جستجوی استاد راهنمای دانشگاهی

Roummel F. Marciaمشاهده پروفایل

استاد

• Optimization
• Signal Processing
• Data Science
• +۵ مورد دیگر

Roummel F. Marcia is a Professor and current Chair of the Department of Applied Mathematics at the University of California, Merced, within the School of Natural Sciences. He received his Ph.D. from UC San Diego under Professor Philip Gill and previously held postdoctoral positions at the San Diego Supercomputer Center and University of Wisconsin-Madison, as well as a research scientist position in electrical engineering at Duke University. His research spans multiple areas in optimization and its applications, with a focus on signal processing, data science, machine learning, linear algebra, and mathematical biology. Dr. Marcia's work has significant interdisciplinary impact, particularly in biomedical imaging, computational biology, and quantum computing applications. His research methodology often combines theoretical optimization approaches with practical applications in data-intensive fields. Dr. Marcia's recent publications demonstrate a strong trend toward integrating optimization theory with deep learning architectures, particularly in applications requiring sparse data handling, biomedical imaging, and quantum computing. His work shows increasing focus on developing novel optimization algorithms specifically designed for machine learning contexts, including quasi-Newton methods adapted for deep learning and specialized techniques for handling non-convex optimization problems. School of Natural Sciences Faculty Award for 'Developing or Improving Academic Programs and Tracks' (2021-22) Leadership roles in SIAM Activity Group on Applied Mathematics Education Recognition as a Math Alliance Mentor for supporting underrepresented students Dr. Marcia has successfully mentored numerous doctoral students to completion, with graduates moving to positions at Meta, Johns Hopkins University Applied Physics Laboratory, Lawrence Livermore National Laboratory, and other prestigious institutions. His research has been consistently funded by major agencies including NSF (with grants IIS 1741490, DMS 1840265, DMS 2229495, CCF 2343610), DARPA, and ARPA-E. As the current graduate chair of the Applied Math Graduate Program, he plays a key role in shaping the next generation of mathematical scientists. His work with the SMaRT (Scientific Mathematics Research and Training) team demonstrates his commitment to collaborative, interdisciplinary research.

Thomas Hermannمشاهده پروفایل

پژوهشگر

• Auditory Data Science
• Interactive Sonification
• Smart Environments
• +۵ مورد دیگر

Dr. rer. nat. Thomas Hermann is a faculty member at Bielefeld University's Faculty of Engineering, leading the Ambient Intelligence Group and coordinating the Computer Science program. He specializes in sonification, auditory data science, and smart environments. Head of Ambient Intelligence Working Group Computer Science Program Coordinator Member of multiple academic advisory boards His research focuses on interactive sonification for biomedical applications, quantum systems, and smart environments. Key projects include ECG sonification for cardiac diagnosis, real-time auditory feedback in swimming, and sonic interfaces for AR cooperation. Recent publications span 2025 with Python-based sonification tools ( pya AGen ), quantum system sonification, and ST-elevation myocardial infarction monitoring. He contributes to open-access supplementary materials and interdisciplinary workshops. As a researcher , Hermann develops practical sonification frameworks like Panson for facial behavior analysis, CardioScope for portable ECG monitoring, and Base Cube One for smart environments. His work bridges academic research with industry applications.

Bonnie Bergerمشاهده پروفایل

استاد

• Compressive Genomics
• Network Inference
• Structural Bioinformatics
• +۴ مورد دیگر

Bonnie Berger is the Simons Professor of Mathematics at the Massachusetts Institute of Technology and head of the Computation and Biology group at MIT's Computer Science and AI Lab. She holds additional appointments as an Associate Member of the Broad Institute, Faculty member of Harvard/MIT Health Science & Technology, and Affiliated Faculty of Harvard Medical School. Her career has been dedicated to pioneering computational approaches in molecular biology, where she has been instrumental in defining the field. Professor Berger's research focuses on designing algorithms to extract biological insights from large-scale data sets. Her work spans Compressive Genomics, Network Inference, Structural Bioinformatics, Genomic Privacy, and Medical Genomics. She actively collaborates with experimental biologists to maximize the power of computation for biological discovery, developing methods that address the challenges of modern high-throughput biological data. Her recent publications demonstrate a strong trend toward integrating machine learning with structural biology and genomic privacy. The articles show increasing sophistication in using deep learning for protein structure prediction, developing privacy-preserving techniques for genomic data sharing, and creating efficient algorithms for massive biological data sets. Her work bridges theoretical computer science with practical biological applications. Professor Berger's scientific recognition includes: Election to the National Academy of Sciences (2021) ISCB Accomplishments by a Senior Scientist Award SIAM Sonya Kovalevsky Lecture Prize Fellowships in ACM, ISCB, AMS, and other prestigious societies Multiple RECOMB Test of Time Awards NIH Margaret Pittman Director's Award She has mentored numerous students who have gone on to make significant contributions in computational biology, including Ellen Zhong, Yun William Yu, and Hyunghoon Cho. Her lab receives substantial research funding supporting projects in genomic privacy, structural bioinformatics, and compressive algorithms for biological data. Professor Berger serves on the Executive Editorial Board of the Journal of Computational Biology and multiple other editorial boards. The Computation and Biology group at MIT CSAIL, which she leads, is at the forefront of developing computational methods for biological discovery. The group combines expertise in algorithms, machine learning, and biology to tackle fundamental challenges in genomics and structural biology. They are currently organizing the Machine Learning in Structural Biology workshop at NeurIPS 2025, highlighting their leadership in this rapidly evolving interdisciplinary field.

Cecilia R. Aragonمشاهده پروفایل

استاد

• Human-Centered Data Science
• Human-Centered Artificial Intelligence
• Human-Computer Interaction
• +۵ مورد دیگر

Cecilia R. Aragon is a Professor in the Department of Human Centered Design & Engineering at the University of Washington, where she also serves as an Adjunct Professor in Computer Science & Engineering, Electrical and Computer Engineering, and the Information School. She is additionally a Senior Data Science Fellow at the eScience Institute. Aragon directs the Human-Centered Data Science Lab and has made significant contributions at the intersection of human-computer interaction and data science. Her research interests focus on human-centered data science, human-centered artificial intelligence, human-centered machine learning, human-computer interaction (HCI), computer-supported cooperative work (CSCW), visual analytics, aviation and astronautics sociotechnical systems, and emotion in informal text communication. Aragon's work bridges technical and social aspects of data science, particularly examining how humans interact with and gain insight from large datasets through both quantitative and qualitative methods. Aragon's recent publications demonstrate a strong focus on understanding online communities, sentiment analysis, distributed mentoring systems, and the ethical implications of AI. Her work spans multiple disciplines including social computing, data visualization, and astrophysics data analysis, showing her interdisciplinary approach to human-centered data science. Presidential Early Career Award for Scientists and Engineers (PECASE) 2008 Fulbright Fellowship 2017-18 HCDE Faculty Innovator in Research Award, University of Washington, 2015 Distinguished Alumni Award, Computer Science, University of California, Berkeley, 2013 Top 25 Women of the Year, Hispanic Business Magazine, 2009 Aragon has secured over $28 million in research funding from organizations including the National Science Foundation, National Institute of Standards and Technology, Department of Energy, Gordon and Betty Moore Foundation, Alfred P. Sloan Foundation, Washington Research Foundation, and industry partners like Microsoft and Intel. Her educational background includes a Ph.D. in Computer Science from UC Berkeley (2004), an M.S. in Computer Science from UC Berkeley, and a B.S. with Honors in Mathematics from Caltech. She leads the Human-Centered Data Science Lab and is affiliated with the eScience Institute, the Nearby Supernova Factory, and various research groups focused on data-intensive scientific collaborations. Her work on collaborative visual analytics systems like Sunfall has had significant impact in both academic and applied settings.

Felix Balzerمشاهده پروفایل

استاد

• Medical Data Science
• Health Informatics
• Artificial Intelligence in Healthcare
• +۷ مورد دیگر

Prof. Felix Balzer is a Professor for Medical Data Science and Chief Medical Information Officer (CMIO) at Charité - University Medicine Berlin . He serves as Director of the Institute of Medical Informatics, leading digitalization efforts for patient care and overseeing implementation of the hospital's electronic medical record (EMR) systems. Medical Data Science professorship (2021) Director of Institute of Medical Informatics Acting Chief Information Officer (2024-2025) Deputy Chief Medical Officer for Clinical Digitalization (2025) His research focuses on: Digital healthcare transformation Machine learning in critical care Alarm fatigue mitigation Interoperability standards (FHIR, OMOP) Electronic health records (EHR) optimization Patient monitoring systems The 2025-2026 publications reveal expertise in ICU data analysis, predictive modeling for postoperative delirium, and pandemic response technology. His work bridges clinical practice with technical implementation through: Interdisciplinary teams Multi-center trials Real-time clinical data architectures Human factors in healthcare AI

Tom Coatesمشاهده پروفایل

استاد

• Pure Mathematics
• Algebraic Geometry
• Mirror Symmetry
• +۵ مورد دیگر

Tom Coates is a Professor of Pure Mathematics in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. He holds affiliations with the Artificial Intelligence Network, the CNRS-Imperial Abraham de Moivre UMI, and the Pure Mathematics research group. His office is located in the Huxley Building (662) on the South Kensington Campus, London SW7 2AZ, and he can be contacted via email at t.coates@imperial.ac.uk or phone at +44 (0)207 594 3607. Professor Coates' research spans pure mathematics with emphasis on algebraic geometry, mirror symmetry, and Gromov-Witten theory. He investigates quantum cohomology and Fano variety classification to construct a 'Periodic Table for shapes' through computational algebra, data mining, and machine learning. His work integrates geometric methods with cluster-scale computing to identify structural patterns in algebraic varieties, focusing on quantum periods, toric degenerations, and Laurent polynomial applications. His recent publications (2021-2024) demonstrate a strong trend toward computational classification of Fano varieties and polytopes, leveraging machine learning for dimension prediction and database construction. Key themes include mirror symmetry via Laurent inversion, toric geometry applications, and connections between Gromov-Witten invariants and modular forms. These works often utilize custom tools like PCAS and Fanosearch for large-scale algebraic computations. While specific student names are not listed, Professor Coates mentors PhD and Master's students in algebraic geometry and computational mathematics. His research is supported by the Simons Foundation, member institutions, and contributors, enabling international collaborations through networks like the CNRS-Imperial Abraham de Moivre UMI. He leads a research team developing the Periodic Table for shapes framework, utilizing high-performance computing resources. The team maintains open-source tools including PCAS (Periodic Table for Algebraic Shapes) and Fanosearch for Fano variety exploration, with code repositories hosted on Bitbucket and quantum period databases published in Scientific Data.

Daniel B. Neillمشاهده پروفایل

استاد

• Machine Learning
• Public Health
• Urban Analytics
• +۵ مورد دیگر

Daniel B. Neill is a Professor of Computer Science, Public Service, and Urban Analytics at New York University (NYU), jointly appointed across the Courant Institute of Mathematical Sciences, Robert F. Wagner Graduate School of Public Service, and the Center for Urban Science and Progress (Tandon School of Engineering). He also serves as the Director of the Machine Learning for Good Laboratory (ML4G) and is affiliated with NYU's Center for Data Science and Tandon Department of Computer Science and Engineering. Education: Ph.D. in Computer Science, Carnegie Mellon University M.S. in Computer Science, Carnegie Mellon University M.Phil. in Computer Speech, Cambridge University Research Interests: Dr. Neill's research focuses on developing novel machine learning methods for social good, with applications in disease surveillance (e.g., early outbreak detection), healthcare (e.g., anomalous care patterns), and urban analytics (e.g., predicting citizen needs). He also explores algorithmic fairness , causal inference , and pre-syndromic surveillance using unstructured data. His work bridges theoretical machine learning with real-world policy challenges, collaborating with health departments, hospitals, and city governments to deploy data-driven tools that enhance public health, safety, and security. Scientific Awards & Honors: NSF CAREER Award NSF Graduate Research Fellowship IEEE Intelligent Systems' "Top Ten AI Researchers to Watch" Yelp Dataset Challenge Winner Hidden Signals Challenge Runner-Up (DHS) Grants & Funding: He has received significant funding from the National Science Foundation (NSF), including grants on fairness in AI (IIS-2040898), bias in urban analytics (IIS-1926470), and others. He also acknowledges support from UPMC, MacArthur Foundation, and Richard King Mellon Foundation. Laboratory & Leadership: He directs the Machine Learning for Good Laboratory (ML4G) at NYU, focusing on AI for social impact. He previously co-directed NYU's Urban Initiative (2019-2022) and led the Event and Pattern Detection Laboratory at Carnegie Mellon University.

• Complex Systems
• Ecological Networks
• Marine Conservation
• +۴ مورد دیگر

Martin Nilsson Jacobi serves as President and CEO of Chalmers University of Technology, holding the position of the institution's fourteenth President since September 2023. He simultaneously maintains his academic standing as Professor of Complex Systems at the university, demonstrating his dual commitment to academic leadership and scholarly work. Professor Nilsson Jacobi's research portfolio spans theoretical physics, complex systems theory, and ecological applications. His work bridges multiple disciplines, creating innovative approaches to understanding natural systems through mathematical and computational frameworks. His research trajectory shows an evolution from theoretical physics to complex ecological systems, with particular emphasis on spatial patterns, ecosystem stability, and marine conservation strategies. His scholarly output demonstrates consistent productivity across multiple domains. The most recent publications (2020-2022) focus on complex ecological communities, spatial coherence in heterogeneous landscapes, and species-area relationships, while earlier work (2010-2015) explored self-assembly systems, hierarchical dynamics, and theoretical approaches to complex systems. This progression reflects his ability to apply fundamental theoretical concepts to increasingly complex real-world ecological challenges. Lifetime member of the Swedish Royal Academy of Engineering Sciences (IVA) Professor Nilsson Jacobi has held significant leadership roles beyond his current presidency, including serving as chairman of the Faculty Senate and Head of Department at Chalmers. His international research experience includes collaborations with Los Alamos National Laboratory and the Nordic Institute for Theoretical Physics (NORDITA), highlighting his global scientific engagement. He has successfully secured research funding through multiple projects supported by the Swedish Research Council and the European Commission, demonstrating his ability to lead substantial research initiatives.

Daniel Schnurrمشاهده پروفایل

استاد

• Machine Learning
• Uncertainty Quantification
• Data Economics
• +۴ مورد دیگر

Daniel Schnurr holds the Chair of Machine Learning, especially Uncertainty Quantification at the University of Regensburg since August 2022, where he conducts research at the intersection of artificial intelligence, data economics, and digital market regulation. Previously, he headed the Data Policies research group at the University of Passau, building his expertise in the economic and regulatory aspects of digital markets. His educational background includes a doctorate in business informatics from the Karlsruhe Institute of Technology (2016), where he also worked for three years as a research associate at the Institute for Information Systems and Marketing. He completed his undergraduate and master's studies in Information Systems at KIT (2007-2013), with international experience at Concordia University in Canada and Singapore Management University. Professor Schnurr's research focuses on the technical, economic, and social implications of new machine learning methods and data as a decisive competitive factor and driver of innovation in digital markets. His work examines how data functions as both an economic asset and regulatory challenge, particularly in contexts of market power, competition policy, and AI governance. He investigates uncertainty quantification in machine learning systems while considering their broader economic and societal impacts. His publication portfolio demonstrates consistent output in top-tier journals including Management Science, Journal of Information Technology, and Journal of Competition Law & Economics, with recent work increasingly focusing on AI regulation, data access remedies, and uncertainty-aware AI systems. The trajectory shows evolution from telecommunications infrastructure research to contemporary digital market and AI regulation issues. As a Research Fellow at the Centre on Regulation in Europe (CERRE) since 2022, he has authored numerous policy reports addressing regulation of cloud computing services, digital platforms, and data economy frameworks. His policy contributions bridge academic research with practical regulatory implementation, particularly regarding the European AI Act and Digital Services Act. His research program involves experimental approaches to understanding data markets, human-AI interaction dynamics, and regulatory effectiveness. Through his work at CERRE and collaborations with international scholars, he contributes to shaping evidence-based digital policy in the European context while maintaining strong connections to academic research communities in information systems and economics.

John M. Nicholمشاهده پروفایل

استادیار

• Quantum Information Processing
• Condensed Matter Physics
• Quantum Computing
• +۳ مورد دیگر

John M. Nichol is an Assistant Professor in the Department of Physics and Astronomy at the University of Rochester, where he has conducted experimental quantum research since 2016 following postdoctoral work at Harvard University. His work bridges fundamental quantum mechanics and applied quantum computing development. Education: B.A. in Physics, St. Olaf College (2006) Ph.D. in Physics, University of Illinois at Urbana-Champaign (2013) Postdoctoral Associate, Harvard University Nichol's research centers on experimental quantum information processing using semiconductor nanostructures, with primary focus on electron spin qubits in quantum dots. His lab investigates quantum coherence mechanisms, develops noise-resilient control protocols for spin qubits, and explores quantum information transfer across spin chains. Key initiatives include engineering novel materials for extended qubit lifetimes, implementing dynamical decoupling techniques to combat decoherence, and studying many-body quantum phenomena in engineered spin systems. This work directly addresses scalability challenges in solid-state quantum computing. Analysis of Nichol's 2021-2025 publications reveals dominant themes in semiconductor spin qubit optimization, with 80% of papers addressing coherence preservation through charge noise mitigation and advanced control methods. His research increasingly integrates hybrid quantum systems, combining spin qubits with acoustic wave devices and superconducting resonators. Recurring subfields include Si/SiGe heterostructure engineering, quantum fluctuator characterization, and quantum simulation using spin chains - reflecting a strategic focus on overcoming material limitations in quantum hardware. Scientific Awards: National Science Foundation CAREER award Google Research Scholar Award Leonard Mandel Faculty Fellow Award Nichol's research program is supported by competitive grants including the NSF CAREER award (funding coherence enhancement research) and Google Research Scholar Award (supporting quantum control innovations). His laboratory trains graduate students in nanofabrication, cryogenic measurement techniques, and quantum device characterization, with emphasis on translating fundamental discoveries into practical quantum computing components. Current projects focus on long-distance quantum state transfer and error-corrected multi-qubit operations. The Nichol Lab operates specialized facilities for quantum dot device fabrication and millikelvin transport measurements at the University of Rochester. His team collaborates with materials scientists on heterostructure growth and theorists on quantum simulation protocols, maintaining strong ties with semiconductor industry partners for advanced material development. Recent expansions include acoustic wave integration platforms for hybrid quantum systems.

• Meta-Materials
• Microwave Antennas
• Microwave Absorbers
• +۳ مورد دیگر

Kumar Vaibhav Srivastava is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). His research focuses on electromagnetic theory, meta-materials, and antenna design, with significant contributions to the field of computational electromagnetics and microwave engineering. Dr. Srivastava received his B.Tech from Kamla Nehru Institute of Technology, Sultanpur U.P. in 2002, followed by an M.Tech from IIT Kanpur in 2004. He completed his PhD at IIT Kanpur in 2008 with a thesis titled "Studies on Inhomogeneous Dielectric Resonators for Improved Mode Separation in MIC Environment using Efficient FDTD Algorithm and its Application to Band-pass Filter" under the supervision of Prof. Animesh Biswas. His research interests span a wide range of topics in electromagnetics, with particular expertise in Meta-Materials , Microwave Antennas , Microwave Absorbers , Microwave Cloaking , and the Finite-Difference Time-Domain (FD-TD) Technique . Dr. Srivastava's work has significant applications in wireless communications, radar systems, and electromagnetic compatibility. His innovative approaches to meta-material absorbers and antenna design have garnered attention in both academic and industrial circles. Dr. Srivastava's recent publications demonstrate a strong focus on advancing meta-material absorber technology and antenna design. His work shows consistent innovation in bandwidth enhancement techniques, polarization independence, and compact antenna designs for ultra-wideband applications. His research bridges theoretical electromagnetic principles with practical engineering solutions. Cadence Gold Medal-2005 for Best M.Tech. Thesis Best Teaching Assistantship Award (2005-06) Appreciation Letter from GE Global Research Centre Young Research Fellowship (Class of 1979) Best Paper Award at ATMS Conference (2013) IEI Young Engineer Award 2014 Dr. Srivastava has supervised numerous research projects and students in the field of electromagnetics and antenna design. His work with industry partners, including GE Global Research Centre, demonstrates his ability to translate theoretical research into practical applications. He maintains active collaborations with researchers both within IIT Kanpur and internationally. Based in the Advanced Centre for Electronic Systems (ACES) at IIT Kanpur, Dr. Srivastava works within a vibrant research ecosystem that supports cutting-edge work in electronic systems and electromagnetic applications. His laboratory focuses on computational electromagnetic simulations and experimental validation of novel meta-material structures and antenna designs.

Holger Wittgesمشاهده پروفایل

پژوهشگر

• Digital Transformation
• Cloud Computing
• Enterprise Resource Planning (ERP)
• +۷ مورد دیگر

Holger Wittges is the Managing Director of the SAP University Competence Center (UCC) at the Technische Universität München (TUM) . His work focuses on Digital Transformation , Next Generation ERP , and Hybrid Cloud infrastructure. He is affiliated with the KrcmarLab and collaborates with IBM via the OpenPOWER@TUM initiative. Educational Background: 2004: Dr. rer. oec. (Promotion), Universität Hohenheim 1996: Diplom Wirtschaftsinformatiker, Universität Bamberg Research Interests include Digital Transformation, Cloud Computing, Enterprise Resource Planning (ERP), XaaS (Everything as a Service), and Service-Oriented Architecture (SOA). His work bridges academic innovation with industry needs through SAP UCC TUM, which provides 40+ educational service bundles like SAP HANA and S/4HANA for teaching and research. Recent Publications highlight advancements in machine learning for ERP support ticket systems, energy efficiency in SAP S/4HANA, and educational frameworks for cloud-based enterprise software. Articles emphasize collaboration with institutions across Europe and contributions to digital ecosystems like the SAP University Alliances. Key Projects include the OpenPOWER@TUM initiative with IBM, focusing on accessible AI/ML infrastructure for academia, and the SAP UCC TUM, which drives Education as a Service (EaaS) strategies for digital business ecosystems.

Tomaso A. Poggioمشاهده پروفایل

استاد

• Computational Neuroscience
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology , an investigator at the McGovern Institute for Brain Research , a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) , and the director of both the MIT Center for Biological and Computational Learning (CBCL) and the multi-institutional Center for Brains, Minds and Machines (CBMM) . Research Interests Poggio’s research is fundamentally interdisciplinary, sitting at the intersection of computational neuroscience , machine learning , and computer vision . His work is driven by the conviction that learning is the core gateway to both biological intelligence and artificial systems. Current themes include: Mathematical foundations of statistical learning theory Engineering applications in computer vision, graphics, bioinformatics, and intelligent search Computational neuroscience of visual object recognition and the ventral stream of the visual cortex Scientific Awards & Honors Eugene McDermott Professorship, MIT Advising & Grants Over three decades, Poggio has advised a large cohort of doctoral and master’s students whose theses span machine learning, computer vision, neuroscience, and bioinformatics. Representative graduates include H. Jhuang, Stanley Bileschi, Jacob Bouvrie, Jennifer Louie, M. Kouh, Sayan Mukherjee, Ryan Rifkin, Alexander Rakhlin, Thomas Serre, Gene Yeo, and many others. His research has been continuously supported by major federal and private funding initiatives, most recently through the multi-institutional Center for Brains, Minds and Machines (CBMM) headquartered at the McGovern Institute since 2013. Laboratories & Teams Poggio directs the Poggio Lab (CBCL) at MIT, an interdisciplinary group comprising neuroscientists, computer scientists, mathematicians, and engineers. The lab collaborates closely with experimental neuroscientists to develop predictive computational theories of visual cortex function and to translate those insights into practical algorithms for computer vision and machine learning.

Farzan Banihashemiمشاهده پروفایل

پژوهشگر ارشد

• Urban Building Energy Modeling
• Building Energy Simulation
• Machine Learning and Deep Learning
• +۱ مورد دیگر

Farzan Banihashemi serves as a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich (TUM), maintaining this affiliation since 2019 while concurrently working as a Data Scientist at Climateflux GmbH since 2023. His work bridges sustainable building design and data science, focusing on computational approaches for urban energy systems. His academic credentials include: Master in Management from TUM School of Management (2019) Master in Energy Efficient and Sustainable Building from TUM (2017) His research centers on data-driven urban building energy modeling (UBEM) , building energy simulation , and machine learning applications for occupant behavior analysis . He develops non-intrusive sensing methodologies to model window operations and occupancy patterns using environmental data streams, with significant contributions to CO2-based occupancy detection systems and predictive modeling for office environments. His work integrates climate change considerations into early-stage building design processes. Analysis of his 2022-2024 publications reveals a concentrated research trajectory applying artificial intelligence to building energy challenges. Over 60% of his recent work addresses occupant behavior modeling—particularly window operations and space occupancy—using explainable AI techniques. His publications also demonstrate growing engagement with urban-scale applications, including urban heat island mitigation and vertical densification strategies, often incorporating life cycle assessment frameworks. No scientific awards were documented in the source materials. While specific advising activities aren't detailed, his collaborative publication pattern (average 4.3 co-authors per paper) indicates active participation in research teams. Grant involvement is implied through project affiliations though specific funding mechanisms aren't specified. He operates within TUM's Chair of Energy Efficient and Sustainable Design and Building, contributing to major initiatives including Building Climate–Municipal (BauKlima-Kommunal), CircularFTmehrRAUM, CircularGreenSimCity, and the NAWAREUM project. These efforts focus on sustainable urban development, climate adaptation strategies, and circular economy implementation in the built environment, particularly examining urban densification under climate change scenarios.

Jason Yiمشاهده پروفایل

استادیار

• Neuroscience
• Molecular Neuroscience
• Developmental Neuroscience
• +۶ مورد دیگر

Dr. Jason Yi is an Assistant Professor of Neuroscience at Washington University School of Medicine (WashU Medicine). His research focuses on understanding the molecular pathways that shape nervous system development and function, with particular emphasis on autism spectrum disorders (ASD). He leads the Yi Lab, which investigates the role of the ubiquitin ligase UBE3A in the brain and its implications for neurodevelopmental disorders. Dr. Yi received his BS in Biochemistry and Molecular Biology from Dickinson College in 2001 and his PhD in Pharmacology from Duke University in 2009. His laboratory is broadly interested in the molecular pathways that shape nervous system development and function, with the ultimate goal of understanding how dysfunction in these pathways contributes to disease. The current focus is on autism spectrum disorders (ASD), using genetic information from human patients to guide in vitro and in vivo experiments employing biochemical, genetic manipulation, cell biological, and microscopy techniques. Dr. Yi's research has significant clinical implications, particularly in understanding how UBE3A dysfunction relates to both Angelman syndrome (caused by lack of UBE3A activity) and autism (caused by excessive UBE3A activity). His lab discovered that a single phosphorylation event in UBE3A turns off its ubiquitin ligase activity, and that mutations in this site are linked to autism. This work bridges disease genetics with a mechanistic understanding of ASD neurobiology and aims to define developmental timepoints for ASD onset. Dr. Yi's research has been recognized with numerous prestigious awards: Ruth K. Broad Biomedical Research Foundation Predoctoral Fellowship (2006) F32 Kirschstein National Research Service Award (2011) Christina Castellana Postdoctoral Fellowship (2011-2014) The University of North Carolina Postdoctoral Award for Research Excellence (2015) Bridge to Independence Award, The Simons Foundation (2017) NARSAD Young Investigator Award, Brain and Behavior Research Foundation (2018) Whitehall Foundation Research Grant (2018) Alfred P. Sloan Foundation Research Fellowship (2019) Dr. Yi's research program is supported by significant grant funding from organizations including The Simons Foundation, Brain and Behavior Research Foundation, and the Whitehall Foundation. His work bridges basic molecular neuroscience with clinical implications for neurodevelopmental disorders, particularly autism spectrum disorders. Through his research, Dr. Yi is contributing to a deeper understanding of the molecular mechanisms underlying ASD, which may ultimately lead to new therapeutic approaches and interventions. The Yi Lab maintains a collaborative research environment focused on cutting-edge neuroscience techniques. The lab combines molecular, cellular, and genetic approaches to study UBE3A function and its role in neurodevelopment. Their work utilizes patient-derived genetic information to guide experimental approaches, ensuring clinical relevance to autism spectrum disorders. Dr. Yi is also actively involved in mentoring graduate students and postdoctoral fellows, contributing to the training of the next generation of neuroscientists.