جست‌وجوی استاد و پژوهشگر

Lorenzo De Stefaniمشاهده پروفایل

مدرس

• Algorithms
• Big Data
• Statistical Learning
• +۳ مورد دیگر

Lorenzo De Stefani serves as an Assistant Teaching Professor (formally titled Lecturer) in Brown University's Department of Computer Science, teaching core courses including Theory of Computation (CSCI1010), Design and Analysis of Algorithms (CSCI1570), and Data Science (CSCI1951A). His academic credentials feature dual doctoral degrees: a PhD in Computer Science from Brown University (2020) under Eli Upfal, and a PhD in Computer Engineering from the University of Padova (2016) under Gianfranco Bilardi. Additional qualifications include a Master of Science (2012) and Bachelor of Science (2009) in Computer Engineering from the University of Padova. PhD, Brown University, 2020 PhD, University of Padova, 2016 MSc, University of Padova, 2012 BSc, University of Padova, 2009 De Stefani's research centers on algorithmic innovation for large-scale data challenges, with emphasis on memory-efficient graph stream processing, statistical learning for multiple hypothesis testing, and Byzantine-resilient computation. His work bridges theoretical computer science and practical data analysis through rigorous probabilistic frameworks. Publication trends reveal consistent contributions to graph algorithm optimization, particularly in single-pass triangle counting for dynamic networks and I/O complexity bounds for fundamental operations like matrix multiplication. His methodologies prioritize computational efficiency while maintaining statistical validity in massive datasets. Scientific recognition includes: KDD Best Student Paper Award (2016) for Tríest streaming algorithms While advising PhD candidates during his doctoral studies at Brown and Padova, current student mentorship details remain unspecified. No major grant awards are documented in available materials. He actively participates in Brown's computer science research ecosystem through collaborative networks focused on algorithmic foundations and data science applications.

Pierre Lafaye de Micheauxمشاهده پروفایل

دانشیار

• Applied Statistics
• Biostatistics
• Statistical Theory
• +۱۱ مورد دیگر

Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.

• Internet of Things
• Human Activity Recognition
• Digital Health
• +۳ مورد دیگر

Jorge Martinez Carracedo is a Lecturer in Computer Science (Internet of Things) at the School of Computing , Ulster University. His research focuses on IoT applications in digital health, human activity recognition, and data analytics. Role: Lecturer in Computer Science (Internet of Things) University: Ulster University School: School of Computing Key research areas include Internet of Things , Human Activity Recognition , and Digital Health . His work spans algorithm development for wearable sensors and cross-disciplinary collaborations in emotional wellbeing monitoring. Recent publications highlight trends in digital mental health interventions , dynamic activity recognition , and health app clustering . Awards include an honorary recognition (2021) and a BCS award shortlisting (2023). Scientific Awards: 2021: Honorary award for 'A Robust Martingale Approach for Detecting Abnormalities in Human Heartbeat Rhythm' 2023: Shortlisted for BCS & Computing UK IT Industry Awards 'Security Innovation of the Year' Active in consultancy projects, including the Fusion Project: Smart Client Assistance (2024–2026) and AI-driven initiatives for large language models. Supervised work includes collaborations on wearable technology and neonatal cardiac diagnostics.

• Urban Economics
• Disaster Risk Management
• Real Estate Finance
• +۵ مورد دیگر

Kerem Yavuz Arslanlı is an Associate Professor at Istanbul Technical University's Faculty of Architecture, Department of Urban and Regional Planning. He has held various academic roles since 2002, including Deputy Head of Department (2020-2022) and Education Coordinator. His international collaborations include positions at the University of Alicante, Cass Business School, and institutions in Switzerland, Germany, and Romania. Education: PhD in Urban and Regional Planning from Istanbul Technical University (2004), MA in Real Estate Development (2001) Research interests span Urban Economics , Disaster Risk Management , Real Estate Finance , and Post-Disaster Reconstruction . His work focuses on seismic risk scenarios , impact chain analysis , and low-carbon real estate investments , often leveraging spatial statistics and social big data . Recent publication trends highlight applications of impact chains for systemic risk assessment, tokenization in real estate, and sustainability factors in consumer behavior. His 2025 Journal of Risk Research paper addresses Istanbul's seismic risk modeling, while 2024 works explore post-earthquake housing affordability and low-carbon investments . Scientific awards include: 1st Prize, Meles Stream National Urban Design Competition (2020) AREUEA Doctoral Session Prize (2009) Advising encompasses 18 theses on topics like real estate tokenization , smart contracts , and post-disaster urban planning . He has led projects including Energy Demand Modeling for Post-Disaster Settlements and Impact Chain Analysis of Kahramanmaraş Earthquakes .

Giorgio Scorzelliمشاهده پروفایل

پژوهشگر

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +۶ مورد دیگر

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

Arijit Bishnuمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Randomized Algorithms
• Approximation Algorithms
• +۶ مورد دیگر

Arijit Bishnu is an Associate Professor at the Indian Statistical Institute in the Advanced Computing and Microelectronics Unit (ACMU). He has taught courses such as Design and Analysis of Algorithms , Randomized Algorithms , Computational Geometry , and Algorithms for Big Data over multiple years (2008–2025), focusing on theoretical and applied aspects of computer science. Research Interests: His work spans Theoretical Computer Science , Randomized and Approximation Algorithms , Computational Geometry , and Combinatorics . He explores problems in sublinear algorithms, streaming computation, geometric data analysis, and complexity theory. Publications: Recent papers include contributions to STOC 2025 , RANDOM 2025 , and APPROX 2024 , covering topics like property testing, triangle counting complexity, and streaming algorithms. Collaborations with researchers like Sourav Chakraborty, Gopinath Mishra, and Sayantan Sen highlight his interdisciplinary approach. Academic Leadership: He has co-organized research courses such as Approximation Algorithms and Topics in Algorithms and Complexity , emphasizing mentorship and knowledge dissemination in theoretical computer science. His comprehensive work integrates algorithmic innovation with rigorous mathematical analysis, advancing computational techniques for large-scale and geometric data.

Michael M. Goldmanمشاهده پروفایل

استاد

• Sport Marketing
• Sport Sponsorship
• Consumer Behavior
• +۴ مورد دیگر

Michael M. Goldman is Professor of Sport Management and Associate Dean of Graduate Programs and Strategic Initiatives at the University of San Francisco’s College of Arts & Sciences. He is a full-time faculty member who also serves as adjunct faculty at the Gordon Institute of Business Science (GIBS) in Johannesburg and sits on the board of the Case Research Foundation. Education & Prior Roles DBA, Gordon Institute of Business Science (GIBS), 2014 MBA, Gordon Institute of Business Science (GIBS), 2005 Former Senior Lecturer in Marketing, University of Pretoria (GIBS) Advisor to the 2018 Rugby World Cup Sevens Host Committee Advisor, Rumble Ventures Sports Innovation Fund Research & Applied Expertise Goldman’s scholarship lies at the intersection of sport marketing, sponsorship analytics, and consumer psychology. He studies how fans form identities with teams, how brands leverage sport partnerships, and how organizations can translate these insights into revenue growth. His work spans professional sport franchises (San Francisco Giants, LA Clippers), global sponsors (MTN Group), and governing bodies such as CONCACAF and Cricket South Africa. Across more than fifteen recent publications, Goldman’s research trajectory reveals a consistent focus on three themes: fan engagement and disengagement , sponsorship ROI and brand building , and innovative case-based pedagogy . He employs mixed methods—qualitative case studies, quantitative market analyses, and experimental designs—to tackle questions ranging from esports broadcasting during COVID-19 to the financial impact of team rebranding. Honours & Awards Visiting Professorship, Universidad Peruana de Ciencias Aplicadas (2020) North American Case Research Association – Silver Best Case Award (2019) Sarlo Prize for Excellence in Teaching, USF (2017) NACRA – Bronze Best Case Award (2017) Paul R. Lawrence Fellowship, Case Research Foundation (2015) Consulting, Grants & Labs Goldman maintains an active portfolio of industry partnerships that function as living laboratories for his research and teaching. Recent collaborations include designing sales-skills workshops with the Los Angeles Clippers, advising MTN on FIFA World Cup activation, and assessing brand equity for the San Francisco Giants’ season-ticket retention campaigns. While specific grant amounts are not disclosed, his ongoing work with teams and federations provides continuous funding for case development and experiential-learning initiatives.

• Arctic Ecosystems
• Climate Change Impacts
• Permafrost Dynamics
• +۶ مورد دیگر

Ken D. Tape is a Professor of Ecology at the University of Alaska Fairbanks, affiliated with the Geophysical Institute. His research focuses on climate change impacts on Arctic ecosystems, emphasizing interconnections between snow, vegetation, permafrost, hydrology, wildlife, and human communities. Founded Arctic Beaver Observation Network (A-BON) (2020), coordinating circumarctic beaver research Developed repeat photography projects documenting landscape change in Alaska Principal Investigator (PI) and Co-PI for 10+ grants from NASA, NSF, USGS, DOE , and IASC His work synthesizes satellite remote sensing , field measurements, and historical data to analyze: Beaver colonization in tundra regions (2018–present) Shrub expansion and carbon dynamics (2008–2020) Hydrological changes in permafrost landscapes (2014–2022) Wildlife-vegetation interactions (2000–2017) Recent publications analyze beaver-engineered Arctic wetlands , shrub-root trait relationships , and permafrost destabilization , with methodological innovations in ground-penetrating radar and pressure transducer monitoring .

Lynn Kamerlinمشاهده پروفایل

استاد

• Computational Biophysics
• Molecular Evolution
• Enzyme Engineering
• +۱۲ مورد دیگر

Lynn Kamerlin is a Professor at the Georgia Institute of Technology and co-leads the Kamerlin Laboratory, which operates across Georgia Tech and Lund University. Her work integrates computational chemistry and biophysics to address fundamental questions in enzyme evolution, catalysis, and protein design. Education MNatSc in Chemistry, University of Birmingham (UK) PhD in Chemistry, University of Birmingham (UK) Her research spans computational biophysics , focusing on mechanistic biochemistry , protein evolution , and enzyme engineering . Key methodologies include machine learning , molecular dynamics simulations , EVB/QM/MM modeling , and natural language models for protein structure prediction. Recent publications highlight trends in AI-driven enzyme design , conformational dynamics , and mechanistic studies of phosphoryl transfer reactions . Tools like WatCon and Q-RepEx demonstrate her commitment to method development in computational biology. Scientific Awards Georgia Research Alliance Eminent Scholar (2022-Present) Wallenberg Scholar (2020-2024) ERC Starting Grant (2012-2017) Wallenberg Academy Fellowship (2014-2019, prolonged 2019-2024) Young Academy of Europe Chair (2014-2015) Fellow of the Royal Society of Chemistry (2017) Her lab collaborates with experimental groups worldwide, leveraging enhanced sampling techniques and structural bioinformatics to engineer enzymes with tailored properties. Grants from the Swedish Research Council and European Research Council underpin her research on enzyme evolution and catalytic mechanisms. Current projects include computational design of thermostable enzymes , allosteric modulators for biomedical targets, and modular protein scaffolds . The lab also investigates non-canonical amino acid incorporation and FAIR data principles in biomolecular simulations.

Tom Wildمشاهده پروفایل

مدرس

• Nature-based solutions (NBS)
• Urban water management
• River restoration and daylighting
• +۷ مورد دیگر

Tom Wild is a Lecturer at the School of Architecture and Landscape , University of Sheffield, specializing in nature-based solutions (NBS) for urban sustainability. His work bridges ecology, environmental planning, and socio-economic policy to address climate change adaptation, river restoration, and equitable green infrastructure investments. Focuses on biophysical and socio-political conditions enabling ecosystem rehabilitation Principal Investigator for Horizon 2020 CONEXUS project (€5M, 30+ partners) Active in economic valuation of blue-green infrastructure and governance frameworks Coordinates ESRC-funded Planning for Nature and NERC-funded GP4Streets projects His research spans urban ecology , river daylighting , and green space equity , with extensive publications on NBS implementation across Europe and Latin America. Current work examines climate adaptation strategies through the lens of community engagement and policy innovation. Key research trends include: Integration of NBS in urban drainage systems Transdisciplinary governance models Cultural and economic barriers to adoption Long-term viability of green infrastructure Professional engagements include Chartered Biologist status (Society of Biology) and prior leadership roles in environmental agencies, reflecting his commitment to translating research into practice through community partnerships and policy advocacy.

Gabriel Dallagoمشاهده پروفایل

استادیار

• Animal Welfare
• Machine Learning Applications
• Livestock Production Systems
• +۳ مورد دیگر

Gabriel Dallago serves as Assistant Professor in the Department of Animal Science within the Faculty of Agricultural and Food Sciences at the University of Manitoba. His research integrates advanced data analytics with livestock production systems to enhance animal welfare and farm decision-making processes. His educational background includes: PhD from McGill University, Canada MSc from Federal University of the Vales of Jequitinhonha and Mucuri, Brazil BSc from Federal University of the Vales of Jequitinhonha and Mucuri, Brazil Dallago's research program centers on machine learning and deep learning applications for livestock management. Key initiatives include developing predictive models for animal bio-responses, integrating multimodal data streams to analyze complex farm systems, and optimizing dairy cow longevity through data-driven interventions. His work bridges computational science with practical agricultural challenges, focusing on tangible improvements in production efficiency and animal well-being. Precision monitoring of dairy cow behavior and welfare Early-life management impacts on herd productivity Computer vision applications for livestock assessment Economic modeling of livestock production systems Analysis of his 15 most recent publications (2022-2025) reveals strong thematic concentration in dairy science (47%), swine production (20%), and animal nutrition (20%), with consistent application of computational methods. Notable trends include increasing use of machine learning for welfare assessment (evident in 60% of recent works), growing emphasis on economic sustainability metrics, and innovative sensor integration for real-time livestock monitoring. Dallago teaches ANSC 7500 (Methodology in Agricultural and Food Sciences) and AGRI 4100 (Current Issues in Agricultural Systems), though specific advising relationships and grant details remain unreported in available materials. His research appears to operate through interdisciplinary collaborations leveraging computational tools and on-farm data collection systems, though dedicated laboratory descriptions are absent from current documentation.

Alvin Cheungمشاهده پروفایل

دانشیار

• Data Management
• Programming Languages
• Software Systems
• +۴ مورد دیگر

Alvin Cheung is an Associate Professor in the Computer Science Division at UC Berkeley's EECS department. He is affiliated with the Data Systems and Foundations group, Programming Systems group, Sky Lab, and SLICE Lab, and serves as a faculty affiliate at the Berkeley Institute for Data Science. He advises the Data Science Discovery Program and provides technical guidance to industry partners. His research spans data management, programming languages, and scalable software systems, with emphasis on helping users process large datasets efficiently. Key innovations include verified lifting (applying formal methods and ML to infer program properties) and systems for optimizing database-backed applications and geospatial analytics. Recent work explores LLM-driven code optimization and transpilation techniques. His publications (2023-2025) show strong trends in ML-enhanced systems, verified compilation, and data management tools. Articles frequently integrate formal methods, program synthesis, and hardware-aware optimizations across domains like databases, distributed computing, and HCI. Scientific Awards: ACSIC Rock Star Award (2025) Dahl-Nygaard Junior Prize (2024) VLDB Early Career Research Contribution Award (2023) IEEE TCDE Rising Star Award (2020) Sloan Fellowship (2019) NSF CAREER Award (2017) 20+ additional honors Advising & Grants: He mentors PhD/MS students (e.g., Lily Liu at OpenAI, Chenglong Wang at Microsoft Research). Research is funded by: NSF DOE ONR ARO Intel Notable grants include ONR Young Investigator Award and ARO Early Career Program Award. Labs & Teams: Leads projects in Berkeley's Data Systems/Programming Systems groups and collaborates with Sky Lab/SLICE Lab. Manages labs focused on verified compilation (e.g., Tenspiler) and data infrastructure (e.g., Spatialyze).

Saman Amarasingheمشاهده پروفایل

استاد

• Programming Languages
• Compiler Design
• High-Performance Computing
• +۵ مورد دیگر

Professor Saman Amarasinghe is a faculty member in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), where he leads the Commit compiler research group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on programming languages and compilers that maximize application performance on modern computing platforms, with a particular emphasis on high-performance domain-specific languages. Professor Amarasinghe received his bachelor's degree in electrical engineering and computer science from Cornell University in 1988, followed by master's and PhD degrees in electrical engineering from Stanford University in 1990 and 1997, respectively. He joined the MIT faculty as an assistant professor in 1997 and has since become a world leader in his field. Professor Amarasinghe's research interests span programming languages, compiler design, and high-performance computing, with a particular focus on domain-specific languages. His group has developed numerous influential languages and compilers including Halide, TACO, Simit, StreamIt, StreamJIT, PetaBricks, MILK, Cimple, and GraphIt, which deliver unprecedented performance for application domains such as image processing, stream computations, and graph analytics. He has also pioneered the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Professor Amarasinghe's publication history reveals a consistent research trajectory toward creating specialized language and compiler solutions that address performance challenges in specific domains while hiding complexity from application developers. His recent work focuses heavily on sparse computing, tensor algebra, graph processing, and the integration of machine learning techniques into compiler technology, demonstrating his ability to identify and address emerging computational challenges. ACM Fellow (2019) As an educator, Professor Amarasinghe has developed the popular Performance Engineering of Software Systems (6.172) class with Professor Charles Leiserson and created innovative project-based courses including the Open Source Software Project Lab, the Open Source Entrepreneurship Lab, and the Bring Your Own Software Project Lab. He also serves as the faculty director of MIT Global Startup Labs, which has helped create more than 20 startups across 17 countries. His research has translated into practical applications through startups like Determina, Inc. (acquired by VMware), demonstrating the real-world impact of his academic work. Professor Amarasinghe co-led the Raw architecture project with Professor Anant Agarwal, which did pioneering work on scalable multicores. His entrepreneurial activities include founding Determina, Inc. based on computer security research from his MIT lab and co-founding Lanka Internet Services, Ltd., the first Internet Service Provider in Sri Lanka, showcasing his ability to bridge academic research with commercial applications.

James Shacklefordمشاهده پروفایل

دانشیار

• Medical Image Processing
• High Performance Computing
• Embedded Systems
• +۲ مورد دیگر

James Shackleford serves as Associate Professor and Interim Associate Dean for Enrollment Management and Graduate Education in the Department of Electrical and Computer Engineering at Drexel University. His research bridges medical image processing, high performance computing, and emerging neuromorphic architectures with significant contributions to radiation therapy applications. Education: PhD in Electrical Engineering, Drexel University, 2011 MS in Electrical Engineering, Drexel University BS in Electrical Engineering, Drexel University Research Focus: Professor Shackleford's work centers on GPU-accelerated medical image registration (forming the core of the open-source Plastimatch software), real-time tumor motion management for radiation therapy, and digital spiking neuromorphic systems . His research integrates computer vision, machine learning, and embedded systems to solve clinical imaging challenges. Publication Trends: Recent work (2020-2024) reveals dual research trajectories: (1) advancing deformable image registration through CycleGAN-based domain adaptation for CT auto-segmentation in radiation oncology, and (2) pioneering neuromorphic computing with configurable hardware architectures, dataflow-based compilers, and resource-aware neural network mapping. These streams converge on high-performance solutions for medical imaging and efficient neural processing.

Stephanie Lansingمشاهده پروفایل

استاد

• Renewable Energy
• Anaerobic Digestion
• Bioplastic Formation
• +۶ مورد دیگر

Dr. Stephanie Lansing is a Professor in the Department of Environmental Science & Technology at the University of Maryland's College of Agriculture and Natural Resources. She directs the Bioenergy and Biotechnology Lab, focusing on renewable energy systems, waste treatment, and the Food-Energy-Water Nexus. Her work spans ecological engineering, antimicrobial resistance mitigation, and sustainable bioplastic production from organic waste. College of Agriculture and Natural Resources Environmental Science & Technology Bioenergy and Biotechnology Lab Her research integrates anaerobic digestion , microbial fuel cells , and nutrient recovery to address global sustainability challenges. Recent projects examine bioplastic formation from food waste and AMR dynamics in manure systems , with fieldwork in the US, Africa, and Latin America. She co-developed the NourishNet platform combining real-time surplus food distribution ( FoodLoops ) with spoilage detection ( Quantum Nose ). Active grants exceed $6 million, including two major DOE awards for biofuel and bioplastic production from food waste. Her lab collaborates with institutions like Virginia Tech, Idaho National Laboratory, and international partners. Current team members include PhD candidates Maureen Nabulime and Adanyro Atilago, MS student Rafian Aziz, and undergrad researchers exploring waste-to-energy systems.