جست‌وجوی استاد و پژوهشگر

Vincenzo Stoicoمشاهده پروفایل

استادیار

• Energy Efficiency
• Machine Learning
• Energy Consumption
• +۵ مورد دیگر

Vincenzo Stoico serves as an Assistant Professor in the Department of Computer Science at the Faculty of Science, Vrije Universiteit Amsterdam (VU Amsterdam), with dual affiliation at the university's Network Institute. His academic profile centers on computational sustainability, specifically optimizing energy consumption in emerging AI-driven systems. His research critically examines energy efficiency across machine learning infrastructure, with particular emphasis on large language models (LLMs). Key investigations include energy trade-offs in LLM-generated content delivery (on-device versus cloud), sustainable monitoring of concept drift in ML systems, and energy-aware self-adaptation mechanisms for robotics. This work bridges theoretical performance modeling with empirical validation in green computing contexts, addressing urgent environmental concerns in AI deployment. Recent publications (2024-2025) demonstrate consistent focus on quantifying energy-performance relationships across diverse computing domains. His methodology combines controlled experimentation with architectural analysis, revealing significant energy savings through optimized LLM operations, concept drift detection frameworks, and behavior tree implementations in robotic systems. These contributions advance sustainable AI engineering practices while highlighting critical accuracy-energy tradeoffs. Stoico actively collaborates with researchers including Ivano Malavolta and Patricia Lago across VU Amsterdam's Computer Science department and Network Institute. He contributes to academic instruction through the 'Green Lab' course, though specific advising relationships and grant funding details remain unreported in available sources. His institutional engagement occurs primarily within VU Amsterdam's Network Institute ecosystem, which fosters interdisciplinary research on digital society challenges. Current projects likely integrate with the university's broader sustainability initiatives, though explicit lab affiliations or team structures are not documented in the provided materials.

Jie Caoمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +۴ مورد دیگر

Dr. Jie Cao is an Assistant Professor at the School of Computer Science, University of Oklahoma, affiliated with the Gallogly College of Engineering and the Data Science and Analytics Institute. He holds a PhD from the University of Utah, and MS/BS degrees from Huazhong University of Science and Technology. His research focuses on Natural Language Processing (NLP), Machine Learning (ML), dialogue systems, structured prediction, and trustworthy AI applications in education and healthcare. Prior to OU, he was a postdoctoral researcher at the NSF AI Institute for Student-AI Teaming (iSAT) at the University of Colorado Boulder, and worked as a research scientist at WeChat AI, Amazon, and in software engineering at Alibaba and Baidu. Education: PhD in Computer Science, University of Utah MS in Computer Science, Huazhong University of Science and Technology BS in Information Security, Huazhong University of Science and Technology Research Interests: Dr. Cao’s work spans multi-party/multimodal dialogue analysis, LLM alignment, efficient structured prediction, and robust AI deployment. Notable projects include AI partners for collaborative classrooms, discourse analysis in psychotherapy, and database workload characterization. His research bridges foundational NLP/ML advancements with interdisciplinary applications in education, healthcare, and material science. Key Achievements: Recipient of the iSAT Trainee Grant (2023) and Alternative Textbook Grant (2025) Lead developer of MatterChat, a multi-modal LLM for material science Published over 20 peer-reviewed papers in top venues like ACL, COLING, and IEEE TVCG Co-instructor for NLP courses integrating LLM and agentic AI materials Labs & Teams: Leads the OUNLP lab at OU, focusing on advancing dialogue systems and trustworthy AI solutions.

• Transportation Safety
• Intelligent Transportation Systems (ITS)
• Machine Learning
• +۳ مورد دیگر

Dr. Mohammed Elhenawy is a Senior Lecturer and Senior Research Fellow at the Centre for Accident Research & Road Safety – Queensland (CARRS-Q), Queensland University of Technology (QUT), Brisbane, Australia. He holds a PhD in Computer Engineering from Virginia Tech. His research focuses on applying machine learning, AI, and statistical learning to transportation safety and intelligent transportation systems (ITS). He has authored over 120 papers and led projects like Australia’s largest Cooperative Intelligent Transport Systems (C-ITS) pilot, the LAARMA system for animal-vehicle collision prevention, and the 'Hold the Red' traffic signal evaluation. His work has been recognized with awards such as the Best Paper Award in Intelligent Vehicle Technologies. Affiliations: CARRS-Q, Faculty of Health (School of Psychology & Counselling) Education: PhD in Computer Engineering (Virginia Tech) Research Interests: Transportation safety, AI-driven traffic solutions, cooperative vehicle-infrastructure systems, and data-driven safety models. His work spans connected vehicles (V2V/V2I), near-miss detection, and multimodal large language models (MLLMs) for real-time traffic control. He also explores applications in pedestrian safety, e-scooter rider protection, and tunnel glare mitigation. Articles Trends: Recent work emphasizes LLMs for traffic safety analysis, multimodal data fusion, and autonomous vehicle perception. Key themes include real-time intersection control, zero-shot learning for crash analysis, and optimizing urban mobility systems. Awards: Best Paper Award in Intelligent Vehicle Technologies Advising & Grants: Leads interdisciplinary projects funded by Australian transport agencies and collaborates globally on ITS initiatives. His work bridges academia and industry, focusing on deployable safety solutions. Labs/Teams: CARRS-Q, collaborating with QUT’s School of Psychology & Counselling and external transport agencies.

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Tomasz Kajdanowicz serves as an Associate Professor in the Department of Artificial Intelligence at Wrocław University of Science and Technology's Faculty of Information and Communication Technology. His research bridges theoretical machine learning with practical cybersecurity and natural language processing applications. His primary research interests focus on graph neural networks , large language model optimization , and interpretable cybersecurity systems . Notable contributions include the MIRAD framework for ransomware detection and PUGG dataset development for Polish language processing. His work demonstrates consistent innovation in graph representation learning, particularly through multiplex graph fusion techniques and knowledge graph enrichment strategies. Analysis of his recent publications reveals a strong trajectory toward trustworthy AI systems with emphasis on hallucination detection (FactSelfCheck), latent space optimization (LatentPrompt), and synthetic data generation for medical applications. His cybersecurity research shows increasing sophistication in interpretable threat detection mechanisms. His academic supervision includes multiple doctoral and master's students evidenced through co-authorship patterns, particularly in graph-based learning and NLP projects. Current collaborations span biomedical signal processing (fNIRS analysis) and efficient LLM deployment strategies. His laboratory work centers on the AI Security Lab within the Department of Artificial Intelligence, focusing on adversarial robustness testing and real-time network monitoring systems. Key initiatives include the development of Polish-language NLP benchmarks and cross-domain graph embedding frameworks.

• Artificial Intelligence
• Game Theory
• Multi-Agent Systems
• +۳ مورد دیگر

Agnieszka Mensfelt is a Postdoctoral Research Assistant at the Department of Computer Science, Royal Holloway University of London. Her work focuses on advancing strategic AI systems through intersections of game theory, multi-agent systems, and large language model (LLM) applications. She contributes to research areas aligned with UN Sustainable Development Goals, particularly in technological innovation and computational problem-solving. Research Themes: Strategic reasoning, quality-diversity optimization, and teleo-reactive behavior modeling Methodologies: LLM-enhanced recursive reasoning, domain-specific dissimilarity metrics, and agent-based simulation Collaborations: Active researcher network across Europe with recent presentations at international conferences Recent publications highlight her expertise in formalizing complex game structures, improving AI behavioral transparency, and developing novel optimization frameworks. Despite no explicit awards mentioned, her frequent conference contributions indicate significant community engagement. Her work spans theoretical and applied aspects of computational intelligence, balancing technical innovation with ethical considerations in AI deployment. Current activities include oral presentations at major AI workshops and collaborations with interdisciplinary research teams.

Amanda Fernandezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Amanda Fernandez is an Assistant Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), where she contributes significantly to research, teaching, and interdisciplinary collaboration. She will be a faculty member in the newly launching College of AI, Cyber and Computing in Fall 2025, reflecting her central role in advancing UTSA's strategic focus on emerging technologies. Her research lies at the intersection of artificial intelligence, deep learning, computer vision, and computer science education . She directs the UTSA Vision and AI Lab (VAIL) and serves as Machine Learning & Deployment Thrust Lead for MATRIX: The AI Consortium for Human Well-Being , and as a Principal Investigator in the Consortium on Nuclear Security Technologies (CONNECT) . Her work emphasizes explainable and robust AI, with applications in nuclear materials, VR/AR, and biomedical signals like ECG. The 15 most recent publications reflect a strong trend in explainable AI, adversarial robustness, and AI-enhanced education . Her work spans technical AI research (e.g., ICML, CVPR workshops) and educational innovation (e.g., SIGCSE, RESPECT), demonstrating a dual commitment to advancing both the science and pedagogy of computing. Themes include model attribution, training optimization, semantic segmentation, and the integration of LLMs in introductory CS courses. Senior Member of the IEEE Senior Member of the National Academy of Inventors (NAI) Funded by the National Science Foundation (NSF) Supported by the U.S. Department of Energy (DOE) Supported by the U.S. Department of Defense (DOD) Fernandez is deeply committed to mentoring undergraduate researchers through her CS-CURE course, which provides hands-on research experience. She advises student organizations including ACM-W and GDSC , and leads initiatives such as CS4SA to increase Latinx participation in computer science. Her grants support both research and educational outreach, emphasizing inclusivity and workforce development. She leads the Vision and AI Lab (VAIL) at UTSA and is an integral member of two major research consortia: MATRIX and CONNECT , fostering collaboration across institutions including Los Alamos National Lab, Argonne National Lab, and UNLV.

ZHANG Jiahengمشاهده پروفایل

استادیار

• Cryptography
• Security
• Artificial Intelligence
• +۷ مورد دیگر

ZHANG Jiaheng is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His work bridges cryptography, artificial intelligence, and system security, with a focus on scalable and privacy-preserving technologies. He teaches CS3235 – Computer Security and leads research in zero-knowledge proofs, LLM safety, and trustworthy AI. Research Interests: His research spans Cryptography , Security , Machine Learning & AI , Privacy , and Algorithms & Theory . He specializes in making zero-knowledge proofs practical at scale and securing large language models against jailbreaking, backdoors, and privacy leaks. His recent projects include zkGPT, BatchZK, and Guardreasoner, highlighting his dual focus on theoretical foundations and real-world applications. The recent publications show a strong trend toward scalable zero-knowledge systems and AI security , particularly in verifying and protecting LLMs. These works integrate cryptographic rigor with modern AI challenges, reflecting a cohesive research vision at the frontier of trustworthy computing. Scientific Contributions: Developed scalable collaborative zk-SNARKs for efficient proof generation. Pioneered techniques for secure LLM inference and jailbreak detection. Advanced GPU-accelerated and distributed zero-knowledge proof systems. Advising & Grants: While specific students and grants are not listed, his active publication record in top-tier venues suggests ongoing research supervision and external funding in cybersecurity and AI. He is likely involved in advising PhD and Master’s students in cryptography and AI security. Labs & Teams: He is part of the NUS School of Computing research ecosystem, potentially affiliated with cybersecurity or AI labs, contributing to Singapore’s leadership in privacy-preserving technologies.

Micah Goldblumمشاهده پروفایل

استادیار

• Machine Learning
• AI Safety
• Automated Data Science
• +۵ مورد دیگر

Micah Goldblum is an Assistant Professor at Columbia University, focusing on machine learning research with emphasis on AI safety, automated data science, and large-scale model training strategies. His work explores foundational topics like generalization theory, Bayesian inference, and algorithmic reasoning. He received a Ph.D. in Mathematics from the University of Maryland, advised by Tom Goldstein and Wojciech Czaja, followed by a postdoctoral fellowship at New York University under Yann LeCun and Andrew Gordon Wilson. His research portfolio includes developing robust benchmarks for large language models (e.g., LiveBench), analyzing inductive biases in ML systems via Kolmogorov complexity, and advancing adversarial detection techniques like Binoculars. Key contributions span LLM evaluation, diffusion model theory, and fairness in computer vision systems. Goldblum’s work on Bayesian model selection and compression bounds for large language models has provided critical theoretical insights. Goldblum has been recognized with the 2022 ICML Outstanding Paper Award for his work on Bayesian model selection. His research also addresses societal challenges, such as protecting privacy in facial recognition systems (LowKey) and mitigating biases in neural architectures. Collaborations include leading projects like the Battle of the Backbones vision benchmark and exploring adversarial robustness in federated learning. His labs and teams focus on interdisciplinary applications of ML, including automated benchmarking, secure AI deployment, and ethical alignment of LLMs. Goldblum’s publications consistently bridge theory and practice, with recent breakthroughs in diffusion model design, continual learning algorithms, and understanding the intrinsic properties of data distributions.

Mark Santolucitoمشاهده پروفایل

استادیار

• Program Synthesis
• Computer Music
• DevOps
• +۴ مورد دیگر

Mark Santolucito is an Assistant Professor of Computer Science at Barnard College, Columbia University. He holds a PhD in Computer Science from Yale University, where his research focused on program synthesis and computer music. His current work explores program synthesis techniques to enhance programmer productivity, particularly in lowering barriers to entry for underrepresented groups and optimizing workflows for advanced developers. He leads the Barnard PL (Programming Languages) Labs, which develops tools like TSL (Temporal Stream Logic) for synthesizing reactive systems and analyzing infrastructure-as-code (IaC). His research intersects formal methods with creative applications in music and live coding, emphasizing accessibility and usability. Education: PhD in Computer Science (Yale University), focusing on program synthesis and computer music. Research interests include program synthesis, temporal logic specifications, infrastructure configuration analysis, and music technology. He emphasizes human-centered design in his work, aiming to make programming more accessible through tools like TSL and interactive synthesis environments. Projects include TSL Move Cube, Block-based Editor for Temporal Logic, and Spiral Analysis for medical software migration. Notable contributions include developing TSL synthesis pipelines, optimizing Arduino configurations, and exploring static analysis for cost prediction in cloud deployments. His work bridges theoretical foundations with practical applications in both software engineering and creative computing. Labs/Teams: Leads Barnard PL Labs, collaborating on projects like TSL Synthesis Engine and Static Analysis for IaC. Active in workshops such as SEConfig and FMCAD.

Wei Yangمشاهده پروفایل

دانشیار

• Software Engineering
• Security
• AI on Edge Devices
• +۵ مورد دیگر

Wei Yang is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, working within the Erik Jonsson School of Engineering and Computer Science. He holds a regular faculty position with an office in ECSS 4.225 and is actively engaged in teaching, research, and mentoring graduate students. His academic journey spans multiple prestigious institutions, and he currently serves on editorial boards for major software engineering journals. Dr. Yang received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2018, advised by Prof. Carl A. Gunter and Prof. Tao Xie. He earned his M.S. in Computer Science from North Carolina State University in 2013 under Prof. Tao Xie, and his B.E. in Software Engineering from Shanghai Jiao Tong University in 2011 under Prof. Jianjun Zhao. He was also a visiting researcher at the University of California, Berkeley, invited by Prof. Dawn Song. Dr. Yang's research spans multiple cutting-edge areas at the intersection of software engineering and security. His primary focus centers on software engineering for AI systems , particularly addressing challenges in deploying AI on edge devices like mobile phones, IoT devices, and autonomous vehicles. His pioneering work on efficiency robustness (initiated in 2019) explores how different inputs can trigger varying computational costs in neural networks, leading to novel attacks and defenses. He also develops infrastructure support for AI deployment , including compiler toolchains for dynamic-shaped neural networks and security analysis for IoT deployments. His research extends to mobile testing (since 2012), malware detection using expectation context analysis, and intelligent tools for software engineers and security researchers. Dr. Yang's publication record demonstrates a clear trajectory toward addressing critical challenges in AI security and efficiency. His recent work shows increasing focus on foundation models, large language models, and their security implications, while maintaining strong connections to practical software engineering challenges. The publications reveal a consistent pattern of high-impact research in top-tier venues across software engineering, AI, and security domains. NSF CAREER Award (2022) ACM SIGSOFT Distinguished Paper Award (2021) Amazon Research Award Dr. Yang actively mentors a large group of graduate and undergraduate students, with several PhD students currently working under his supervision. He serves as a faculty advisor for the ASTRO (AI Security and Trustworthiness Operations) team, which was selected as a red teaming participant in the Amazon Nova AI Challenge. His research is supported by significant grants, including the NSF CAREER award providing approximately $500,000 over five years. He emphasizes practical student development, helping them navigate the job market and transition from academic training to professional careers. Dr. Yang leads a vibrant research group focused on software engineering and security challenges in AI systems. His team, including the ASTRO group participating in the Amazon Nova AI Challenge, develops innovative techniques for testing, securing, and improving AI-based systems. The research environment fosters collaboration across multiple domains, with students working on projects ranging from mobile security to foundation model engineering.

Santu Karmakerمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Science
• Natural Language Processing
• +۲ مورد دیگر

Santu Karmaker is an Assistant Professor at the University of Central Florida in the Department of Computer Science within the College of Engineering. His research focuses on democratizing AI and data science through advancements in natural language processing, information retrieval, and machine learning. He earned a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign and a Master of Science in Computer Science from Bangladesh University of Engineering and Technology. Ph.D. in Computer Science – University of Illinois Urbana-Champaign M.S. in Computer Science – Bangladesh University of Engineering and Technology Karmaker's research explores semantic similarity alignment, conversational data science, infrastructure as code defects, and embedded NLP applications. His work addresses zero-shot learning, Bangla language processing, and prompt taxonomy for LLMs. He has secured over $1.4 million in grants from the NSF, AFOSR, ARO, and USDA. Recent publications span premier venues like EMNLP, ACL, TMLR, and ACM Transactions on Intelligent Systems and Technology. He actively contributes to academic service as an action editor for ACL Rolling Review and communication/tutorial chairs for conferences. Current affiliations include the Laboratory for Information and Decision Systems (MIT) postdoctoral collaboration and UCF's embedded systems research initiatives.

• Clinical Natural Language Processing (NLP)
• Medical Informatics
• Machine Learning
• +۳ مورد دیگر

Jens-Michalis Papaioannou is a prominent Researcher in clinical natural language processing (NLP) and medical informatics, with extensive publications in top-tier venues like ACL, LREC, and EMNLP. His work focuses on improving clinical decision support systems through advanced machine learning techniques. 2024 : Revisiting clinical outcome prediction for MIMIC-IV with biomedical transformers 2023 : Developing MEDBERT.de for German medical NLP and MedAlpaca conversational AI 2022 : Introducing ProtoPatient for interpretable diagnosis prediction 2021 : Creating self-supervised knowledge integration frameworks for admission note analysis His research spans seven major themes : Clinical outcome prediction from admission notes Cross-lingual knowledge transfer in medical NLP Prototypical network applications Data drift analysis in longitudinal datasets Knowledge integration techniques Model optimization for healthcare LLM interpretability frameworks He has collaborated with Wolfgang Nejdl, Alexander Löser, and Betty van Aken on 13+ publications , with over 445 citations. Notable contributions include: Novel patient similarity modeling approaches ICD code hierarchy integration methods Multilingual clinical model strategies Adversarial robustness analysis Medical conversational AI frameworks

Navya Martin Kollapallyمشاهده پروفایل

استادیار

• Ontology Engineering
• Natural Language Processing (NLP)
• AI Ethics
• +۲ مورد دیگر

Navya Martin Kollapally is an Assistant Professor at Kean University , affiliated with the College of Science, Mathematics and Technology and the Department of Computer Science and Technology . She holds an M.S. in Computer Science (2021) and a Ph.D. in Computer Science (2024), both from the New Jersey Institute of Technology. Education M.S. in Computer Science, May 2021, New Jersey Institute of Technology Ph.D., Computer Science, August 2024, New Jersey Institute of Technology Her research focuses on the intersection of ontology and natural language processing (NLP) , particularly on structuring context with ontologies to improve NLP task efficiency. She specializes in extracting Social Determinants of Health (SDoH) from unstructured clinical text using large language models (LLMs) . Additionally, she investigates ethical challenges in AI deployment to ensure responsible and equitable technological applications. Current courses taught include: CPS 2231 Computer Programming TECH 3740 IT Database System Management Contact: nmartink@kean.edu | Office: GLAB 244 | Phone: (908) 737-74715

• Machine Learning
• Explainable AI
• Fairness in AI
• +۳ مورد دیگر

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.

• Machine Learning
• Relational Databases
• Quantum Computing
• +۳ مورد دیگر

R. Hai is a researcher active in the fields of Machine Learning , Relational Databases , and Quantum Computing . Their work bridges the integration of large language models (LLMs) with database systems, focusing on optimizing query processing and data management through linear algebraic methods. Hai's research emphasizes seamless data-ML workflows and innovative applications of relational databases in emerging domains. Key Research Themes : LLM compilation to SQL, quantum circuit simulation via RDBMS, and convergence of data integration with ML. Collaborations : Active in international academic networks, with contributions to conferences like SIGMOD and IEEE journals. Scientific Awards: Veni grant AES2022 (2023) Publications demonstrate expertise in overcoming data barriers, enhancing database performance for ML tasks, and simulating quantum computations using relational database management systems.