جست‌وجوی استاد و پژوهشگر

Francesca Stradoliniمشاهده پروفایل

مدرس

• Bioelectronics
• IoT for Medical Monitoring
• Anesthesia Delivery Systems
• +۳ مورد دیگر

Francesca Stradolini is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Electrical Engineering Doctoral School (EDEE). She holds a PhD in Electrical Engineering from EPFL (2018), with prior education in Bio Engineering and Neuroscience from the University of Genoa (2012, 2015). Her current work focuses on R&D engineering for medical devices, emphasizing system lifecycle management, human factors engineering, and compliance with CE/FDA regulations. As an invited guest, she contributes to usability engineering education and interdisciplinary research at EPFL. PhD: IoT Bio-Electronic Multi-Panel Device for On-line Monitoring of Anaesthesia Delivery (EPFL, 2018) MSc: Bio Engineering (University of Genoa, 2015) BSc: Bio Engineering (University of Genoa, 2012) Her research spans bioelectronics, IoT-enabled medical monitoring, and electrochemical sensor development for clinical applications. Key areas include anesthesia delivery systems, wearable diagnostics, implantable devices, and human factors engineering. Her work integrates fluidic systems, noble metal nanostructures, and open-source hardware (e.g., Raspberry Pi) to address challenges in traceability, fouling mitigation, and pharmacokinetic modeling. Scientific recognition includes the Ville de Lausanne Award (2019) , EPFL Outstanding PhD Thesis Distinction (2018) , and a Best Paper Award at MOBIHEALTH (2016) . She has published extensively in journals like IEEE TBIOCAS, Sensors and Actuators B, and IEEE Sensors Journal, with collaborative studies on propofol monitoring, carbon nanoallotrope drug detection, and implantable sensor validation. Her technical expertise includes managing medical device development lifecycles, designing IoT architectures for intensive care, and optimizing electrochemical sensors for stability in undiluted biological fluids. She has contributed to conferences such as IEEE BioCAS, ISCAS, and MOBIHEALTH, focusing on translational studies in personalized medicine and 3D-printed medical systems.

Joel Gohمشاهده پروفایل

دانشیار

• Health services and systems
• Transportation, logistics and supply chains
• Operations research
• +۶ مورد دیگر

Joel Goh is Associate Professor at the Department of Analytics and Operations, NUS Business School, National University of Singapore. He serves as Director of the J.Y. Pillay Comparative Asia Research Centre (under NUS Global Asia Institute) and PhD Program Director at the Institute of Operations Research and Analytics (IORA). Previously, he was Assistant Professor at Harvard Business School (2014-2017) and Visiting Scholar (2017-2022). BSc, MSc, PhD in Operations, Information, and Technology from Stanford University His research focuses on healthcare analytics (preventing health conditions, hospital operations, frailty assessment), supply chain analytics (digital business models, platform leakage), and service platform operations (hospital-at-home programs, incentive design). He co-created the Robust Optimization Made Easy (ROME) software. Recent publications analyze workplace psychological safety (2024), hospital-at-home models (2024), and platform leakage dynamics (2023). His work spans 18+ journals with 740+ citations for burnout cost studies (2022) and 606+ citations for physician well-being research (2017). Teaching Honors : 2023: Best MBA Teaching & Skinner Innovation Award 2021: NUS Annual Teaching Excellence Award 2020: Early Career Research Excellence Award & 40 Under 40 Best MBA Professors Advising & Grants : Served as PhD Program Director. Received NUS Start-Up Grant R-314-000-110-133 (2021) and Humanities & Social Sciences Fellowship (2021). Editorial roles include Associate Editor at Management Science , Manufacturing & Service Operations Management , and Senior Editor at Production and Operations Management .

Petros Koumoutsakosمشاهده پروفایل

استاد

• Machine Learning
• Computational Fluid Dynamics
• Turbulence Modeling
• +۳ مورد دیگر

Petros Koumoutsakos is the Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he also serves as Area Chair for Applied Mathematics. His research integrates machine learning with computational science to advance understanding of complex systems, including fluid dynamics, turbulence modeling, and biomedical applications. He leads the CSE Lab, focusing on high-performance computing and interdisciplinary collaborations such as a recent study with Citadel Securities and Google Cloud to simulate heart disease in cloud environments. Key research interests include reinforcement learning for turbulence closures, generative models for PDE solutions, and physics-informed AI for biomedical imaging and wildfire prediction. He was awarded the PRACE HPC Excellence Award (2023) for contributions to high-performance computing. His work bridges computational methods with real-world applications, emphasizing interpretability and scalability in multiscale systems. Grants & Collaborations: Leadership in multi-institutional projects, including turbulence modeling via reinforcement learning and cloud-based HPC studies. Labs/Teams: Director of the CSE Lab, advancing AI, computational fluid dynamics, and biomedical simulations.

Jiyuan Tuمشاهده پروفایل

استاد

• Computational Fluid Dynamics (CFD)
• Multiphase Flows
• Thermal Sciences
• +۴ مورد دیگر

Professor Jiyuan Tu is a Professor in the Department of Mechanical and Automotive Engineering at RMIT University's School of Engineering. He specializes in computational fluid dynamics (CFD), multiphase flows, and their applications in renewable/nuclear energy, biomedical engineering, and built environment systems. His research has led to over 500 peer-reviewed articles, 9 books, and $10M+ in ARC grants. He has supervised over 50 postgraduate students and received prestigious awards such as the RMIT Research Excellence Award (2012) and Fulbright Senior Scholar Award (2008). Research interests include CFD modelling of bioaerosol transport, drug delivery systems, and thermal energy storage. He pioneered numerical models for multiphase flows, contributing to software implementations in industries. Notable works include books on CFD and multiphase flow analysis, and leadership in international conferences like COBEE 2018. He holds honorary professorships at Tsinghua University and is Editor-in-Chief of the Experimental and Computational of Multiphase Flow journal. Industry experience includes roles at ANSTO (1996-2001). Awards span fellowships from JSPS, KOSEF, and Fulbright programs. Grants include ARC Discovery, Linkage, and LIEF projects. His work ranks him among the world’s top researchers in pebble bed reactors and airborne infection studies (SciVal 2016-2025).

• Machine Learning
• Deep Learning
• Gaussian Processes
• +۵ مورد دیگر

Geoffrey Pleiss is an Assistant Professor in the Department of Statistics at the University of British Columbia's Faculty of Science. He is also a CIFAR AI Chair at the Vector Institute and an inaugural member of CAIDA's AIM-SI (AI Methods for Scientific Impact) cluster. His work bridges statistical theory, machine learning, and computational methods with applications across various scientific domains. Pleiss received his PhD from the Computer Science department at Cornell University in 2020, where he was advised by Kilian Weinberger and worked closely with Andrew Gordon Wilson. Prior to his faculty position at UBC, he was a postdoctoral researcher at Columbia University with John P. Cunningham. His research focuses on the intersection of deep learning and probabilistic modeling, particularly on developing heuristic and approximate notions of uncertainty from machine learning models. His work has significant implications for reliable and optimal decision-making in experimental design and scientific discovery. Major research thrusts include neural network uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Pleiss develops theoretical frameworks while maintaining strong connections to practical applications across scientific domains. An analysis of his recent publications reveals a strong focus on uncertainty quantification in deep learning models, with particular attention to the limitations and capabilities of ensemble methods in the era of overparameterized models. His work increasingly addresses practical challenges in Bayesian optimization for scientific discovery, especially in materials science. There's also a growing emphasis on computational efficiency in Gaussian process methods, reflecting his commitment to making advanced statistical techniques accessible for real-world applications. CIFAR AI Chair Pleiss currently advises several graduate students including Donney Fan (PhD, Computer Science), Tim G. Zhou (MSc, Computer Science), Zachary Lau (MSc, Statistics), Nathan Cantafio (BSc, Statistics), and Tristan Cinquin (Research Intern at Vector Institute). His research is supported by multiple funding sources including his CIFAR AI Chair position, which provides significant research resources for advancing machine learning methodologies with scientific impact. Pleiss co-created and maintains GPyTorch, a highly efficient and modular implementation of Gaussian processes in PyTorch designed for speed, modularity, and prototyping. He is also involved with CoLA (Compositional Linear Algebra), a library for structured linear algebra operations in JAX and PyTorch that enables fast linear algebra computations by automatically exploiting matrix structure.

Bradley D. Olsenمشاهده پروفایل

استاد

• Polymer Science
• Materials Engineering
• Soft Condensed Matter Physics
• +۴ مورد دیگر

Bradley D. Olsen is a full professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research at the intersection of polymer science, soft matter physics, and bioengineering. His work focuses on designing materials for critical applications in biotechnology, hemostasis, and sustainable polymer development while advancing fundamental understanding of polymer network mechanics and self-assembly. Education: Ph.D. in Chemical Engineering, University of California Berkeley (2007) S.B. in Chemical Engineering, Massachusetts Institute of Technology (2003) Olsen's research spans protein-based materials, block copolymer phase behavior, and mechanochemical hydrogels. He has pioneered methods for quantifying polymer network topology, developing hemostatic nanoparticles, and creating bio-inspired materials for selective biomolecular transport and medical applications. His recent publications emphasize data-driven approaches to polymer characterization and educational outreach in materials science. Scientific Awards: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters Young Investigator Award (2021) MIT Committed to Caring Honor (2019) AIChE Owens Corning Early Career Award (2019) APS Dillon Medal (2018) Kavli Emerging Leader in Chemistry (2017) ACS Polymer Division Fellow (2016) Camille Dreyfus-Teacher Scholar (2015) Alfred P. Sloan Research Fellow (2014) NSF Career Grant (2013) NIH Postdoctoral Fellowship (2008-2009) Hertz Fellow (2003-2007) Barry M. Goldwater Scholarship (2002) Olsen has received significant grant support including NSF Career (2013) and AFOSR (2012) awards. His teaching activities include innovative international outreach like the 2025 soccer-themed science camp in Brazil. The Olsen Group at MIT explores advanced materials with applications ranging from trauma care to sustainable polymers.

Scott Sissonمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Statistics
• Big Data
• +۴ مورد دیگر

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

Kai Gongمشاهده پروفایل

استادیار

• Sustainable Infrastructure Materials
• Environmental Sustainability
• Materials Science
• +۴ مورد دیگر

Dr. Kai Gong is an Assistant Professor of Civil and Environmental Engineering at Rice University, with affiliations at the Rice Advanced Materials Institute and Ken Kennedy Institute. His research focuses on sustainable infrastructure materials, environmental sustainability, and materials science. He holds a Ph.D. in Civil & Environmental Engineering and Materials Science from Princeton University, an MEngSci from Monash University (Australia), and dual B.S. degrees from Monash University and Central South University (China). Research Interests: Development of durable, sustainable infrastructure materials Waste encapsulation and conversion to value-added products Carbon mineralization and utilization Advanced characterization techniques (synchrotron/neutron scattering) Data-driven modeling and atomistic simulations Notable Awards: 2023 Le Chatelier Medal (Cement and Concrete Research) 2024 Giatec Award for Best Paper in Sustainability Walbridge Fund Graduate Award (2019) His work integrates computational methods (e.g., molecular dynamics) with experimental techniques to address decarbonization challenges in infrastructure. The Gong Research Group actively seeks motivated researchers for opportunities in sustainable materials innovation.

Mingchen Gaoمشاهده پروفایل

دانشیار

• Medical Imaging Informatics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Mingchen Gao is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He serves as Program Director for the Engineering Sciences (Artificial Intelligence) MS Program and is affiliated with the Institute for Artificial Intelligence and Data Science. Previously, he was a Postdoctoral Fellow at the NIH Clinical Center's Radiology and Imaging Science Department (2014–2017). His research focuses on medical imaging informatics, computer vision, and machine learning applications in healthcare. Notable projects include NSF-funded work on continual learning and federated domain adaptation. He teaches advanced courses like CSE674 (Advanced Machine Learning) and CSE703 (Deep Learning for Medical Imaging). Dr. Gao earned his Ph.D. in Computer Science from Rutgers University (2014), advised by Dimitris N. Metaxas, and a B.S. from Southeast University, China (2007). His lab develops AI systems for medical diagnosis, with recent work on robust neural networks and federated learning frameworks. His team has produced impactful algorithms for segmentation, classification, and domain adaptation in imaging tasks. Current research includes NSF CAREER Award (2023–2028) for deployable medical diagnosis systems and collaborations on drug discovery and toxicity prediction. He advises four PhD students and has authored over 60 peer-reviewed publications in top venues like NeurIPS, CVPR, and MICCAI.

Knut Drescherمشاهده پروفایل

دانشیار

• Bacterial Biofilms
• Microbial Multicellularity
• Biophysics
• +۵ مورد دیگر

Prof. Knut Drescher is an Associate Professor at the Biozentrum, University of Basel , leading a research group focused on bacterial biofilms , swarming , and microbial multicellularity . Previously, he served as a Professor of Biophysics and Max Planck Research Group Leader at Philipps-Universität Marburg (2015-2021) and conducted postdoctoral research at Princeton University. Research Interests: Physical and biological mechanisms of biofilm formation Cell-cell interactions in microbial communities Antibiotic resistance in biofilms Hydrodynamics of bacterial swarms Evolution of cooperation in multispecies biofilms Development of bioimaging software (BiofilmQ, BacStalk) Scientific Awards: 2023: SNSF Consolidator Grant 2019: Heinz Maier-Leibnitz Prize (DFG), VAAM Research Prize, IUPAP Young Scientist Prize 2016: ERC Starting Grant Advising & Grants: Advises PhD and Master's students in microbiology, biophysics, and bioinformatics Secured major grants from ERC , HFSP , and DFG

Zhe Jiمشاهده پروفایل

استادیار

• Data Science
• Computational Biology
• Functional Genomics
• +۴ مورد دیگر

Zhe Ji is an Assistant Professor in the Department of Biomedical Engineering at McCormick School of Engineering and the Department of Pharmacology at Feinberg School of Medicine, Northwestern University. His research integrates computational and experimental genomics to study gene transcription and RNA translation in cell fate commitment and oncogenic processes, aiming to develop precision medicine strategies. **Education**: Postdoctoral Fellow in Cancer Systems Biology, Harvard Medical School Postdoctoral Fellow in Computational Biology, Broad Institute of MIT and Harvard Ph.D. in Computational Genomics, Rutgers University B.S. in Biotechnology, Nanjing University, China **Research Focus**: Keywords include Data Science, Computational Biology, Functional Genomics, RNA, Cancer, Inflammation, and Machine Learning. The lab explores regulatory mechanisms underlying disease, with a focus on translational control, cancer metastasis, and inflammatory networks. **Grants & Advising**: No specific grants or student advisees listed. The lab emphasizes collaborative projects and computational-experimental approaches. **Lab Affiliations**: Zhe Ji’s lab is part of Northwestern’s interdisciplinary environment, bridging engineering and medicine to advance genomic technologies and therapeutic strategies.

• Statistics
• Public Health
• Medicine
• +۱۱ مورد دیگر

Zhiling Gu is a Research Fellow at Yale School of Public Health, having earned her Ph.D. in Statistics at Iowa State University. Her work integrates statistical theory with applications in public health and medicine. Her research spans Functional Data Analysis Network Analysis Spatiotemporal Modeling Statistical AI Foundations Nonparametric Learning applied to neuroimaging, electronic health records, and environmental health studies. Recent publications focus on Adaptive spatiotemporal models Neuroimaging data processing Pandemic forecasting frameworks Environmental exposure modeling with methodological rigor and practical implementation. Scientific achievements include Runner-up in SMI 2023 Student Paper Competition She has taught STAT 305: Engineering Statistics (ISU) STAT 226: Business Statistics Statistical Computing Statistical Learning and actively engages in academic presentations at conferences like SMI 2024 and CMStatistics 2022.

Aaron Loganمشاهده پروفایل

دانشیار بالینی

• Hematology
• Oncology
• Immunology
• +۳ مورد دیگر

Dr. Aaron Logan is a Clinical Associate Professor of Medicine at the University of California, San Francisco (UCSF). He specializes in the clinical management of acute leukemias, myelodysplastic syndromes, aplastic anemia, and hemophagocytic lymphohistiocytosis. Additionally, he directs a research laboratory focused on immune repertoire profiling following hematopoietic cell transplantation. MD and PhD in Molecular Microbiology and Immunology from Keck School of Medicine (2006) MPhil in History of Medicine from University of Cambridge (2005) Internal Medicine Residency and Hematology Fellowship at Stanford University Dr. Logan's research explores clonotypic diversity in B and T lymphocyte repertoires, utilizing techniques like TREC/KREC quantification, high-throughput sequencing, and mixed lymphocyte reactions. His work has significant applications in tracking immune responses post-transplantation and quantifying measurable residual disease in lymphoid cancers. Notable scientific contributions include advancements in CAR T-cell therapy and immune profiling for blood malignancies. His publications cover topics in B-cell ALL , AML , and measurable residual disease monitoring. 2020 - UCSF Hematology-Oncology Fellowship Teaching Award 2011 - ASH Abstract Award and Research Training Award 2003 - Keck School of Medicine Outstanding Teaching Award As director of the UCSF Hematologic Malignancies Tissue Bank, Dr. Logan provides deidentified patient samples to support collaborative research on blood cancers and post-transplantation immune dynamics.

Margaret P. Chapmanمشاهده پروفایل

استادیار

• Risk-Averse Control Theory
• Stochastic Control Theory
• Safety Analysis for Dynamical Systems
• +۴ مورد دیگر

Margaret P. Chapman is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She leads the DATA Lab (Decision Analysis for Trustworthy Autonomy), focusing on risk-averse and stochastic control theory with applications to environmental and human health. Education: B.S. and M.S. in Mechanical Engineering from Stanford University (2012, 2014), Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2020, advised by Claire Tomlin) Her research bridges robust and stochastic optimal control via risk measure theory, emphasizing safety-critical applications in healthcare and sustainable cities. Key challenges include scalable risk-sensitive control methods, integrating physics-based and data-driven models for safety analysis, and promoting technologies that enhance planetary and human well-being. Recent publications focus on risk-averse autonomous systems, CVaR-based safety analysis, and multi-time-scale modeling for cancer treatment. She has advised students in both graduate and undergraduate research roles, including NSERC awardees and thesis participants. Awards: US National Science Foundation Graduate Research Fellowship, Berkeley Fellowship, Terman Engineering Scholastic Award, Leon O. Chua Award She teaches courses like ECE 557 (Linear Control Theory) and ECE 1643 (Risk-Averse Control with Learning). Her invited talks span institutions such as MIT, Princeton, and Georgia Tech, highlighting risk-sensitive analysis and control for trustworthy autonomy.

Lee Yong Limمشاهده پروفایل

استاد

• Drug Delivery Systems
• Pediatric Medicinal Products
• Taste-Masked Medicines
• +۴ مورد دیگر

Lee Yong Lim is a Professor of Pharmaceutics at The University of Western Australia (UWA), leading the Laboratory for Drug Delivery. She specializes in innovative drug delivery systems, particularly for pediatric populations and marine aquaculture. Her work focuses on overcoming medication challenges for children through taste-masked formulations and stable drug-loaded scaffolds for ear diseases. She has secured over $4.5 million in research grants from diverse funding bodies. Education: BSc (Pharmacy) Hons, National University of Singapore PhD, University of Manchester Research: Her team developed a patented chocolate-based formulation for bitter drugs and conducts pediatric trials. Collaborations include WA hospitals for safe compounded medicines and international projects in nanotechnology. Key research areas include pediatric medicinal products, veterinary drug delivery, and sustainable drug formulations aligned with UN SDGs. Grants & Funding: Major supporters include NHMRC, ARC, Telethon, and industry partnerships. Current active grants focus on pre-procedural chewables for children and perioperative analytics. Labs/Teams: Heads the Laboratory for Drug Delivery and collaborates with the Centre for Optimisation of Medicines and Institute for Paediatric Perioperative Excellence.