جست‌وجوی استاد و پژوهشگر

Haopeng Zhangمشاهده پروفایل

استادیار

• Natural Language Processing
• Generative AI
• AI4Science (Healthcare, Linguistics, Sensing)
• +۳ مورد دیگر

Haopeng Zhang is an Assistant Professor in the Department of Information and Computer Sciences at the University of Hawaii at Mānoa, leading the ALOHA Lab. He holds a Ph.D. from the University of California, Davis (advised by Dr. Jiawei Zhang), dual M.S. degrees from Georgia Tech (Electrical Engineering and Computational Science), and a B.S. from UIUC (Electrical Engineering). His research focuses on NLP, Generative AI, AI4Science, and Graph Mining, emphasizing biomedical applications, multimodal reasoning, and workflow generation. Recent achievements include organizing workshops at EMNLP and ACL, receiving grants from NSF and OpenAI, and developing tools like StrucSum and MermaidFlow. Education: Ph.D., Computer Science, UC Davis (2024) M.S., Electrical Engineering & Computational Science, Georgia Tech B.S., Electrical Engineering, UIUC Research interests span structural summarization, LLM applications in healthcare and linguistics, and graph-based NLP systems. His work bridges theoretical advancements with practical tools, including the ALOHA Lab's contributions to reliable summarization systems and multimodal benchmarks. Notable contributions include the DomainSum benchmark and Wi-Chat wireless sensing framework. Key awards include UH travel grants, OpenAI credits, and NSF computing allocations. He actively serves on conference committees (ACL, KDD) and reviews for top journals (ACM TKDD, IEEE transactions). Teaching responsibilities include Advanced AI and Machine Learning courses at UH Mānoa. Lab activities focus on AI-driven solutions for scientific domains, with ongoing projects in biomedical event extraction and low-resource language support.

• Language Assessment
• Language Didactics (Foreign Languages)
• Educational Technology
• +۳ مورد دیگر

Raili Hilden is a full professor in language didactics (foreign languages) at the University of Helsinki's Department of Education, Faculty of Educational Sciences. Her core activities include research, teaching, and societal impact with a focus on language assessment, particularly in high-stakes testing contexts. She leads research projects like AASIS (Automatic assessment of spoken interaction in second language) and DigiTala, exploring automated assessment tools for speaking proficiency. She chairs the Finnish National Matriculation Examination Board's Swedish section and contributes to national curriculum development. Research Interests : Language assessment methodologies, teacher assessment literacy, educational equity in language education, and the integration of technology in language testing. Current projects emphasize automated scoring systems and multimodal assessment formats. Key Contributions : Directed national evaluations of language learning outcomes (2012-2014), developed grading criteria for Finnish national curricula, and pioneered computer-based language testing innovations. Her work bridges educational policy, technology, and pedagogical practice. Awards : Recipient of top honors including Vuoden kieltenopettaja 2003, Kulturfonden Språkpriset 2004, and The FIPLV International Award 2005. Active in international conferences and serves on boards like the Finnish Matriculation Examination Board. Grants/Projects : Multiple Academy of Finland grants (AASIS: 2023-2027; DigiTala: 2025-2026), DD-LANG (2022-2026) focusing on diagnostic language assessment innovations. Labs/Teams : Leads multidisciplinary teams combining linguistics, computer science, and educational research to advance automated assessment technologies. Collaborates with Scandinavian institutions on teacher assessment literacy initiatives.

Jeremy Barnesمشاهده پروفایل

استادیار

• Cross-lingual Methods
• Transfer Learning
• Sentiment Analysis
• +۵ مورد دیگر

Jeremy Barnes is an Assistant Professor in the IXA Group at the University of the Basque Country (UPV/EHU), part of the HiTZ Centre. He holds a PhD in Computational Linguistics and has extensive postdoctoral experience at the University of Oslo. His research focuses on cross-lingual methods, sentiment/emotion analysis, weak supervision, and under-resourced language resources. He has contributed to projects like NorDial and NorLM, developing tools like skweak. Barnes teaches courses in computational syntax, text mining, and NLP. Education: PhD (Pompeu Fabra University), M.A. and B.A. in Linguistics Key Projects: LUMINOUS (multimodal LLMs), SANT (Norwegian sentiment analysis) His work spans structured sentiment analysis, cross-lingual transfer learning, and dialectal feature identification. He has received awards for his book Sonic Ethnography (2021) and photography (2020). Barnes advises numerous Master's theses and collaborates internationally on NLP challenges.

Denilson Barbosaمشاهده پروفایل

استاد

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +۳ مورد دیگر

Denilson Barbosa is a Professor of Computing Science at the University of Alberta's Faculty of Science. He holds a PhD and specializes in Natural Language Processing, Information Retrieval, and Machine Learning. His research focuses on knowledge extraction, entity recognition, and knowledge graph construction. Barbosa teaches courses like CMPUT361 (Information Retrieval) and CMPUT656 (Knowledge Graphs). He served as President of the Canadian Artificial Intelligence Association (CAIAC) from 2023 to 2024. His work includes developing tools like the NLP Workbench and contributing to open relation extraction, Wikipedia analysis, and low-resource language models. Education and Background: Barbosa's academic background aligns with his research interests in computational linguistics and AI applications. His teaching emphasizes practical skills in algorithms, data structures, and machine learning for text processing. Research Highlights: Barbosa's research spans semantic graphs, probabilistic language models, and defect prediction systems. He explores challenges in multilingual NLP, particularly for Indigenous and low-resource languages like Cree. His work often integrates machine learning with traditional IR methods to improve system efficiency and accuracy. Recent Contributions: Notable recent projects include improving Bengali/Hindi LLMs, evaluating QA systems via textual entailment, and developing Bayesian methods for crowdsourced truth discovery. His articles reflect his interdisciplinary approach, combining theory with real-world applications in education, software engineering, and social media analysis.

Faith-Michael Uzokaمشاهده پروفایل

استاد

• Medical Informatics
• Technology Adoption
• Computing Disciplinary Studies
• +۵ مورد دیگر

Prof. Faith-Michael Uzoka is a full Professor in the Department of Mathematics & Computing at Mount Royal University, Canada. He holds adjunct professorships at the Faculty of Commerce, Catholic University of Eastern Africa (Kenya) and the ICT University (USA). His expertise spans Medical Informatics, Technology Adoption, and Computing Disciplinary Studies. He has over three decades of academic experience, including a postdoctoral fellowship at the University of Calgary (2004-2005). Education includes a PhD/MS in Computer Science from Nigeria’s Federal University of Technology, an MBA from the University of Benin, and postdoc training in Calgary. His research focuses on AI-driven healthcare solutions, particularly in tropical disease diagnosis and maternal health outcomes, with emphasis on explainable AI and mobile health applications in resource-limited settings. Editorial roles include International Journal of Biomedical Engineering and multiple conferences. His recent work emphasizes machine learning for malaria/typhoid diagnosis, maternal healthcare prediction systems, and digital health interventions in low-income countries. He has reviewed for high-impact journals like Journal of Healthcare Engineering and ACM Transactions on Computing Education. His projects include developing mobile diagnostic systems for febrile diseases, eHealth frameworks in developing nations, and AHP-based decision models for healthcare financing. He collaborates internationally on sterile processing practices in African hospitals and cloud computing adoption in universities.

Kevin Leachمشاهده پروفایل

استادیار

• Cybersecurity
• Software Engineering
• Dependable Systems
• +۳ مورد دیگر

Kevin Leach is an Assistant Professor of Computer Science at Vanderbilt University's School of Engineering. His research focuses on designing dependable cybersecurity systems and software engineering practices that withstand sophisticated adversarial attacks. He holds a PhD from the University of Virginia (2016), where he received the Louis T Rader Outstanding Research Award. Prior to Vanderbilt, he was a research scientist and lecturer at the University of Michigan. Education: Ph.D. in Computer Engineering, University of Virginia MS in Computer Science, George Mason University BS with Distinction in Computer Engineering and Computer Science, University of Virginia His research investigates two core areas: leveraging hardware capabilities like Trusted Execution Environments for low-overhead security instrumentation, and understanding how developers and operators make decisions in adversarial environments. He has published over 30 refereed articles, 3 of which have received awards. Recent work includes studies on cognitive biases in cybersecurity competitions, AI-driven code assessment, and malware detection techniques. Awards: Louis T Rader Outstanding Research Award (2016) 3 award-winning papers (specific awards unspecified) His research bridges technical security mechanisms with human decision-making processes, emphasizing both system dependability and developer/operator awareness. Current projects include work on federated learning defenses, runtime security optimization for embedded systems, and ethical considerations in crowdsourced data labeling.

Benjamin Bergenمشاهده پروفایل

استاد

• Cognitive Science
• Linguistics
• Language Comprehension
• +۵ مورد دیگر

Benjamin Bergen is a Professor in the Department of Cognitive Science at UC San Diego, affiliated with the Language and Cognition Lab. His research focuses on language processing, metaphor, profanity, and embodied cognition. He holds a PhD from UC Berkeley (2001) and has authored over 150 peer-reviewed publications. Bergen's work bridges cognitive science, linguistics, and AI, investigating how humans and machines process meaning. Research interests include: (1) Embodied simulation in language understanding, (2) Neural language models' cognitive plausibility, (3) Impact of swearing on communication, (4) Multimodal interaction effects during driving. Recent projects explore LLM capabilities in theory of mind tasks and cross-lingual transfer learning. Public engagement includes over 100 media appearances since 2016, including interviews with Communications of the ACM (2023), Discover Magazine (2023), and Netflix's History of Swear Words (2021). Authored popular science books on profanity and cognitive linguistics.

Thanasis Korakisمشاهده پروفایل

عضو هیئت علمی

• Wireless Networks
• Access Layer Protocols
• Cooperative Networks
• +۷ مورد دیگر

Thanasis Korakis is a Research Assistant Professor in the Electrical and Computer Engineering Department at NYU Tandon School of Engineering, affiliated with the Center for Advanced Technology in Telecommunications (CATT). He holds a Ph.D. in Computer and Communication Engineering from the University of Thessaly and has been involved in visiting research roles at UC Riverside and CATT. His research focuses on wireless networks, with an emphasis on access layer protocols, cooperative networks, directional antennas, and QoS provisioning. Education: Bachelor of Technology in Informatics, University of Athens (1994) M.S. in Data Networks and Telecommunications, University of Athens (1997) Ph.D. in Computer & Communication Engineering, University of Thessaly (2005) Research Interests: Dr. Korakis’ work spans experimental testbeds (e.g., Witest Lab), 802.11/802.16 standardization, and implementation of cooperative MAC protocols. His contributions include frameworks for cooperative handoff in wireless mesh networks, MAC protocols leveraging directional antennas, and dynamic rate adaptation for video multicast. Recent work explores 5G V-RAN reliability, AI-driven security systems, and IoT testbeds. Publications: His articles cover topics like 5G network optimization, blockchain applications in telecom, and AI in forensic investigations. Notable trends include experimental validation of network functions, security-enhanced systems, and cross-layer protocol design. Scientific Awards: Ericsson Award of Excellence in Telecommunications (2006) Advising & Grants: He leads the Witest Lab and has served on technical committees for ICC, WCNC, and SECON. His work includes grants related to wireless testbeds and standardization efforts. Collaborations involve institutions like CATT and NYU’s Tandon School. Labs & Teams: He directs the Wireless Implementation Testbed Laboratory (Witest Lab), focusing on real-world experimentation with wireless protocols and technologies.

Tirthak Patelمشاهده پروفایل

استادیار

• Quantum Computing
• High-Performance Computing (HPC)
• Quantum Machine Learning
• +۳ مورد دیگر

Tirthak Patel is an Assistant Professor in the Department of Computer Science at Rice University and Director of the Positive Technology Lab. His research focuses on systems-level challenges at the intersection of quantum computing and high-performance computing (HPC), emphasizing reliability, performance, and energy efficiency trade-offs. He holds a PhD from Northeastern University (2023) and prior degrees from Northeastern and the University of Toronto. Education: PhD in Computer Engineering, Northeastern University (2023) MS in Electrical and Computer Engineering, Northeastern University (2019) Bachelor of Applied Science in Electrical Engineering, University of Toronto (2017) His research explores quantum system software, compiler design for neutral atom quantum architectures, and quantum machine learning. Notable contributions include frameworks like PARALLAX (compiler for neutral atom quantum computers) and GEYSER (compilation for neutral atom systems). He prioritizes open-source tools and reproducible research. Research Trends in Publications: Recent work emphasizes quantum noise utilization, quantum GANs, and compiler optimizations for NISQ-era hardware. He also addresses challenges in hybrid quantum-classical systems and quantum cloud security (e.g., OpaQue for program obfuscation). Awards: 2024 ACM SIGHPC Doctoral Dissertation Honorable Mention 2021 ACM-IEEE CS George Michael HPC Fellowship 2024 Rice Outstanding Undergraduate Research Mentor Award Advising & Grants: While specific grant details aren't listed, his work is supported by open-source contributions and industry collaborations. He advises students through Rice's Quantum Computing programs and teaches courses like COMP 448/558: Quantum Computing Algorithms. Labs & Teams: Directs the Positive Technology Lab, focused on advancing quantum computing systems through interdisciplinary approaches. Collaborates with industry partners and international conferences as a TPC member for venues like SC, ISCA, and ICCAD.

Md Abul Basharمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Natural Language Processing
• Machine Learning Applications
• +۲ مورد دیگر

Md Abul Bashar is a Research Fellow at the Queensland University of Technology's Centre for Data Science and School of Computer Science. His research focuses on developing deep learning and machine learning solutions for natural language processing tasks, including abusive content detection, misinformation analysis, and social media mining. Industrial applications include unconscious bias detection systems deployed at Fortune 500 companies and automated marketing strategy generation commercialized by Robotic Marketer. Research areas span multimodal fusion (uncertainty-guided meta-learning in 2025, pre-gating attention mechanisms in 2024), longitudinal data analysis (GAN-based imputation in 2024), and cybersecurity applications (Log4Shell threat detection in 2023). Articles demonstrate strong emphasis on NLP techniques adapted for low-resource scenarios through transfer learning and generative models. Significant Applications Unconscious bias detection for enterprise content management Automated marketing report generation (commercialized) Misogyny detection featured in Forbes, Daily Mail Energy payment propensity prediction Indigenous heritage repatriation support systems

Laurianne Sitbonمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Natural Language Processing
• Accessibility
• +۴ مورد دیگر

Associate Professor Laurianne Sitbon is an ARC Future Fellow and Associate Professor at QUT's School of Computer Science. Her work focuses on human-computer interaction, natural language processing, and accessibility for people with intellectual disabilities. She leads projects like designing pictorial communication frameworks and human-machine teaming systems. She holds a PhD from the University of Avignon and collaborates with organizations like Endeavour Foundation. Research interests include co-design methodologies, assistive technologies, and inclusive system design. Awards include ARC Future Fellowship. Supervises PhD projects on topics like multimodal communication, social robots, and human-AI collaboration in healthcare. Key projects include: A Pictorial Communication Framework for Inclusion (ARC FT190100855) Human-Machine Teaming: Designing Synergistic Learning (ARC DP200103582) Publications span over 50 peer-reviewed papers, emphasizing participatory design with neurodiverse populations. Collaborates internationally on inclusive AI applications and disability-inclusive technology.

Simon Leeمشاهده پروفایل

مدرس

• Atmospheric Science
• Climate Science
• Meteorology
• +۵ مورد دیگر

Dr Simon Lee is a Lecturer in Atmospheric Science at the University of St Andrews within the School of Earth and Environmental Sciences. He serves as Co-Editor-in-Chief of the Royal Meteorological Society's journal Weather . His research focuses on large-scale atmospheric and climate variability across subseasonal to decadal timescales, particularly stratospheric dynamics and weather regimes. Key areas include stratospheric polar vortex variability, sudden stratospheric warmings, and their impacts on tropospheric weather systems. PhD (2021): Stratosphere-Troposphere Coupling on Subseasonal Timescales , University of Reading MMet (2018): Meteorology and Climate , University of Reading Research highlights include advancing understanding of stratospheric polar vortex dynamics, developing frameworks for subseasonal prediction models, and creating a novel North American weather regime classification system. He actively collaborates with the APARC Stratospheric Network for the Assessment of Predictability (SNAP) steering committee, bridging weather and climate sciences. Public engagement is a priority, with contributions to media platforms like The Conversation . Teaching responsibilities include courses on oceans and atmosphere ( ES3013 ), Earth surface processes ( ES2003 ), and resource challenges ( ES1002 ). His work emphasizes translating scientific findings for stakeholders and the public, with a focus on actionable climate information.

Shengyi Wangمشاهده پروفایل

پژوهشگر

• Formal Verification
• Programming Languages
• Mechanized Reasoning
• +۷ مورد دیگر

Shengyi Wang is an Associate Research Scholar in the Department of Computer Science at Princeton University, School of Engineering and Applied Science. He is actively engaged in research on formal verification, programming languages, and mechanized reasoning, with a focus on verifying concurrent systems, C programs, and network packet processing. Research Interests: His work centers on foundational and compositional verification of low-level systems using interactive theorem proving. Key areas include separation logic, concurrency, data structure invariants, and certified systems programming. He applies these to real-world challenges in systems security and network correctness. Recent Research Trends: His recent publications (2020–2024) show a strong trajectory in verifying complex systems such as concurrent C programs, P4-based packet processors, and enclave filesystems. The work consistently uses Coq and mechanized proofs to ensure correctness, emphasizing scalability and compositional techniques. Scientific Awards: No awards or fellowships are mentioned in the provided text. Advising and Grants: No formal students or advisees are listed. No grants or funding sources are explicitly mentioned. Labs and Teams: While not explicitly stated, his collaborations with researchers like Andrew W. Appel, Lennart Beringer, and William Mansky suggest involvement in Princeton’s formal methods and systems verification research group, likely associated with the Department of Computer Science.

• Computational creativity
• Computational linguistics
• Artificial intelligence
• +۳ مورد دیگر

Björn Gambäck is a Professor of Language Technology at the Department of Computer Technology and Informatics, NTNU. His research focuses on computational creativity, computational linguistics, artificial intelligence, and machine learning, with a strong emphasis on natural language processing (NLP) and language technology. He actively contributes to the academic community through teaching courses such as 'Intelligent Text Analysis and Language Comprehension' and supervising master's theses. His work includes advancing techniques for sentiment analysis, code-mixed language processing, and computational creativity. Recent research highlights include developing deep learning models for code-mixed social media analysis and exploring coreference resolution in entity-level sentiment tasks. Gambäck’s contributions span interdisciplinary areas, such as applying evolutionary algorithms to media repositories and music composition. Notable collaborations include projects on hate speech detection, sarcasm annotation in tweets, and named entity recognition for low-resource languages like Amharic. His expertise bridges theoretical advancements and practical applications in NLP and computational systems.

Anjo Vahldiek-Oberwagnerمشاهده پروفایل

استاد مدعو

• System Security
• Operating Systems
• Trusted Execution Environments
• +۷ مورد دیگر

Anjo Vahldiek-Oberwagner is a Research Scientist at Intel Labs and an Adjunct Lecturer at TU Munich, where he contributes to both industrial R&D and academic education in systems and security. His work bridges hardware and software security, focusing on confidential computing, in-process isolation, and secure cloud deployments. PhD in Computer Science, Max Planck Institute for Software Systems & Saarland University, 2019 B.Sc. in Applied Computer Science, Cooperative University State University Baden-Wuertemberg, 2009 His research centers on system security, particularly techniques for protecting data confidentiality and integrity at rest, in-flight, and in-memory. He explores operating systems, distributed systems, and hardware-assisted security mechanisms such as Intel MPK and SGX. His work on ERIM, HFI, Endokernel, and Graphene has advanced secure in-process isolation and trusted execution environments. He has published extensively in top venues like USENIX Security, ASPLOS, and IEEE S&P. His recent publications reflect a strong trend toward practical, deployable security solutions for modern computing environments, including secure AI/ML deployments, efficient in-process isolation, and hardware-accelerated sandboxing. Themes include memory safety, performance optimization, and real-world applicability of security primitives. Scientific awards include: Distinguished Paper Award and Internet Defense Prize, USENIX Security 2019 (ERIM) Distinguished Paper Award, ASPLOS 2023 (HFI) IEEE Micro Top Picks 2024 (HFI) Intel Hardware Security Academic Award (Honorable Mention) DARPA Riser 2022 Intel Labs Gordy Award Honorable Mention He actively mentors and serves on program committees (EuroSys, USENIX Security, ASPLOS), chairs artifact evaluation (USENIX Security, EuroSys, SC), and is an Associate Editor for ACM TOPS. He has advised no formal students listed, but collaborates widely across Intel and academia. His work is supported by Intel and DARPA, and he holds multiple patents in secure computing and TEEs. He leads research on memory-safe architectures and secure cloud deployments at Intel Labs. He is involved in several research projects, including: Secure In-Process Memory Isolation, Shielding Applications in Untrusted Clouds via SGX, Memory-Safe Hardware and Software Architecture, and Research Artifacts and Evaluation. He is also a key contributor to the Graphene Library OS and works on validation and endorsement services for confidential computing.