جستجوی استاد راهنمای دانشگاهی

Paul E. Handمشاهده پروفایل

دانشیار

• Applied Mathematics
• Compressed Sensing
• Deep Learning
• +۷ مورد دیگر

Paul E. Hand is an Associate Professor in the Department of Mathematics and Computer Science at Northeastern University, affiliated with the College of Science and Khoury College of Computer Sciences. He holds a Ph.D. in Mathematics from New York University (2009) and specializes in signal recovery, deep learning, and optimization. His research focuses on developing mathematical frameworks for inverse problems, phase retrieval, and generative models with provable guarantees. He has taught advanced courses in machine learning, algorithms, and deep learning at Northeastern and Rice University. Education: Ph.D. in Mathematics, New York University (2009); M.S. and B.S. in Mathematics, not explicitly stated but inferred from academic trajectory. Research Interests: Applied mathematics, compressed sensing, deep learning, phase retrieval, signal recovery, and optimization. His work bridges theory and practice, addressing challenges in imaging, robustness, and generative modeling. Grants: NSF CAREER Grant DMS-1848087 (2018). Outreach: Directed STEM summer camps at Rice University (2017) and developed LeadingLesson , a platform for multivariable calculus problem-solving resources. Teaching: Courses include Machine Learning (CS 6140), Deep Learning (CS 7150), Algorithms (CS 3000), and Analysis at Northeastern and Rice. He emphasizes rigorous proof techniques and pedagogical innovation. Labs/Teams: Collaborates with researchers in computational mathematics, computer vision, and machine learning. Active in authoring peer-reviewed papers and reviewing for top conferences (NeurIPS, ICML, ECCV).

Ramana Vinjamuriمشاهده پروفایل

دانشیار

• Brain-Machine Interfaces
• Neuroprosthetics and Exoskeletons
• Machine Learning
• +۴ مورد دیگر

Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Ling Zhao is a distinguished Professor at the School of Management, Huazhong University of Science and Technology, China, with extensive research contributions spanning artificial intelligence, machine learning, information systems, and biomedical applications. With over 150 publications since 2008, Dr. Zhao has established herself as a leading researcher in multiple interdisciplinary domains, particularly in applying computational methods to solve complex real-world problems. Dr. Zhao's research interests encompass a broad spectrum of cutting-edge topics including artificial intelligence, machine learning, data mining, control systems, and information systems. Her work demonstrates exceptional versatility, bridging theoretical computer science with practical applications in healthcare, transportation, cybersecurity, and business management. Notably, she has made significant contributions to sentiment analysis, medical image processing, algorithmic management, and privacy-preserving data analysis. Her research methodology often combines deep learning approaches with domain-specific knowledge to develop innovative solutions. Analysis of Dr. Zhao's recent publications (2023-2025) reveals a strong focus on interdisciplinary applications of AI, with particular emphasis on healthcare informatics (medical image analysis, disease diagnosis), human-computer interaction (algorithmic management effects), and advanced machine learning techniques (graph neural networks, multimodal learning). Her work shows a consistent trend toward increasingly complex and integrated systems that address real-world challenges across multiple domains. Dr. Zhao has made substantial contributions to academic advising and research mentorship, though specific student names aren't detailed in the available publications. Her research has been supported by various grants enabling work in AI applications, biomedical engineering, and information systems. Dr. Zhao maintains active collaborations with researchers across China and internationally, as evidenced by her co-authorship patterns. While specific laboratory information isn't explicitly mentioned in the publication records, Dr. Zhao appears to lead or be significantly involved in research groups focusing on AI applications in management and healthcare. Her work on medical imaging, sentiment analysis, and control systems suggests involvement in multiple specialized research teams addressing different application domains through computational approaches.

Sandrine Dudoitمشاهده پروفایل

استاد

• Statistical Methodology
• Computational Biology
• Genomics
• +۵ مورد دیگر

Sandrine Dudoit is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She earned her PhD in Statistics from UC Berkeley in 1999 and joined the faculty in 2001. Her research focuses on statistical methodology and computing with applications to genomics, biomedical research, and precision health. She co-founded the Bioconductor Project , an open-source software initiative for biological data analysis, and leads interdisciplinary projects in single-cell transcriptomics and computational biology. Education: PhD in Statistics (UC Berkeley, 1999), M.Sc. in Mathematics (Carleton University, Canada). Research interests include high-dimensional statistical learning, single-cell RNA-Seq analysis, stem cell differentiation in the olfactory system, and statistical computing. She collaborates with biologists like John Ngai to study neuroepithelial regeneration using cutting-edge sequencing technologies. Recent work emphasizes trajectory inference, biomarker discovery, and methodological advances in handling high-dimensional genomic data. Her lab develops tools for normalization, clustering, and differential expression analysis in large-scale biological datasets. She teaches courses on statistical genomics and serves as a leader in UC Berkeley’s Division of Computing, Data Science, and Society (CDSS). Advising: Supervises PhD students in statistical methodology, computational biology, and bioinformatics. Grants: Active in securing funding for interdisciplinary research projects in genomics and data science. Labs/Teams: Core member of the Center for Computational Biology (CCB) and contributes to the Bioconductor community.

Abani Patraمشاهده پروفایل

استاد

• Computational Sciences
• Data Driven Modeling
• Computer Science
• +۵ مورد دیگر

Abani Patra is a Professor of Computer Science, Mathematics, Mechanical Engineering, and Civil and Environmental Engineering at Tufts University. He also serves as the Center Director for Data Science at the Tufts Institute for Artificial Intelligence (TIAI). His research focuses on computational sciences and data-driven modeling, with applications spanning environmental systems, biomedical imaging, and geophysical hazards. He has directed major initiatives at the National Science Foundation (NSF) and U.S. Department of Energy (DOE), and previously founded the Institute for Computational and Data Sciences at the University at Buffalo. Education: PhD in Mathematics, University of Texas, 1995 MS in Mechanical Engineering, University of Missouri, 1990 BSc in Engineering, Birla Institute of Technology & Science, India Research Interests: Large-scale computational modeling and uncertainty quantification Data-driven approaches for geophysical hazards (e.g., debris flows, volcanic eruptions) Biomedical imaging and metabolic analysis Open science platforms for glaciology and volcanology Key Projects: Developed the Ghub platform for open cryosphere research Launched VICTOR, a cyberinfrastructure for volcanology Advanced AI-driven techniques for postfire debris flow prediction Grants & Leadership: Directed NSF and DOE programs in computational science PI for NSF Cyberinfrastructure grants Former director of the Institute for Computational and Data Sciences

Muhammad Azmi UMERمشاهده پروفایل

مدرس

• Machine Learning
• Data Science
• Cyber Physical Systems
• +۳ مورد دیگر

Dr. Muhammad Azmi UMER is a Lecturer at DHA Suffa University and a Ph.D. Scholar at Karachi Institute of Economics and Technology, Pakistan. His research focuses on Machine Learning applications in Cyber Physical Systems (CPS), particularly intrusion detection in industrial control systems like the SWaT testbed. He holds a Master’s in Computer Science from Karachi Institute of Economics and Technology and a Bachelor’s from the University of Karachi. His academic work emphasizes cybersecurity challenges in smart grids, IoT healthcare systems, and adversarial machine learning techniques. Key contributions include developing decision tree-based intrusion detection frameworks and adversarial attack simulations for industrial systems. He collaborates with researchers like Dr. Jit BISWAS and Dr. Eyasu G. CHEKOLE within interdisciplinary teams. Publications span machine learning applications in smart cities, CPS security protocols, and IoT conceptual frameworks. His research bridges theoretical models with practical implementations in critical infrastructure security and urban technology systems.

Onur Mutluمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Hardware Security
• +۵ مورد دیگر

Onur Mutlu is a Professor of Computer Science at ETH Zurich, affiliated with the Department of Information Technology and Electrical Engineering. He also holds adjunct professorships at Carnegie Mellon University and Bilkent University. His research focuses on computer architecture, systems security, bioinformatics, and energy-efficient computing. He has pioneered work on memory-centric computing paradigms, RowHammer security vulnerabilities, and bio-inspired computing systems. He teaches courses such as Digital Design & Computer Architecture and supervises the SAFARI research group, which explores cutting-edge topics in memory systems, AI accelerators, and genomics. Recent activities include keynote talks at ISCA, HiPEAC, and IEEE conferences, emphasizing emerging hardware-software co-design principles. Key contributions include foundational work on memory reliability, cross-layer system design, and accelerating genomic data analysis. His research has been showcased in over 200 publications and industry collaborations with tech leaders like Intel, Huawei, and Micron.

George Chalhoubمشاهده پروفایل

استادیار

• Cybersecurity
• Privacy and Data Protection
• Human-Computer Interaction
• +۱۲ مورد دیگر

Dr. George Chalhoub is a Lecturer (Assistant Professor) in Human-Computer Interaction at the UCL Interaction Centre (UCLIC), Department of Computer Science, University College London (UCL). He is also an Associate Member at the Department of Computer Science, University of Oxford, and a 2024–2025 Berkman Klein Fellow at the Berkman Klein Center for Internet & Society, Harvard Law School, Harvard University. His multidisciplinary research bridges cybersecurity, privacy, and human-centered computing, focusing on real-world technology use. DPhil in Cyber Security, University of Oxford (supported by Information Commissioner’s Office) MSc in Computer Science, University of Southampton (supported by Lloyd’s Register) BS in Computer Science, Lebanese American University His research centers on the security, privacy, and safety of digital technologies through a user-centered lens. Key areas include AI-powered systems (e.g., LLMs, smart assistants), emerging technologies in the wild (e.g., smart homes, IoT), embedded devices (e.g., routers), marginalized communities, data workers in AI, and online content creators. His work integrates UX principles to improve data protection in healthcare (e.g., NHS records) and children’s apps, with implications for GDPR compliance and responsible AI innovation. The analysis of his recent publications reveals a consistent focus on empirical studies of user experience in security and privacy, particularly in smart homes and data-intensive applications. His work spans design interventions, ethical frameworks, and policy-relevant findings, published in top venues like CHI, CSCW, SOUPS, and IJHCS. Themes include consent design, communal privacy, vulnerability patching, and developer support for privacy. UK Global Talent Visa recipient, UK Research and Innovation 2024–2025 Berkman Klein Fellow, Harvard University Dr. Chalhoub has advised on research projects related to secure networking by design and responsible AI (e.g., EWADA, RoboTIPS). He has received grant support from the Information Commissioner’s Office for his doctoral work. He is available for consultancy, collaborative research, grant assessment, and supervision of research degrees. His professional experience includes internships at Microsoft Research (Calc Intelligence) and Nokia Bell Labs (Social Dynamics), contributing to projects in AI and social computing. He is affiliated with research groups including the Human-Centered Computing group at Oxford, the UCL Interaction Centre (UCLIC), and the Berkman Klein Center at Harvard. His work is supported by tools and frameworks developed in collaboration with interdisciplinary teams focused on cybersecurity ethics, data governance, and platform accountability.

Edward Kimمشاهده پروفایل

دانشیار

• Computer Vision
• Sparse Coding
• Neuromorphic Computing
• +۱۲ مورد دیگر

Edward Kim is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics. His research spans computer vision, sparse coding, neuromorphic computing, and AI, with a focus on neuro-inspired machine learning and robust, interpretable models. Research Interests: Computer Vision Sparse Coding and Dictionary Learning Neuromorphic and Spiking Neural Networks Explainable and Adversarially Robust AI Multimodal Learning Medical Image Processing His recent publications highlight a strong trend in developing biologically inspired, robust, and interpretable machine learning models, particularly using sparse coding and spiking neural networks. Themes include adversarial robustness, model confidence calibration, and cross-modal integration. His work often bridges neuroscience and AI, aiming to create more human-like and trustworthy systems. Scientific Awards: NSF CAREER Award (2019) Longsview Fellow (collaborative project, 2021) Dr. Kim advises several graduate students in the SPARSE Lab and has secured significant research funding from the NSF, DARPA, and the Bill & Melinda Gates Foundation. His grants focus on ethical AI, racial bias in ML, and digital health platforms. He also contributes to academic leadership as a Provost Fellow at the Drexel Solutions Institute and co-chair of computer vision tracks at major conferences. Labs and Teams: He leads the SPARSE (SPiking And Recurrent SOFTwarE) Coding Lab, which investigates biologically inspired learning models beyond traditional deep learning. The lab integrates neuroscience principles to improve stability, interpretability, and robustness in AI systems.

Matt Bonneyمشاهده پروفایل

مدرس

• Digital Twins
• Nonlinear Structural Dynamics
• Mechanical Joint Modelling
• +۴ مورد دیگر

Dr. Matt Bonney is a Lecturer in Space Engineering at Swansea University, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds a position in the Department of Aerospace Engineering and is actively involved in postgraduate supervision. His research focuses on digital twin technology, nonlinear structural dynamics, mechanical joint modeling, seismic reliability, and uncertainty quantification, with recent emphasis on digital twin security and thermo-mechanical coupling in assembled structures. Dr. Bonney's expertise spans multi-physics joint modeling and multi-disciplinary development of digital twins, with international collaborations. He teaches modules such as 'Advanced Space Systems' (EG-M334) and 'Aerospace Systems' (EGA220), emphasizing space system design, orbital mechanics, and cyber-physical security. His research highlights include the development of a Python Flask-based digital twin operational platform, contextualization of information in digital twin processes, and experimental studies on frictional interfaces. His work on uncertainty quantification and seismic reliability has applications in nuclear reactor systems and civil engineering structures. Dr. Bonney currently supervises a PhD student focusing on nonlinearities in thermal-mechanical joints. His research outputs include over 30 peer-reviewed publications, with contributions to journals like Mechanical Systems and Signal Processing and Data-Centric Engineering .

Éric Tanterمشاهده پروفایل

استاد

• Programming Languages
• Gradual Typing
• Software Engineering
• +۹ مورد دیگر

Éric Tanter is a Full Professor in the Computer Science Department (DCC) at the University of Chile, where he also leads the PLEIAD Lab. He holds an Inria International Chair (2025–2029) and is an Associate Researcher at the Millenium Institute for Foundational Research on Data (IMFD). His academic leadership includes prior roles as Director and Deputy Director of the DCC, and Coordinator of the PhD Program in Computer Science. His research centers on programming languages and software engineering, with a strong focus on gradual typing, type systems, program verification, and secure programming. He explores both theoretical foundations and empirical practices, aiming to bridge the gap between static and dynamic language paradigms. His recent work emphasizes gradual verification, refinement types, and applications in proof assistants and security. The trends in his recent publications show a deep engagement with foundational aspects of gradual and dependent typing, often aiming to enhance the reliability and security of software systems. These works frequently involve formalization in proof assistants like Coq and explore applications in differential privacy, secure interoperability, and symbolic execution. Inria International Chair (2025–2030) Best Paper Awards at POPL 2019, OOPSLA 2018, ICFP 2018, MSR 2011, AOSD 2010, SBLP 2008, DAIS 2006 Most Influential/Most Notable Paper Awards at DLS, Programming, DLS Facebook Research Testing and Verification Award (2018) Google Faculty Research Awards (2015, 2016) Best Professor Award, University of Chile (2011) Éric Tanter has advised numerous PhD and Master’s students, many of whom have gone on to publish influential work in top venues. He has led multiple research projects funded by FONDECYT, ANID, INRIA, and other national and international agencies. His service includes editorial roles in journals such as the Journal of Functional Programming and Science of Computer Programming, and extensive participation in program committees of major conferences like POPL, ICFP, and OOPSLA. He leads the PLEIAD Lab at the University of Chile, a research group focused on programming languages, software engineering, and formal methods. The lab fosters collaboration with international institutions and emphasizes both theoretical rigor and practical impact.

• Multi-access Edge Computing
• High-Performance Networks
• Cognitive IoT Systems
• +۷ مورد دیگر

Prof. Ahmed Al-Dubai is a Professor at the School of Computing, Engineering and the Built Environment, Edinburgh Napier University, where he leads the IoT and Networked Systems Research Group and serves as Cybersecurity and Cyber Physical Systems Research Lead. His interdisciplinary research spans Multi-access Edge Computing, High-Performance Networks, Cognitive IoT Systems, VANETs, AI, E-Health, Smart Cities, and Security . He earned his PhD in Computing Science from the University of Glasgow in 2004. His recent publications focus on edge computing architectures, digital twin security, Arabic NLP, wireless energy harvesting, and vehicular communication . His work has been recognized with IEEE Outstanding Service Award, Best Paper Awards at IEEE IUCC 2015 and ACM MoMM 2013 , and fellowships like Senior IEEE Member and British Higher Education Academy Fellow . He supervises 20+ PhD students and has served on 60+ IEEE/ACM conference committees. Current projects include AI-driven fish identification (Innovate UK £265K) , secure IoT protocols (Royal Society £12K) , and COG-MHEAR (EPSRC £3.26M) . He has held visiting professorships at Universite de Valenciennes, University of Sydney, and University of Shenyang.

Abhik Roychoudhuryمشاهده پروفایل

استاد

• Software Testing
• Program Analysis
• Program Repair
• +۴ مورد دیگر

Abhik Roychoudhury is a Provost's Chair Professor of Computer Science at the National University of Singapore (NUS), leading the Trustworthy and Secure Software (TSS) research group since 2001. His work focuses on automated program repair, software testing, security, and agentic AI. He is a Senior Advisor at SonarSource following the acquisition of his startup AutoCodeRover. He holds an ACM Fellowship and has received the ICSE Most Influential Paper Award for program repair research. Education: M.S. and Ph.D. in Computer Science from State University of New York at Stony Brook (1997-2000). Research interests include program analysis, software security, and AI-driven software engineering. His team has pioneered techniques like SemFix and Angelix for program repair, and AFLNet for protocol fuzzing. He has served as editor-in-chief of ACM TOSEM and conference chair for ICSE and FSE. Awards include the NUS Outstanding Graduate Mentor Award (inaugural recipient) and IEEE New Directions Award. His work bridges academia and industry, with contributions to projects like the DesCartes initiative for critical urban systems. Key collaborations include Microsoft on API repair and IBM on AI research centers. His recent focus includes agentic AI for software engineering, reflected in AutoCodeRover's acquisition by SonarSource.

Lingming Zhangمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Machine Learning
• +۶ مورد دیگر

Lingming Zhang is an Associate Professor at the Department of Computer Science, University of Illinois Urbana-Champaign, affiliated with the Grainger College of Engineering. His research focuses on the intersection of Software Engineering, Programming Languages, and Machine Learning, with a particular emphasis on automated program repair, compiler testing, and large language model (LLM) applications in software engineering. He has published over 100 papers, achieving an h-index of 50+, and holds an ACM Distinguished Member status. Research Interests: LLM-based software testing, repair, and synthesis Fuzzing of deep-learning libraries and compilers Open-source code LLMs (e.g., StarCoder2, Magicoder) with over 1M downloads Automated program repair systems (e.g., AlphaRepair, ChatRepair, Agentless) Recent Contributions: Developed TitanFuzz for coverage-guided compiler fuzzing Released Agentless , an LLM-based coding tool adopted by OpenAI and DeepSeek Proposed SWE-RL to enhance LLM reasoning via reinforcement learning Service Roles: Program Co-Chair for ASE 2025 and LLM4Code 2025 Associate Chair for OOPSLA 2024 and Area Chair for ICSE 2025/2026 Recipient of NSF CAREER Award and ACM SIGSOFT Early Career Award Lab/Teams: Develops open-source tools like UniAPR for efficient patch validation Active in releasing industry-adopted LLM-based software engineering tools

John D. Kubiatowiczمشاهده پروفایل

استاد

• Computer Architecture
• Quantum Computing
• Distributed Systems
• +۷ مورد دیگر

Professor John D. Kubiatowicz is a faculty member at the University of California at Berkeley in the Department of Electrical Engineering and Computer Sciences since 1998. He holds a PhD in Electrical Engineering and Computer Science (minor in Physics) from MIT (1998), an M.S. in EECS (1993), and a double B.S. in Electrical Engineering and Physics (1987) from MIT. His research interests span Quantum Computing Architectures Distributed Systems and Storage Network Security and Peer-to-Peer Protocols Introspective and Manycore Operating Systems Edge and Fog Computing Hardware-Assisted Security He has pioneered systems like OceanStore , a global-scale distributed file system, and Tessellation , a manycore OS with continuous adaptation. The scientific awards he has received include Presidential Early Career Award (PECASE, 2000) Scientific American 50 (2002) Diane S. McEntyre Teaching Award (2003) IEEE ICRA Best Paper (2025) George M. Sprowls Award for MIT PhD thesis (1998) Okawa Research Grant (1998) Best Paper at International Conference on Supercomputing (1993) His recent publications focus on Quantum Circuit Design and Optimization Edge/Fog Computing Architectures Secure Runtime Systems Distributed Garbage Collection Manycore OS Innovations Hardware-Assisted Security Mechanisms He leads the Quantum Architecture Research Center and co-founded the SWARM Lab at Berkeley, advancing a vision of self-adapting, secure systems from the chip level to internet scale.