جستجوی استاد راهنمای دانشگاهی

Brandon M. Luciaمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Programming Languages
• +۷ مورد دیگر

Brandon M. Lucia is the Kavčić-Moura Professor of Electrical and Computer Engineering at Carnegie Mellon University and CEO/co-founder of Efficient Computer Company. He leads the Abstract research group and focuses his work on the intersection of computer architecture, systems, and programming languages. Education: Ph.D. in Computer Science and Engineering, University of Washington (2013) — advised by Luis Ceze Research Focus: Brandon's research is broadly centered on intermittent computing , edge computing , and energy-efficient architectures . Two major thrusts define his current work: Intermittent Computing: Making battery-free, energy-harvesting devices programmable and reliable despite frequent power failures. Applications include medical implants, space systems, and large-scale sensing. Orbital Edge Computing: Designing nanosatellite constellations that perform on-orbit data processing, enabling low-latency, high-resolution sensing in space-constrained environments. Scientific Awards: NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS 2020 Best Paper Award OOPSLA 2015 Distinguished Paper & Artifact Awards IEEE Micro Top Picks (2016, 2018 Honorable Mention) Advising & Grants: Brandon actively advises a strong cohort of Ph.D. students including Zhuo Cheng, Bradley Denby, Souradip Ghosh, Harsh Desai, Kiwan Maeng, Emily Ruppel, and others. His group is funded by the NSF (CAREER and SHF grants), the Sloan Foundation, and industry partnerships. Labs & Teams: He directs the Abstract research group at CMU ECE, which hosts interdisciplinary projects spanning hardware design, compiler construction, and system software for ultra-low-power and space-borne computing platforms.

Martin T. Wellsمشاهده پروفایل

استاد

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

• Computational Physics
• Machine Learning
• Applied Mathematics
• +۷ مورد دیگر

Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.

Brian Schollمشاهده پروفایل

استاد

• Visual Cognition
• Object Persistence
• Event Perception
• +۴ مورد دیگر

Brian Scholl is a Professor of Psychology at Yale University, specializing in visual cognition, perception, and cognitive science. His research explores how visual perception interacts with higher-level cognition, including object persistence, event perception, causality, and intuitive physics. He leads the Yale Perception & Cognition Lab, focusing on dynamic events, attention, and computational models. He earned his Ph.D. from Rutgers University in 1999. His work spans experimental psychology, neuroscience, and computational modeling, with a focus on phenomena like inattentional blindness, scaffolded attention, and event segmentation. He has authored numerous papers and presented at international conferences, contributing to debates on perception vs. cognition interactions. Key research areas include the perception of dynamic event types, the role of intuitive physics in visual processing, and the influence of event boundaries on memory and decision-making. His lab employs computer-based experiments and collaborations with computational models to study perception across species and clinical populations.

• Soil Science
• Pedology
• Geomorphology
• +۱۱ مورد دیگر

Carol Smith is an Associate Professor in the Department of Soil & Physical Sciences at Lincoln University, New Zealand, where she serves as Head of Department since 2017. She has been an elected academic staff member of the Lincoln University Council from 2018-2022. Dr. Smith holds a PhD from the University of Aberdeen, an MSc from the University of Reading, and a BSc(Hons) from the University of Portsmouth, forming the foundation of her expertise in soil science and physical geography. Her research spans both fundamental and applied aspects of pedology. On the fundamental side, she investigates Quaternary pedology and paleoenvironmental reconstruction using loess stratigraphy, geomorphology, and micromorphology, which provides critical data for verifying future climate change predictive models. Applied research focuses on sustainable use of recycled organic matter and rehabilitation of degraded soils. She collaborates internationally on multidisciplinary projects involving paleoclimate reconstruction, paleoliquefaction, Antarctic soils, and viticulture. Dr. Smith is passionate about teaching and science communication, employing experiential learning methods to develop practical field skills in soil science through 'soil judging competitions' in New Zealand and Australia. Her research addresses UN Sustainable Development Goals including Life on Land (15), Climate Action (13), and Quality Education (4). Among her notable recognitions are the Norman H Taylor Memorial award 2020 from the New Zealand Society of Soil Science for outstanding contributions to soil science in New Zealand and Fellowship in the Royal Geographical Society, London. She serves as Associate Editor of Natural Sciences Education and was previously editor of Quaternary Australasia. Dr. Smith has supervised numerous graduate students through research-based supervision, with completed projects covering diverse topics from soil patterns in Southland to Antarctic soil ecology. She teaches advanced courses in field research, soil science, and physical landscapes, and has developed innovative approaches to teaching during the pandemic, including virtual field trips.

Brandon Luciaمشاهده پروفایل

استاد

• computer architecture
• computer systems
• programming languages
• +۸ مورد دیگر

Brandon Lucia is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He holds the Kavčić-Moura Professorship and leads the Abstract research group. As CEO and co-founder of Efficient Computer Corp., he bridges academic research with commercial applications in energy-efficient computing. Dr. Lucia received his Ph.D. in Computer Science and Engineering from the University of Washington in 2013, following an MS from the same institution in 2010 and a BS in Computer Science from Tufts University in 2007. His research focuses on the intersection of computer architecture, computer systems, and programming languages, particularly in energy-constrained environments. His primary research interests include intermittent computing, energy harvesting computers, orbital edge computing, and parallel computing systems. Lucia's work addresses fundamental challenges in creating programmable, reliable computing devices that operate without batteries by harvesting energy from their environments, with applications in sensing, medical implants, and space systems. He also investigates software systems and architectures for making parallel computing correct, reliable, and efficient in the post-Moore's Law era. Lucia's publication record shows a clear trajectory toward orbital edge computing and nanosatellite systems, with recent work focusing on computational constellations, visual navigation for satellites, and energy-efficient processing in space. His research spans both theoretical foundations of intermittent computing and practical implementations in hardware and software. 2021 Sloan Research Fellowship 2018 NSF CAREER Award 2018 ASPLOS Best Paper Award IEEE MICRO Top Picks in Computer Architecture (2009, 2010, 2016) 2015 OOPSLA Best Paper Award 2019 IEEE TCCA Young Computer Architect Award 2022 Engineering Faculty Award As an advisor, Lucia has mentored numerous graduate students including Brad Denby, Zhuo Cheng, and Kyle McCleary, many of whom have become co-authors on his significant publications. His lab developed the world's first batteryless PocketQube nanosatellite (Tartan-Artibeus-1), which was deployed to low-Earth orbit aboard the SpaceX Transporter-3 Rocket. Lucia's research has received funding from sources including NSF, DARPA, Google, and VMware, supporting both fundamental research and practical implementations of energy-harvesting computing systems.

Naoki Yoshinagaمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Multilingual and Multimodal NLP
• +۴ مورد دیگر

Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .

Gautham Narayanمشاهده پروفایل

دانشیار

• Cosmology
• Time-domain Astrophysics
• Survey Science
• +۵ مورد دیگر

Gautham Narayan is an Associate Professor in the Department of Astronomy at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in Physics and the National Center for Supercomputing Applications (NCSA). He holds roles as Deputy Director for Astrophysics Research at the NSF-Simons SkAI Institute and Deputy Director of the Center for AstroPhysical Surveys. His research focuses on multi-messenger and time-domain astrophysics, cosmology, and machine learning applications in astronomy. Education: PhD in Physics from Harvard University (2013) and BS (Hons) in Physics from Illinois Wesleyan University (2005). His work includes pioneering AI methods for transient detection, leading collaborations like the Young Supernova Experiment (YSE), and developing standards for LSST and WFIRST. He is a Simonyi NSF-CAREER Fellow and Analysis Coordinator for the LSST Dark Energy Science Collaboration. Research interests span cosmology, supernovae, and survey science. Key projects include establishing spectrophotometric standards via HST observations and advancing real-time analysis pipelines like ANTARES. Recent work emphasizes Bayesian models for supernova cosmology and multi-messenger astrophysics. Awards: Simonyi NSF-CAREER Fellowship. Collaborations include DESC, SCiMMA, and the KEGS team. Teaching includes courses on astrophysics and data science, with mentorship of students across undergraduate and graduate levels. Public outreach efforts include Astronomy on Tap events and science communication initiatives.

Richard M. Sternمشاهده پروفایل

استاد

• Speech Recognition
• Audio Signal Processing
• Music Information Retrieval
• +۳ مورد دیگر

Richard M. Stern is a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in the Language Technologies Institute and Department of Computer Science, and serving as an Artist Lecturer in the School of Music since 2007. His interdisciplinary work bridges engineering and music technology through the School of Music's programs. Education: Ph.D. in Electrical Engineering from Massachusetts Institute of Technology (MIT), 1976 Professor Stern's research spans sound, speech, hearing, and music, with core emphases on robust speech processing in variable acoustic environments, music information retrieval, automated accompaniment, and foundational contributions to binaural perception theory. His work integrates psychoacoustic principles with machine learning to address challenges in speech recognition and human-robot interaction. Recent publications (2022-2025) reveal intensified focus on deep learning for speech enhancement in reverberant/noisy conditions, human-robot interaction scenarios, and music tagging—highlighting innovations in beamforming, source separation, and temporal modulation modeling. Awards and Honors: Fellow of the IEEE Fellow of the Acoustical Society of America Fellow of the International Speech Communication Association (ISCA) ISCA Distinguished Lecturer Allen Newell Award for Research Excellence (1992) Lutron Award for Teaching Excellence (2018) Professor Stern has advised numerous graduate students in speech and audio research, though specific names are unlisted in source materials. His grant portfolio includes significant National Science Foundation and industry-funded projects in speech technology, with leadership roles in initiatives like Interspeech 2006. He actively collaborates with CMU's Language Technologies Institute and Music and Technology program. He maintains strong ties to CMU's interdisciplinary ecosystem through the Language Technologies Institute and School of Music's Music and Technology program, contributing to research that merges acoustic engineering with musical applications.

Ye Zhishengمشاهده پروفایل

دانشیار

• Inventory Control
• Reliability Engineering
• Emergency Response Systems
• +۴ مورد دیگر

Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.

• Computational Linguistics
• Natural Language Processing (NLP)
• Dependency Parsing
• +۵ مورد دیگر

Joakim Nivre is a Professor at Uppsala University's Department of Linguistics and Philology. He is a leading researcher in computational linguistics, with a focus on dependency parsing, Universal Dependencies (UD) framework development, and multilingual NLP applications. His recent work explores LLMs in climate change discourse analysis, pharmacovigilance explainability, and historical text processing. Key research areas: Dependency parsing theory, Universal Dependencies standardization, LLM evaluation Collaborations: SweSAT-1.0 benchmark development, ClimateEval project, PARSEME integration His 2025-2023 publications demonstrate expertise in explainable AI for healthcare, synthetic data generation for idioms, and multilingual benchmark design. Notably, he co-developed SweSAT-1.0 to evaluate Swedish LLMs and contributed to typology-informed UD revisions. Despite extensive work in NLP, no scientific awards are mentioned in available texts.

Alex Liuمشاهده پروفایل

پژوهشگر

• Paleobiology
• Early Animal Evolution
• Ediacaran Biota
• +۸ مورد دیگر

Alex Liu is a Researcher at the Department of Earth Sciences within the University of Cambridge. He specializes in palaeobiology , focusing on the origin and early evolution of animals, particularly the Ediacaran biota (~579–539 million years ago), and their co-evolution with Earth's environment. Research Focus: Refines understanding of animal body plan evolution, taphonomy, sedimentology, and macroevolutionary trends. Methodologies: Combines fieldwork, experimental studies, petrology, phylogenetics, and big-data analyses. Collaborations: Works with international museums and research teams on fossil sites in Namibia, Canada, and Brazil. Additional Interests: Explores meiofaunal evolution, paleogeography, paleoclimate, and conservation paleobiology. He supervises Cambridge NERC Doctoral Landscape Awards (DLA) projects and welcomes student contributions to taxonomic, paleoecological, and analytical studies.

Ruben Portuguesمشاهده پروفایل

استاد

• Neuroscience
• Systems Neuroscience
• Behavioral Neuroscience
• +۷ مورد دیگر

Ruben Portugues is a Professor of Brain Circuit Function and Dysfunction at the Institute of Neuroscience, Technical University of Munich (TUM). He is a full member of the Graduate School of Systemic Neurosciences (GSN), an associate and advisory board member of the Munich Center for Neurosciences (MCN), and leads a research group focused on understanding the neural basis of behavior. His lab uses larval zebrafish as a model organism to investigate sensorimotor control, decision-making, and motor learning through whole-brain imaging and circuit analysis. His research interests lie at the intersection of systems neuroscience and behavior. He investigates how brain circuits process sensory information, integrate it with motor output, and enable adaptive and flexible behavior. Key areas include the function of the cerebellum, heading direction networks, sensorimotor transformations, and the neural mechanisms of decision-making. His lab employs cutting-edge techniques including custom-built microscopes, behavioral assays, and computational analysis. The recent publications and preprints from his lab demonstrate a strong trend in decoding distributed neural circuits underlying navigation and decision-making in zebrafish. There is a clear focus on identifying specific brain regions (e.g., interpeduncular nucleus, cerebellum) and cell types involved in processing visual, motor, and spatial information. The work increasingly emphasizes whole-brain functional imaging and the emergence of cognitive-like representations such as allocentric heading direction. FENS-Kavli Network of Excellence (FKNE) PhD Thesis Prize (awarded to student Luigi Petrucco) Ruben Portugues actively mentors PhD students, including current advisees Luigi Petrucco, Ot Prat, and Shuhong Huang, and has successfully graduated Dr. Elena Dragomir and Dr. Vilim Štih. His lab engages in extensive collaborations, hosts visiting researchers, participates in teaching (e.g., CSHL Imaging Course, Cajal Course), and secures resources for advanced research. The lab is known for building its own microscopes and software, fostering technical innovation. The Portugues Lab operates as a dynamic, interdisciplinary team that combines experimental neuroscience with computational and engineering approaches. They regularly hold retreats, participate in scientific events, and contribute to community initiatives like the Munich Brain Day. The lab is preparing to relocate to the Department of Neurobiology and Behavior at Cornell University, marking a new phase in its research trajectory.

Akihiko Nishimuraمشاهده پروفایل

استادیار

• Bayesian Statistics
• Statistical Computing
• Public Health Data Science
• +۹ مورد دیگر

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

• Hydrogeology
• Groundwater hydraulics
• Seepage flow
• +۳ مورد دیگر

Dr. FARKAS Dávid is an Assistant Professor in the Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). His office is located in Building K, basement level, room 12/7, and he holds weekly consultation hours on Mondays from 1–3 pm. Educational contributions include teaching core courses in hydrogeology and groundwater engineering: Groundwater (BMEEOVVMV63) Hydrogeology (BMEEOGMMG62) Infrastructural Design Project (BMEEODHAI41) Research activities concentrate on quantitative hydrogeology, with a special emphasis on karstic groundwater systems, seepage hydraulics, and the safety of flood-protection levees. He couples field investigations in iconic Hungarian cave systems (Buda Castle Cave, Molnár János Cave) with laboratory sandbox experiments and numerical modelling to advance understanding of flow and transport processes in fractured carbonates. Over the past decade his work has evolved from fundamental hydrogeological mapping toward the design and deployment of automated monitoring networks that integrate classical hydrological instruments with modern sensor technologies. This progression is evident in his most recent publications (2024-2025) that document the establishment of high-resolution cave monitoring systems capable of capturing rapid responses to precipitation events. Scientific awards currently listed: none. Advising and grants: no specific student names, grant numbers, or funded project titles are provided in the supplied text. Laboratories and teams: while no dedicated laboratory name is stated, his affiliation with the Department of Hydraulic and Water Resources Engineering implies access to the faculty’s hydraulics and hydro-environmental laboratories, including sandbox and seepage modelling facilities.