جستجوی استاد راهنمای دانشگاهی

Gonzalo Guillén Gosálbezمشاهده پروفایل

استاد

• Process Systems Engineering
• Sustainable Chemistry
• Environmental Impact Assessment
• +۷ مورد دیگر

Prof. Dr. Gonzalo Guillén Gosálbez is a Full Professor at the Department of Chemistry and Applied Biosciences , ETH Zürich. He holds a PhD in Process Systems Engineering (UPC, 2005) and has held academic positions at Imperial College London (Reader), University of Manchester (Senior Lecturer), and Universitat Rovira i Virgili (Assistant/Associate Professor). His research focuses on Sustainable Chemical Processes , integrating life cycle assessment, optimization techniques, and planetary boundary analysis to evaluate and design low-carbon technologies. Current position: Full Professor, ETH Zürich (2019–present) Prior roles: Imperial College London (2016–2019), University of Manchester (2014–2016), URV Spain (2008–2014) Education: PhD (UPC, 2005), MEng/BEng (University of Murcia) His research explores CO2 valorization , green methanol synthesis , circular marine fuels , and planetary boundary compliance in energy and chemical systems. Recent work emphasizes machine learning for process modeling, single-atom catalysis , and decentralized ammonia production . Scientific contributions include 15+ peer-reviewed articles (2023–2025) in journals like Nature Chemical Engineering , ACS Sustainable Chemistry & Engineering , and Energy & Environmental Science . Key themes: Optimization of hybrid fossil/renewable carbon systems Environmental impacts of energy transitions Catalyst design for sustainable chemistry Life cycle assessment of emerging technologies Awarded UPC Top Doctoral Student Award and Top National Student Award , he combines process systems engineering with sustainability metrics to address global challenges in chemical and energy systems.

Harri Lähdesmäkiمشاهده پروفایل

دانشیار

• Probabilistic Machine Learning
• Deep Generative Models
• Artificial Intelligence
• +۴ مورد دیگر

Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.

• 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.

Tengyao Wangمشاهده پروفایل

استاد

• High-dimensional Statistics
• Change-point Analysis
• Sparse Signal Detection
• +۷ مورد دیگر

Tengyao Wang is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), serving as the MSc Statistics (Financial Statistics) Programme Director. Prior to LSE, he held positions as a Lecturer at University College London and a Research Fellow at the Cantab Capital Institute for the Mathematics of Information, University of Cambridge. His research focuses on high-dimensional statistics, computational efficiency, and statistical limitations imposed by computational constraints. Education: PhD in Statistics under Prof Richard Samworth at the University of Cambridge, with earlier studies including a Part III Essay in Empirical Process Theory. Research interests include sparse signal detection, change-point analysis, dimension reduction, robust statistics, and applications in medical statistics, financial data analysis, and material discovery. Key contributions include methodologies for handling missing data, high-dimensional change-point detection algorithms, and statistical learning techniques. Publications span theoretical advancements and applied innovations, with recent work emphasizing deep learning with missing data, residual permutation tests, and semi-supervised learning via random projections. His work has been recognized with awards such as the Royal Statistical Society Research Prize (2019) and the Guy Medal in Bronze (2023). He is an Associate Editor of the Journal of the Royal Statistical Society, Series B (JRSS B), and actively contributes to open-source tools like the 'ocd' and 'MissInspect' R packages for changepoint detection and missing data analysis.

• Computational Physiology
• Cardiac Electrophysiology
• Atrial Fibrillation
• +۷ مورد دیگر

Molly Maleckar is a Research Professor at the Computational Physiology Department of Simula Research Laboratory , Oslo, Norway. Her work bridges computational modeling, cardiac electrophysiology, and biomedical applications, with a focus on arrhythmia mechanisms, fibrosis modeling, and machine learning integration in cardiac risk prediction. Research Interests include: Computational Cardiology Ion Channel Dynamics Machine Learning in Medicine Excitable Tissue Modeling Cardiac Fibrosis Analysis Biomedical Simulation Scientific Contributions span 15+ publications (2018-2024) addressing atrial fibrillation, calcium handling, and AI-driven ECG analysis. Key collaborative projects involve patient-specific ventricular modeling and educational initiatives like the Simula Summer School in Computational Physiology .

David Junckerمشاهده پروفایل

استاد

• Microfluidics
• Nanotechnology
• Lab-on-a-Chip
• +۶ مورد دیگر

David Juncker is a Professor and Department Chair of the Department of Biomedical Engineering at McGill University. He serves as a Principal Investigator at the McGill University & Genome Quebec Innovation Centre and holds associate memberships in the Department of Neurology and Neurosurgery, Department of Electrical and Computer Engineering, Division of Experimental Medicine, Department of Surgery, and Goodman Cancer Research Centre. His research focuses on micro- and nano-bioengineering technologies for bioanalysis, precision medicine, and organs-on-chips. Key areas include microfluidics, lab-on-a-chip devices, biomedical sensors, medical diagnostics, biomaterials, tissue engineering, and cancer biomarker discovery. His lab develops scalable antibody microarrays, self-powered diagnostic platforms, microfluidic probes for brain tissue perfusion, and nanogradients for neuronal navigation, with applications in cancer diagnostics, global health, and neuroscience. Recent publications (2023-2025) reveal strong emphasis on extracellular vesicle analysis, single-cell proteomics, 3D-printed microfluidic/organ-on-a-chip systems, and capillary-driven circuits. Key trends include low-cost point-of-care diagnostics, advanced circulating tumor cell isolation methods, and biomimetic synthetic vesicles for drug delivery, demonstrating translational potential in early disease detection. Dr. Juncker leads a highly interdisciplinary team comprising undergraduate and graduate students, post-doctoral fellows, and staff from diverse scientific, engineering, and cultural backgrounds. His lab actively recruits Canadian/permanent resident graduate students for projects on single extracellular vesicle and protein detection in cancer and infectious diseases, leveraging microfluidics and wearables for biomarker discovery. The Juncker Lab operates from the McGill University & Genome Quebec Innovation Centre (740 Dr. Penfield Avenue, Room 6206). It maintains a collaborative, multicultural environment focused on developing transformative micro- and nano-bioengineering technologies with significant potential impact on human health diagnostics and treatment.

• Synthetic Biology
• Enzyme Engineering
• Genetic Circuits
• +۳ مورد دیگر

Dr. Seung-gu Lee is a Principal Scientist and Director of the Synthetic Biology Institute at the Korea Research Institute of Bioscience and Biotechnology (KRIBB). He holds a Ph.D. in Biotechnology from KAIST and serves as the President of the Korean Society of Enzyme Engineering. His leadership extends to multiple national research initiatives focused on advancing synthetic biology infrastructure and applications. Dr. Lee is a leading expert in genetic circuits and enzyme engineering for synthetic biology. He pioneered the development of GESS (Genic Enzyme Screening System/Circuits) technology, which enables ultra-high-speed screening of trace enzyme activities at the single-cell level. His research focuses on applying intelligent genetic circuits to biosensors, metabolic control, and bioprocess development. He is actively integrating AI-based automated workflows with synthetic biology to revolutionize the speed and scale of biological research. His work spans multiple disciplines including enzyme engineering, genetic circuit design, metabolic engineering, and biofoundry development. Dr. Lee currently leads several major research initiatives including the Korea-North America Biofoundry Collaboration Global Joint Research Center, the development of core technologies for advanced synthetic biology, and the establishment of biofoundry infrastructure. His research team at KRIBB works at the intersection of synthetic biology, enzyme engineering, and AI-based automation to develop next-generation bio-manufacturing platforms. The lab has made significant contributions to genetic circuit design, biosensor development, and plastic biodegradation technologies.

Xihong Linمشاهده پروفایل

استاد

• Statistical Machine Learning and AI
• Statistical Genetics and Genomics
• Causal Inference and Mediation Analysis
• +۴ مورد دیگر

Xihong Lin is a Professor of Statistics at Harvard University and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a distinguished academic, holding membership in both the National Academy of Sciences and the National Academy of Medicine. Her research focuses on scalable statistical inference for big data, statistical machine learning, causal inference, and integrative data analysis, with applications in genomics, public health, and precision medicine. Lin’s work addresses challenges in analyzing large-scale genomic and multi-ancestry data, including methods for rare variant association testing, ancestry-adjusted sample analysis, and scalable computing frameworks. Her contributions span biobank studies (e.g., UK Biobank, TOPMed) and clinical applications in lung cancer, cardiovascular health, and smoking cessation. Her scientific awards reflect her leadership in statistical genetics and public health. Key research trends include leveraging single-cell sequencing for functional genomics, developing ensemble machine learning methods for health subtyping, and enhancing polygenic risk prediction across diverse populations. Lin’s methodologies prioritize interpretability and scalability, enabling impactful analyses of complex observational and genomic datasets. Awards: Member, National Academy of Sciences; Member, National Academy of Medicine Her grants and advising efforts focus on interdisciplinary collaborations, bridging statistics, AI, and domain sciences. Lin leads initiatives to improve genomic data management and ethical use of federated data (e.g., FADI framework). She is affiliated with labs advancing statistical genetics and cloud-based workflows (e.g., STAAR workflow).

Chao Liuمشاهده پروفایل

پژوهشگر

• Surgical Robotics
• Teleoperation
• Haptics
• +۲ مورد دیگر

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

Junier Olivaمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Nonparametric Statistics
• +۸ مورد دیگر

Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.

• Plant Developmental Biology
• Epigenetics
• Plant Genetics
• +۵ مورد دیگر

Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement

• Biomedical Engineering
• Nanotechnology
• Cancer Research
• +۳ مورد دیگر

Yen-Cheng Liu is a former researcher at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences (BIOS) and multiple departments including LCOM and CMI. His work focuses on interdisciplinary research at the intersection of biomedical engineering, nanotechnology, and cancer biology. Key research areas include nanoplasmonic biosensors, single-cell analysis, optofluidic systems, and cancer immunology. He holds collaborations with institutions like the University of Lausanne and the Centre Hospitalier Universitaire Vaudois (CHUV). His research emphasizes real-time monitoring of cellular secretions, tumor microenvironment dynamics, and diagnostic platform development for pathogens and genetic diseases. Notable contributions include high-throughput microarray technologies for single-cell secretion profiling and optofluidic platforms for molecular diagnostics. Liu’s publications span journals like Advanced Science , Advanced Functional Materials , and Biosensors and Bioelectronics . His work integrates engineering principles with biological systems to address challenges in precision medicine and cancer therapy.

Roland Zengerleمشاهده پروفایل

استاد

• Microfluidics
• Lab-on-a-Chip
• Bio-MEMS
• +۶ مورد دیگر

Prof. Dr. Roland Zengerle serves as Full Professor for Application Development at the Institute of Microsystems Technology within the Faculty of Engineering at Albert Ludwigs University of Freiburg, concurrently holding the position of Director at Hahn-Schickard Institute for Microanalysis Systems in Freiburg. His academic leadership spans microsystems engineering with a focus on translational research bridging fundamental science and clinical applications. Zengerle's research expertise centers on Microfluidics, Lab-on-a-Chip systems, Bio-MEMS, Electrochemical Energy Systems, and Tomographic Reconstruction of Mesoporous Materials. He pioneers hybrid manufacturing techniques integrating molten metal printing with polymer processing to develop point-of-care diagnostic platforms and advanced energy systems. Current projects include UTI-Diag for urinary tract infection diagnosis and PhotonMed, a 32-million-euro medical technology initiative where his MEMS Applications Laboratory develops centrifugal microfluidic solutions. Analysis of his recent publications reveals a dominant trend toward multi-technology integration: centrifugal microfluidics combined with 3D bioprinting for organoid-based drug testing, molten metal printing for flexible electronics, and bead-based immunoassays for infectious disease detection. The work demonstrates strong clinical translation focus, particularly in cancer diagnostics (circulating tumor cell isolation), infectious disease testing (TB diagnostics), and regenerative medicine (spheroid/organoid handling). His laboratory has secured significant funding for high-impact projects including: UTI-Diag: Molecular diagnostics for urinary tract infections PhotonMed: Medical technology innovation consortium livMatS: Living, Adaptive and Energy-autonomous Materials Systems Zengerle actively mentors researchers through Freiburg's Master Lab program and Writer's Studio initiative while promoting young talent via Bootcamp training. His group maintains strategic alliances with Hahn-Schickard spin-offs and industry partners, leveraging university cleanroom facilities and specialized service centers for microfabrication. The MEMS Applications Laboratory operates as a hub for interdisciplinary innovation, combining microfabrication expertise with clinical insights to develop commercializable diagnostic solutions. Current infrastructure supports centrifugal microfluidic cartridge development, 3D-bioprinting of tissue models, and electrochemical sensor integration, with ongoing work focused on automating complex biological workflows for point-of-care applications.

Osman Kaan Erolمشاهده پروفایل

دانشیار

• Optimization Algorithms
• Heuristic and Metaheuristic Methods
• Control Systems
• +۳ مورد دیگر

Assoc. Prof. Dr. Osman Kaan Erol is affiliated with Istanbul Technical University , where he serves in the Department of Control and Automation Engineering . His primary research focuses on optimization algorithms and their applications in engineering systems. Doctorate in Control and Computer Engineering Teaching and research at Istanbul Technical University since 2003 Industry experience at BEKO Electronics (2000-2003) and ABC CEDETAŞ (1995-2000) Research Interests include: Big Bang-Big Crunch optimization algorithm Differential Evolution methods Heuristic and metaheuristic approaches Applications in structural engineering, control systems, and transportation Search space reduction techniques Hybrid algorithm design Publication Trends show a consistent focus on optimization algorithms ( Big Bang-Big Crunch , Differential Evolution ), with applications spanning airport operations , structural design , and preventive control . His work emphasizes algorithm hybridization and convergence improvements. Project Leadership includes: Industrial data collection systems AI-assisted service systems for concrete plants Hydrophore control systems under ERP regulations Electrically heated underwear design

• Bayesian Statistics
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Sara Wade is a Lecturer in Statistics and Machine Learning at the University of Edinburgh , within the School of Mathematics . Her research focuses on Bayesian statistics, machine learning, and their applications in health sciences, particularly in dementia diagnosis and predictive modeling. She holds a PhD from the University of Milan and has held academic positions at the University of Cambridge and University of Warwick before joining Edinburgh. She teaches a popular Machine Learning and Python course for Master’s and final-year undergraduate students, attracting nearly 200 enrollments annually. Her work integrates Bayesian methods with modern machine learning, emphasizing interdisciplinary applications such as scalar-on-image regression and biomarker analysis. Notable contributions include developing hierarchical Dirichlet processes for clustering and uncertainty quantification in RNA velocity studies. She secured a Royal Society of Edinburgh grant for her dementia research project, which aims to improve early diagnosis through statistical modeling. Education: PhD in Statistics, University of Milan Bachelor’s in Mathematics, University of Maryland Wade advocates for diversity in STEM, actively participating in the Women in Machine Learning community. Her research bridges statistical rigor and computational tools, fostering collaborations across academia and healthcare sectors.