Sandro Zanetti, Full Professor of General and Comparative Literature at the University of Zurich's Faculty of Arts and Humanities, specializes in avant-garde research, artistic production processes, and poetics of (im)probability. He studied German, History, and Philosophy at Basel, Freiburg, and Tübingen (1993-1999), earned his PhD in 2005 at Basel's SNSF project, and held positions including Junior Professor at Hildesheim (2008-2011) and DFG network head (2010-2014). Since 2011 at UZH, he progressed from Assistant to Full Professor (2022) and serves as Co-Dean of Studies since 2021. Current affiliations: Department of General and Comparative Literature (UZH), Center for Arts and Cultural Theory (ZKK), Literary Radio Zurich Former roles: ZGW Center (2010-2022), SNF project leader (2015-2019), Co-editor of History of the Present (2016-present) His research explores: Avant-garde genealogies and postwar literary modernity Time perception and aesthetic temporality Writing as cultural technique Interdisciplinary intersections with philosophy, media studies, and art Publications reveal trends in: 2022-2024: Dada historiography, AI-generated literature, Paul Celan's late works 2019-2021: Aesthetic theory, transactuality, Barthesian concepts 2014-2017: Improvisation networks, multilingualism, collaborative writing He co-founded interdisciplinary platforms and maintains editorial roles across 15 anthologies since 2004.
Narges Sharif Razavian is an Assistant Professor at NYU Grossman School of Medicine , holding appointments in both the Department of Population Health and Department of Radiology . She earned her PhD from Carnegie Mellon University and completed postdoctoral training at New York University's Courant Institute in Computer Science's Machine Learning group. Research focuses on applying Machine Learning and Artificial Intelligence to healthcare challenges, including Predictive Analytics for disease outcomes, Biomarker Discovery , and Medical Imaging analysis. Recent publications highlight her work on AI-driven diagnosis in oncology (lung and pancreatic cancer), hematoma expansion prediction in neurology, and real-time models for infectious disease outcomes (e.g., COVID-19). She utilizes Electronic Health Records (EHRs) and multimodal data to develop clinical decision support systems, with applications in public health surveillance and personalized medicine. Contact: Email | Phone: 212-263-2234 | Office: 227 East 30th Street, 6th Floor, Room 639, New York City
Dr. Catrin Lundström is an Assistant Professor of Ethnicity and Migration at the Department of Migration, Ethnicity and Society (REMESO) within the Department of Culture and Society (IKOS) at Linköping University's Faculty of Philosophy. Her research focuses on the intersection of migration, whiteness, and gender, examining how Swedish women experience transnational migration and return migration processes. Dr. Lundström received her PhD in Sociology from Uppsala University in 2007 and became an Associate Professor (Docent) in Sociology there in 2011. Her academic journey includes teaching and research positions at multiple Swedish institutions including Uppsala University, Stockholm University, Mälardalen University, Södertörn University, Umeå University, and Linköping University. She has also been a visiting researcher at the University of Arizona and the University of California. Dr. Lundström's research centers on whiteness as both a place-bound and transnational construction. She challenges conventional migration studies by focusing on privileged Swedish women migrants, developing the concept of 'white capital' to understand how whiteness functions as transferable cultural capital across national borders. Her work examines how racial privilege, gender, and class intersect in migration experiences, particularly among women who migrate for lifestyle, companionship, or professional reasons. Her recent publications demonstrate an expanding focus on the relationship between race, whiteness, and educational systems in Sweden, as well as critical examinations of contemporary political debates through the lens of critical race theory. Lundström's work increasingly addresses how colorblind discourse operates in Swedish society while simultaneously exploring the politics of national identity in a globalized world. Dr. Lundström has led significant research projects including: 'White Migrations: Gender, Whiteness and Privilege in Transnational Migration' (2014) 'Tre faser av hegemonisk vithet' (Three Phases of Hegemonic Whiteness) with Tobias Hübinette 'Att återintegrera svenskheten' (Reintegrating Swedishness) on returning Swedish migrant women, funded by the Swedish Research Council Her research has been supported by organizations including the Sweden-America Foundation and the Swedish Research Council. Dr. Lundström is actively involved in several academic networks: IMISCOE Standing Committee on Gender, Migration and Transnational Lives White Spaces Network at Leeds University Lifestyle Migration Hub, an international network of researchers studying lifestyle migration She regularly lectures within and outside academia, both in Sweden and internationally, and has received significant media attention for her research on school segregation, colorblind language, and gender equality in migration contexts.
Hadi Daneshmand is an Assistant Professor of Computer Science at the University of Virginia, specializing in theoretical machine learning. Prior to joining UVA, he completed postdoctoral research at FODSI (jointly hosted by MIT and Boston University), Princeton University, and INRIA Paris following his 2020 PhD in Computer Science from ETH Zurich. Education Ph.D. in Computer Science, ETH Zurich, 2020 His research bridges computational perspectives and neural network theory, focusing on theoretical guarantees for deep learning systems. Key interests include understanding neural network mechanisms through optimization frameworks, foundations of machine learning, and stochastic processes in learning systems. His work reveals how neural networks implement computational primitives like gradient descent and optimal transport through architectural components. Recent publications demonstrate a cohesive trajectory analyzing transformers' computational capabilities, batch normalization's theoretical properties, and optimization dynamics in deep learning. His studies consistently establish formal connections between neural architectures and classical optimization methods, particularly in in-context learning scenarios. Scientific Awards Stanford CPAL Rising Star Award Spotlight award at ICML In-context Learning workshop (2024) Postdoc fellowship of the Foundation of Data Science Institute (FODSI) Early Postdoc Mobility grant from SNSF Best poster award at Max Planck ETH deep learning workshop (2016) Reviewer awards for ICML (2022, 2019) and NeurIPS (2020) Dr. Daneshmand actively mentors graduate students, with advisees including PhD candidates at ETH Zurich who have secured positions at Harvard, Yale, Meta, and NVIDIA. His research is supported by competitive grants including the SNSF Early Postdoc Mobility award and FODSI fellowship. He serves the community as Area Chair for NeurIPS 2023-2024 and ICML 2025, and regularly reviews for top machine learning conferences and journals. He teaches specialized courses including "Neural Networks: A Theory Lab" at UVA, emphasizing experimental-theoretical connections in neural computation through hands-on coding exercises.
Prof. Dr. Ingo Scholtes is Chair of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). His research spans network science, graph machine learning, and computational social science, with applications in software engineering, ecology, biology, and physics. He received a Juniorfellowship from the German Informatics Society (2014) and an SNSF Professorship (CHF 1.5Mio, 2018). Current affiliations: JMU Würzburg (since 2021), University of Zurich (2018-2024), Bergische Universität Wuppertal (2019-2021) Research focus: Higher-order network modeling, temporal graph analysis, AI for collaborative systems, causality-aware machine learning His recent publications demonstrate strong trends in temporal network analysis , graph neural networks for time-series, and higher-order models across software engineering and social science domains. He co-chairs multiple international workshops on complex networks and serves as associate editor for EPJ Data Science and Advances in Complex Systems. Key scientific contributions: Foundational work on higher-order network models published in Nature Physics Methodological innovations in temporal network visualization (HOTVis) and path-based analysis (pathpy) As both educator and organizer, he leads the Computational Social Science Section at GI e.V., mentors across disciplines, and develops tools like git2net for collaboration analysis. His work bridges theoretical foundations with practical applications in network science.
Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .
Franz Franchetti is the Kavčić-Moura Professor of Electrical & Computer Engineering at Carnegie Mellon University. He serves as Associate Dean for Research and Director of the Engineering Research Accelerator at CMU. Education: Ph.D. in Computational Mathematics (Vienna University of Technology, 2003) M.Sc. in Technical Mathematics (Vienna University of Technology, 2000) His research interests focus on automatic performance tuning and program generation for emerging parallel computing platforms , including multicore CPUs , GPUs , and 3DIC chip design . He leads the SPIRAL effort to automate highly optimized software libraries and explores domain-specific compiler transformations in HPC applications for smart grids and material sciences . Recent work extends SPIRAL to quantum computing . The scientific awards Franchetti has received include the Gordon Bell Prize (2006) , HPC Challenge Class II Award (2010) , and the CIT Dean's Early Career Fellowship (2013) . He and his students have won multiple Best Paper Awards at HPEC, DAC, and ISPA ACM TODAES Best Paper (2014) Student Research Competition wins (PACT 2024, CGO 2023) Franchetti has advised students like Richard Veras and Thom Popovici . He has secured significant grants from agencies such as DARPA, DOE, NSF, and industry partners (Intel, NVIDIA, Mercury). He co-founded SpiralGen, Inc. and holds leadership roles in organizations like ASciNA Western Pennsylvania and as Honorary Consul of Austria in Pittsburgh.
Mohammad T. Alhawary is Professor of Arabic Linguistics and Second Language Acquisition at the University of Michigan's Middle East Studies department within the College of Literature, Science, and the Arts. He serves as Director of both the MA Program in Arabic for Professional Purposes (APP) and the MA Program in Teaching Arabic as a Foreign Language (TAFL). His educational background includes a Ph.D. from Georgetown University (1999). Prior to joining the University of Michigan, he contributed to developing Arabic and Middle Eastern Studies programs at various US institutions. Professor Alhawary's research spans both theoretical and applied Arabic linguistics, with particular focus on second language acquisition processes. His work examines how factors like age, input quality, output practice, and first language transfer affect Arabic language learning. He has made significant contributions to understanding Arabic language pedagogy, curriculum design, proficiency testing, and the application of technology in language learning. His research also extends to bilingualism, multilingualism, language impairment, and Arabic medieval grammatical traditions. His publications reflect a strong trajectory in Arabic linguistics research, with recent works focusing on practical language teaching applications, reading comprehension mechanisms, code-switching patterns in digital communication, and multilingual acquisition processes. The 2023 publication 'Teaching Arabic as a Foreign Language' represents his latest contribution to language pedagogy methodology. 2019 AATA Book Award for 'Arabic Second Language Learning and Effects of Input, Transfer, and Typology' As an academic leader, Professor Alhawary serves as Executive Director of the American Association of Teachers of Arabic and edits both the Journal of Arabic Linguistics Tradition and Al-'Arabiyya journal. He continues to develop empirical research on Arabic second language acquisition to inform teaching practices both in America and globally.
Professor Janet McColl-Kennedy is a leading figure in marketing and service science at The University of Queensland's Business School. She serves as Program Lead for Innovation Pathways (FaBA) and co-founded the Service Innovation Alliance (SIA) research hub, focusing on customer experience, AI, digital transformation, and sustainability. With Fellow status in the Academy of the Social Sciences in Australia and the Australian and New Zealand Marketing Academy, she holds international recognition including Research.com's World's Best Business Scientists ranking and the Christopher Lovelock Career Contributions to the Services Discipline Award (2025). University of Queensland Business School Honorary Visiting Professor, Cambridge Service Alliance Research Collaborator, University of Cambridge Her research spans customer experience management, service ecosystems, digital transformation, and healthcare services. She has pioneered frameworks for customer value co-creation and service recovery strategies, integrating AI and behavioral science. Her 2019 paper on customer experience insights was implemented by a major B2B organization, while her work on value co-creation improved outcomes at Lutheran Community Care. Recent publications focus on AI impacts in food and beverage services, sustainable service ecosystems, and digital health interventions. She has secured over $89 million in competitive grants, including multiple ARC Linkage and Discovery Projects, and leads cross-disciplinary teams with institutions like Cambridge University and Arizona State University. 2024 - ARC College of Experts appointment 2023 - Bo Edvardsson Industry Impact in Services Award 2022 - Elected Fellow of Academy of the Social Sciences in Australia 2021 - Clarivate Highly Cited Researcher 2018 - Ranked most influential marketing academic in Australia With over 220 publications and a Google Scholar H-index of 62, she has mentored 15 PhD students and examined theses at multiple universities. Her teaching spans 30+ years across undergraduate, postgraduate, and executive programs in Australia, USA, Italy, China, and Korea, with awards for blended learning and corporate education.
Nathaniel D. Daw serves as the Huo Professor in Computational and Theoretical Neuroscience and Professor of Neuroscience and Psychology at Princeton University, based at the Princeton Neuroscience Institute. His research integrates computational modeling with experimental neuroscience to investigate fundamental mechanisms of learning and decision-making. Daw's research focuses on computational and theoretical neuroscience, specializing in reinforcement learning, memory systems, and decision-making processes. He examines how neural circuits represent value, update beliefs through experience, and balance model-based versus model-free control strategies. His work frequently bridges theoretical frameworks with behavioral and neural data to explain phenomena ranging from habitual behavior to flexible cognitive control. Analysis of his 2025 publications reveals dominant themes in neural replay mechanisms, individual differences in learning trajectories, and clinical applications to eating disorders. His work increasingly incorporates large language models for psychological assessment while maintaining core focus on interpretable cognitive architectures and hierarchical planning. Daw maintains active research operations through the Princeton Neuroscience Institute, an interdisciplinary hub fostering collaboration between computational modelers, neuroscientists, and psychologists to advance understanding of neural mechanisms underlying cognition.
Jason Cong is the Volgenau Chair for Engineering Excellence and Distinguished Chancellor's Professor in the Computer Science Department at UCLA's Samueli School of Engineering. He directs the Center for Domain-Specific Computing (CDSC) and the VLSI Architecture, Synthesis, and Technology (VAST) Laboratory, and serves as Associate Vice Provost for Internationalization and Co-Director of UCLA/PKU Student and Scholar Program. Dr. Cong's research spans electronic design automation, customizable computing for machine learning and big-data applications, quantum computing, and highly scalable algorithms. His work has produced over 500 publications with more than 41,000 citations and an H-index of 106. His recent work focuses on quantum computing compilation, domain-specific acceleration for AI workloads, and high-level synthesis optimization techniques that leverage machine learning. His publication trend shows a strong emphasis on quantum computing and machine learning acceleration in recent years, with numerous papers on quantum layout synthesis, LLM acceleration, and high-performance FPGA implementations. His team has developed frameworks like TAPA for task-parallel dataflow programming and RapidStream for automated parallel implementation of FPGA designs. Member of National Academy of Engineering (2017) IEEE Robert N. Noyce Medal recipient (2022) Phil Kaufman Award recipient (2024) ACM Chuck Thacker Breakthrough Award recipient (2024) 18 Best Paper Awards across major conferences Multiple 10-Year Retrospective Most Influential Paper Awards Dr. Cong has graduated 50 PhD students, many of whom are now faculty at major research universities or hold key positions at leading tech companies. He has led over 100 research projects funded by DARPA, NSF, SRC, and industry sponsors. His entrepreneurial activities include founding three successful companies (Aplus Design Technologies, AutoESL, and Falcon Computing Solutions), all acquired by major EDA players. His VAST Laboratory continues to push boundaries in domain-specific computing, with active research in quantum computing, AI acceleration, and high-performance FPGA implementations.
Gary King is the Albert J. Weatherhead III University Professor at Harvard University and Director of the Institute for Quantitative Social Science. He is based in the Department of Government within Harvard's Faculty of Arts and Sciences. One of only 22 University Professors at Harvard, this represents the institution's most distinguished faculty position. King received his B.A. from SUNY New Paltz in 1980 and his Ph.D. from the University of Wisconsin-Madison in 1984. His academic journey has led him to become one of the most influential scholars in political methodology and quantitative social science. Professor King's research spans numerous areas of methodological innovation in the social sciences. His work focuses on developing and applying empirical methods across various domains. Key research interests include: Ecological Inference - developing methods to infer individual behavior from group-level data Automated Text Analysis - creating techniques for extracting knowledge from massive text collections Causal Inference - methods for detecting and reducing model dependence in causal effect estimation Missing Data and Measurement Error - statistical approaches to handle incomplete or imperfect data Survey Research - developing methods for more accurate cross-cultural survey comparisons Unifying Statistical Analysis - integrating diverse methodological approaches into coherent frameworks King's recent publications demonstrate a continued focus on methodological innovation with practical applications. His work spans political science, public health, and data science, with particular emphasis on privacy-preserving data analysis, maternal health metrics, survey methodology, and media effects. A notable trend is the increasing interdisciplinary nature of his research, bridging political methodology with public health, computer science, and demography. His work on census data privacy, maternal mortality disparities, and media influence represents cutting-edge applications of social science methodology to critical societal issues. His scientific achievements have been recognized with numerous prestigious awards: Fellow of the National Academy of Sciences (2010) Fellow of the American Statistical Association (2009) Fellow of the American Academy of Arts and Sciences (1998) Guggenheim Foundation Fellow (1994-1995) Career Achievement Award (2010) Warren Miller Prize (2008) Multiple awards for research software and methodology King has mentored numerous students and postdocs, many of whom now hold faculty positions at leading universities. His research has been supported by major funding agencies including the National Science Foundation, Centers for Disease Control and Prevention, World Health Organization, and National Institute of Aging. He has collaborated with over seventy scholars on research publications and served on numerous editorial boards and professional organization councils. His work on the Mexican universal health insurance program represents one of the largest randomized health policy experiments to date, demonstrating his commitment to rigorous evaluation of real-world policy interventions. As Director of the Institute for Quantitative Social Science, King leads a vibrant research community focused on methodological innovation. His work has practical applications in diverse areas including legislative redistricting (used by the U.S. Supreme Court), health policy evaluation (including the largest randomized health policy experiment to date in Mexico), Chinese censorship analysis (revealing government fabrication of 450 million social media comments annually), and automated text analysis (through Crimson Hexagon, a company he co-founded).
Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .
Dr. Kaye Mitchell is a Senior Lecturer in Contemporary Literature within the Department of English and American Studies at the University of Manchester. She serves as Co-Director of the Centre for New Writing and is affiliated with the Centre for the Study of Sexuality and Culture. Her academic career spans over fifteen years at Manchester, following previous teaching at the University of Westminster. Her educational background includes a BA in English Literature from Queen Mary University of London, an MA in Philosophy from Birkbeck, and a PhD from Birkbeck completed in 2004. She has held visiting positions including a Visiting Scholar at NYU in 2011 and an 18-month Humboldt Experienced Research Fellowship in Berlin from 2014-2015. Dr. Mitchell's research focuses on contemporary literature with particular emphasis on gender and sexuality studies, literary theory, and experimental writing by women. Her work explores the relationships between form, meaning, and gender/sexual identity and representation. She has published extensively on topics including literary intention, women's experimental writing, and the politics and poetics of shame in contemporary literature. She is currently working on a monograph titled Gendered Experiments which analyzes gender ramifications of literary experiment through strategies of appropriation, constraint, and excess. Her research output shows a consistent trajectory toward increasingly specialized work on women's writing, gender, and experimental literary forms. The publications demonstrate interdisciplinary connections between literary studies, gender theory, and affect studies, with particular attention to how negative affects like shame operate within contemporary literary texts. Humboldt Experienced Research Fellowship (2014-2015) Co-Editor of Contemporary Women's Writing (OUP journal) Member of editorial boards for Open Gender Journal and C21 Dr. Mitchell actively supervises PhD students working on diverse topics spanning gender studies, contemporary literature, and experimental writing. Her external engagement includes talks at major festivals and events including Ladyfest Manchester, Queer Up North, Homotopia, and the Manchester Literature Festival. She also participates in the annual Sexuality Summer School through her affiliation with the Centre for the Study of Sexuality and Culture.
Heng Ji is a Professor at the Siebel School of Computing and Data Science , affiliated with the Department of Computer Science , Electrical and Computer Engineering Department , and multiple research labs including the Coordinated Science Laboratory and Carl R. Woese Institute for Genomic Biology at the University of Illinois Urbana-Champaign. She serves as an Amazon Scholar and Founding Director of the Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE) and CapitalOne-Illinois Center on AI Safety and Knowledge Systems (ASKS) . B.A. and M.A. in Computational Linguistics from Tsinghua University M.S. and Ph.D. in Computer Science from New York University Her research bridges Natural Language Processing with Vision-Language Models , Knowledge-Enhanced LLMs , and AI for Science (e.g., chemical language modeling). She leads major multi-institutional projects such as DARPA ECOLE MIRACLE , KAIROS RESIN , and DEFT Tinker Bell , while advising governments (U.S. Air Force Data Analytics Expert Panel) and industry (Amazon, Google, IBM). Her work on multimodal reasoning, agent-based systems, and chemical language models (e.g., mCLM ) has been supported by NSF, DARPA, and corporate partners. Recent publications (2025) focus on LLM agents , vision-language integration , and scientific knowledge acquisition . Awards include NSF CAREER , IEEE Intelligent Systems' AI's 10 to Watch , and multiple Outstanding Paper Awards at ACL/NAACL. She advises students like Chi Han (ACL/NAACL awardee) and post-docs Xiusi Chen and Yuji Zhang , and leads the BLENDER Lab , which develops frameworks like WiNELL (Wikipedia updating) and ProteinZero (protein generation). She has also served as NAACL Secretary and Program Co-Chair for ACL-IJCNLP2022. Outstanding Paper Award at ACL2024 Two Outstanding Paper Awards at NAACL2024 Young Scientist by World Laureates Association (2023-2024) AI's 10 to Watch by IEEE (2013) NSF CAREER (2009) Google/IBM/Bosch Research Awards
