جست‌وجوی استاد و پژوهشگر

• Deep Learning
• Pattern Recognition
• Energy Systems Modeling
• +۳ مورد دیگر

Jenny Alexandra Cifuentes Quintero is an Assistant Professor at the Department of Quantitative Methods, School of Engineering (ICAI), Universidad Pontificia Comillas, Madrid. She holds a PhD in Automation and Mechanical and Mechatronic Engineering from a double degree program between National University of Colombia and INSA Lyon, France. Her research focuses on pattern recognition, deep learning, and machine learning applications in energy systems, biomedical engineering, and data science. Education: PhD in Automation and Mechanical/Mechatronic Engineering (double degree: National University of Colombia & INSA Lyon, France) Research: Energy Systems Modeling, Pattern Recognition, Medical Gesture Analysis, Data Science, Urban Mobility Her recent publications span deep learning , energy systems , biomedical signal processing , and interpretable AI . Key trends include surgical gesture classification , renewable energy forecasting , and neural network interpretability . She has received recognition for her work on wind power forecasting (Best Paper, IREC 2022) and contributes as a reviewer for journals like IEEE Access and IEEE Transactions on Biomedical and Health Informatics . Scientific Awards: Best paper on wind energy forecasting (IREC 2022) Mentorship: Directed Master thesis by Mora, E. (2021) Research Grants: Participated in projects for Endesa Medios y Sistemas S.L. (2022) and Enel Iberoamérica S.R.L. (2021)

Debin GAOمشاهده پروفایل

استاد

• Cybersecurity
• Software Engineering
• Human-Machine Collaborative Systems
• +۹ مورد دیگر

Debin GAO is a Full-time Professor of Computer Science at the Singapore Management University , affiliated with the School of Computing and Information Systems (SCIS) . He serves as Co-Director of the Centre on Security, Mobile Applications & Cryptography and Faculty Manager for the SMU BSc (IS)-CMU Fast-Track Programme . His research focuses on Android security , trusted execution environments , and malware detection . PhD from Carnegie Mellon University (2006) Supervisor to SCIS undergraduate instructors Research Advisor to EE Fook Ming GAO's research explores security vulnerabilities in mobile platforms , with emphasis on cache side-channel attacks and Android app debloating . His recent work investigates LLM-driven malware classification and secure code partitioning for smart contracts . His publications demonstrate a focus on mobile security (15/15), including malware analysis (9/15), trusted execution environments (5/15), and side-channel attack mitigation (4/15). Notable contributions include DynDebloater (2025), AutoTEE (2025), and CacheAlarm (2025). As Co-Director of the Centre on Security, Mobile Applications & Cryptography , GAO leads initiatives in trustworthy app delegation (AGChain, 2024) and user-centric security (OTO, 2012). His teaching covers Information Security & Trust , Networking , and Software Engineering .

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۱۲ مورد دیگر

Ahmet Cüneyd Tantuğ is an Associate Professor in the Department of Artificial Intelligence and Data Engineering at the Faculty of Computer and Informatics, Istanbul Technical University (ITU). He has been a core faculty member since 2007 and is among the founders of the ITU Natural Language Processing Group. He has held key administrative roles, including Vice Dean (2011–2015) and Head of IT Department at ITU (2012–2020). BSc, MSc, PhD: Istanbul Technical University, Computer Engineering Guest Researcher: University of Copenhagen, Centre for Language Technology (2006–2007) Dr. Tantuğ’s research spans artificial intelligence, natural language processing, machine learning, and deep learning, with a focus on Turkish and other agglutinative languages. His work includes named entity recognition, dependency parsing, machine translation, word sense disambiguation, and text normalization. He has developed linguistic resources for Turkish and low-resource languages such as Amharic, and has contributed to financial AI and temporal expression extraction. His recent publications highlight advancements in Turkish NLP, including optimal vocabulary sizing, tokenization granularity for LLMs, and transformer-based models for named entity recognition. He has led multiple research projects funded by TÜBİTAK, the European Union, and the Ministry of Development. Scientific awards include: Bedri Karafakioğlu Special Award (2000) Siemens Excellence Award (2007) TeknoGirişim Sermaye Desteği Ödülü (2010) He has supervised numerous master’s and PhD students, with theses on topics such as dependency parsing, named entity recognition, and text normalization. He has also served as a consultant for private sector projects in banking, software, and telecommunications, and as a referee for TÜBİTAK, TEYDEB, and the Ministry of Industry and Technology. Dr. Tantuğ leads and participates in active research projects including Turkish text normalization from user-generated content and improving dependency parsing with augmented features. He is a founding member of the ITU Natural Language Processing Group, which continues to be a central hub for NLP research in Turkey.

• Natural Language Processing
• Biomedical Informatics
• Medical Text Simplification
• +۴ مورد دیگر

Byron C. Wallace is a prominent researcher in Natural Language Processing with a focus on biomedical and clinical applications . His work spans model distillation, factuality evaluation, and medical text simplification. Key contributions include developing methods for tracing teacher models in distillation and creating benchmarks like FactPICO and RedHOT. Research interests include LLM interpretability , medical evidence synthesis , and cross-modal alignment in clinical NLP His recent publications analyze syntactic template repetition in LLMs, token erasure effects , and definition-augmented biomedical NER Wallace's work has advanced zero-shot summarization techniques, pharmacovigilance automation , and patient data privacy analysis in clinical models. Current projects explore chain-of-thought distillation , multilingual medical simplification , and interpretable feature extraction from EHR data. Key collaborators include researchers from institutions like Allen Institute for AI, MIT, and various biomedical NLP teams. His methodological innovations in instance attribution , data augmentation , and counterfactual reasoning have influenced modern NLP research paradigms.

• Natural Language Processing
• Computational Linguistics
• Large Language Models
• +۷ مورد دیگر

Yulan He is an active researcher in Natural Language Processing and Computational Linguistics with numerous publications in top-tier conferences including ACL, EMNLP, and COLING from 2023-2025. Their work spans both theoretical advancements in Large Language Model architectures and practical applications in healthcare, social media analysis, and information retrieval. Research interests focus on Large Language Model optimization , including improving faithfulness in rationale generation, enhancing reasoning capabilities, personalizing outputs to user preferences, and optimizing computational efficiency. Significant contributions include frameworks for debiasing opinion summarization, improving depression detection in clinical interviews, and developing methods for Theory-of-Mind reasoning in LLMs. Their work addresses critical challenges in LLM reliability, interpretability, and efficiency. Analysis of recent publications reveals consistent focus on bridging the gap between theoretical LLM capabilities and practical applications , with particular attention to healthcare contexts, social media analysis, and complex reasoning tasks. Their research demonstrates how to make LLMs more reliable, efficient, and aligned with human needs across diverse domains. Scientific contributions include: Novel frameworks for LLM faithfulness and reasoning (Drift, EnigmaToM) Efficient inference methods (SCOPE, PECAN) Bias mitigation techniques (LASS, Rehearse With User) Personalization approaches (PROPER) Healthcare applications (Explainable Depression Detection) As evidenced by senior authorship positions across numerous publications, Yulan He leads research projects and likely supervises graduate students in NLP research. Their work demonstrates strong technical expertise combined with practical problem-solving approaches to real-world NLP challenges.

• Computational Linguistics
• Artificial Intelligence
• Text Generation
• +۶ مورد دیگر

Sujian Li is an active researcher in computational linguistics and natural language processing, with recent contributions to advanced large language model applications. Their work spans multiple critical areas including hierarchical memory frameworks for Wikipedia generation, self-refining entity grounding systems, and long-context embedding model extensions. Key Research Areas: Continual learning in NLP, multimodal reasoning, cross-lingual knowledge transfer, and factual consistency evaluation. Notable Methods: MOG framework for structured generation, ISR self-refinement scheme, LongAttn token-level analysis, and IPR step-level process refinement. Article Trends show a focus on improving LLM robustness through adversarial training, enhancing coherence via discourse-level graph modeling, and developing benchmarks like WIKIGENBENCH for real-world evaluation. Their research also addresses knowledge integration in biomedical multilingual models (KBioXLM) and mathematical parsing via tree-structured decoding. Collaborations include leading researchers like Yifan Song, Dawei Zhu, and Wenhao Wu across institutions and projects.

Besim Bilalliمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Governance
• Big Data
• +۳ مورد دیگر

Dr. Besim Bilalli is a researcher at the Universitat Politècnica de Catalunya (Technical University of Catalonia) within the Barcelona School of Informatics (FIB) and the Department of Service and Information Systems Engineering . He contributes to the inSSIDE and DTIM research groups, focusing on data governance, machine learning, and software engineering for data-intensive systems. His research interests include: Fair and interpretable machine learning Knowledge graph integration with language models Automated data lifecycle management Pre-processing pipeline automation AI ethics in healthcare applications Key projects include: EXPeriment driven analytics Automated data governance frameworks Erasmus Mundus doctoral program AutoETL pipeline generation Contact: besim.bilalli@upc.edu | ORCID: 0000-0002-0575-2389

• Artificial Intelligence
• Graph Neural Networks
• Biomedical Informatics
• +۷ مورد دیگر

Dr. Siwei Liu is an Assistant Professor at the School of Natural and Computing Sciences, University of Aberdeen, UK. He previously held a postdoctoral position at MBZUAI and completed his PhD with the Terrier team under the supervision of Prof. Iadh Ounis and Prof. Craig Macdonald. He is actively involved in research, teaching, and PhD supervision. His research focuses on advancing artificial intelligence methods, particularly in graph neural networks, large language models, and recommender systems, with applications in bioinformatics, biomedical image analysis, and multi-modal single-cell data. He is a co-founder and main contributor to the open-source Beta-Recsys project, promoting reproducibility and evaluation in recommendation systems. Dr. Liu's recent publications (2020–2025) reflect a strong trajectory in deep learning for biomedical applications and intelligent systems. Key themes include GNNs for gene-disease and RNA-disease association prediction, cold-start recommendation using heterogeneous graphs, pre-training strategies, and hybrid Transformer-Mamba architectures for radiology report generation. His work appears in top-tier venues such as IEEE TPAMI, ACM Transactions, and Briefings in Bioinformatics. He teaches courses in Data Mining and Visualisation and Natural Language Processing, contributing to the education of future AI practitioners. While no specific awards or student names are listed, his active research and leadership in open-source initiatives highlight his growing impact in the AI and biomedical informatics communities.

• Large Language Models
• Multilingual Machine Translation
• Vision-Language Models
• +۳ مورد دیگر

Xiaocheng Feng is a prominent researcher in computational linguistics and large language models (LLMs), with extensive contributions to multilingual systems, vision-language integration, and knowledge alignment. His work spans from 2016 to 2025, focusing on advanced topics like Mitigating hallucinations in multimodal models Efficient knowledge editing across multiple models Length-controlled text generation for black-box LLMs Improving contextual faithfulness via retrieval heads Cross-lingual connection mechanisms for fine-tuning His recent publications address critical challenges in LLMs, including knowledge misalignment, entity-level unlearning, and Pareto optimization in multilingual translation. He has explored probability density estimation for text control, adaptive context modeling in visual storytelling, and cross-lingual annotation projection for low-resource languages. Collaborations with researchers like Bing Qin, Lei Huang, and Weitao Ma highlight his central role in advancing NLP methodologies.

• Blockchain
• Distributed Ledger Technologies
• Supply Chain Management
• +۳ مورد دیگر

Dr. Tan Gürpinar is an Assistant Professor of Business Analytics & Information Systems at Quinnipiac University's School of Business. He also serves as a Faculty Fellow in Residence and Advisor to the Living Learning Experience Leadership Council, mentoring students in leadership development. Additionally, he holds an affiliation with the Fraunhofer Institute for Material Flow and Logistics (Dortmund, Germany), focusing on translating blockchain research into industrial applications. Dr. Gürpinar is an Editorial Board Member for the Blockchain & Cryptocurrency Journal and sits on the Academic Advisory Board of the International Association of Trusted Blockchain Applications. Education Dr. Gürpinar earned his PhD from Technische Universität Dortmund. Research Interests His research explores the organizational, societal, and economic impacts of emerging technologies, with a specialized focus on blockchain and distributed ledger technologies (DLT). Key areas include: Blockchain & DLT : Governance, enterprise integration, and supply chain applications. Technology & Innovation : Management strategies for adopting AI, IoT, and Industry 4.0 solutions. Supply Chain Optimization : Enhancing transparency and efficiency through decentralized systems. Organizational Leadership : Fostering innovation in technology-driven environments. Publications Overview Dr. Gürpinar's recent work (2023–2025) emphasizes blockchain-DLT convergence with AI, supply chain digitization, and educational frameworks. Themes include enterprise blockchain cost modeling, industrial metaverse applications, and decentralized learning systems. His research consistently addresses real-world implementation challenges across automotive, logistics, and higher education sectors. Scientific Awards No awards are mentioned in the provided materials. Advising and Grants As Faculty Fellow, he mentors students through Quinnipiac's Living Learning Experience Leadership Council. No specific grants are detailed. Labs and Teams He collaborates with the Fraunhofer Institute (Germany) to industrialize blockchain research and previously led the Blockchain Team at Dortmund University of Technology, co-founding a research group and industry consortium on DLT.

Ndackyssa Oyima-Antseleveمشاهده پروفایل

استادیار

• Finance
• Technology
• Cyber Risk
• +۴ مورد دیگر

Ndackyssa Oyima-Antseleve serves as an Assistant Professor of Finance at the Orfalea College of Business, California Polytechnic State University (Cal Poly), where he bridges traditional finance principles with cutting-edge technological applications. Education: Ph.D. in Finance from University of Texas at Dallas' Naveen Jindal School of Management Master of Science in Finance from University of Texas at Dallas Dual undergraduate degrees: Bachelor of Science in Petroleum Engineering and Bachelor of Business Administration in Finance from Texas Tech University Dr. Oyima-Antseleve's research centers on the critical intersection of finance and technology, with particular focus on cyber risk and blockchain technology. His work investigates the complex challenges and risks arising from rapid digital innovations in financial systems. Employing advanced methodologies including Big Data analysis, Text Analysis, and Machine Learning, he develops innovative risk management and corporate governance frameworks. His research aims to contribute to academic scholarship while driving meaningful changes in professional practice and society at large. As an educator, Dr. Oyima-Antseleve teaches Investment Management, Intermediate Financial Management, Personal Finance, and Fundamentals of Corporate Finance courses. He has developed specialized FINTECH educational materials, including a comprehensive lecture on building SEC filings applications using Streamlit, Docker, and Google Cloud Run with Retrieval-Augmented Generation techniques. Dr. Oyima-Antseleve has presented his research at prestigious conferences including the American Finance Association (AFA), Southwestern Finance Association (SWFA), Chicago Rising Scholars, and University of Texas at Dallas workshops. His most recent presentation was at the Annual Financial Association Conference Poster Session in January 2024.

Krishnamurthy Dvijothamمشاهده پروفایل

پژوهشگر

• AI Safety
• Robustness in AI
• Differential Privacy
• +۵ مورد دیگر

Krishnamurthy Dvijotham (Dj) is a research scientist with a focus on developing safe, reliable, and secure AI systems. His current role as a Research Lead at ServiceNow Research (2024-2025) builds on his extensive experience at Google DeepMind (Researcher, 2017-2024), Pacific Northwest National Laboratory (Researcher, 2016-2017), and a postdoctoral fellowship at Caltech's Center for Mathematics of Information. Research Interests: Mathematical optimization, control theory, AI robustness, differential privacy, neural network verification, power systems optimization. Scientific Awards: Best Paper at ICML 2024, Best Paper at UAI 2018, Best Paper at Constraints 2016, Best Student Paper at UAI 2014. Dvijotham's work emphasizes the application of rigorous mathematical frameworks to AI systems. His publications span certified robustness , adversarial defense mechanisms , privacy-preserving learning , and power grid optimization , reflecting interdisciplinary contributions to both theoretical and applied domains. He has mentored numerous PhD students and postdoctoral fellows, many of whom now hold academic or research positions at institutions like UC Berkeley, Google DeepMind, and Microsoft Research. His recent articles highlight advancements in diffusion models , selective deferral systems , and formal verification techniques , with a strong focus on security and reliability in AI deployment.

Helena Galhardasمشاهده پروفایل

دانشیار

• Databases
• Data Cleaning
• Data Integration
• +۴ مورد دیگر

Helena Galhardas is an Associate Professor at the Department of Computer Science and Engineering at Instituto Superior Técnico, University of Lisbon , and a researcher at INESC-ID . Her work focuses on data cleaning, data integration, information extraction, and ETL systems , with applications in clinical data management and big data frameworks. Academic Affiliation: Associate Professor, Department of Computer Science and Engineering, Instituto Superior Técnico, University of Lisbon Research Unit: INESC-ID (Information and Decision Support Systems) Contact: helena.galhardas@tecnico.ulisboa.pt | +351 21 423 32 46 Her research spans theoretical and applied domains, including clinical data systems like Umedicine for structured medical data management and PiCaSSo for pediatric palliative care. She has developed frameworks like AJAX for data cleaning, REEL for relation extraction, and ∂u∂u for distributed duplicate detection. Her work integrates machine learning with database systems , particularly in workflow optimization and one-to-many data transformations. Scientific contributions include: Best Student Paper Award (2009) Nomination for Best Paper Award (2009) Research projects: PiCaSSo (2025-26): Pediatric Palliative Care Support System LAIfeBlood : AI for Blood Management Data2Help : Data Science for EMS Optimization DataStorm : Cloud-based Data Management

Alfredo Ferreiraمشاهده پروفایل

دانشیار

• Computer Graphics
• Human-Computer Interaction
• Immersive Analytics
• +۵ مورد دیگر

Alfredo Ferreira is an Associate Professor at the Department of Computer Engineering of Instituto Superior Técnico, University of Lisbon, and a researcher at INESC-ID Lisboa. His research focuses on Computer Graphics, Human-Computer Interaction, Immersive Analytics, 3D Shape Analysis and Retrieval, and Extended Reality. Education: PhD in Information Systems and Computer Science, Technical University of Lisbon, 2009 MSc in Information Systems and Computer Science, Technical University of Lisbon, 2005 Research Interests: His expertise spans Computer Graphics , Human-Computer Interaction , and Immersive Analytics . He explores natural interfaces, immersive environments, 3D object retrieval, and extended reality applications in domains like civil engineering and facilities management. Publications: He has authored over 40 peer-reviewed conference papers and 15 journal articles, with recent works focusing on immersive technologies for dam monitoring, extended reality in construction, and sketch-based 3D modeling. Teaching: Information Visualization Usability and Information Systems Web Computation Human-Computer Interaction Computer Graphics Supervision: He has supervised more than 30 MSc students, 2 PhD candidates, and 1 post-doc researcher. Professional Memberships: Association for Computing Machinery (ACM) Institute of Electrical and Electronics Engineers (IEEE) Eurographics

Giovanni Trappoliniمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Multimodal Deep Learning
• +۵ مورد دیگر

Giovanni Trappolini serves as Assistant Professor at Sapienza University of Rome within the Department of Computer, Control, and Management Engineering, conducting research at the RSTLess Lab under Prof. Fabrizio Silvestri. Previously, he completed his Ph.D. in Machine Learning at Sapienza under Prof. Emanuele Rodolà, following an MSc in Data Science where he graduated cum laude as a Sapienza honor graduate. His educational background includes: MSc in Data Science, Sapienza University of Rome (cum laude, Sapienza honor graduate) Ph.D. in Machine Learning, Sapienza University of Rome (2022) BSc from Luiss Guido Carli Trappolini's research bridges Machine Learning and Deep Learning with emphases on multimodal systems and information retrieval . He pioneers applications in Graph Neural Networks security, Federated Learning architectures, and Italian-language Large Language Models —notably creating Fauno , Italy's leading LLM. His work extends to operating system innovation through generative AI, 3D shape analysis using transformers, and creative applications like AI-driven music generation. Analysis of his 2023 publications reveals dominant trends toward integrating retrieval systems with generative models (RAG), developing robust neural databases, and enhancing cross-modal understanding. Key themes include adversarial defense for graph networks, privacy-preserving federated retrieval, and transformer-based geometric learning—demonstrating consistent focus on foundational AI infrastructure. Scientific recognition includes: Sapienza honor graduate Cum laude graduate distinction Trappolini actively contributes to academic instruction through courses including Advanced Data Mining and Language Technologies (Sapienza, 2023) and multiple iterations of Python Programming for Data Science (2019-2023). He maintains significant research collaborations with Stanford, Technion, Meta, Amazon, TII, and UniPi while preparing new PhD-level coursework in Geometric Deep Learning for 2024. His research operates within the RSTLess Lab ecosystem, focusing on scalable AI systems and multimodal integration.