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Edmon BegoliView profile

Adjunct Professor

• Artificial Intelligence Security
• Adversarial Machine Learning
• Health Informatics
• +4 more

Edmon Begoli serves as Director of Oak Ridge National Laboratory's Center for AI Security Research (CAISER) and holds an Adjunct Professor appointment in the Department of Electrical Engineering and Computer Science at the University of Tennessee-Knoxville. As a distinguished ORNL research staff member and senior scientist, he specializes in developing resilient, secure, and scalable machine learning architectures with national impact in AI security, veteran suicide prevention, and precision medicine initiatives. Begoli's educational foundation includes undergraduate and graduate studies at the University of Colorado-Boulder and a doctorate from the University of Tennessee, all in Computer Science. Following his PhD, he conducted research as a visiting scholar at UC Berkeley's EECS department, where he maintains an active association with the SKY Computing lab. His research program bridges adversarial machine learning and healthcare analytics, focusing on real-time decision systems for critical applications like suicide risk prediction and infectious disease monitoring. Begoli pioneers frameworks that integrate cybersecurity principles into AI development to withstand sophisticated attacks while maintaining functionality in sensitive environments. Analysis of his publication trajectory reveals a consistent emphasis on adversarial techniques applied across cybersecurity and healthcare domains, with increasing focus on veteran health outcomes and medical data security. His work demonstrates the convergence of transformer model vulnerabilities, medical informatics, and national security applications. Notable recognition includes: IEEE Computer Society Distinguished Contributor status Google Research Innovator award (2022) for robust streaming and language processing architectures Begoli leads major national initiatives including the PERC/REACH VET veteran suicide prevention collaboration and MVP CHAMPION precision medicine program. He designed DOE's foundational Knowledge Discovery Infrastructure (KDI) and Citadel platforms for protected data computation, establishing critical capabilities for secure analytics on leadership-class systems. As co-leader of ORNL's internal AI safety initiative and collaborator with UC Berkeley's RISE Lab, he advances real-time analytic monitoring frameworks for high-stakes clinical events through projects like Realm, which implements Ray's actor model for context-specific model updates across distributed healthcare systems.

Philip KennewegView profile

Researcher

• Machine Learning
• Deep Learning
• Computer Vision
• +11 more

Philip Kenneweg is a Researcher at the Faculty of Engineering , University of Bielefeld , working within the Machine Learning Group at Center for Cognitive Interaction Technology (CITEC) . Since 2023, he serves as a Research assistant with teaching duties , supporting courses like Deep Learning and Introduction to Machine Learning. Education : PhD in Computer Science (2020–2025), M.Sc. in Natural and Computer Sciences (2016–2019), B.Sc. in Natural and Computer Sciences (2013–2016), Abitur (2005–2013) His research focuses on advancing optimization techniques for neural networks and transformer models , with significant contributions to line search adaptation , debiasing embeddings , and generative modeling . Philip has pioneered the SaLSa optimizer for automated learning rate determination and explored diffusion models for synthetic genotype generation in bioinformatics. Recent publications span transformer fine-tuning , retrieval-augmented generation systems , and reinforcement learning architectures , reflecting his expertise in bridging theoretical innovation with practical AI solutions. His work emphasizes ethical considerations in NLP and computational efficiency in large-scale model training. Scientific Awards : 1st place, it's OWL Makeathon 2022 1st place, RoboCup@Home 2016 As part of CITEC, Philip contributes to cognitive interaction technology research, combining machine learning with real-world applications in robotics and data analysis.

• Machine Learning
• Natural Language Processing
• Computer Vision
• +3 more

Andreas Babic is a Junior Researcher at the Institute of Creative Media and Technologies within the University of Applied Sciences St. Pölten , affiliated with the Department of Media and Digital Technologies . His work focuses on applying AI and machine learning techniques to analyze animation films and address online hate speech. Research Interests : Machine Learning Natural Language Processing Computer Vision Artificial Intelligence Digital Media Multimedia Technologies Publication Trends : His recent publications highlight applications of AI in media analysis (e.g., hybrid animations) and social computing, particularly in developing multilingual models for automatic sexism detection using transfer learning and toxicity embeddings. Projects : YCSAD (Youth Cyber Security Awareness Development) AniVision: Computer Vision in Animation Analysis Counter Speech - Young People Against Online Hate Laboratory : Member of the Media Computing Research Group , which explores intersections of digital media, AI, and human-computer interaction.

Diana TrandabatView profile

Researcher

• Natural Language Processing
• Computational Linguistics
• Semantic Role Labeling
• +5 more

Diana Trandabat is a researcher in Computational Linguistics and Natural Language Processing at the Faculty of Computer Science, University Al. I. Cuza Iasi, and the Institute for Computer Science, Romanian Academy. She obtained her PhD in March 2010 with a thesis focused on Semantic Role Labelling (SRL) for Romanian language, having developed a language-independent SRL tool and applied it to various NLP processes. Her research interests span Natural Language Processing, Computational Linguistics, Semantic Role Labeling, Sentiment Analysis, Social Media Analysis, and Romanian Language Processing. She has extensive experience in developing NLP systems for Romanian language, particularly in semantic analysis, with applications in social media monitoring, medical text analysis, and educational technology. Her publication record shows a strong focus on semantic analysis, with numerous contributions to the SemEval evaluation challenges across multiple years. Her work demonstrates expertise in applying semantic role labeling to various domains including social media analysis, medical text processing, and educational applications. She has developed systems for detecting offensive language in Romanian social media, sarcasm detection, word-in-context disambiguation, and generating tests from medical reference texts. Mining Social Media to Extract Structured Knowledge through Semantic Roles (2017) Medi-Test: Generating Tests from Medical Reference Texts (2018) Special issue on the curative power of medical data (2019) Hope at SemEval-2019 task 6: Mining social media language to discover offensive language (2019) FII_CROSS at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (2021) Detecting Offensive Language in Romanian Social Media (2022) Trandabat has been actively involved in the development of language resources for Romanian, including the COBILIRO Project for building bimodal corpora. She has also contributed to educational applications through voice-controlled games and augmented reality cooking experiences. Her work bridges theoretical NLP research with practical applications in healthcare, education, and social media analysis.

• Natural Language Processing
• Artificial Intelligence
• Information Extraction
• +4 more

Assoc. Prof. Pavel Pecina, Ph.D., is a faculty member at the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. His primary academic role as an Associate Professor focuses on Natural Language Processing , Artificial Intelligence , and related areas. Research Interests: Information extraction, information retrieval, machine translation, multimodal data interpretation, optical music recognition Teaching: Courses since 2012-2022 including Natural Language Processing , Information Retrieval , and Statistical Methods in NLP Key Research Projects include GI-Insight (AI for healthcare), RES-Q+ (global stroke care registry), and MEMORISE (heritage digitization). His recent publications (2023-2025) demonstrate expertise across clinical NLP , historical document analysis , and music technology domains. Advising: Supervised 10+ graduate students in areas ranging from semantic search to optical music recognition. Collaborates with international institutions on multilingual translation and medical informatics challenges through initiatives like IWSLT and CLEF eHealth.

Hanspeter PfisterView profile

Professor

• Computer Science
• Visualization
• Computer Vision
• +3 more

Hanspeter Pfister serves as the An Wang Professor of Computer Science within Harvard University's School of Engineering and Applied Sciences (SEAS), specifically in the Department of Computer Science. His research bridges theoretical and technological domains through interdisciplinary applications. His primary research interests include: Connectomics and neural circuit mapping Biomedical and scientific visualization techniques Computer vision for medical imaging 3D/4D reconstruction and Gaussian splatting Sports analytics through XR technologies Analysis of his 2023-2025 publications reveals a pronounced focus on machine learning integration with visualization, particularly diffusion models for histological analysis, language-guided 3D reconstruction, and connectome annotation systems. His work demonstrates consistent interdisciplinary collaboration between computer science and neuroscience. No scientific awards are documented in the provided materials. Details regarding student advising and grant funding remain unspecified in the source text. The Pfister group maintains active research streams in Connectomics, Information Visualization, Biomedical Imaging, Computer Vision, and SportsXR, with recent projects including CAVE (Connectome Annotation Versioning Engine) and SmartEM (machine-learning guided electron microscopy).

Nada LavračView profile

Professor

• machine learning
• data mining
• inductive logic programming
• +5 more

Professor Nada Lavrač is a leading researcher at the Jožef Stefan Institute and Full Professor at the University of Nova Gorica , Slovenia. With over 40 years of experience in machine learning and data mining, she has made groundbreaking contributions to inductive logic programming, computational creativity, and biomedical data analysis. Her work spans applications in medicine, public health, bioinformatics, and industrial systems, with a focus on explainable AI and knowledge discovery. Head of ICT Programme at Jozef Stefan International Postgraduate School Principal researcher in EU projects H2020 FNS-Cloud and EMBEDDIA Author of 163-page monograph Representation Learning: Propositionalization and Embeddings (Springer, 2021) Research highlights include developing neuro-symbolic AI frameworks for conference scheduling, creating ontology-driven graph-based learning systems, and pioneering feature selection methods for high-dimensional biomedical datasets. Her work on tax2vec for taxonomy-based text classification has transformed short text analysis approaches. She has also led advancements in grapevine disease modeling using multi-omics data integration. Scientific accolades: 1984 national research excellence prize , 1997 Ambassador of Science of Slovenia , ECCAI Fellow (2007) , and Zois Recognition Award (2013) . Her citation metrics include 16,141 Google Scholar citations (h=54) and 4,622 WoS citations (h=31).

Nick StreetView profile

Professor

• Data Science
• Machine Learning
• Business Analytics
• +3 more

Nick Street is the Leonard A. Hadley Chair in Leadership and Professor at the University of Iowa, with appointments in the Tippie College of Business (Business Analytics), Computer Science, and Nursing. He serves as Associate Dean for Research and PhD Programs in the Tippie College of Business. PhD in Computer Science, University of Wisconsin-Madison MS in Computer Science, DePaul University BA in Mathematics and Computer Science, Drake University Street's research bridges data science with healthcare analytics , business intelligence , and social network analysis . His work spans predictive modeling in cancer symptom trajectory analysis, federated learning for medical imaging, and influence diffusion in social networks for viral marketing. He has developed methodologies for inverse classification, correlation search, and real-time risk prediction in clinical and financial domains. Recent publications focus on machine learning applications in healthcare (e.g., surgical site infection prediction, warfarin dosing optimization) and urban informatics (e.g., traffic accident forecasting). His work integrates NLP for processing clinical narratives and deep learning for longitudinal EHR analysis. INFORMS Society on Data Mining Prize (2022) Henry B. Tippie Research Professor (2014) Dean's Teaching Award (2012) Master of Business Analytics Instructor of the Year (2023) Street has advised numerous graduate students and secured grants from the NIH , NSF , and institutional sponsors. Current projects include federated learning for medical imaging and multimorbidity pattern analysis in Alzheimer’s disease.

Neil RanlyView profile

Assistant Professor

• Software Engineering
• Information Management
• Portfolio Decision Analysis
• +3 more

Dr. Neil Ranly serves as an Assistant Professor of Data Science at the Air Force Institute of Technology (AFIT), leveraging over 20 years of software development experience and 15 years in information management research to advance defense-oriented data science applications. His academic credentials include: Doctorate in Operations Research from the Air Force Institute of Technology (2018) Master of Science in Management Science from the University of Dayton (2009) Bachelor of Science in Computer Science from the University of Dayton (2004), graduating Magna Cum Laude with the Addison-Wesley Senior Book Award for Excellence in Computer Science Ranly's research integrates software engineering with complex decision systems, specializing in portfolio optimization under time-sensitive constraints and information aging dynamics. His recent work demonstrates a strategic pivot toward AI-driven methodologies, particularly large language models for natural language processing and computer vision applications in defense contexts. This evolution reflects his commitment to solving real-world problems in information management through cutting-edge computational approaches. Analysis of his 2024-2025 publications reveals a concentrated focus on AI/ML applications across three critical domains: defense-oriented computer vision (satellite imagery analysis), natural language processing advancements (word sense disambiguation), and software engineering innovation (rapid experimentation frameworks). These works collectively emphasize practical implementations of theoretical models in resource-constrained military environments, with significant contributions to data analytics for system sciences. His scholarly recognition includes: Magna Cum Laude Graduation Honor Addison-Wesley Senior Book Award for Excellence in Computer Science As a research advisor, Ranly mentors students including R. Everman, J. H. Yae, and N. C. Skelly on defense-focused AI projects, with current grants supporting satellite imagery analysis and NLP applications. He actively collaborates with the Air Force Research Laboratory and IISE on information management systems requiring multi-user coordination and temporal data decay modeling. Though institutional lab affiliations aren't explicitly stated, his leadership in conference minitracks on NLP and LLMs indicates active participation in AFIT's data science research ecosystem, particularly teams developing AI solutions for Air Force operational challenges.

Carolina ScartonView profile

Senior Lecturer

• Natural Language Processing
• Machine Translation
• Online Misinformation Detection
• +6 more

Dr. Carolina Scarton is a Senior Lecturer in Natural Language Processing at the Department of Computer Science, University of Sheffield, UK, and serves as Research Innovation Grant Support Lead. She is an active member of the Natural Language Processing research group and part of the GATE team, with significant contributions to European projects including WeVerify and SIMPATICO. Her educational background includes a PhD in Computer Science from the University of Sheffield (2017), supervised by Professor Lucia Specia and funded by the EXPERT Marie Curie ITN network, along with MSc and BSc degrees from the University of São Paulo, Brazil (awarded 2013), where she was a member of the Interinstitutional Center for Computational Linguistics (NILC). Dr. Scarton's research focuses on Natural Language Processing with particular emphasis on text adaptation, machine translation, online misinformation detection and verification, evaluation of NLP task outputs, NLP applied to healthcare and robotics, and dialog systems. Her work bridges theoretical NLP advances with practical applications in disinformation combat and accessibility. Analysis of her recent publications (2023-2025) reveals strong trends in multilingual misinformation detection, biomedical text simplification, idiomaticity representation, and leveraging large language models for zero-shot tasks. Her research demonstrates consistent focus on real-world applications while advancing core NLP methodologies across multiple domains. Secretary for the European Association for Machine Translation (EAMT) since 2018 (re-elected 2020) Secretary for the International Association for Machine Translation (IAMT) since 2019 Dr. Scarton currently leads as Principal Investigator for the ExU project (€399,926, 2023-2025) and VIGILANT project (Horizon Europe, £476,955, 2022-2025), while serving as Co-PI for vera.ai (Horizon Europe, £776,703, 2022-2025) and Co-I for multiple UKRI and EPSRC grants including XAIvsDisinfo (£288,337) and Modeling Idiomaticity (£446,163). Her research group actively collaborates with the GATE team on NLP tool development and applications.

Xi WangView profile

Lecturer

• Natural Language Processing
• Conversational AI
• Retrieval-Augmented Generation
• +3 more

Dr Xi Wang is a Lecturer in the School of Computer Science at the University of Sheffield, specializing in Natural Language Processing. His research focuses on conversational AI systems, personalization techniques, retrieval-augmented generation, and fairness in AI applications. He actively contributes to the school's Natural Language Processing research group and maintains strong industry connections through publications in top-tier conferences. Dr Wang earned his PhD from the University of Glasgow under Prof. Iadh Ounis and Prof. Craig Macdonald, where he developed review text-based recommender systems. He subsequently conducted postdoctoral research at University College London's web intelligence group with Prof. Emine Yilmaz, focusing on knowledge-augmented task-oriented dialogue systems. His research spans critical NLP domains with emphasis on: Conversational AI: Building dialogue systems that handle complex user interactions Retrieval-Augmented Generation: Integrating external knowledge into language models Personalization: Developing adaptive systems that respect user context Fairness: Addressing bias in recommendation and dialogue systems Machine Learning: Applying advanced techniques to information retrieval challenges Analysis of Dr Wang's 15 most recent publications (2021-2025) reveals a clear trajectory toward more sophisticated conversational systems. His work increasingly integrates retrieval methods with generative models, particularly in recommendation contexts. Key trends include cold-start problem solutions, bias mitigation in conversational agents, and novel approaches to clarification question handling. The research demonstrates growing emphasis on fairness and knowledge grounding in dialogue systems. Dr Wang received a Google research workshop grant ($20,000) in 2022 for developing knowledge-enriched task-oriented dialogue systems during his postdoctoral work at UCL with Prof. Emine Yilmaz. While no current advisees are listed, Dr Wang's grant activity demonstrates research leadership. His Google-funded project focused on action-oriented dialogue systems, showing capability in securing competitive research funding. The Natural Language Processing research group at Sheffield provides his primary academic home, where he contributes to the school's strengths in speech and language technologies. His recent publications indicate active collaboration with researchers across multiple institutions including UCL, University of Glasgow, and international partners.

Eva DarulovaView profile

Associate Professor

• Programming Languages
• Software Verification
• Program Synthesis
• +3 more

Eva Darulova is an Associate Professor at the Department of Information Technology, Uppsala University, and Adjunct Faculty at Max Planck Institute for Software Systems (MPI-SWS). Previously held tenure-track faculty at MPI-SWS and completed her PhD at École Polytechnique Fédérale de Lausanne (EPFL) under Viktor Kuncak. Current research: Applications in numerical/embedded domains, program synthesis, software verification, approximate computing Key projects: Blossom (fuzzing), Lassie (HOL4 tactics), Pine (floating-point loops), LTLTalk (robot instruction), Incarnate (3D printing), Icing (compiler semantics), Flipper (NLP interface), FloVer (error checking), Daisy (accuracy analysis), Rosa (real compiler) Teaching: Program Design (2021/22), Program Synthesis (2021), Program Analysis (multiple years), Advanced Program Analysis (2019), Static Program Analysis (2017), Approximate Computing seminar (2016) Service: Artifact evaluation chair (ASPLOS'22), PC member (ECOOP'22, ESEC/FSE'22, EMSOFT'21, PLDI'21) Research Trends: Focus on numerical computing verification, approximate computing frameworks, and program synthesis techniques. Publications span TACAS, EMSOFT, ISSTA, POPL, CAV, FMCAD, TOPLAS, with emphasis on floating-point error analysis, mixed-precision optimization, and compiler verification. Scientific Recognition: Nominated for EAPLS best paper award (TACAS'21). Co-advisor for Anastasiia Izycheva and mentor for multiple MSc/BSc students including Mustafa Hafidi, Joachim Bard, Nikita Zyuzin, Youcef Merah, Safya Alzayat, Anastasiia Izycheva. Collaborations: Works with researchers at EPFL, MPI-SWS, University of Saarland, University of Washington, and Saarbrücken/Kaiserslautern institutions.

• Natural Language Processing
• Deep Learning
• Text Summarization
• +3 more

Valentin Nyzam is a Researcher at the University of Tours , affiliated with the Polytechnic School and Department of Networks and Telecommunications (RT) . He works at the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT, UR 6300) and is associated with the University Institute of Technology of Blois (IUT Blois) . Research Focus: Specializes in Natural Language Processing with emphasis on automatic summarization and comparative summarization Develops modular open-source tools (MOTS) for multi-document summarization Active in deep learning applications for text analysis and word embeddings Conducts cross-lingual research and short text language identification Focus on French language processing and implementation of NLP tools Contact: valentin.nyzam@univ-tours.fr

Nickvash KaniView profile

Academic

• Mathematical Language Processing
• Natural Language Processing
• Artificial Intelligence
• +4 more

Nickvash Kani is a Teaching Assistant Professor in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign since August 2023. He is affiliated with the Coordinated Science Lab and leads research in Mathematical Language Processing, focusing on developing systems that can process mathematical data with the sophistication currently applied to natural language. His educational background includes: Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2017) M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2013) B.S. in Computer Engineering from Boston University (2010) Dr. Kani's research centers on Mathematical Language Processing, which he describes as essential for achieving true artificial intelligence since mathematics represents axiomatic knowledge. His work bridges natural language processing with mathematical reasoning systems, developing algorithms to extract knowledge from mathematical literature. He emphasizes that "true intelligence is axiomatic" and believes focusing on mathematical literature enables automated collection of axiomatic knowledge from unlabeled sources. His publication record shows a clear evolution from traditional electrical engineering topics like nanomagnetics and spintronics to cutting-edge AI research. Recent publications (2023-2025) focus on mathematical expression representation, formula search, and enhancing mathematical reasoning in language models. His work spans multiple venues including Transactions on Machine Learning Research, IEEE/ACM Transactions, and NeurIPS workshops, demonstrating interdisciplinary impact across computer science and engineering. His notable achievements include: George Anner Excellence in Undergrad Teaching Award (September 2023) 1st Place - NL4Opt-Task2 challenge at NIPS 2022 (December 2022) 2023 Engineering Council Outstanding Advisors award Dr. Kani actively mentors students and has built a research group dedicated to Mathematical Language Processing. His graduate teaching assistants, including Neeraj Gangwar and Suyuan Wang, have been instrumental in his research and teaching. He has taught ECE 374 (Introduction to Algorithms and Models of Computation) to 200-300 students and developed ECE 364 (Data Science and Engineering), focusing on implementation with auto-differentiation tools like PyTorch. He leads the Mathematical Language Processing Group, which is dedicated to developing algorithms that process mathematical data and literature, with the long-term goal of enabling AI systems to reason axiomatically. His teaching philosophy emphasizes "slow, iterative changes" to improve course accessibility and support student learning objectives in STEM education.

Artem SokolovView profile

Professor

• Machine Translation
• Natural Language Processing
• Structured Prediction
• +4 more

Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.