جست‌وجوی استاد و پژوهشگر

Robert Rohlingمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging
• Medical Information Systems
• +۳ مورد دیگر

Robert Rohling is a Professor at the University of British Columbia's Faculty of Applied Science, affiliated with the Department of Mechanical Engineering and holding a joint appointment with the Department of Electrical and Computer Engineering. As Director of the Institute of Computing, Information and Cognitive Systems (ICICS), his research focuses on biomedical engineering, medical imaging, robotics, and computational methods. B.A.Sc. (UBC) M.Eng. (McGill) Ph.D. (Cambridge) Rohling's work spans three primary research areas: medical imaging (3D ultrasound, spatial compounding, elasticity reconstruction), medical information systems (radiologist navigation tools for large image datasets), and robotic calibration for surgical applications. His multidisciplinary approach integrates mechanical and electrical engineering principles with clinical needs. Rohling's publications (2020-2022) reveal trends in advanced ultrasound techniques (e.g., shear wave vibro-elastography), AI-driven image processing (cycleGAN translation), and computational optimization for diagnostic accuracy. Keywords across his work include Medical Imaging, Biomedical Engineering, Robotics, and Computational Modeling. As director of the Robotics and Control Laboratory , Rohling leads interdisciplinary collaborations with industry and clinical partners to address practical challenges in medical diagnostics and surgical robotics. His research emphasizes translating engineering innovations into clinical practice.

Ville Vuorinenمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics (CFD)
• Large Eddy Simulation (LES)
• Combustion
• +۷ مورد دیگر

Ville Vuorinen is an Associate Professor at the Department of Energy and Mechanical Engineering, Aalto University. His research focuses on computational fluid dynamics (CFD) in energy technology, particularly using Large-Eddy Simulation (LES) and hybrid LES-RANS approaches with OpenFOAM. Research Interests: Combustion, Turbulence, Hydrogen, Emission Reduction, Biofuels, Marine Engine Hydrodynamics, Primary Atomization, Liquid Cooling. His team explores hydrogen-enriched flames, ammonia combustion, two-phase flows, and virus transmission modeling. Article Trends : Recent work spans hydrogen pre-ignition in engines, LES of ammonia/methanol flames, aerosol transmission in choir rehearsals, atomic layer deposition conformality, underwater noise analysis, and techno-economic waste-to-hydrogen systems. Keywords include combustion modeling, sustainable energy, and cross-disciplinary CFD applications. Scientific Awards Teknologiateollisuus ry Diesel- ja kaasumoottoritoimialaryhmän tunnustusapuraha (2010) Collaborations : Works closely with experimentalists. Advisees include Shervin Karimkashi Arani, Parsa Tamadonfar, Ossi Kaario, and others. Research impacts energy-efficient ships, marine engines, and biomedical applications.

Adam Yalaمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Precision Medicine
• +۵ مورد دیگر

Adam Yala is an Assistant Professor of Computational Precision Health, Statistics, and Electrical Engineering and Computer Science at UC Berkeley and UCSF. He is also the Founder & CEO of Voio Inc., a company focused on clinical translation of AI tools. PhD in Computer Science from MIT (2022) His research lies at the intersection of Machine Learning and Precision Medicine, with a focus on robust AI tools for clinical deployment, personalized screening policies, and private data sharing. Current work includes multi-modal imaging analysis, decision guarantees in clinical workflows, and prospective trials in oncology and radiology. Recent publications highlight advancements in AI for cancer risk prediction, vision-language models in healthcare, and data privacy techniques. Tools like Mirai are implemented in 66 hospitals across 30 countries. Bakar Fellows Spark Award (2024) Eppy Award: Investigative Reporting (2022) Falling Walls Finalist: Life Science (2022) NSF Fellowship (2016) He advises PhD students in AI-driven healthcare and collaborates with hospital systems globally. His lab emphasizes clinical translation of machine learning methods in radiology and oncology.

Kevin Kochersbergerمشاهده پروفایل

دانشیار

• Dynamics and control
• Autonomous aerial systems
• Applied aerodynamics
• +۷ مورد دیگر

Dr. Kevin Kochersberger is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech , with a career spanning academic research, technical innovation, and educational leadership. His work focuses on autonomous aerial systems , robotic control , and applied aerodynamics , particularly through the Uncrewed Systems Laboratory . Kochersberger's research has pioneered UAV-based radiation detection , 3D terrain mapping , and low-resource drone applications , including establishing the African Drone and Data Academy in Malawi . Education: Ph.D., Mechanical Engineering, Virginia Tech (1994) M.S., Mechanical Engineering, Virginia Tech (1984) B.S., Mechanical Engineering, Virginia Tech (1983) A.S., Engineering Science, Jamestown Community College (1981) Kochersberger's publications demonstrate expertise in UAV path planning , smart material actuation , and radiation source localization , with over $9M in research funding. His scientific awards include AIAA Associate Fellow (2009) and Aviation Week Aerospace Laureate (2003). Notable projects involve helicopter-deployable robotic systems and urban canyon navigation without GPS. Recent articles highlight BVLOS drone simulators , 2.5D terrain mapping , and autonomous negative obstacle traversal , reflecting his focus on real-time adaptive control and heterogeneous robotic systems . He teaches Drone Technology and Flight Operations and Advanced Design Projects , emphasizing student-driven innovation and industry collaboration .

Amit Singerمشاهده پروفایل

استاد

• Computational Mathematics
• Structural Biology
• Cryo-Electron Microscopy
• +۳ مورد دیگر

Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.

Rina Foygel Barberمشاهده پروفایل

استاد

• Statistics
• Statistical Inference
• Distribution-Free Inference
• +۷ مورد دیگر

Rina Foygel Barber is the Louis Block Professor in the Department of Statistics at the University of Chicago, where she also serves as Co-chair of the Committee on Community, Diversity, and Inclusion (CCDI) and is a member of the Committee on Computational and Applied Mathematics (CCAM). Her educational background includes: PhD in Statistics, University of Chicago (2012), advised by Mathias Drton and Nati Srebro MS in Mathematics, University of Chicago (2009) ScB in Mathematics, Brown University (2005) NSF postdoctoral fellow, Stanford University Department of Statistics (2012-13), supervised by Emmanuel Candès Professor Barber's research focuses on the theoretical foundations of statistical problems in estimation, prediction, and inference, particularly in high-dimensional settings where classical methods may not be reliable. She specializes in distribution-free inference methods such as conformal prediction, multiple testing methods, algorithmic stability, and shape-constrained inference. Her work also extends to modeling and optimization problems in medical imaging reconstruction. Her recent publications demonstrate a strong focus on distribution-free inference, with particular emphasis on conformal prediction, false discovery rate control, and algorithmic stability. Her work bridges theoretical statistics with practical applications, especially in the medical imaging domain. Professor Barber has received numerous prestigious awards: Elected to National Academy of Sciences (2025) MacArthur Fellowship (2023) IMS Fellow (2023) COPSS Presidents' Award (2020) Peter Gavin Hall Early Career Prize (2020) She actively mentors students and collaborators, with many co-authored publications across statistics, machine learning, and medical imaging. Her research has been supported by significant grants that enable her work on theoretical foundations of statistical inference and practical applications in medical imaging. Professor Barber also co-organizes the International Seminar on Selective Inference. Her research group focuses on developing and analyzing estimation, inference, and optimization tools for structured high-dimensional data problems. They work on false discovery rate control, distribution-free inference, and applications in medical imaging reconstruction.

Hamsa Bastaniمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Healthcare Operations
• +۵ مورد دیگر

Hamsa Bastani is an Associate Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania, with a secondary appointment in Statistics and Data Science. She co-directs the Wharton Healthcare Analytics Lab and serves as an Associate Editor for Operations Research, M&SOM and OR Letters. Her academic journey began with summa cum laude graduation from Harvard in 2012 with an A.M. in physics and A.B. in physics and mathematics. She completed her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, followed by a Herman Goldstine postdoctoral fellowship at IBM Research. Professor Bastani's research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications spanning healthcare operations, social good, and revenue management. Her work demonstrates particular expertise in sequential decision-making (bandits, reinforcement learning), learning from auxiliary data sources (transfer learning, meta-learning), and designing effective human-AI interfaces (interpretability, fairness). She has made significant contributions to understanding how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. Her publications reveal a strong trend toward high-impact applications of machine learning in critical societal domains. A significant portion of her recent work focuses on healthcare applications, including optimizing health supply chains in low- and middle-income countries, designing clinical trial protocols, and creating targeted public health interventions. Another major theme examines the complex relationship between humans and AI systems, particularly how AI affects learning outcomes and decision-making processes. Her work frequently bridges theoretical advances with practical implementation, as evidenced by country-scale deployments in Greece and Sierra Leone. Wagner Prize for Excellence in Operations Research Practice (2021) Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Behavioral OM Best Paper Award (2021) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Award (2019, 2020, 2021) Professor Bastani has advised numerous PhD students who have gone on to prominent positions, including Pia Ramchandani (Director of Responsible AI at PwC), Arielle Anderer (Assistant Professor at Cornell Johnson), and Kan Xu (Assistant Professor at ASU Carey). Her research has been supported by collaborations with national governments, including the Greek government where she co-designed Eva, the national-scale reinforcement learning system for targeted COVID-19 testing, and the Government of Sierra Leone where she improved patient access to essential medicines by nearly 20% via decision-aware learning. She has also conducted the first large field study deploying generative AI tutors in high school math classes. She leads the Wharton Healthcare Analytics Lab and serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, demonstrating her commitment to translating academic research into practical applications.

• Cybersecurity
• Maritime Cyber Risk Assessment
• Dynamic Positioning Systems
• +۱۲ مورد دیگر

Pelin Bolat is an Associate Professor in the Department of Fundamental Sciences at Istanbul Technical University (ITU), College of Maritime Studies. She is actively engaged in maritime cybersecurity, risk assessment, and maritime safety research. Her work is supported by multiple BAP and EU-funded projects, with ongoing research extending into 2025. Her research interests include cybersecurity in maritime navigation systems, dynamic positioning, port state control, and GHG emissions in the maritime sector. She applies advanced methodologies such as fuzzy FUCOM, CORAS framework, and association rule mining to analyze cyber and operational risks. Her recent publications (2023–2025) reflect a strong trend in maritime cybersecurity, focusing on ECDIS, RADAR, ransomware, and cyber hygiene. These works span high-impact journals in maritime engineering and technology, demonstrating interdisciplinary engagement with computer science, safety engineering, and policy analysis. She serves as a principal investigator on several key projects, including cyber risk assessment of bridge navigation equipment and system dynamic modeling of maritime GHG emission measures. She also mentors 15 theses in progress, indicating her active role in student supervision. Her collaborative network includes researchers like Gökhan Kayişoğlu and international partners. While no formal awards are listed, her leadership in EU and BAP projects underscores her academic prominence.

Michele J. Gelfandمشاهده پروفایل

استاد

• Cross-Cultural Organizational Behavior
• Negotiation
• Conflict Management
• +۵ مورد دیگر

Michele Gelfand is a Professor of Organizational Behavior at the Stanford Graduate School of Business and Professor of Psychology by courtesy at Stanford University . She is renowned for her research on cross-cultural organizational behavior, negotiation, conflict management, and cultural psychology. Her work explores the concept of tight and loose cultures and their impact on global leadership, organizational dynamics, and societal responses to threats. PhD in Social and Organizational Psychology, University of Illinois, Urbana-Champaign (1996) Her research spans interdisciplinary approaches, integrating computational methods, neuroscience, and behavioral science to understand cultural evolution and its multilevel consequences. She has led groundbreaking studies on the effects of cultural tightness-looseness on political polarization, pandemic responses, and corporate mergers. Recent publications highlight her work on social norm dynamics, threat-driven cultural adaptation, and the psychological underpinnings of political and terrorist ideologies. Her articles frequently appear in top journals such as Science , Nature Human Behaviour , and Proceedings of the National Academy of Sciences . Notable scientific awards include the 2020 Katzell Award , 2016 Diener Award , and 2011 Anneliese Maier Research Award . She has also been elected to the National Academy of Sciences and the American Academy of Arts and Sciences . Gelfand’s Culture Lab at Stanford investigates topics like cultural norms, revenge, forgiveness, and diversity. She actively mentors postdoctoral researchers and serves as an editor for the Oxford Handbook of Cross-Cultural Management . Her executive education programs focus on global leadership and negotiation strategies.

Samuel W.K. Wongمشاهده پروفایل

دانشیار

• Data Science Methodology
• Protein Structure Analysis
• Reliability Engineering
• +۴ مورد دیگر

Samuel W.K. Wong is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a Ph.D. in Statistics from Harvard University (2013) under Prof. Samuel Kou. His research focuses on statistical methodology for complex data science challenges in protein structure modeling, dynamic systems inference, and reliability engineering of wood-based products. He has held academic positions at the University of Florida (2013–2018) and has been at Waterloo since 2018. His research interests include Bayesian computation, statistical inference for dynamic systems, and spatial-temporal data analysis. Notable contributions include the development of manifold-constrained Gaussian processes (MAGI package) and sequential Monte Carlo methods for protein folding studies. He has advised over 15 graduate students and researchers, many of whom are now in academic or industry roles worldwide. Wong has received teaching distinctions at Harvard and holds awards including the Nash Medal (2008) for academic excellence. His work bridges computational statistics with applications in bioinformatics, structural engineering, and environmental science. He has published extensively in top-tier journals like Journal of Computational and Graphical Statistics and Biometrics , and collaborates with wood scientists to improve real-time lumber quality assessment using laser imaging data. His teaching portfolio includes courses on probability theory, statistical inference, and spatial data analysis at both undergraduate and graduate levels. Beyond academia, he maintains an active passion for classical piano performance, having performed recitals combining music with his statistical research interests.

Nicholas M. Kanaanمشاهده پروفایل

استاد

• Neurodegenerative Diseases
• Alzheimer's Disease
• Parkinson's Disease
• +۶ مورد دیگر

Nicholas M. Kanaan is a Professor of Translational Neuroscience and holds the Maibach Smiley Professorship of Alzheimer's Research at Michigan State University's College of Human Medicine. He serves as Director of Advanced Microscopy and is faculty in both the MSU Neuroscience Program and the MSU BioMolecular Science Gateway. His research is centered at the Grand Rapids Research Center where he leads the Kanaan Laboratory. Dr. Kanaan received his B.S. in Neuroscience, Psychology, and Sociology from Central Michigan University in 2001, followed by a Ph.D. in Neurological Sciences from Rush University Medical Center in 2007. He completed postdoctoral training at Northwestern University from 2007-2010 under Dr. Lester Binder. His research focuses on neurodegenerative diseases, particularly Alzheimer's disease (AD) and Parkinson's disease (PD), with emphasis on tau protein pathology. The Kanaan Lab investigates mechanisms underlying degenerative diseases using a combination of in vitro and in vivo model systems. A major focus is understanding how disease-related alterations in tau cause neuronal dysfunction through disruption of axonal transport. His lab has identified a phosphatase-activating domain (PAD) in tau that inhibits anterograde fast axonal transport. Analysis of Dr. Kanaan's publication record reveals a consistent focus on tau protein biology, with recent work emphasizing iPSC models, CRISPR screening, tau proteostasis, and therapeutic interventions targeting tau phosphorylation and oligomerization. His research spans basic molecular mechanisms to translational applications, with increasing use of primate models and advanced screening technologies in recent years. Scientific Awards and Recognition: Maibach Smiley Alzheimer's Research Professor Maibach Smiley Professor of Alzheimer's Research Dr. Kanaan mentors numerous students and postdoctoral fellows in his laboratory, with research supported by multiple grants focused on understanding and treating neurodegenerative diseases. His lab employs a wide range of technical expertise including recombinant protein purification, cell culture, monoclonal antibody production, various microscopy techniques, and behavioral testing in rodent models. Outside the lab, Dr. Kanaan enjoys photography, woodworking, and fishing.

Vasily Klucharevمشاهده پروفایل

استاد

• Neuroeconomics
• Social Neuroscience
• Emotional Neuroscience
• +۴ مورد دیگر

Vasily Klucharev is a Tenured Professor at the Higher School of Economics University (HSE University) since 2013, where he serves as Laboratory Head of the Institute for Cognitive Neuroscience and the International Laboratory of Social Neurobiology. He is also a Professor at the Graduate School of Business in the Department of Strategic and International Management, and Programme Scientific Supervisor of the Cognitive Sciences and Technologies: From Neuron to Cognition program. His educational background includes: 2003: Candidate of Sciences (PhD) in Neurobiology from St Petersburg State University with thesis titled 'Neurobiological mechanisms of emotions' 1994: Degree in Physiology from St Petersburg State University, Faculty of Biology Klucharev's research focuses on neuroeconomics, social and emotional neuroscience, and cognitive neuroscience, with particular emphasis on understanding decision-making processes, social influence, and conformity. His work integrates psychological theories with advanced neuroimaging techniques including fMRI, EEG, depth and scalp ERP, TMS, and MEG. His research has significant implications for understanding consumer behavior, persuasion, and the neural mechanisms underlying social conformity. His recent publications demonstrate a clear trend toward investigating the neural correlates of price perception, social influence on decision-making, and the processing of disinformation and deepfakes. His work bridges multiple disciplines, combining methods from neuroscience, psychology, and economics to address fundamental questions about human behavior in social contexts. Scientific awards: Best Teacher 2023-2024 Best Teacher 2018-2020 Best Teacher 2015 Klucharev has supervised numerous doctoral students including I. Ntoumanis, M. Martinez-Saito, O. Zinchenko, and Z. Yaple. His research has been supported by significant grants from Saint Petersburg State University, the Swiss National Science Foundation, and the European Commission. He has also served as an invited reviewer for prestigious journals including Psychological Science, Neuron, and Social Cognitive & Affective Neuroscience. As Director of the Institute of Cognitive Neuroscience at HSE University, Klucharev leads a multidisciplinary team exploring the neural mechanisms of social behavior, decision-making, and cognitive processes. His laboratory has gained international recognition, with research featured in major media outlets including BBC TV, Financial Times, The Telegraph, and CNN.

• Software Architecture
• Microservices
• Cloud Computing
• +۷ مورد دیگر

Uwe Zdun is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice-Director of Studies for Computer Science and Head of the Research Group Software Architecture. His teaching portfolio includes core courses such as Software Engineering 2, Advanced Software Engineering, and Practical Software Courses for Bachelor's and Master's theses across multiple semesters (2024W-2025S). His research spans software architecture with emphasis on microservices, cloud computing, and DevOps. Key focus areas include architectural design decisions, infrastructure-as-code conformance, security in distributed systems, and the integration of machine learning operations (MLOps/RLOps). He investigates cognitive aspects of architecture practices through controlled experiments and develops model-driven approaches for quality assessment in complex systems. Recent publications (2024-2026) reveal three dominant trends: (1) Security and coupling analysis in infrastructure-as-code deployments, (2) MLOps/RLOps integration for Industry 4.0 cyber-physical systems, and (3) Performance optimization patterns for CI/CD pipelines and autoscaling. His work bridges theoretical architecture models with industrial practice, particularly in microservice ecosystems and reinforcement learning applications. Professor Zdun leads the Research Group Software Architecture at the University of Vienna's Faculty of Computer Science. The group focuses on empirical validation of architectural patterns, tool development for conformance checking, and advancing design decision methodologies in cloud-native and AI-driven systems.

David Ackerleyمشاهده پروفایل

استاد

• Enzymes
• Synthetic biology
• Microbiology
• +۴ مورد دیگر

Professor David Ackerley (Victoria University of Wellington) is a leading microbiologist and enzyme engineer specializing in directed evolution of bacterial enzymes for biotechnological applications. As Biotechnology Programme Director since 2006, he lectures in foundational courses like BTEC101 and BTEC201. Academic rank: Professor of Biotechnology Institutional affiliation: Victoria University of Wellington Research focus areas: Microbial Biotechnology, Drug Discovery, Synthetic Biology His research employs Darwinian evolutionary principles to engineer enzymes with enhanced activities, particularly targeting non-ribosomal peptide synthetases and nitroreductases for antibiotic development and cancer therapy. Recent work explores metagenomic domain substitution in pyoverdine biosynthesis and Purpuramine R from marine sponges. Key publications demonstrate innovations in metagenomic library construction , CRISPR screening for regeneration genes, and structural characterization of engineered enzymes. His team has developed NTR 2.0 , a high-efficacy nitroreductase for targeted cell ablation. Current research projects include: Clean solutions from dirty genes: Plastic-degrading enzyme discovery Engineering enzymes for CAR T-cell-chemotherapy synergy Repurposing niclosamide against Gram-negative superbugs Grants from the Health Research Council of New Zealand, Royal Society of New Zealand, and Cancer Society of NZ support his work. Collaborations span biomedical research, synthetic biology, and environmental applications.

• 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