Ana Klimovic is an Assistant Professor at the Department of Computer Science, ETH Zürich, and Deputy Head of the Institute for Computing Platforms. She leads the Efficient Architectures and Systems Lab (EASL) and focuses on optimizing cloud computing performance and resource efficiency. Education: Ph.D. in Electrical Engineering (Stanford University), M.S. in Electrical Engineering (Stanford), B.A.Sc. in Engineering Science (University of Toronto) Previous Roles: Research Scientist at Google Brain Her research spans cloud computing, operating systems, distributed systems, and their intersection with machine learning. Key areas include cloud-native programming models, elastic storage, GPU sharing, and data pipeline optimization. The 2025-2024 articles highlight trends in serverless computing, large language model (LLM) serving, GPU optimization, and data-centric ML systems. Themes include elasticity, resource multiplexing, heterogeneous computing, and infrastructure for AI/ML workloads. Scientific Awards: Microsoft Research PhD Fellowship, Stanford Graduate Fellowship She has led research projects on ephemeral storage, distributed training automation, and cost-efficient ML preprocessing, with grants from Microsoft Research and Stanford.
Roles & Affiliations: Manolis Savva is an Associate Professor at Simon Fraser University's School of Computing Science and holds a Canada Research Chair in Computer Graphics. He leads research in 3D scene understanding, with applications in graphics, vision, and robotics. Previously, he was a researcher at Facebook AI and Princeton University. Education: Ph.D. in Computer Science (2016), Stanford University, advised by Pat Hanrahan B.A. in Physics and Computer Science (2009), Cornell University Research Interests: His work focuses on analyzing, organizing, and generating 3D content, particularly for holistic scene understanding. Key areas include articulated objects, embodied AI, and datasets like ScanNet , Matterport3D , and Habitat . His methods drive applications in robotics, autonomous agents, and virtual environments. Publications Trends: Recent work emphasizes generative models (e.g., SINGAPO for articulated object parts), embodied AI benchmarks (Habitat), and multimodal scene analysis. His papers often address challenges in scalability, realism, and cross-modal fusion for 3D environments. Awards: CHCCS Early Career Researcher Award (2022) ICLR 2023 Outstanding Paper Award ICCV 2019 Best Paper Nomination (Habitat) Advising & Grants: Supervised over 15 graduate students, many advancing to top PhD programs and tech companies. Active in grants for embodied AI, scene understanding, and robotics. Labs/Teams: Leads the 3DLG (3D Learning Group) and GrUVi (Graphics and Vision) groups at SFU. Collaborates extensively with industry (e.g., Meta, NVIDIA) on AI-driven 3D research.
Catherine Lai is a Reader (~Associate Professor) in the Department of Linguistics and English Language at the University of Edinburgh, with strong affiliations to the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition and Computation (ILCC) in the School of Informatics. She is based in the School of Philosophy, Psychology and Language Sciences and is actively involved in research, teaching, and academic service. Department: Department of Linguistics and English Language School: School of Philosophy, Psychology and Language Sciences Research Institutes: Centre for Speech Technology Research, Institute for Language, Cognition and Computation Email: C.Lai@ed.ac.uk Her research centers on the role of prosody—non-lexical aspects of speech—in spoken communication. She investigates how prosody contributes to discourse structure, information structure, and affect in dialogue, using interdisciplinary methods from linguistics and machine learning. Her work bridges theoretical linguistics and practical speech technology, aiming to improve spoken language understanding and synthesis systems. She is particularly interested in how prosody shapes listener expectations and how affect and topic are expressed and perceived in conversation. Her recent publications reflect a strong focus on self-supervised learning in speech models, emotion recognition, ASR error correction using large language models, cognitive state classification, and ethical considerations in language technology. She explores topics such as the uncanny valley in synthetic speech, gender expression through voice, and community-centered development of language technologies. Prize from Scopus Profile Catherine Lai has supervised several PhD students, including Leimin Tian and Yuanchao Li, and has been involved in significant research projects, such as a Toyota-funded initiative on spoken dialogue for robot companions. She has secured multiple grants and leads a research agenda that integrates theoretical inquiry with real-world applications in assistive technologies and social science. Her academic service includes organizing major conferences like Interspeech and UK and Ireland Speech. She is a key member of research teams at CSTR and ILCC, collaborating across disciplines to advance the understanding of spoken communication and the development of robust, ethical speech technologies.
John B. Whitman is a Professor of Linguistics at Cornell University's College of Arts and Sciences. His research focuses on language variation, syntax, historical linguistics, and East Asian languages, particularly Japanese and Korean. He explores typological universals, syntactic variation, and diachronic developments, contributing to debates on configurationality and structural universals. Whitman has published extensively on topics ranging from Japanese historical phonology to comparative syntax across East Asian languages. His work often integrates cross-linguistic analysis with neuroscientific insights, as seen in studies on semantic number processing. He has received grants for projects including language revitalization initiatives and syntactic research, and he teaches courses on Japanese language history and language endangerment. Whitman collaborates with interdisciplinary teams and actively engages in linguistic fieldwork and historical text analysis, maintaining affiliations with the East Asia Program and research labs at Cornell. Research interests include syntactic variation, historical syntax, and the interaction between language structure and diachronic change. His recent work examines the evolution of verbal conjugations in Japanese/Korean and the typological implications of syntactic reanalysis. Whitman's publications also address methodological questions in glossing practices and comparative lexicography, bridging historical linguistics with modern computational analysis. Grants and projects include funding from the New Frontier Grants for collaborative research on language documentation and historical syntax. He advises graduate students in the linguistics program and contributes to Cornell's language revitalization efforts, such as Cayuga language preservation initiatives. Whitman's work frequently intersects with cognitive science, as seen in neuroimaging studies of linguistic structures.
Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, holding the Canada CIFAR AI Chair at the Vector Institute and serving as Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Her research spans AI safety, ethics, reinforcement learning, and knowledge representation, with a focus on human-compatible AI and long-term societal impacts. Her career includes six years as a Research Scientist at Stanford University and a year at Xerox PARC. McIlraith’s work has been recognized through ACM and AAAI fellowships, as well as prestigious awards like the SWSA 10-Year Award (2011) and the CAIAC Lifetime Achievement Award (2024). Research Interests: AI Safety and Alignment Human-Compatible AI Reinforcement Learning with Ethical Constraints Semantic Web Services Cognitive Robotics and Diagnostic Systems Probabilistic and Logical Reasoning Recent Contributions: Her work emphasizes ethical AI integration, such as the Embedded Ethics Education Initiative (E3I), and addresses challenges in long-term AI risks, multi-agent systems, and interpretable decision-making frameworks. Awards and Honors: ACM Fellow AAAI Fellow 2023 IJCAI-JAIR Best Paper Prize 2024 CAIAC Lifetime Achievement Award Labs and Teams: McIlraith leads initiatives at the Schwartz Reisman Institute and contributes to the Vector Institute, focusing on societal and ethical dimensions of AI technology.
Professor Janet B. Pierrehumbert is a leading academic in computational linguistics and natural language processing, holding the position of Professor of Language Modelling at the Oxford e-Research Centre, University of Oxford. She is also a Senior Research Fellow at Trinity College and affiliated with the Faculty of Linguistics, Philology and Phonetics. Her work bridges interdisciplinary research in phonology, sociolinguistics, and computational models of language dynamics. Education: PhD in Linguistics from MIT (1980), A.B. in Linguistics from Harvard University (1975). Research Interests: Focuses on computational linguistics, dialect variation, language dynamics, and the societal impacts of NLP. Her group develops algorithms for analyzing social media discourse, forecasting trends, and modeling language evolution. Recent work includes studies on dialect fairness in LLMs and semantic shifts in political discourse. Key Projects: EPSRC-funded research on online forum dynamics, the Wordovators project on lexical innovation, and collaborations with institutions like the Oxford Man Institute. Her work emphasizes robust NLP systems and theoretical linguistics. Awards: ISCA Medal (2020), National Academy of Sciences membership (2019), Fellowships from the American Academy of Arts and Sciences and Cognitive Science Society. Grants & Advising: Over £6M in research funding, including EPSRC and Templeton grants. Supervised over 30 PhD students and postdocs, many now leading academics and industry researchers in NLP and linguistics. Labs/Teams: Leads the Pierrehumbert Language Modelling Group, collaborating with the Oxford e-Research Centre and international partners like Stanford and the University of Canterbury.
Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.
Elisa Ricci is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento and serves as Head of the Research Unit Deep Visual Learning at Fondazione Bruno Kessler. She coordinates the Doctoral Program in Information Engineering and Computer Science at the University of Trento and holds prestigious fellowships from ELLIS and IAPR. Her research focuses on advancing computer vision and deep learning systems capable of operating in open-world environments. Key interests include domain adaptation, continual learning, and self-supervised learning for visual and multi-modal data processing. Her work addresses critical challenges in enabling machines to adapt to new domains without forgetting prior knowledge, with applications spanning robotics perception, medical imaging, and privacy-preserving AI systems. Recent publications reveal a dominant trend toward leveraging vision-language models for open-vocabulary tasks, training-free adaptation methods, and federated learning architectures. Significant research thrusts include machine unlearning for privacy, robustness against bias in visual classifiers, and novel class discovery using foundation models—particularly evident in 2025 publications addressing medical imaging, 3D segmentation, and collaborative generative systems. Her major recognitions include: ELLIS Fellow IAPR Fellow As Doctoral Program Coordinator at the University of Trento, she oversees PhD training while leading the Deep Visual Learning unit at Fondazione Bruno Kessler. Her research group secures competitive grants in AI-driven perception systems, though specific funding sources aren't detailed in the source material. The Deep Visual Learning Research Unit specializes in open-world computer vision challenges, developing frameworks for domain adaptation, continual learning, and multi-modal perception. Current projects integrate generative models with robotics applications while addressing privacy concerns in vision-language systems through unlearning techniques.
David Alvarez-Melis is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data-Centric Machine Learning (DCML) group and holds affiliations with the Kempner Institute, Harvard Data Science Initiative, and the Center for Research on Computation and Society. His research focuses on making machine learning more data-efficient and trustworthy, with applications in natural and medical sciences. He also serves as a researcher at Microsoft Research New England. Affiliations: SEAS, Kempner Institute, Harvard Data Science Initiative, CRCS Education: PhD in Computer Science (MIT), MS in Mathematics (NYU Courant), BSc in Applied Mathematics (ITAM) Research Interests: Optimal Transport, dataset distillation, interpretable AI, medical imaging, robustness, and large language models. His work bridges theory and applications, emphasizing geometric and probabilistic methods. Recent Trends in Publications: Focused on advancing optimal transport for data manipulation, distributional deep equilibrium models, and repurposing LLMs for specialized domains. Key themes include synthetic dataset generation, gradient flows in probability spaces, and robust interpretability frameworks. Awards: Aramont Fellowship, Dean’s Competitive Fund, Top Reviewer awards at major conferences (ICLR, NeurIPS, ICML). Grants: Supported by the Aramont Fund and Harvard’s Dean’s Fund. His lab advises students across Harvard and MIT, with notable contributions to medical imaging, NLP, and foundational ML theory. He actively mentors interns and fosters collaborations with industry and academia.
Nate Foster is a Professor of Computer Science at Cornell University and currently serves as the Associate Dean for Research in the Ann S. Bowers College of Computing and Information Science. He is also a Visiting Researcher at Jane Street and served as a Visiting Professor at École Polytechnique Fédérale de Lausanne during the 2023-24 academic year. His research uses ideas from programming languages to solve problems in networking, databases, and security. BA in Computer Science, Williams College (2001) MPhil in History and Philosophy of Science, University of Cambridge (2008, all work completed in 2003) PhD in Computer and Information Science, University of Pennsylvania (2009) Foster's research focuses on developing languages and tools that make it easy for programmers to build secure and reliable systems. His current work centers on the design and implementation of languages and tools for programmable networks, particularly using the P4 language. His past work includes bidirectional languages (also known as 'lenses'), database query languages, data provenance, type systems, mechanized proof, and formal semantics. His research group at Cornell has made significant contributions to network verification, software-defined networking, and formal foundations for programmable data planes. Analysis of Foster's recent publications reveals a strong focus on network verification and programming language foundations for networking. His work consistently applies formal methods to practical networking problems, with a particular emphasis on the NetKAT and P4 languages. Over the past five years, his research has evolved toward more complex network verification techniques, including infinite state verification, active learning of network models, and dependently-typed approaches to network programming. His work bridges theoretical computer science with practical networking systems, making formal methods accessible to network engineers. ACM Fellow (2025) ACM SIGPLAN Robin Milner Award (2023) ACM SIGCOMM Rising Star Award (2018) NSF CAREER Award (2013) Alfred P. Sloan Fellowship (2012) Multiple distinguished paper awards across top conferences including POPL, PLDI, and SIGCOMM Foster has advised numerous PhD and Master's students who have gone on to prominent positions in both industry and academia, with many continuing work in programming languages and networking. He has led multiple significant research grants including an NSF CAREER Award and has been involved in the P4 Language Consortium, serving as Chair of the P4 Language Governing Board. His work has been supported by various organizations including NSF, DARPA, and industry partners like Intel and Jane Street. Foster is also active in the programming languages research community, serving on numerous program committees and as Vice Chair of DARPA's Information Science and Technology (ISAT) study group. Foster leads a vibrant research group at Cornell focused on programming languages for networks, with collaborators from academia and industry. His group has developed several influential tools and frameworks including NetKAT, Petr4, and KATch. They maintain strong connections with the P4 community and work closely with industry partners to ensure their research has practical impact on real-world networking systems.
Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.
Roberto Navigli is a Full Professor in the Department of Computer, Control and Management Engineering at Sapienza University of Rome, where he leads the Sapienza NLP Group. He has held academic positions at Sapienza since 2007, progressing from Researcher to Associate Professor, and now Full Professor. Navigli is internationally recognized for his groundbreaking work in multilingual natural language processing and computational semantics. His research spans multiple areas within Natural Language Processing, with a particular focus on multilinguality , lexical semantics (Word Sense Disambiguation and induction), sentence-level semantics (Semantic Role Labeling and Semantic Parsing), and Large Language Models . As the leader of the Minerva LLM project, he pioneered the development of the first pretrained LLM in Italian. His creation of BabelNet, the largest multilingual encyclopedic computational dictionary covering 271 languages and about 14 million concepts, represents a landmark achievement in computational linguistics. Professor Navigli's recent publications demonstrate a strong focus on advancing multilingual capabilities of language models, addressing challenges in semantic understanding across languages, and developing robust evaluation frameworks. His work on Italian LLM optimization, lexical ambiguity in machine translation, and cross-lingual safety gaps reflects his commitment to building more accurate and reliable language technologies that work effectively across diverse linguistic contexts. AAAI Fellow (2025) EurAI Fellow (2024) ELLIS Fellow (2024) ACL Fellow (2023) Outstanding Paper Awards at ACL 2024, ACL 2023, and NAACL 2021 Best Resource Paper Award at ACL 2022 Prominent Paper Awards from the Artificial Intelligence Journal (2023, 2017) META Prize 2015 for BabelNet Professor Navigli has successfully secured substantial research funding, including two prestigious ERC Grants (MultiJEDI ERC Starting Grant 2010-2016 and MOUSSE ERC Consolidator Grant 2017-2023, totaling around 3 million euros), PI of PRIN 2017 and PRIN 2020 projects (overall 1.4 million euros), and a Google Focused Research Award (around 300k$). He has mentored over 30 PhD students, many of whom have gone on to successful careers in academia and industry. His leadership extends to major international conferences, serving as General Chair of ACL 2025 and Program Co-Chair of ACL-IJCNLP 2021. As the director of the Sapienza NLP Group and co-leader of the Transversal Project on Large Language/Multimodal Models in the FAIR (Future Artificial Intelligence Research) project, Professor Navigli oversees a vibrant research ecosystem focused on advancing the state-of-the-art in natural language understanding across languages. His spinoff company, Babelscape, translates academic research into practical applications for industry.
Anna Berge is a Professor of Linguistics at the University of Alaska Fairbanks, directing the Alaska Native Language Archive. She holds a PhD from the University of California, Berkeley (1997), specializing in the documentation, description, and historical analysis of Eskimo-Aleut languages, particularly Unangam Tunuu (Aleut). Her research integrates linguistics with archaeology, genetics, and environmental studies to explore prehistoric language contact and divergence patterns along the North Pacific Coast. Her work focuses on morphosyntax, discourse analysis, and language revitalization for highly endangered languages. She teaches courses in Morphology, Field Methods, Community Language Documentation, and Eskimo-Aleut Linguistics, emphasizing practical skills for language preservation. Berge's multidisciplinary approach addresses Aleut language divergence from Eskimoan branches, leveraging data from multiple disciplines to reconstruct linguistic prehistory. She actively collaborates with Indigenous communities in Russia, Alaska, Canada, and Greenland to archive linguistic materials and support language maintenance initiatives.
Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.
Sara Stymne is a Senior Lecturer in Computational Linguistics at the Department of Linguistics and Philology, Uppsala University, where she has been working since 2012. She initially joined as a post-doc (2012-2015), then worked as a researcher (2015-2017), and served as an assistant professor (2017-2023) before her current position as Senior Lecturer. Prior to Uppsala, she was a researcher at Linköping University's Department of Computer and Information Science. Dr. Stymne earned her PhD in Computational Linguistics from Linköping University in 2012 with the thesis 'Text Harmonization Strategies for Phrase-Based Statistical Machine Translation,' following a Licentiate degree in Computational Linguistics (2009) and a Master's degree in Cognitive Science (2006), both also from Linköping University. During her doctoral studies, she spent the autumn of 2010 and spring of 2009 at Xerox Research Centre Europe in Grenoble, France. Her primary research interests focus on cross-lingual natural language processing and digital humanities, with particular emphasis on multilingual dependency parsing. Dr. Stymne is passionate about applying computational linguistics to solve research questions in other fields, including language history, literary analysis, and political science. Her earlier work concentrated on machine translation, with specific interests in discourse-aware translation, compound processing, and error analysis. She has made significant contributions to the development of language technology tools for analyzing dialogue, narrative, and stylistic features in literature. Analysis of Dr. Stymne's recent publications reveals a strong focus on cross-lingual and cross-domain natural language processing. Her work spans multiple subfields including dependency parsing across genres and topics, discourse relation analysis in low-resource languages like Egyptian Arabic, direct speech identification in Swedish literature, and causality detection in governmental documents. A notable trend is her application of NLP techniques to digital humanities problems, particularly in analyzing literary texts and historical language change. Her research often involves creating and utilizing specialized datasets for specific linguistic phenomena across multiple languages. Dr. Stymne actively supervises graduate students, having guided numerous master's and bachelor's theses on topics ranging from speech recognition to multilingual parsing and causality detection. She leads or participates in several research projects including 'Fictional prose and language change' (funded by VR, 2021-2023) and 'Enabling climate-resilient development' (funded by Marianne and Marcus Wallenberg Foundation, 2023-2027), demonstrating her commitment to interdisciplinary research with practical applications. Her work has resulted in several notable software resources including uuPronPred for cross-lingual pronoun prediction, uuparser for dependency parsing, and Docent for document-level machine translation. Within the Computational Linguistics and Language Technology group at Uppsala University, Dr. Stymne contributes to multiple research initiatives focused on developing language technology tools for digital humanities applications. Her team works closely with literary scholars and historians to create computational methods for analyzing large corpora of literary texts, particularly focusing on Swedish literature across different historical periods. Her research bridges the gap between theoretical computational linguistics and practical applications in the humanities, creating new methodologies for quantitative analysis of literary and historical texts.
