جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Embodied Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Yonatan Bisk is an Assistant Professor at Carnegie Mellon University (CMU) in the School of Computer Science , with dual appointments in the Language Technologies Institute and Robotics Institute . His research bridges Natural Language Processing (NLP) with robotics, focusing on grounded and embodied language understanding. Assistant Professor, Language Technologies Institute, CMU (2021–Present) Courtesy Appointment, Robotics Institute, CMU Research Themes : Language as a social codification of embodied experience Interpretable multimodal model training Human-robot collaboration frameworks Embodied question-answering systems Selected Trends : His recent publications show increasing focus on cross-modal attention mechanisms (Vid2Robot), error detection in toolchains (Tools Fail), and theory-of-mind reasoning in language agents (SOTOPIA). Multimodal integration spans vision, audio, and robotic control contexts (ANAVI). Labs & Collaborations : Founder of CLAW Lab (Connecting Language to Action and the World) Collaborations with Microsoft Research, Meta Inc, and CMU's REAL (Robotics, Embodied AI, Learning) community

• Internet of Things (IoT)
• Wireless Sensor Networks (WSN)
• Active Learning Classrooms
• +۵ مورد دیگر

Maher Elshakankiri is an Assistant Professor, Teaching Stream at the University of Toronto's Faculty of Information. He holds a Ph.D. in Computer Engineering from Ain Shams University (Egypt), followed by a postdoctoral fellowship at the University of Regina. His research focuses on IoT, Wireless Sensor Networks (WSN), and pedagogical integration of technology and gaming. He has supervised over 100 student projects and authored a book on WSNs. Education: Ph.D. in Computer Engineering, Ain Shams University, Egypt M.Eng., B.Eng. in Engineering, Ain Shams University, Egypt Postdoctoral Fellowship, University of Regina, Canada Research Interests: IoT in healthcare, agriculture, and sports Active learning classrooms and technology in education Wireless communication protocols (V2V, UAV, RFID) Leadership & Grants: Director, Bachelor of Information (BI) Program (2024–present) Coordinator, Information Systems and Design (ISD) Concentration (2023) SSHRC Grant: 'Gaming in teaching towards a more inclusive class' (2022–2023) Professional Activities: Member, SCC IoT & Digital Twins Standards Committee Reviewer for journals including Wireless Networks , Telematics and Informatics , and Computational Intelligence Technical Program Committee member at multiple conferences Teaching: Courses include INF1340 (Programming for Data Science), INF1005/1006 (IoT Workshops), and INF452 (Information Design Coding).

Yanzhi Wangمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Neuromorphic Computing
• +۷ مورد دیگر

Yanzhi Wang is a Professor in the Department of Electrical and Computer Engineering at Northeastern University , affiliated with the Institute for Experiential AI and the Institute for the Wireless Internet of Things . He holds a PhD from the University of Southern California (2014). His research focuses on real-time AI systems, deep neural network compression, neuromorphic computing, and non-von Neumann architectures. Notable projects include NSF-funded initiatives on age-inclusive urban design, superconducting computing (DISCoVER), and edge device optimization (PatDNN). He has received prestigious awards such as the Army Research Office Young Investigator Award and the Constantinos Mavroidis Translational Research Award. His work emphasizes algorithm-hardware co-design for energy efficiency, with grants from NSF, ARO, and industry partners like Google. Recent research trends reflect his focus on accelerating vision transformers, diffusion models, and large language models for edge computing. He has pioneered methods like AutoViT and Fastcar, addressing latency and resource constraints in mobile platforms. Collaborations span academia and industry, driving innovations in superconducting circuits and neuromorphic systems.

José F. Martínezمشاهده پروفایل

استاد

• Computer Architecture
• Microprocessor Design
• Multiprocessor and Multicore Architectures
• +۵ مورد دیگر

José F. Martínez holds the Lee Teng-hui Professorship of Engineering at Cornell University's College of Engineering, where he leads research in computer architecture and systems. His roles include Vice Chair of ACM SIGARCH, IEEE Fellow, and past leadership roles in ISCA, MICRO, and IEEE Computer Society journals. He has received prestigious awards such as the NSF CAREER Award, IBM/Qualcomm Faculty Awards, and multiple teaching accolades including Tau Beta Pi Professor of the Year (2011). Education: Licenciado en Informática de Sistemas (1996), Universidad Politécnica de Valencia M.S. (1999) and Ph.D. (2002) in Computer Science, University of Illinois at Urbana-Champaign His research focuses on computer architecture , including microprocessors, multiprocessors, memory subsystems, embedded systems, and processing-in-memory (PIM) innovations. He co-leads the DARPA/SRC ACE Center for Evolvable Computing and NSF's CROPPS Center, and previously co-founded Cornell's Institute for Digital Agriculture (CIDA). His work bridges hardware-software co-design and sustainable computing. Key Contributions: Advancing energy-efficient multiprocessor architectures Developing PIM frameworks like PUMICE and Membrane Market-based resource allocation systems (e.g., XChange, ReBudget) His advising spans over 15 graduate students and has guided Merrill Presidential Scholars. Current research explores evolvable computing paradigms and AI hardware acceleration, reflecting his dual focus on foundational architecture and applied systems innovation.

Junjian Qiمشاهده پروفایل

دانشیار

• Electric power systems
• Cascading failure
• Microgrid control
• +۲ مورد دیگر

Junjian Qi serves as the Hohbach Endowed Associate Professor in the Department of Electrical Engineering and Computer Science at South Dakota State University's College of Engineering, holding this position since 2023. His academic journey includes prior appointments as Assistant Professor at Stevens Institute of Technology (2020-2023) and University of Central Florida (2017-2020), along with research roles at Argonne National Laboratory and University of Tennessee. His educational background includes: Ph.D. in electrical engineering from Tsinghua University, Beijing, China (2013) B.E. in electrical engineering from Shandong University, Jinan, China (2008) Dr. Qi's research centers on electric power systems resilience, with particular expertise in cascading failure mechanisms, microgrid control architectures, cyber-physical security vulnerabilities, and synchrophasor applications. His work integrates advanced data analytics and machine learning techniques to enhance grid stability against extreme weather events and cyber threats. Current investigations focus on developing distributed control strategies for inverter-dominated grids and modeling system interdependencies during failure propagation. Analysis of his 15 most recent publications (2021-2024) reveals a strong methodological shift toward data-driven approaches for power system challenges. Key trends include machine learning applications for cascading failure prediction, novel distributed control frameworks for AC/DC microgrids, and cybersecurity enhancements for inverter-based resources. His work consistently bridges theoretical models with real-world utility data, particularly evident in multiple Best Paper Award-winning publications analyzing actual outage sequences. Dr. Qi's scientific recognition includes: NSF CAREER Award (2020) Three consecutive Best Paper Awards at IEEE PES General Meetings (2022-2024) World's Top 2% Scientist designation in energy (2020-2023) IEEE PES Outstanding Working Group Award (2023) Multiple journal Best Paper Awards (IEEE Transactions on Power Systems, Journal of Modern Power Systems) He currently leads significant research initiatives including an NSF CAREER project ($500k) on cascading failure analysis and an NSF collaborative grant ($219k) for grid stability, alongside previous DOE funding ($1.8M) for cybersecurity of distributed energy resources. His service includes editorial roles for IEEE Transactions on Power Systems and IEEE Power Engineering Letters, plus leadership in IEEE PES technical committees focused on voltage control and smart grid security.

Kostas Bekrisمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Kostas Bekris is a Professor in the Department of Computer Science at Rutgers University, specializing in Robotics and Artificial Intelligence. His research focuses on motion planning, autonomous manipulation, and robot control, with notable contributions to tensegrity robotics, perception-driven systems, and large-scale package handling. He leads a team conducting groundbreaking work in robotics, supported by grants from NSF, NASA, and industry collaborators like ExxonMobil. His group emphasizes interdisciplinary approaches, combining machine learning, topological methods, and differentiable physics modeling to advance robot capabilities in complex environments. Education details are not explicitly stated in the provided texts, but his academic career has included significant mentorship of PhD students and postdoctoral researchers. Key projects involve vision-driven manipulation pipelines, obstacle detection systems (PROBE), and resilient robot designs inspired by biological structures. He has been recognized for his work through prestigious awards including the NASA Early Career Grant and multiple NSF grants, as well as team achievements in robotics competitions like the Amazon Picking Challenge. Research interests span robotics subfields such as: Autonomous manipulation in cluttered environments Learning-based control for dynamic systems Topological data analysis for motion reasoning Tensegrity and soft robotics architectures Sim-to-real transfer in robotic tasks His team's work has produced open-source software tools and datasets, advancing benchmarks in manipulation and perception. Recent articles emphasize scalable solutions for industrial automation and robust navigation strategies in unstructured settings. Scientific achievements include: Development of PROBE for proprioceptive obstacle detection Advances in differentiable physics engines for tensegrity systems NSF-funded projects on robotic rearrangement and modular morphologies Advising contributions span over a decade, with current advisees focusing on topics like non-prehensile manipulation and large-scale storage optimization. Collaborations with industry (e.g., ExxonMobil) and academic partners (Yale University) reflect his commitment to applied robotics research. Labs and teams under his leadership include the Rutgers CS Robotics Group, contributing to projects like the ARIAC challenge platform and packing/industrial automation systems. Future work targets improved robot resilience in disaster scenarios and enhanced human-robot collaboration paradigms.

Antti Honkelaمشاهده پروفایل

استاد

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +۳ مورد دیگر

Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.

• AI for Business
• Data Science
• Responsible AI
• +۵ مورد دیگر

David Martens is a Professor of Data Science at the University of Antwerp , where he directs the Applied Data Mining Research Group within the Faculty of Business and Economics . He also serves as Chair of the Department of Engineering Management and Director of the Antwerp Center on Responsible AI . His academic work spans data mining , interpretable machine learning , and the societal impact of AI . PhD in Applied Economic Sciences (KU Leuven, 2008) Director, Antwerp Center on Responsible AI Chair, Department of Engineering Management Martens' research focuses on responsible AI and data ethics , with applications in finance, public policy, and behavioral analysis. His recent publications emphasize counterfactual explanations , LLM interpretability , and privacy implications in AI systems. His articles reveal trends in Explainable AI (XAI) , including narrative-driven explanations , graph neural networks , and ethical challenges like monetization risks and algorithmic bias. Keywords span Computer Science , Artificial Intelligence , and Behavioral Data . Martens is a leading voice in data science ethics , authoring the book Data Science Ethics: Concepts, Techniques, and Cautionary Tales (Oxford University Press, 2022). He combines academic rigor with industry experience, having consulted for banks, telecom firms, and startups in fraud detection and digital advertising .

Mohamed Amaraمشاهده پروفایل

استاد

• Partial Differential Equations
• Mechanics in Porous Media
• Fluid Mechanics
• +۵ مورد دیگر

Mohamed Amara is a full-time Professor at the University of Pau and the Pays de l'Adour (UPPA) since 1996, affiliated with the Laboratory of Mathematics and their Applications (CNRS-UMR 5142). He served as its director (1999-2007), Director of the Doctoral School of Exact Sciences (ED211, 2007-2008), and UPPA's Scientific Council Vice-President (2008-2012). He has been UPPA's President since 2012 (re-elected until 2020). Education: Mathematics from University of Algiers (1973), Pierre and Marie Curie University (DEA 1974, Doctorate 1978, State Doctorate 1983) Academic Roles: Research Associate at Ecole Polytechnique (1978-1982), Algerian Electricity and Gas Company (1983-1992), Professor in Algiers (1988-1994), Tunis (1994-1995), and Associate Professor at Paris 6 (1995-1996) His research focuses on numerical simulation of partial differential equations for environmental/energy applications, including mechanics in porous media (petroleum engineering, geoscience), fluid mechanics (aerodynamics, estuarine hydrodynamics), non-Newtonian flows, and wave propagation. Articles highlight expertise in discontinuous Galerkin methods, Helmholtz problems, finite element discretization, and multiphysics systems. He managed 20 doctoral theses and led national mathematics programs at ANR (2007-2011). He chairs the Cocktail association for higher education IT systems and collaborates with INRIA's Magique 3D team (since 2006).

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.

• Cryptography
• Computer Security
• Blockchain Technology
• +۴ مورد دیگر

Elaine Shi is a Professor with a joint appointment in the Department of Computer Science (CSD) and the Department of Electrical and Computer Engineering (ECE) at Carnegie Mellon University (CMU). She is also an Adjunct Professor of Computer Science at the University of Maryland. Her research focuses on cryptography, security, mechanism design, algorithms, blockchains, and programming languages. Shi co-founded Oblivious Labs, Inc., and her work on Oblivious RAM and differentially private algorithms has been adopted by major companies like Signal, Meta, and Google. Affiliations: CMU’s Crypto Group CMU’s Cylab Crypto Seminar Series (co-founder) Research Interests: Cryptography and Security: Focused on oblivious computation, private information retrieval, and secure protocols. Blockchain Technology: Including consensus mechanisms, transaction fee design, and decentralized systems. Privacy-Preserving Algorithms: Such as differential privacy and secure multi-party computation. Algorithm Design: With applications to parallel computing and efficient data structures. Awards: Packard Fellow Sloan Fellow ACM Fellow IACR Fellow Advising & Grants: Current advisees include PhD students like Benjamin Chan, Yanyi Liu, Mingxun Zhou, and Nikhil Vanjani. Past students have taken academic roles at institutions like UVA, University of Michigan, and Duke University. Grants supported research in secure computation, blockchain mechanisms, and privacy-preserving technologies. Labs & Teams: Co-founder of Oblivious Labs, Inc. Leading CMU’s Crypto Group and Cylab Crypto Seminar Series.

Aram Harrowمشاهده پروفایل

استاد

• Quantum Information Science
• Quantum Algorithms
• Quantum Computing
• +۳ مورد دیگر

Aram Harrow is a Professor of Physics at the Massachusetts Institute of Technology (MIT) , affiliated with the MIT Center for Theoretical Physics and MIT Center for Quantum Engineering . He focuses on quantum information science and quantum algorithms , with additional interests in representation theory and optimization . His recent work explores quantum computing applications in chemical physics and statistical mechanics . Undergraduate and graduate degrees in Physics at MIT Faculty positions: MIT (2013-present), University of Washington (2010-12), University of Bristol (2005-10) Research Interests: His work bridges quantum information theory and many-body physics , including: Quantum algorithm design for chemistry and optimization Quantum circuit complexity and t-designs Entanglement dynamics in quantum systems Quantum-classical hybrid computing models Key Publications: Recent articles demonstrate quantum speedups for biomolecular free energy calculations , Hamiltonian simulation , and jet clustering algorithms . His research combines quantum complexity theory with practical implementations on near-term quantum devices. Scientific Awards: 2023 Simons Investigator 2018 APS Bennett Award 2017 IEEE Best Paper Award 2016 Kavli Frontiers Fellow Mentorship: He advises current PhD students Shankar Balasubramanian , Angus Lowe , and Norah Tan , with 12 former advisees including Anand Natarajan and Saeed Mehraban . His 2026 recruitment seeks one new graduate student.

• Trustworthy Machine Learning
• Conformal Prediction
• Explainable Artificial Intelligence
• +۲ مورد دیگر

Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.

Roghayeh (Leila) Barmakiمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Multimodal Machine Learning
• Virtual Reality
• +۵ مورد دیگر

Roghayeh (Leila) Barmaki is an Assistant Professor in the Department of Computer & Information Sciences at the University of Delaware, where she leads the Human-Computer Interaction Lab (HCI@UD) and is affiliated with the Data Science Institute. Her research centers on Human-Centered Computing with applications in healthcare and medical education. Education: Postdoctoral Fellowship (2018): Johns Hopkins University, Laboratory for Computational Sensing and Robotics, Whiting School of Engineering PhD (2016): University of Central Florida, Computer Science MSc (2012): Iran University of Science and Technology, Artificial Intelligence BSc: Kharazmi University, Software Engineering Her research spans Multimodal Machine Learning, Virtual and Augmented Reality, and Embodied Interface Design, focusing on innovative applications in healthcare delivery and science education. She teaches core courses including Introduction to Human-Computer Interaction (CISC 482/682) and Introduction to Data Mining (CISC 483/683), emphasizing hands-on project development and theoretical foundations. Dr. Barmaki directs the HCI@UD laboratory, which develops cutting-edge interfaces for medical training and patient care through interdisciplinary collaboration between computer science, engineering, and healthcare domains.

Roberto Manduchiمشاهده پروفایل

استاد

• Computer Vision
• Assistive Technology
• Human-Computer Interaction
• +۲ مورد دیگر

Roberto Manduchi is a Professor of Computer Science and Engineering at the University of California, Santa Cruz, within the Baskin School of Engineering. His primary affiliation is with the Computer Science and Engineering department where he leads research in assistive technology for visual impairments. He holds a Dottorato di ricerca in Electrical Engineering from the University of Padova, Italy, and previously worked at Apple and NASA JPL before joining UCSC in 2001. His research focuses on mobile computer vision, inertial sensors, and location-aware systems to enhance spatial awareness and information access for blind and low-vision individuals. Key research areas include indoor navigation systems, screen magnification for low-vision readers, obstacle detection using augmented reality, and text accessibility assessment through specialized OCR pipelines. His work bridges computer vision, human-computer interaction, and accessibility design. Analysis of his recent publications (2022-2025) reveals strong emphasis on inertial-based indoor navigation (e.g., PALMS localization system, backtracking algorithms), screen magnification usability studies, and novel approaches to scene text access for blind users. His research consistently targets practical applications for visual impairment, with significant contributions to pedestrian dead reckoning, magnetic signature localization, and gaze-contingent interfaces. Manduchi serves on the scientific advisory board of Aira and is a board member of the Vista Center for the Blind and Visually Impaired. He leads the UCSC Computer Vision Lab where his team develops accessible computing solutions. His work includes both theoretical contributions to computer vision and tangible assistive applications, with recent projects focusing on smartphone-based inertial odometry, multi-scale tactile maps, and real-time obstacle cueing systems.