جستجوی استاد راهنمای دانشگاهی

Scott A. Haleمشاهده پروفایل

دانشیار

• Human-Computer Interaction (HCI)
• Machine Learning
• Natural Language Processing (NLP)
• +۷ مورد دیگر

Dr Scott A. Hale is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute (OII), University of Oxford, and a Fellow of the Alan Turing Institute. His work bridges computer science and social sciences, focusing on equitable information access, multilingual online dynamics, and misinformation mitigation. He holds degrees in Computer Science, Mathematics, and Spanish from Eckerd College, followed by a DPhil (PhD) in Social Data Science from the OII. Hale’s research has been supported by grants from UK Research and Innovation, the US National Science Foundation, and organizations like the Omidyar Network and the Alan Turing Institute. Key Roles: Programme on AI, Government & Policy; Director of Research at Meedan; Co-Director of the Social Data Science MSc Research Focus: Misinformation, multilingual systems, social media impact, and AI ethics Education: Eckerd College (BS), OII (MSc, DPhil). His DPhil explored social media design’s role in cross-language information sharing. Recent projects include the Digital Good Network and AI alignment studies. Articles highlight trends in multilingual misinformation detection, LLM cultural biases, and hate speech dynamics. Hale’s work bridges technical innovation with social science rigor to address global digital challenges. Awards: Alan Turing Institute Fellowship, recognition in Oxford’s Teaching Excellence Awards. Grants: Over 20 funding sources including DSO National Laboratories and Meta.

Tianhao Wangمشاهده پروفایل

استادیار

• Differential Privacy
• Machine Learning Privacy
• Data Security
• +۳ مورد دیگر

Tianhao Wang is an Assistant Professor in the Department of Computer Science at the University of Virginia School of Engineering and Applied Science. His work focuses on advancing differential privacy and machine learning privacy, with particular expertise in privacy-preserving technologies for data synthesis, adversarial machine learning, and secure AI systems. His research interests span differential privacy mechanisms, secure data sharing, and mitigating privacy risks in modern AI systems. He explores how to protect sensitive information in machine learning models, synthetic data generation, and network analysis while maintaining utility. Recent work highlights include developing benchmarks for private image synthesis (DPImageBench), safeguarding text data from misuse (ExpShield), and analyzing privacy threats in pre-trained language models. His publications reflect a strong emphasis on both theoretical foundations and practical applications of privacy-preserving techniques. Dr. Wang's contributions address cutting-edge challenges in AI ethics, secure machine learning, and privacy engineering, with implications for healthcare, cybersecurity, and data-driven decision-making systems.

• Artificial Intelligence
• Electronic Commerce
• Intelligent Agents and Multi-Agent Systems
• +۶ مورد دیگر

Thomas Tran is a Full Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa. He holds a Ph.D. in Computer Science from the University of Waterloo (2004) and a B.Sc. (Double Major in Mathematics and Computer Science) from Brandon University (1999). His research focuses on Artificial Intelligence, Electronic Commerce, Multi-Agent Systems, Trust and Reputation Modeling, and Recommender Systems. He has published over 80 refereed papers and supervised 27 graduate students (4 PhDs and 23 Masters). Education: Ph.D. in Computer Science, University of Waterloo (2004) B.Sc. (Double Major in Mathematics and Computer Science), Brandon University (1999) Research Interests: AI Applications in E-Commerce and Mobile Business Trust Establishment Models in Multi-Agent Systems Recommender Systems and Deep Learning Clinical Data Analysis for Hidradenitis Suppurativa Awards: Governor General's Gold Medal (2004) NSERC Postgraduate Scholarships (PGS A/B) AAAI Doctoral Consortium Participant (2002) Advising and Grants: Supervised 27 graduate students Recipient of multiple research grants (details unspecified) Labs/Teams: Active in AI and E-Commerce research groups within EECS.

Dilian Gurovمشاهده پروفایل

استاد

• Software Specification and Verification
• Multi-Agent Strategic Planning
• Formal Methods
• +۳ مورد دیگر

Dilian Gurov is a Professor in Computer Science at KTH Royal Institute of Technology, associated with the Digital Futures Faculty and the Division of Theoretical Computer Science. He also coordinates the Doctoral Programme in Computer Science at the CSC school. Before joining KTH in 2002, he earned a Ph.D. from the University of Victoria, Canada (1998), and worked at the Swedish Institute of Computer Science (1997-2002). His research focuses on software specification and verification, including contracts, program models, logics, and tools, as well as multi-agent strategic planning involving knowledge-based strategies in imperfect information settings. Key contributions include the CAV Distinguished Paper Award 2023 for 'Automatic Program Instrumentation for Automatic Verification' and an EASST award for 'Checking Absence of Illicit Applet Interactions: A Case Study' (2004). He leads projects funded by VR (SEFROS, ContraST) and Vinnova (AVerT2) and collaborates with industries like Scania on formal verification of C programs. His service roles span over 30 conference committees and organization roles, including PC memberships for iFM, TAP, and ISoLA. Teaching responsibilities include courses such as 'Formal Methods,' 'Program Semantics and Analysis,' and 'Knowledge in Games with Imperfect Information.' His work emphasizes practical applications of formal methods, bridging academic research with industry needs through collaborations and tool development (e.g., CVPP, ProMoVer, TriCo).

Ziquan Liuمشاهده پروفایل

مدرس

• Reliable Machine Learning
• Uncertainty Quantification
• Computer Vision
• +۴ مورد دیگر

Dr. Ziquan Liu is a Lecturer (Teaching & Research) at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Multimodal AI. He holds a PhD from City University of Hong Kong (2023) and dual B.Sc./B.Eng. degrees from Beihang University (2017). His research focuses on trustworthy machine learning, adversarial robustness, and uncertainty quantification in foundation models. He has served as a reviewer for top conferences like NeurIPS, ICLR, and CVPR, earning an Outstanding Reviewer Award in 2021. His teaching includes modules on machine learning for visual data analysis and principles of machine learning. He supervises PhD students in AI safety and reliability, with notable work on conformal prediction, adversarial attacks, and multimodal learning. His research outputs span top venues such as ICML, CVPR, and NeurIPS, addressing challenges in algorithmic fairness, model certification, and cross-modal alignment.

Azhar Zamمشاهده پروفایل

دانشیار

• Biomedical Optics
• Optical Coherence Tomography (OCT)
• Photoacoustics
• +۵ مورد دیگر

Azhar Zam is an Associate Professor of Bioengineering at NYU Abu Dhabi (NYUAD) and associated faculty at NYU Tandon School of Engineering's Biomedical and Electrical Engineering departments. He holds a B.Sc. from University of Indonesia, M.Sc. from University of Luebeck (Germany), and Ph.D. from Friedrich-Alexander-University Erlangen-Nuremberg (Germany). His research focuses on developing smart optical devices for medical imaging/diagnostics, including laser surgery, OCT, photoacoustics, and AI-driven imaging systems. He leads the Laboratory for Advanced Bio-Photonics and Imaging (LAB-π) at NYUAD and has authored 85+ publications/patents. Education: Bachelor of Science, University of Indonesia M.Sc. Biomedical Engineering, University of Luebeck Ph.D. Engineering, Friedrich-Alexander-University Erlangen-Nuremberg Research Interests: Innovations in biomedical optics, optical-based smart sensors, AI-enhanced diagnostics, and miniaturized medical imaging systems. His work integrates advanced optical technologies with surgical robotics and clinical applications. Professional Contributions: Associate Editor for Frontiers in Photonics Biophotonics section; Reviews Editor for Frontiers in Ophthalmology Retina section. Previously held positions at University of Basel (Assistant Professor), University of Waterloo, and other institutions globally. Labs & Teams: Directs NYUAD's LAB-π lab focusing on bio-photonics innovations. Collaborates across NYU's global network and international partners.

Stephen Leeمشاهده پروفایل

استادیار

• Computer Systems
• Cyber-Physical Systems
• Data Mining and Data Science
• +۳ مورد دیگر

Stephen Lee is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with Pitt Cyber. His research focuses on distributed systems, cyber-physical systems, and sustainability, emphasizing energy efficiency and cost optimization. Dr. Lee holds a PhD from the University of Massachusetts Amherst, a Master’s from Chennai Mathematical Institute, and a Bachelor’s from St. Stephen’s College, Delhi. He actively seeks students for his research group. Education: PhD, Computer Science, University of Massachusetts Amherst Master’s, Chennai Mathematical Institute Bachelor’s, St. Stephen’s College, Delhi Research Interests: Dr. Lee’s work integrates distributed systems, machine learning, and optimization to enhance sustainability. Key areas include IoT-enabled energy systems, emission-aware computing, and privacy-preserving frameworks. He leads projects like GreenWhisk (serverless emission reduction) and Sat2map (3D building modeling from satellite imagery). Recent Achievements: Best Paper Award in IEEE TPS 2024 DOE-funded Cyber Energy Center (2024) MCSI Seed Grant for Pitt building sustainability (2024) NSF Grant on sustainable distributed infrastructures (2023) Grants & Advising: Secured over $2M in grants, including NSF and DOE funding. Advises on energy-efficient systems and IoT security. Teaches CS 2510 (Operating Systems) and CS 1699 (Systems & Sustainability). Labs & Teams: Directs the Sustainable Systems Research Group, focusing on decarbonizing IT and optimizing renewable energy systems. Collaborates with industry partners on smart grid solutions and edge-cloud systems.

• Computational Linguistics
• Text Summarization
• Information Extraction
• +۶ مورد دیگر

Horacio Saggion is the Chair in Computer Science and Artificial Intelligence at the Department of Information and Communication Technologies, Universitat Pompeu Fabra. He leads the TALN Group and the Large Scale Text Understanding Systems Lab. His research focuses on Computational Linguistics, with specialties in Text Summarization, Information Extraction, and Semantic Analysis. He coordinates the Horizon Europe iDEM project on inclusive democratic spaces and previously led the SignON project for Sign Language Translation. Key technologies include the SUMMA Summarization system and the Dr Inventor Text Mining Library. Education: PhD, MSc, and Licenciatura in Computer Science. Research Interests: Text simplification for accessibility, sign language translation, misinformation detection, and ethical AI applications. His work bridges natural language processing with societal needs such as clear communication in public administration. Grants & Projects: Coordinator of iDEM (Horizon Europe), PI of SignON, Simplext, and Able to Include. Involved in BEA shared tasks and CLEF labs. Active in organizing workshops like TSAR at EMNLP. Labs & Teams: Head of TALN Group and Text Understanding Lab. Collaborations include Universitat Pompeu Fabra's interdisciplinary initiatives and industry partnerships for technology commercialization.

Chanan Singhمشاهده پروفایل

استاد

• Reliability of Electric Power Systems
• Integration of Renewable Energy Sources
• Cyber-Physical Systems Reliability
• +۳ مورد دیگر

Chanan Singh is a distinguished academic serving as a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. He holds the Irma Runyon Chair and is a Regents Professor. His affiliations include the College of Engineering and a Guest Professorship at Tsinghua University's Department of Electrical Engineering (2010–2015). Dr. Singh earned his Ph.D. in Electrical Engineering from the University of Saskatchewan, alongside M.S. and B.S. degrees from the same institution and Punjab Engineering College, respectively. His research focuses on reliability and security of electric power systems , including renewable energy integration and cyber-physical systems resilience. He pioneered methodologies for hurricane impact analysis, cyber-malfunction modeling, and wind farm optimization. Key achievements include the IEEE-PES Roy Billinton Award (2010), PMAPS Merit Award (2008), and Fellow of IEEE (1991). His work has been recognized globally, including through over 20 major awards and fellowships. Dr. Singh advises students like Hangtian Lei and leads funded projects on power system resilience. He is affiliated with the Electric Power System Group , advancing interdisciplinary research in energy systems and reliability engineering.

Sharmistha Guhaمشاهده پروفایل

استادیار

• Bayesian Methods
• High-Dimensional Regression
• Causal Inference
• +۴ مورد دیگر

Sharmistha Guha is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. Her research focuses on Bayesian methods for analyzing complex, high-dimensional data, with applications in neuroscience, network security, and social sciences. She develops techniques for supervised network data analysis, causal inference, and data privacy preservation. Education: Ph.D. in Statistical Science (Duke University, implied by dissertation recognition). Research Interests: Bayesian high-dimensional regression, object-oriented regression, causal inference in randomized trials, probabilistic record linkage, Bayesian nonparametric mixture models, and integration of heterogeneous data types like networks and functional data. Her work addresses challenges in neuroimaging (dMRI/fMRI) and big data analytics. Recent Contributions: Her articles span Bayesian methodologies for network analysis, differential privacy in treatment effect estimation, and applications in environmental health (e.g., PM2.5 exposure studies). Notable trends include leveraging Bayesian hierarchical models for complex data structures and advancing causal inference frameworks. Awards: 2022 Blackwell-Rosenbluth Award (ISBA) 2021 Savage Award Honorable Mention 2024 NSF-DMS Grant 2413721 Grants & Advising: Secured NSF funding for heterogeneous data integration research. Mentored students like Jose Rodriguez-Acosta (NSF GRFP Fellow) and Jacob Pagel (NIH-funded CoSIBS participant). Lab/Team: Leads a research group integrating statistical method development with domain collaborations in neuroscience and network security. Active in organizing workshops and serving on panels to foster academic engagement.

Marcus Botacinمشاهده پروفایل

استادیار

• Malware Analysis
• Hardware-Assisted Security
• Antivirus Technology
• +۴ مورد دیگر

Marcus Botacin is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University (TAMU), USA. Previously, he held positions as Visiting Assistant Professor (2022-2024) and Lecturer at the Federal University of Paraná (Brazil). His research focuses on malware analysis, hardware-assisted security, and antivirus technology. He earned his Ph.D. in Computer Science from UFPR (2021), M.Sc. from UNICAMP (2017), and B.Sc. in Computer Engineering from UNICAMP (2015). Education: Ph.D., Computer Science, Federal University of Paraná, Brazil (2021) M.Sc., Computer Science, University of Campinas, Brazil (2017) B.Sc., Computer Engineering, University of Campinas, Brazil (2015) Research Interests: Botacin's work addresses challenges in malware detection (static/dynamic methods, sandboxing), hardware-enhanced security mechanisms (e.g., branch monitoring), and antivirus internals. He emphasizes practical solutions and ethical considerations in cybersecurity, advocating for culturally aware threat models (e.g., Brazilian financial malware studies). Publications & Trends: His recent work explores adversarial ML attacks on malware detectors, HPC-based detection frameworks, and automated malware generation risks. He frequently publishes in top venues like ACM CCS, USENIX Security, and IEEE TDSC. Awards: Top-3 Best Ph.D. Thesis in Computer Security (Brazilian Computer Society, 2022) Best PhD Thesis Award from UFPR (2022) MLSec Evasion Challenge 1st place (2021/2020) Advising & Grants: Supervises 15+ students at TAMU and leads NSF-funded projects (e.g., $523K grant for HPC malware detection frameworks). Serves on 21+ conference program committees and reviews for over 50 journals. Lab & Projects: Develops Corvus (public malware analysis sandbox) and explores hardware-accelerated AV solutions like Terminator and HEAVEN.

Peter Chinمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +۸ مورد دیگر

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Subodha Kumarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Blockchain
• +۱۱ مورد دیگر

Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science at Temple University’s Fox School of Business. He also serves as Founding Director of the Center for Business Analytics and Disruptive Technologies and holds a secondary appointment in Information Systems. With prior faculty roles at the University of Washington and Texas A&M University, he has been recognized with prestigious awards including the INFORMS ISS Distinguished Fellow Award (2023) and POMS Fellow (2019). His research focuses on artificial intelligence, machine learning, blockchain, fintech, and cybersecurity, with over 240 publications in top journals and two books. He actively contributes to academic leadership as Deputy Editor of the Production and Operations Management Journal and Founding Executive Editor of Management and Business Review. Research Interests: Artificial Intelligence Machine Learning Blockchain Fintech Healthcare Analytics Social Media Analytics Supply Chain Analytics Awards: INFORMS ISS Distinguished Fellow Award (2023) POMS Fellow (2019) Changjiang Scholars Chair Professorship (China) Namesake Award: The Subodha Kumar and Geoffrey G. Parker Award for Digital Transformation Research Grants & Advising: Secured NIH grants, advises PhD students in Operations & Supply Chain Management, and oversees the Center for Business Analytics. His editorial roles include multiple top journals and conference leadership (e.g., POMS Annual Conference 2018). Labs/Teams: Leads the Center for Business Analytics and Disruptive Technologies, fostering innovation in analytics and tech-driven business strategies.

Arpan Gujaratiمشاهده پروفایل

دانشگاهی

• Real-Time Systems
• Distributed Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Arpan Gujarati is a Sessional Lecturer in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. He teaches graduate and undergraduate courses such as CPSC 538G (Distributed Systems), CPSC 416 (Operating Systems), and CPEN 432 (Real-Time System Design). He holds a PhD from the Max Planck Institute for Software Systems and TU Kaiserslautern, where he was supervised by Björn B. Brandenburg. PhD: Max Planck Institute for Software Systems & TU Kaiserslautern (2020) Undergraduate: Birla Institute of Technology and Science (BITS Pilani) Postdoctoral Researcher: MPI-SWS Research Associate: UBC Software Development Engineer: Citrix R&D, India His research focuses on real-time and distributed systems, with applications in cyber-physical systems, fault tolerance, and machine learning reliability. He investigates scheduling algorithms, reliability analysis, and the integration of learning-enabled components into safety-critical systems. His work combines theoretical analysis with practical system implementations, often involving real-world testbeds and open-source tools. His recent publications span top-tier venues including RTSS, OSDI, ECRTS, DSN, and Middleware, with a strong emphasis on performance predictability, resilience of ML systems, and real-time communication. His work frequently addresses challenges in timing guarantees, fault tolerance, and system reliability in both cloud and embedded environments. SIGBED Paul Caspi Memorial Dissertation Award Best Paper Award at RTSS 2022 Distinguished Artifact Award at OSDI 2020 Best Student Paper Award at Middleware 2017 Outstanding Paper Award at RTCSA 2025 He advises several PhD students and undergraduate researchers at UBC, including Heng Zhao, Aida Aminian, Zainab Saeed Wattoo, and Philip Schowitz. He has led multiple research projects involving robotic arms, NVIDIA Holoscan, FreeRTOS, and distributed key-value stores. His lab work emphasizes reproducibility, open datasets, and practical system building. He has served on program committees for RTSS, RTAS, ECRTS, and Middleware, and contributes to journals such as Real-Time Systems and JSys.

John Clarkمشاهده پروفایل

استاد

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

John Clark is a Professor of Computer and Information Security at the University of Sheffield since 2017 and Director of the Siemens Digital MINE. Previously, he held roles as Professor of Critical Systems at the University of York (1992–2017) and worked at Logica in security R&D. He studied Mathematics and Applied Statistics at the University of Oxford. His research focuses on cybersecurity, software engineering, and AI applications, particularly in threat modeling, intrusion detection, quantum cryptanalysis, and secure autonomous systems. Clark leads the Security of Advanced Systems research group and has secured grants totaling over £36 million. Notable projects include the EPSRC-funded DAASE (2012–2019) and the Active Building Centre (2018–2022). His work on phishing detection (e.g., analyzing user behavior) and malware analysis has been widely recognized. He has been awarded the Royal Society Wolfson Merit Award (2013), GEECO medals (2005, 2013), and multiple best-paper prizes. Clark’s research spans theoretical and applied domains, including evolutionary computation for cryptanalysis, robotic system security, and smart grid protection. His labs explore areas like digital twin authentication and privacy-aware energy theft detection. He has supervised numerous grants and maintains active collaborations with industry and academia.