جست‌وجوی استاد و پژوهشگر

Jared Kutzinمشاهده پروفایل

دانشیار مدعو

• simulation-based education
• educational technologies
• behavior change in healthcare
• +۶ مورد دیگر

Jared Kutzin serves as an Adjunct Associate Professor at the MGH Institute of Health Professions and holds a dual appointment as Associate Professor of Emergency Medicine and Medical Education at the Icahn School of Medicine at Mount Sinai, where he concurrently serves as Senior Director of the Simulation, Teaching and Research Center at The Mount Sinai Hospital. His educational foundation includes: Doctor of Philosophy, Nursing (candidate), Texas Woman’s University MS in Medical Education Leadership, University of New England - College of Medicine DNP in Public Health Nurse Leadership, UMASS Amherst MPH in Health Policy and Management, Boston University BS in Nursing, Columbia University BS in Community Health Education, Hofstra University Dr. Kutzin's research centers on leveraging simulation-based education and educational technologies to drive behavior change that enhances healthcare quality and safety. His specialized focus spans emergency medicine, nursing practice, pre-hospital systems, and disaster response protocols. Current investigations examine virtual reality educational environments and the critical relationship between built clinical environments and clinician performance. His methodological approach integrates human factors engineering with educational theory to optimize simulation design and implementation. Analysis of his 15 most recent publications reveals a dominant trajectory toward technology-enhanced simulation methodologies, particularly virtual reality and telemedicine applications, with increasing emphasis on global health contexts and economic evaluations of simulation programs. The work consistently bridges clinical practice with educational innovation across emergency medicine, nursing, and prehospital care domains. His distinguished recognition includes: Society for Simulation in Healthcare Educator of the Year (2022) MedEdPORTAL Associate Editor of the Year (2020) Fellow of the Institute for Medical Education Mount Sinai Health System (2020) Fellow of the New York Academy of Medicine (2018) Inaugural Fellow of the Society for Simulation in Healthcare Academy (2017) Long Island Business News 40 under 40 (2015) As an educator and leader, Dr. Kutzin has developed and implemented simulation curricula across multiple clinical settings, significantly impacting staff competency development and patient safety initiatives. His grant-funded work focuses on simulation technology integration and healthcare systems improvement, though specific funding sources aren't detailed in available materials. His leadership extends to the REBEL Lab and Center of Excellence in Healthcare Simulation Research, where he drives innovation in simulation operations, educator development, and cross-disciplinary collaboration to transform clinical education and practice.

• Labor Economics
• Family Economics
• Economics of Education
• +۲ مورد دیگر

Bernhard Schmidpeter is an Assistant Professor at Vienna University of Economics and Business (WU Wien) and a Research Affiliate at the IZA - Institute of Labor Economics since March 2020. Previously, he worked as a Researcher at the Institute for Social and Economic Research (ISER), University of Essex. He earned his Ph.D. from Johannes Kepler University (JKU). His research focuses on labor economics, family economics, and the economics of education , using applied microeconometric methods to investigate how changing labor market conditions affect individual and firm decisions. Schmidpeter's work spans several critical areas including parental bereavement effects on mortality, grandmothers' labor supply decisions, immigration enforcement impacts on families, and the relationship between automation and unemployment. His research consistently employs high-quality administrative data, particularly from Austria, to provide causal evidence on important economic questions. Schmidpeter's publications appear in top economics journals including the Oxford Bulletin of Economics and Statistics, Journal of Human Resources, Journal of Population Economics, and European Economic Review. His work on suicide contagion in the workplace, parental bereavement effects, and the impact of immigration enforcement on children's human capital represents innovative applications of economic methods to important social issues. His research has received significant media attention from major outlets including The Telegraph, Die Presse, Handelsblatt, ORF Science, Süddeutsche Zeitung, and the WSJ Economics Blog, demonstrating the policy relevance of his work. Schmidpeter frequently collaborates with prominent researchers including Wolfgang Frimmel, Martin Halla, Rudolf Winter-Ebmer, Lukas Laffers, and Esther Arenas-Arroyo. Currently, Schmidpeter is working on projects examining the equilibrium effects of cross-firm pay transparency and the demand for green skills in the evolving labor market. His research contributes to understanding how economic policies and social phenomena affect individual behavior, family dynamics, and labor market outcomes across multiple dimensions.

Veronique Hosteمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Veronique Hoste is a Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy , where she serves as Department Head of the Department of Translation, Interpreting and Communication and Director of the LT3 Language and Translation Technology Team . Her work bridges machine learning with natural language processing, focusing on semantics, discourse modeling, and practical applications in emotion detection, irony recognition, and customer service dialogue analysis. PhD in Computational Linguistics from University of Antwerp (2005) 2023-2024: Francqui Chair at Université Libre de Bruxelles 2024: Elected to Royal Flemish Academy of Belgium (KVAB) Research Interests: Specializes in machine learning approaches to coreference resolution, sentiment analysis, and multimodal emotion detection. Leads projects like FlandersAI (empathy in conversational agents), METRICS (emotion trajectories in service dialogues), and SENTiVENT (financial event extraction). Develops high-quality datasets (e.g., EmotioNL , ENCORE ) for broader NLP community use. Recent Publications span Dutch social media irony detection, Classical Chinese poetry sentiment analysis, and multimodal emotion datasets. Collaborates on interdisciplinary initiatives like NewsDNA (news recommendation) and SentEMO (commercial sentiment analysis). Scientific Awards Francqui Chair (2023-2024) KVAB Membership (2024) Outreach includes co-founding AlfaSent (LT3 spin-off for customer feedback analysis) and authoring the first Dutch-language NLP book Taaltechnologie Ontrafeld (2024). Coordinates AI education initiatives like the AI at School project.

Jimmy Linمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Data Management
• +۲ مورد دیگر

Jimmy Lin is a Professor and David R. Cheriton Chair at the University of Waterloo's David R. Cheriton School of Computer Science. His research focuses on information retrieval, natural language processing, and data management, with applications in large-scale data analysis and AI systems. Previously at the University of Maryland, he earned his PhD from MIT in 2004. PhD in Electrical Engineering and Computer Science, MIT (2004) MS, Mechanical Engineering, Penn State University BSc, Mechanical Engineering, Addis Ababa University His research integrates information retrieval, NLP, and data management to build tools for understanding large datasets. Current interests include neural retrieval models, cross-lingual systems, and efficient search algorithms. Recent publications focus on retrieval-augmented generation (RAG), model efficiency, multilingual NLP, and evaluation methodologies. Trends include hybrid dense-sparse retrieval systems, robustness improvements, and practical toolkits like Anserini and Tevatron. ACM Fellow SIGIR Academy ACL Fellow SAE Ralph R. Teetor Educational Award (2014) NSF CAREER Award (2011) He advises 36+ PhD/Master's students and leads multiple industry collaborations (Primal, RSVP, Twitter). His lab develops open-source tools for retrieval and NLP, focusing on scalable and multilingual applications.

• Causal Machine Learning
• Healthcare Informatics
• Medical Data Analysis
• +۳ مورد دیگر

Dr. Vinod Kumar Chauhan is a Research Fellow at the Institute of Biomedical Engineering, University of Oxford. His work focuses on causal machine learning and healthcare applications, including individualized treatment effects estimation and addressing sample selection bias in medical data. Previously, he held a postdoctoral position at the University of Cambridge and earned his PhD from Panjab University, Chandigarh, India. Education: BSc, MCA, PhD (Panjab University, India). Research interests include causal inference, healthcare informatics, and graph neural networks for EHR analysis. Recent projects involve postoperative atrial fibrillation prediction and optimizing treatment effects for composite outcomes. Key Awards: 2021 Institute for Manufacturing Postdoctoral Award (Cambridge), Travel Grant for Asian Conference on Machine Learning 2017. Selected Talks: Keynote on HyperNetworks (2023 Panjab University), invited talks on treatment effects estimation (IISER Pune, 2023). Labs/Teams: Computational Health Informatics group under Prof. David Clifton. Active in departmental EDI committee roles.

• Computer Vision
• Object Recognition
• Scene Understanding
• +۷ مورد دیگر

Derek Hoiem is a Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School for Computing and Data Science since 2009. He holds a PhD in Robotics from Carnegie Mellon University (2007) and completed a Beckman Postdoctoral Fellowship (2008). As co-founder and Chief Science Officer of Reconstruct, he develops AI-driven tools for construction progress monitoring. His research focuses on computer vision, object recognition, scene understanding, and graphics. Key honors include IEEE Fellow (2022), University Scholar (2022), and the Sloan Research Fellowship (2013). He has received NSF CAREER and Intel Early Career awards, alongside best paper awards at CVPR (2006) and WACV (2015). His teaching excellence is reflected in numerous 'List of Teachers Ranked as Excellent' accolades across multiple semesters. Research interests span 3D reconstruction, neural networks, and multimodal models. His work addresses challenges in visual program generation, neural radiance fields, and construction monitoring through photogrammetry. He leads projects in continual learning, efficient AI systems, and explainable multimodal interactions. Professional contributions include grants from NSF and industry partnerships. He advises on cutting-edge AI applications in construction, graphics, and vision-language integration. His lab’s innovations bridge theoretical advancements with real-world systems like Reconstruct’s Visual Command Center.

Marcin Copikمشاهده پروفایل

پژوهشگر

• Parallel Computing
• High-Performance Computing (HPC)
• Serverless Systems
• +۳ مورد دیگر

Dr. Marcin Copik is a Researcher in the Professorship for Scalable Parallel Computing at ETH Zürich, focusing on advancing high-performance computing (HPC), serverless systems, and cloud computing. His work emphasizes optimizing resource utilization, improving scalability, and enhancing performance in distributed and parallel environments. His research interests span parallel computing architectures, serverless benchmarking (e.g., SeBS suite), and sustainable HPC practices. He explores techniques like automatic loop scheduling, acceleration-as-a-service models, and fault-tolerant distributed systems (e.g., FaaSKeeper). His contributions address challenges in cloud elasticity, stateful services, and energy-efficient computing. Recent publications highlight innovations in serverless workflows, AI reasoning frameworks, and hardware-accelerated protocols. His work bridges theoretical foundations with practical implementations, such as Cppless for high-performance serverless programming and XaaS for HPC-cloud integration. While no formal awards or grants are listed, his contributions to open-source projects like STEllAR-GROUP/hpx and Extrapeak demonstrate active engagement in the HPC community. He collaborates closely with academic and industry partners to advance scalable computing solutions.

Tom Van Cutsemمشاهده پروفایل

دانشیار

• Distributed Systems
• Software Security
• Blockchain
• +۵ مورد دیگر

Tom Van Cutsem is an Associate Professor of computer science at KU Leuven in Belgium, where he is affiliated with the Distributed and Secure Software (DistriNet) research group within the Faculty of Engineering Science and Department of Computer Science. He also maintains an affiliation with Nokia Bell Labs, where he previously served as a Research Department Head with research interests in decentralized systems, IoT, stream processing and machine learning for software engineering. His academic career includes a previous position as Assistant Professor at Vrije Universiteit Brussel from 2010 to 2014. Dr. Van Cutsem holds a PhD in Computer Science from Vrije Universiteit Brussel. His educational background has provided the foundation for his expertise in distributed systems, programming languages, and software security. His primary research interests span Distributed Systems , Software Security , Blockchain , and Programming Language Design . With the DistriNet research group, he pursues work on decentralized systems, IoT, stream processing, and machine learning for software engineering. His research focuses on making distributed systems more secure, reliable, and accessible, with particular attention to blockchain technology and Web3 infrastructure. He is a founding member of IFIP WG 2.16 on Programming Language Design. His recent publications demonstrate a strong focus on blockchain security and infrastructure, with numerous papers on Web3, smart contracts, and decentralized systems from 2023-2025. His work addresses critical challenges in blockchain validation, smart contract security, and Web3 infrastructure, reflecting his commitment to making decentralized systems more secure and accessible to low-resource participants. Several of his current projects focus on secure middleware for smart contracts and scalable infrastructure for self-sovereign applications. Dr. Van Cutsem supervises PhD students including Weihong Wang, with whom he has co-authored multiple recent publications. He is currently leading several research projects including SODISA (Scalable Software Development and Infrastructure for Self-sovereign Applications), Secure Sandboxing for WebAssembly-based Smart Contracts, and Programming languages for privacy-preserving and verifiable computing, with funding extending through 2028-2029. He leads research within the Distributed and Secure Software (DistriNet) group at KU Leuven, which focuses on creating secure and dependable distributed systems. His team works on cutting-edge challenges in blockchain infrastructure, smart contract security, and decentralized application development, with connections to both academic and industrial partners including Nokia Bell Labs.

Christopher Manningمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Christopher Manning is the Thomas M. Siebel Professor in Machine Learning and Professor of Linguistics and Computer Science at Stanford University. He serves as a Founder and Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and former Director of the Stanford Artificial Intelligence Laboratory (SAIL). His research spans Natural Language Processing (NLP), Deep Learning, computational linguistics, and open-source tools like Stanford CoreNLP and Stanza. Manning has pioneered work on sentiment analysis, neural machine translation, and the GloVe model. He holds a B.A. from ANU and a Ph.D. from Stanford, with an honorary doctorate from U. Amsterdam (2023). He previously held faculty roles at Carnegie Mellon University and the University of Sydney. Education: B.A. (Hons), Australian National University, 1989 Ph.D., Stanford University, 1994 Honorary Doctorate, University of Amsterdam, 2023 Research Interests: Manning's work focuses on robust NLP techniques, including deep learning for language understanding, dependency parsing, Universal Dependencies, and low-resource language processing. His contributions span theory (e.g., syntactic parsing), applications (e.g., machine translation), and education (e.g., Foundations of Statistical NLP ). Recent efforts include studying large language models' emergent properties and addressing ethical AI challenges like hallucination detection. Awards: IEEE John von Neumann Medal (2024) ACL Test of Time Award ACM/AAAI/AACL Fellowships Elected to American Academy of Arts and Sciences (2025) National Academy of Engineering (2025) Advising & Labs: Advisor to numerous PhD graduates (see his dissertations page ). Leads the Stanford NLP Group and collaborates with SAIL and HAI on projects like legal AI tools and scalable NLP education. Notable Projects: Development of Stanford Dependencies , Universal Dependencies , and open-source NLP libraries. Co-created CS224N (Deep Learning for NLP), one of the most-watched online courses in the field.

Miriam Leeserمشاهده پروفایل

استاد

• Reconfigurable Computing
• FPGA Accelerators
• Wireless Communications
• +۴ مورد دیگر

Miriam Leeser is a Professor and Associate Chair of Research in the Department of Electrical and Computer Engineering at Northeastern University. She leads the Reconfigurable and GPU Computing Lab, focusing on heterogeneous architectures, FPGA accelerators, and their applications in wireless communications, machine learning, and data privacy. Her work emphasizes practical implementations of theoretical advancements, such as cloud-edge computing frameworks and secure multi-party computation using FPGAs. Leeser holds a PhD in Computer Science from the University of Cambridge and a BS in Electrical Engineering from Cornell University. She has been at Northeastern since 1996 and is a Senior Member of both the IEEE and ACM. Notable recognitions include the National Science Foundation Young Investigator Award and her status as a Charter Member of the IEEE Computer Society Distinguished Contributor Program. Her research portfolio includes projects funded by the NSF on topics like resilient cloud infrastructure (CAREFREE), cross-layer wireless coexistence, and FPGA-enabled secure computation. The Reconfigurable Computing Lab develops tools and libraries to simplify accelerator usage, particularly for emerging applications in IoT, medical imaging, and 5G/6G systems. Key Projects: CAREFREE, P4-based FPGA Testbeds, Secure Function Evaluation via FPGAs, MIMO Systems Acceleration Grants: Over $2M in NSF awards for cloud infrastructure and cybersecurity Labs: Reconfigurable and GPU Computing Lab (RCL) Leeser’s publications span FPGA optimization, hardware-software co-design, and privacy-preserving technologies. Recent work highlights include accelerating large language models with token-adaptive quantization and low-latency optical wireless transceivers.

Warren Grossمشاهده پروفایل

استاد

• Integrated Circuits and Systems
• Error-Correcting Codes
• Hardware Acceleration
• +۵ مورد دیگر

Warren Gross is a Professor and Chair of the Department of Electrical & Computer Engineering at McGill University's Faculty of Engineering. Holding the prestigious James McGill Professor title, he leads research in integrated hardware systems from his office in Montreal's McConnell Engineering Building. His work bridges theoretical coding theory with practical VLSI implementations for next-generation communication networks. Professor Gross specializes in Integrated Circuits and Systems with deep expertise in error-correcting codes, particularly polar and Reed-Muller codes. His research spans hardware acceleration for machine learning, stochastic computing architectures, and VLSI design for low-latency wireless systems. Current projects focus on 6G air interface technologies, energy-efficient edge AI, and novel decoding algorithms that balance performance with hardware constraints. The ISIP Lab (Integrated Systems and Image Processing) serves as his primary research hub, advancing both theoretical frameworks and physical implementations. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Hardware-optimized decoding algorithms for polar codes targeting sub-1ms latency in 6G systems; (2) Model compression techniques for transformer networks deployed on resource-constrained edge devices; (3) Stochastic computing approaches for combinatorial optimization problems. These themes reflect his dual focus on communication theory and efficient hardware realization, with increasing emphasis on machine learning integration. Scientific recognition includes: James McGill Professor (McGill University's highest academic honor) As Department Chair and research leader, Professor Gross oversees multiple collaborative projects with industry partners in telecommunications and semiconductor sectors. His lab maintains active partnerships with 5G/6G standardization bodies and chip manufacturers, though specific grant details aren't publicly enumerated. The ISIP Lab operates advanced VLSI design facilities and FPGA testbeds supporting both academic research and industry prototyping. Research infrastructure includes specialized labs for stochastic computing implementation, polar code decoder validation, and edge AI acceleration. Current efforts focus on quantum-inspired annealing techniques for MIMO detection and hardware-friendly transformer architectures, with several patents pending in decoding methodology and model compression.

Liu Hanمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Systems
• Machine Learning
• +۷ مورد دیگر

Liu Han is the Orrington Lunt Professor of Computer Science at Northwestern University, with a courtesy appointment in Industrial Engineering & Management Sciences. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI (CFMG). Previously, he served as a professor at Princeton University and Johns Hopkins University, and as Director of the Deep Reinforcement Learning Center at Tencent AI Lab. His research focuses on integrating AI with computer systems, leveraging large foundation models and probabilistic graphical models to advance science, engineering, and business. He holds a joint PhD in Machine Learning and Statistics from Carnegie Mellon University, advised by John Lafferty and Larry Wasserman. Key research interests include transformer architectures, edge AI deployment, reinforcement learning, and genomic modeling . He actively contributes to academic governance as an associate editor for journals like the Journal of American Statistical Association and Technometrics , and serves as area chair for NeurIPS, ICML, and ICLR. His work emphasizes scalable machine learning solutions for heterogeneous data, with applications in genomics, traffic prediction, and proton beam control systems. Awards: Presidential Early Career Award (2025?), Alfred P Sloan Fellowship (Mathematics), IMS Tweedie New Researcher Award, ASA Noether Young Scholar Award, NSF CAREER Award, Howard B Wentz Award, and Umesh Gavaskar Memorial Dissertation Award. Advising & Grants: Advised students at top-tier institutions (specific names omitted due to lack of explicit listing). His grants focus on edge AI, foundation models, and ethical AI frameworks. Collaborates with Fermilab and CERN on real-time control systems using ML. Labs/Teams: MAGICS Lab (Northwestern) and CFMG Center, previously led Tencent's Deep Reinforcement Learning Center. Involved in interdisciplinary teams like Hetegraph-Mamba and the KGML-xDTD project.

• Software Engineering
• Mobile Security
• Program Analysis
• +۲ مورد دیگر

Jacques Klein is Full Professor of Software Engineering and Mobile Security at the University of Luxembourg. His research bridges software engineering principles with security challenges in modern computing environments. Research focuses on software security, mobile systems analysis, and increasingly on large language model applications for program analysis. Recent work examines Android ecosystem security, infrastructure-as-code vulnerabilities, and AI-enhanced program repair techniques. Klein's 2025 publications demonstrate strong emphasis on LLM capabilities for automated compliance checking, malware detection, and program understanding. The research combines empirical software engineering with AI methodologies to address security and maintenance challenges.

Ruqi Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Probabilistic Methods
• +۵ مورد دیگر

Ruqi Zhang is an Assistant Professor in the Department of Computer Science at Purdue University. Previously, she was a postdoctoral fellow at the Institute for Foundations of Machine Learning, UT Austin (2021-2022), and earned her PhD in Statistics from Cornell University (2016-2021). Her research focuses on scalable probabilistic methods for machine learning, including alignment of foundation models, uncertainty quantification, and Bayesian deep learning. She has received awards such as the Ross-Lynn Research Scholar Fund and ICML Best Reviewer recognition. Education: PhD in Statistics, Cornell University (2021) MS in Computer Science, Cornell University (2021) Bachelor of Science in Mathematics, Renmin University of China (2012-2016) Research Interests: Trustworthy AI: Safety of LLMs/VLMs, alignment mechanisms Probabilistic Inference: Bayesian methods, MCMC, variational inference Generative Models: Diffusion models, energy-based models Uncertainty Estimation: Calibration, out-of-distribution detection Notable Contributions: Developed the Discrete Langevin Sampler for high-dimensional discrete spaces Pioneered Low-Precision SGLD for efficient Bayesian neural networks Designed DP-Fast MH for privacy-preserving Bayesian inference Teaching: CS57800 - Statistical Machine Learning (2022-Present) CS37300 - Data Mining and Machine Learning (2024) CS59200 - Probabilistic Machine Learning (2022)

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +۵ مورد دیگر

Torsten Hoefler is a Professor affiliated with ETH Zurich, leading research in parallel computing, distributed systems, and high-performance computing (HPC). His work bridges theoretical foundations and practical implementations, focusing on optimizing algorithms, network topologies, and hardware-software co-design. Research Interests: His primary areas include parallel algorithms, distributed systems, machine learning infrastructure, and network architectures. He emphasizes scalable solutions for large-scale applications, particularly in data-centric computing and serverless environments. Publications: Recent work highlights include innovations in network topologies (e.g., HammingMesh), serverless benchmarking frameworks (SeBS), and optimizations for large language models (LLMs). His publications often address performance bottlenecks and energy efficiency in HPC and cloud systems. Awards & Grants: While no specific awards are listed here, his prolific publication record and leadership in HPC indicates significant recognition in the field. Active in grant-funded projects related to exascale computing and AI infrastructure. Labs & Teams: Leads the Communication Systems Lab at ETH Zurich, collaborating with industry partners like NVIDIA and IBM on hardware-accelerated computing and cloud-native systems.