Genomics
• +۲ مورد دیگر

Simone D. Castellarin is a Professor in the Department of Applied Biology at the University of British Columbia's Faculty of Land and Food Systems, and holds the Canada Research Chair Tier 2 in Viticulture. His research focuses on the molecular and physiological mechanisms governing berry ripening and composition in grapes, blueberries, and raspberries, with emphasis on genomic regulation under environmental stressors like heatwaves and drought. PhD in Plant Biology from the University of Udine (2007) Postdoctoral training at Hochschule Geisenheim University and University of California Davis Research areas include terpene biosynthesis, jasmonate signaling, cuticular wax dynamics, and agronomic strategies for climate change mitigation. Key projects involve remote sensing for vineyard zoning , hormone application effects , and genetic studies of berry quality traits . His work spans collaborations with industry bodies like the BC Wine and Grape Council and academic partners including the Cantu Lab at UC Davis. Recent publications highlight genomic analyses of terpene synthases, water deficit impacts on metabolites, and postharvest quality assessments. Awards include the 2009 Rudolf Hermanns Prize for viticultural research. He supervises numerous PhD and Master's students, and leads the Castellarin Lab at UBC's Wine Research Centre.