Find Academic Supervisors

• Protein Translocation
• Quality Control in Proteins
• Membrane Protein Structure
• +3 more

Dr. Peter Oatley is a Lecturer at the School of Medicine and Dentistry, University of Central Lancashire, where he teaches on the two-year accelerated Medical Sciences BSc course. His research focuses on protein translocation mechanisms, membrane protein structure analysis, and bacterial cell biology. He employs advanced microscopy techniques and investigates quality control processes in protein secretion pathways. Dr. Oatley's work spans molecular biology, microbiology, and biophysics, with particular emphasis on the Sec machinery involved in protein translocation and the structural organization of membrane proteins like polycystin-1. His recent studies include bacterial S-layer biogenesis and antibiotic resistance mechanisms in Clostridium difficile. His publications highlight interdisciplinary research linking protein dynamics, membrane interactions, and infectious disease processes. While no awards or grants are explicitly mentioned, his contributions to understanding protein secretion and bacterial physiology demonstrate sustained scholarly engagement.

Michael WimmerView profile

Professor

• Computer Graphics
• Real-Time Rendering
• Point Clouds
• +4 more

Univ.Prof. Michael Wimmer is a Professor in the Department of Computer Graphics and Visualization at TU Wien's Faculty of Informatics. His research focuses on real-time rendering, point cloud processing, GPU computing, and computational design. He leads projects involving architectural visualization, thermal simulation, and integrative design frameworks combining 4D sketching with material modeling. His work bridges computer graphics with applications in architecture and engineering. ORCID: 0000-0002-9370-2663 Research Group: Network Lab Recent research emphasizes GPU-accelerated algorithms (e.g., LOD generation for 2 billion points), real-time rendering techniques, and deep learning approaches for surface reconstruction. Collaborations span thermal simulation with civil engineers and 4D design tools for architects. Notable contributions include: Developing PPSurf for detailed surface reconstruction using point convolutions Advancing Vulkan-based rendering pipelines in academic settings Integrating light polarization for HDR imaging His lab explores applications in architectural design metaphors, computational material assessment via GeoRadar, and thermal simulation using precomputed radiative transport. Students under his supervision focus on GPU optimization, 3D reconstruction, and VR ergonomics.

• Electrochemical Engineering
• Renewable Energy Systems
• Material Science
• +3 more

Dr. Melanie Bühler is the Head of Electrochemical Energy Systems - Applications at the Department of Microsystems Engineering (IMTEK) at the University of Freiburg. She holds a PhD from the University of Freiburg (2020), focusing on 'Development of novel electrodes for PEM water electrolysis.' Her academic journey includes a Master's in Microsystems Engineering (2016) with work on bio fuel cells and a Bachelor's (2012) in the same field. She is currently the contact person for PEM water electrolysis projects. Her research interests revolve around electrochemical systems, specifically proton exchange membrane (PEM) electrolyzers, catalyst development, material science, and renewable energy systems. Key areas include optimizing electrode configurations, catalyst activity, and membrane assembly technologies. She has contributed to advancements in non-noble metal catalysts and core-shell microparticle designs for improved electrolyzer performance. Dr. Bühler's lab, the Laboratory for MEMS Applications, focuses on applied electrochemical engineering. Her publications highlight innovations in PEM electrolyzer design, electrode fabrication techniques, and material characterization. Her work bridges fundamental electrochemistry with practical applications in sustainable energy systems.

• Human-Computer Interaction
• Artificial Intelligence
• Natural Language Processing
• +3 more

Joseph Chee Chang is a researcher at Carnegie Mellon University's Human-Computer Interaction Institute. His work focuses on Human-Computer Interaction Artificial Intelligence Natural Language Processing He develops tools for scholarly synthesis, literature review, and research ideation. Research Highlights Chang designs mixed-initiative systems like IdeaSynth for research idea development and Social-RAG for socially grounded AI generation. His PaperWeaver system recontextualizes academic recommendations, while Qlarify offers hierarchical paper summaries. Current projects include SciArena (2025) for evaluating scientific foundation models and PaperMage (2023) for document processing.

• Medical Image Analysis
• Computer Vision
• Deep Learning
• +3 more

Pan Pan is a Professor in the Department of Biomedical Engineering at Huazhong University of Science and Technology, with extensive research contributions spanning medical image analysis, computer vision, and underwater wireless communications. Their work demonstrates strong interdisciplinary collaboration between biomedical engineering and computer science, with significant industry partnerships including Alibaba. Research interests focus on medical image analysis (particularly automatic breast ultrasound systems), deep learning applications in healthcare diagnostics, and secure underwater communications . Their work bridges theoretical advances with practical clinical applications, developing innovative segmentation algorithms, tumor detection systems, and secure communication protocols for specialized environments. Analysis of recent publications reveals a strong trend toward integrating multi-modal data fusion techniques with uncertainty-aware deep learning models for medical diagnostics. The research spans both fundamental algorithm development (novel segmentation networks, feature matching optimization) and domain-specific applications (ABUS tumor detection, ICU mortality prediction, underwater sensor networks). Pan Pan maintains active collaborations with major Chinese technology companies and academic institutions, evidenced by the consistent publication record in top-tier conferences including CVPR, ICCV, and NeurIPS. While specific awards aren't documented in the provided materials, the research impact is demonstrated through numerous high-impact publications across computer vision and biomedical engineering venues. The research program shows particular strength in translating computer vision techniques to medical applications, with significant contributions to semi-supervised learning approaches for medical image segmentation where labeled data is scarce. Recent work also demonstrates growing interest in secure communications for specialized environments like underwater sensor networks.

• Explainable Artificial Intelligence
• Machine Learning
• Data Clustering
• +4 more

Nicolas Labroche serves as a Researcher in the Department of Computer Science at the Polytechnic School of the University of Tours. He is affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT) within the UFR of Sciences and Technology. His academic work spans multiple research domains with significant contributions to machine learning methodology and applications. His primary research focuses on Explainable Artificial Intelligence , particularly for medical applications where model interpretability is critical. He has pioneered methods in clustering of local attributive explanations and discernibility in medical ML models . Additional expertise includes exploratory data analysis through mathematical programming for comparison queries, time series forecasting for environmental monitoring, and preference-based explanations in recommender systems. His work bridges theoretical computer science with practical applications in healthcare, environmental science, and business intelligence. Analysis of his publication trends reveals consistent innovation in making data exploration more efficient and interpretable. Recent work emphasizes user-centric explainability (2024-2025), mathematical optimization for analytics (2022-2023), and synthetic data generation (2022). His research demonstrates strong interdisciplinary connections between computer science, medicine, and environmental science. His laboratory affiliation with LIFAT provides the infrastructure for his computational research. While specific grant details aren't public, his publication record indicates sustained research productivity across multiple European-funded projects focused on data science applications.

Hassan RazaView profile

Senior Lecturer

• Nanoelectronics
• Graphene Nanoelectronics
• Nanotechnology
• +5 more

Hassan Raza is a Senior Lecturer in the Department of Electrical and Computer Engineering at Clemson University. He joined the faculty in 2020 and has previously taught at the University of Iowa, Rowan University, and Georgia Southern University. Dr. Raza focuses on nanoscale device modeling, simulation, and education, with expertise in graphene nanoelectronics, spintronics, and computational methods. Ph.D. in Electrical and Computer Engineering (2007), Purdue University M.S. in Electrical and Computer Engineering (2002), Purdue University M.S. in Operations Management (2016), University of Arkansas B.S. in Electrical Engineering (2001), University of Engineering and Technology, Lahore, Pakistan His research spans nanoscale materials and devices, emphasizing theoretical, computational, and experimental approaches. He has contributed to understanding transport phenomena in graphene systems, molecular electronics, and memory devices. Recent work involves electric-field modulation, misalignment effects, and bandgap engineering in 2D materials. Dr. Raza’s publications reveal a strong focus on graphene nanoelectronics, molecular memory, and low-voltage transistor design. Key subfields include Dirac fermion dynamics, nanoribbon properties, and quantum transport modeling. His 2019 textbooks on nanoelectronics and nanotechnology further solidify his educational contributions. Presidential Faculty Fellowship (University of Iowa, 2010) Old Gold Fellowship (University of Iowa, 2011) As Undergraduate Assessment Coordinator and Chair of the ECE Laboratory Committee, Dr. Raza shapes curriculum and lab infrastructure. He edited the 2012 book Graphene Nanoelectronics and developed interactive seminars for freshman nanotechnology education.

Amy J. KoView profile

Professor

• Computing Education
• Human-Computer Interaction (HCI)
• Equity & Justice in Computing
• +6 more

Amy J. Ko is a Professor and Associate Dean for Academics at the Paul G. Allen School of Computer Science & Engineering and The Information School at the University of Washington, Seattle . She serves as Editor-in-Chief of ACM TOCE , co-directs Reciprocal Reviews and CS for All Washington , and leads the CENSOR Research Lab . Her work prioritizes equitable, liberatory computing education , bridging human-computer interaction and social justice . Research Focus: Equity in CS education, AI literacy, inclusive design, developer productivity, and ethical technology use Awards: Best Paper Award (2024, 2022) Diversity + Inclusion Award (2024) Most Influential Paper Honorable Mention (2014) Honorable Mention (2024, 2023) Publications: Over 184 works spanning program comprehension, assessment design, ethical pedagogy, and democratizing computing education Books: Foundations of Information , Critically Conscious Computing , and Teaching Accessible Computing Her Google Scholar profile reflects citations under her deadname, which she advocates to correct. She emphasizes student agency and community collaboration , particularly with marginalized groups in computing spaces.

• Organic Chemistry
• Materials Science
• Sensor Development
• +4 more

Rui Pedro Carvalho Lima de Sousa is a postdoctoral researcher at the Textile Science and Technology Center (2C2T) of the University of Minho. His career focuses on organic-inorganic hybrid materials for sensors and advanced textile applications, with expertise in sol-gel chemistry , electrospinning , and optical sensing . PhD in Applied Chemistry (University of Minho, 2023), thesis: "Sol-gel based optical sensors for monitoring of biocides and other analytes" MSc in Chemical Characterization and Analysis Techniques (2018), BSc in Biochemistry (2016) Research interests span: Development of smart textile materials for filtration and adsorption via electrospinning Biocide detection using quinoline and hydrazone derivatives Hybrid sol-gel matrices for corrosion protection and concrete durability monitoring Integration of machine learning in sensor design Recent publications highlight work in NIR fluorescent probes , ion chemosensors , and environmental monitoring applications. Key collaborations include projects with the Institute of Systems Engineering and Computers (INESC) and University of Trás-os-Montes . Scientific recognitions : Best Doctoral Thesis in Chemistry (PYCA Award) FlashChem Photography Contest (2024) Advising includes mentoring students like Oscar Martinez-Rico and Joana Rocha on electrospun membrane projects, with past contributions to organic-inorganic hybrid material research.

• Computer Vision
• Medical Imaging
• Deep Learning
• +3 more

Yu Huang is a Researcher in the Department of Mathematical and Statistical Sciences at Clemson University's College of Science. His work focuses on advancing computational methods in medical imaging, computer vision, and deep learning applications. His research includes developing multimodal models for ophthalmology, enhancing 3D graphics rendering, and improving biomedical image analysis through active learning frameworks. Notable projects involve generative models for retinal imaging and shadow removal techniques in computer vision. His research interests span computational ophthalmology, neural rendering, and AI-driven biomedical solutions. Recent efforts emphasize scalable diffusion models for high-resolution synthesis and robust medical image segmentation using test-time augmentation. Yu Huang's publications reflect a strong emphasis on interdisciplinary research at the intersection of computer science and healthcare. His work addresses challenges in both foundational AI methodologies and their practical applications in clinical settings.

Ying ChenView profile

Assistant Professor

• Tissue Engineering
• Regenerative Medicine
• Biomedical Engineering
• +4 more

Ying Chen serves as Research Assistant Professor in the Department of Biomedical Engineering within Tufts University's School of Engineering. Her laboratory develops advanced 3D in vitro tissue models of human intestine using silk scaffolds and intestinal organoids to mimic native structure and function. Ph.D. in Biomedical Science and Engineering, University of Maine (2013) M.S. in Environmental Toxicology, Xiamen University (2007) B.S. in Environmental Science, Fujian Normal University (2004) Her research focuses on tissue engineering and regenerative medicine , specifically creating human intestinal models that maintain mucus layers and oxygen gradients. These models enable studies of inflammatory bowel disease, drug delivery mechanisms, nanotoxicity effects, and microbial infections. Her work bridges biomaterials science , organoid technology , and translational medicine , with recent publications emphasizing silk-based protective coatings and cultivated meat applications. Analysis of her publication record (2022-2026) reveals strong emphasis on silk protein biomaterials for cellular protection and tissue modeling, with growing applications in sustainable food technology. Key thematic areas include intestinal disease modeling (32% of recent work), silk-based cell encapsulation (28%), and novel imaging techniques for engineered tissues (15%). Dr. Chen actively secures competitive research funding, including a National Science Foundation grant for 'Dynamic protein-based biomaterial designs for bionic coatings' (2021-2024) and industry collaboration with Sofregen Medical Inc. Her teaching portfolio consistently features 'Tissue Engineering Research Lab' courses since 2019, training students in advanced biomaterial techniques. Her laboratory maintains two key patents: 'Innervated artificial intestine compositions' (2017) and 'Microphysiologic methods and compositions' (2014), demonstrating translational impact beyond academic publications.

Wahida KhandkerView profile

Senior Lecturer

• Process Philosophy
• Critical Animal Studies
• Philosophy of Biology
• +4 more

Dr. Wahida Khandker is a Senior Lecturer in Philosophy at Manchester Metropolitan University, specializing in process philosophy, critical animal studies, and environmental humanities. She holds a BA/MA/PhD from the University of Warwick and is a Higher Education Academy Fellow. Her research explores intersections between continental philosophy and the life sciences, emphasizing non-human consciousness and ecological thought. Education: BA (Hons) Philosophy and Literature (Warwick), MA Continental Philosophy (Warwick), PhD Philosophy (Warwick) Professional Affiliations: Co-Chair of the Society for European Philosophy, Co-editor of Palgrave Perspectives on Process Philosophy Her work challenges anthropocentric frameworks through projects like the AHRC-funded Process Ecologies Network. Key publications include Philosophy, Animality, and the Life Sciences (2014) and Process Metaphysics and Mutative Life (2020), which re-examine environmental concepts using Bergson's 'dimensional milieu' theory. Current research focuses on rethinking space through Teilhard de Chardin's philosophy. Teaching includes undergraduate courses on philosophy of the body and education, and postgraduate modules on Bergsonian thought and ethics of the Anthropocene. She supervises PhDs in process philosophy, French philosophy, and environmental humanities. Grants: Principal Investigator for AHRC-funded Process Ecologies Network Labs/Teams: Active in Society for European Philosophy and Process Philosophy book series

Marco GrangettoView profile

Professor

• Computer Vision
• Image Processing
• Artificial Intelligence
• +5 more

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Emile Rugamika ChimusaView profile

Professor

• Computational Population Genomics
• Bioinformatics
• Genome-Wide Association Studies
• +5 more

Prof. Emile Rugamika Chimusa is a Professor at Northumbria University , College of Engineering and Science, specializing in Computational Population Genomics and Bioinformatics . Previously, he served as Associate Professor and Programme Director at the University of Cape Town for postgraduate programs in Computational Health Informatics and Human Genetics. His research focuses on genomic methodologies for analyzing large-scale genomic data, including Genome-wide association studies Admixture mapping in mixed-ancestry populations Pharmacogenetics of disease treatments Multi-omics integration for disease-drug associations Development of bioinformatics tools Population-specific disease risk prediction He has received over £17.7 million in research funding from organizations like the NIH , Wellcome Trust , and National Research Foundation . His work spans translational genomics in both non-communicable and infectious diseases .

Jonas BraaschView profile

Professor

• Acoustic Virtual Reality
• Binaural Hearing
• Auditory Modeling
• +3 more

Jonas Braasch is a Professor at Rensselaer Polytechnic Institute's School of Architecture and Associate Director for Research at the Experimental Media and Performing Arts Center (EMPAC). He teaches in the Graduate Program in Architectural Acoustics and leads research on collaborative virtual reality, binaural hearing, auditory modeling, and sensory substitution devices. His work has been funded by NSF, NSERC, and European agencies, and he holds dual PhDs in Electrical Engineering/Information Science and Musicology. His research integrates acoustic virtual reality systems, immersive museum soundscapes, telematic music, and assistive instrument design. Recent publications focus on networked immersive environments, spatial audio techniques, museum acoustics classification, and innovations in wind instrument design for accessibility. Publications emphasize the application of deep learning to acoustic prediction and multisensory integration.