Professor Desiree Frieson teaches COMM 1000 Survey of Communication Studies at Brooklyn College during the Spring 2024 semester. She conducts synchronous classes on Tuesdays from 6:30-8:15 PM ET with virtual Zoom office hours held immediately afterward from 8:15-9:15 PM ET. Her expertise spans multiple communication domains: Interpersonal & Intrapersonal Communication Intercultural Communication Organizational Communication Gender Communication Nonverbal Communication Rhetorical Communication Conflict & Negotiation Professor Frieson's course examines how people use messages to generate meaning across various contexts, emphasizing communication's role in relationships, institutions, and society. Her pedagogical approach combines theoretical foundations with practical application through structured assignments. Course assessment includes: Bi-Weekly Connection Blogs (35%) requiring 500-750 word Wordpress.com posts with embedded multimedia Midterm Paper (25%) Discussion Boards (25%) Final Reflection Blog (15%) She encourages students to utilize the Learning Center/Writing Center for extra credit opportunities, emphasizing proper writing mechanics and research skills essential for communication studies.
Kartik Hosanagar is the John C. Hower Professor of Technology and Digital Business and a Professor of Marketing at The Wharton School, University of Pennsylvania. He is also the Co-Director of the Wharton Human-AI Initiative. His work spans the Department of Operations, Information and Decisions, focusing on the intersection of technology, business, and society. Education: PhD in Management Science and Information Systems, Carnegie Mellon University MPhil in Management Science, Carnegie Mellon University Masters in Information Systems, Birla Institute of Technology and Sciences (BITS, Pilani), India Bachelors in Electronics Engineering, Birla Institute of Technology and Sciences (BITS, Pilani), India Research Interests: Kartik’s research focuses on the digital economy, particularly the impact of AI, algorithms, and analytics on consumers, businesses, and society. His work explores internet marketing, e-commerce, digital media, information diffusion, platform economics, and the ethical implications of AI. He investigates how technology transforms business models and consumer behavior in online environments. Publication Trends: His recent research combines machine learning, causal inference, and behavioral modeling to study digital platforms, user behavior, and AI applications in marketing and operations. The articles reflect a strong focus on empirical analysis of social media, search engines, and platform design, with applications in advertising, content sharing, and product adoption. Scientific Awards: Best Information Systems paper published in Management Science, 2013-2016 Finalist for Best Information Systems paper in Management Science (2012-2015) Recognized as one of the world’s top 40 business professors under 40 Eleven-time recipient of teaching excellence awards at Wharton MBA Excellence in Teaching Award, 2007 “Goes above and beyond the call of duty” award (multiple years) Advising and Grants: While no formal PhD students are listed, Kartik has supervised independent studies and mentored numerous students through research projects. His entrepreneurial ventures, such as Yodle and Jumpcut Media, reflect real-world applications of his research. He has also secured significant industry engagement through consulting and executive education with major firms like Google, American Express, and Citi. His work is supported by academic recognition, editorial roles, and media outreach. Labs and Teams: Kartik co-directs the Wharton Human-AI Initiative, a research hub exploring the integration of human and artificial intelligence in business. He also leads research groups focused on digital platforms and AI ethics, collaborating with scholars across disciplines to advance understanding of algorithmic decision-making and its societal impact.
Professor Wolfgang Vondey serves as Professor of Christian Theology and Pentecostal Studies in the Department of Theology and Religion at the University of Birmingham, where he directs the Centre for Pentecostal and Charismatic Studies. A classically trained systematic theologian with expertise spanning philosophy, linguistics, and ethics, Vondey teaches in the Master of Arts programme in Evangelical and Charismatic Studies while maintaining an active research agenda that bridges academic theology with practical ecclesial concerns. His academic journey includes: MA in Japanese Studies, Japanese Linguistics, and Media Science from Philipps University Marburg, Germany (1994) MDiv from Pentecostal Theological Seminary, USA (1999) PhD in systematic theology and ethics from Marquette University, USA (2003) Postdoctoral fellowship at Boston College Faculty position at Regent University Divinity School (2005-2015), where he founded and directed the Regent Center for Renewal Studies Vondey's research focuses on Pentecostal and Charismatic theology with particular emphasis on pneumatology, ecclesiology, ecumenical theology, and the intersection of theology and science. His scholarly work spans diverse theological traditions from Catholic to postmodern thought, exploring ritual and liturgical studies, theological method, Christology, soteriology, and political theology. With a multilingual background (German, French, Italian, Spanish, Dutch, and Japanese), he brings international perspective to his analysis of global Pentecostalism. His publications reveal consistent engagement with contemporary Pentecostal theological challenges, examining political dimensions of glossolalia, corporate embodiment in Pentecostal tradition, synodality, and the public nature of Pentecostal ecclesiology. Vondey's work demonstrates how Pentecostal theology navigates tensions between mainstream and extreme expressions while maintaining theological coherence in global contexts. As an academic leader, Vondey co-edits the 'Christianity and Renewal – Interdisciplinary Studies' book series with Amos Yong and serves as founding chair of the ecumenical studies group of the Society for Pentecostal Studies. His supervision of numerous PhD students across diverse topics—from Pentecostal orality in Denmark to Word of Faith theology in Bolivia—demonstrates his commitment to developing the next generation of Pentecostal scholars worldwide.
Shannon Barrios is an Associate Professor in the Department of Linguistics at the University of Utah, where she has served since 2022. She co-directs the Speech Acquisition Lab and specializes in second language acquisition, phonetics/phonology, and psycholinguistics. Her research explores how adult learners develop perceptual and lexical representations of novel phonological contrasts, with a focus on cross-language speech perception, orthographic effects, and social factors in input processing. BA, Spanish (SUNY Geneseo, 2004) MA, Linguistics (Syracuse University, 2007) PhD, Linguistics (University of Maryland, 2013) Her research interests span adult second language acquisition , phonolexical development , and accent bias . She investigates how learners process phonological contrasts, the role of orthography in speech perception, and the influence of social roles (e.g., teachers vs. peers) on language learning. Her work combines behavioral experiments, ERP/MEG neuroimaging, and computational modeling. Barrios’ recent publications (2024–2020) focus on representational fuzziness , talker variability , and lexical contrast mechanisms . Her 2024 Languages paper proposes a factorial typology for evaluating auditory word recognition scenarios, while her 2024 JASA Express study highlights individual listener variation in cross-language speech perception. Earlier works examine allophone acquisition, orthographic input effects, and neural correlates of phonological mapping. She has received two teaching awards from the University of Maryland (2013). Her teaching portfolio includes undergraduate and graduate courses in phonetics , psycholinguistics , and second language acquisition theory . She also leads workshops on research ethics, mentee development, and academic literacies.
Joakim Nivre is a Professor at Uppsala University's Department of Linguistics and Philology. He is a leading researcher in computational linguistics, with a focus on dependency parsing, Universal Dependencies (UD) framework development, and multilingual NLP applications. His recent work explores LLMs in climate change discourse analysis, pharmacovigilance explainability, and historical text processing. Key research areas: Dependency parsing theory, Universal Dependencies standardization, LLM evaluation Collaborations: SweSAT-1.0 benchmark development, ClimateEval project, PARSEME integration His 2025-2023 publications demonstrate expertise in explainable AI for healthcare, synthetic data generation for idioms, and multilingual benchmark design. Notably, he co-developed SweSAT-1.0 to evaluate Swedish LLMs and contributed to typology-informed UD revisions. Despite extensive work in NLP, no scientific awards are mentioned in available texts.
James Zou is an Associate Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. His research focuses on advancing machine learning methodologies for healthcare applications, emphasizing reliability, fairness, and statistical rigor. He holds a Ph.D. from Harvard University and has held positions at Microsoft Research, Cambridge University (as a Gates Scholar), and UC Berkeley (Simons Fellow). Zou leads the Stanford Data4Health hub and is a Chan-Zuckerberg Investigator. His work spans AI-driven diagnostics, spatial transcriptomics, and ethical AI frameworks. Key achievements include the EchoNet AI system for echocardiography and foundational contributions to data valuation (e.g., Data Shapley). Awards include the Sloan Fellowship, NSF CAREER Award, and Google/Tencent AI awards. Education: Ph.D., Harvard University (2014); Postdoctoral roles at Microsoft Research, Cambridge, and Berkeley. Research Interests: Machine learning for healthcare, algorithmic fairness, interpretable AI, spatial omics, and translational bioinformatics. His lab develops tools like TextGrad (PyTorch for text agents) and frameworks for evaluating medical AI systems. Recent work addresses LLMs in peer review and clinical decision-making. Grants/Grants: Supported by NSF, Sloan Foundation, Chan-Zuckerberg Initiative, and industry partnerships (Google, Amazon, Adobe). Advises on over 20 doctoral students, many contributing to high-impact papers in Nature , Science , and top conferences (NeurIPS, ICML). Leads collaborations in cardiology, oncology, and veterinary medicine. Labs/Teams: Stanford AI Lab, Stanford Data4Health, and interdisciplinary groups in precision medicine. Active in open-source projects like FrugalML and MetaViz.
Adriana I. Kovashka is an Associate Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. She serves as Chair of the Department of Computer Science. Her research focuses on computer vision, machine learning, and their intersections with human-machine communication and visual rhetoric analysis. Kovashka earned her BA in Computer Science and Media Studies from Pomona College (2008) and her PhD in Computer Science from the University of Texas at Austin (2014). She joined Pitt in 2015. Her work emphasizes improving image retrieval systems through semantic attributes, human-in-the-loop feedback, and crowd-sourced data. Notable projects include analyzing advertisements' persuasive strategies, developing object detection models resilient to domain shifts, and exploring multimodal learning with linguistic and visual inputs. She has secured significant grants, including NSF awards for geographic diversity in object detection (2023), CAREER funding for weak supervision methods (2021), and multiple Google Faculty Research Awards. Kovashka advises PhD students on topics ranging from multimodal intent modeling to domain generalization. She has organized workshops on advertising understanding and subjective attributes in vision conferences. Her lab's datasets, such as the 64,832-image ad repository and video ad collections, are widely used in vision research. Recent efforts include quantifying perceptual diversity in multilingual systems and mitigating bias in CNNs through shape regularization. Awards and recognitions include the NSF CAREER Award, Pitt's CRDF grants, and leadership roles in CVPR and WACV conferences. Her research bridges technical innovation with societal impact, addressing challenges in visual communication, ethical AI, and educational robotics.
Brandon M. Stewart is an Associate Professor of Sociology at Princeton University with extensive interdisciplinary affiliations. He serves as Director of the Statistics Core at the Office of Population Research and maintains formal connections with the Politics Department, Princeton Institute for Computational Science and Engineering, Center for Information Technology Policy, and Center for the Digital Humanities. Stewart holds editorial leadership as Co-Editor-in-Chief of Political Analysis and Associate Editor at Sociological Methods & Research . His educational background includes: Ph.D. in Government from Harvard University (2015) Master's degree in Statistics from Harvard University (2014) Stewart's research pioneers innovative quantitative methods for social science applications, specializing in automated text analysis and modeling complex heterogeneity in regression. His methodological frameworks enable researchers to uncover hidden structures in large datasets that were previously too costly or impossible to analyze. While his recent work has focused on using newspaper archives to study propaganda mechanisms in contemporary China, his tools are deliberately designed for broad applicability across diverse domains including education, human trafficking, forced migration, international relations, constitutional law, and psychology. His publication record demonstrates consistent innovation at the intersection of statistics, machine learning, and social inquiry. Stewart's work shows a clear trajectory from foundational methodological development to practical implementation across numerous substantive areas, with recurring themes of enhancing causal inference with textual data, developing robust topic modeling techniques, and creating accessible computational tools for social scientists. Stewart's scholarly excellence has been recognized through multiple prestigious awards: 2024 Leo Goodman (Early Career) Award from the Methodology Section of the American Sociological Association 2023 Emerging Scholar Award from the Political Methodology Society Edward R Chase Dissertation Prize Gosnell Prize for Excellence in Political Methodology Political Analysis Editor's Choice Award Recognition for Excellence in Mentoring Graduate Students As a mentor, Stewart has guided several successful graduate students to faculty positions at institutions including UCLA and Georgetown. His collaborative approach is evident in numerous multi-author projects spanning disciplines from political science to computational linguistics. His leadership extends to the Sociology Statistics Reading Group, which he founded to foster interdisciplinary methodological exchange, and his summer methods camp that trains social scientists in advanced quantitative techniques.
Ahmad Al-Dabbagh is an Assistant Professor in Manufacturing Engineering and holds a Principal's Research Chair in Control Systems (Tier 2) with the School of Engineering at The University of British Columbia. As a Senior Member of IEEE and ISA, he contributes significantly to the field of resilient automation and control systems through research, teaching, and professional service. His academic journey includes postdoctoral fellowships at Imperial College London, the University of Toronto, and the University of Alberta, where he also earned his PhD in Electrical and Computer Engineering. Dr. Al-Dabbagh's research focuses on designing resilient automation and control systems by addressing critical challenges in fault diagnosis, cyber security, and alarm management. His work spans theoretical foundations and practical applications in industrial control systems, with particular emphasis on detection and isolation of faults and cyber attacks, control reconfiguration, event-triggered control, remote state estimation, and alarm systems design. His research interests also extend to causality analysis, prediction methods, and root cause analysis for industrial processes. His extensive publication record demonstrates consistent contributions to control systems security and reliability, with recent work focusing on sophisticated methods for detecting false data injection attacks, analyzing alarm correlations using advanced machine learning techniques, and developing recommender systems for human operators in industrial environments. The trajectory of his research shows an evolution from foundational control theory toward increasingly complex applications in cyber-physical security and human-system interaction in industrial settings. NSERC Postdoctoral Fellowship NSERC Alexander Graham Bell Canada Graduate Scholarship (CGS – D3) Queen Elizabeth II Graduate Scholarship Governor General's Academic Medal (Gold) As a graduate student supervisor, Dr. Al-Dabbagh mentors the next generation of control systems engineers while maintaining an active research program. He serves as an Associate Editor on the IEEE Control Systems Society Conference Editorial Board and is a licensed Professional Engineer in British Columbia and Ontario. His teaching portfolio includes courses such as System Identification, Digital Enterprise, Systems and Control, and Internet of Things, reflecting the breadth of his expertise. Dr. Al-Dabbagh leads the Okanagan Laboratory for Control Systems Research, where his team develops innovative approaches to enhance the security and reliability of industrial automation systems. The laboratory serves as a hub for interdisciplinary research that bridges theoretical control engineering with practical industrial applications, particularly in the energy, manufacturing, and process industries.
Jon Brennan is an Assistant Professor in the Department of Linguistics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts (LSA). His research focuses on neurolinguistics, computational linguistics, and psycholinguistics, particularly investigating how the brain processes language structure and meaning. He leads the Computational Neurolinguistics Lab, which develops neurocomputational models to study language comprehension mechanisms. Brennan received an NSF Grant for collaborative research with Christophe Pallier (Paris) on neurocomputational models of natural language processing. His work integrates EEG, fMRI, and MEG techniques to decode linguistic features in neural signals. Key research areas include syntax-semantics interfaces, multilingual processing, and developmental disorders like dyslexia. Notable contributions include studies on hierarchical syntactic structure, minimal pairs in language models, and neural correlates of theory of mind in children. Brennan collaborates internationally, exemplified by the US-French NSF-CRCNS grant. He has published extensively on topics like neural decoding of grammatical features, LLM internal representations, and bilingual processing mechanisms. Scientific awards include the NSF Collaborative Research in Computational Neuroscience (CRCNS) Grant (2016). His research bridges computational modeling and experimental neuroscience, aiming to reveal how language mechanisms are implemented in neural systems.
Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.
Barbara Landau is the Dick and Lydia Todd Professor of Cognitive Science at Johns Hopkins University (since 2001). She previously served as Vice Provost for Faculty (2011-2014) and Director of the Science of Learning Institute (2013-2018). Her research focuses on the interplay between language and spatial cognition, studying typical and atypical development through experimental psychology, linguistic analysis, and brain imaging. PhD in Cognitive Science, University of Pennsylvania Landau investigates the cognitive primitives underlying early development, including how children learn spatial language (e.g., prepositions), how spatial impairments affect word learning in Williams syndrome, and how language and spatial cognition interact. Her work spans typical development, congenital blindness, Williams syndrome, and post-stroke spatial representation. She leads the Language and Cognition Lab, part of the JHU Vision Sciences Group (Cognitive Science, Psychological and Brain Sciences, Neuroscience). Her research has been featured in Time , New York Times , NPR, and The New Yorker, with a focus exhibit at the Walters Art Museum (2011). She received the Guggenheim Fellowship (2009), William James Fellow Award (2018), and is a member of the National Academy of Sciences. Cognitive Science Society Fellow American Academy of Arts and Sciences Fellow American Association for the Advancement of Science Fellow Landau’s lab collaborates with University of Pennsylvania researchers (John Trueswell, Lila Gleitman) on NSF-funded projects connecting symmetry to linguistic/perceptual development. She trains students like Zihan Wang (2023 Glushko Award) and Rennie Pasquinelli (Science of Learning Fellow). Her studies involve participants aged 18 months to 18 years, including Williams syndrome individuals.
Roberto Navigli serves as an Associate Professor in the Department of Computer Science at Sapienza University of Rome, conducting pioneering research in Natural Language Processing. He holds editorial leadership positions including Associate Editor of the Artificial Intelligence Journal and membership on the Journal of Natural Language Engineering editorial board. His research program centers on multilingual semantic technologies, with foundational contributions to word sense disambiguation, ontology learning from unstructured text, and large-scale knowledge acquisition systems. Navigli's work bridges theoretical linguistics with practical applications in relation extraction and open information extraction, emphasizing cross-lingual capabilities and resource scalability. Publication analysis reveals a sustained focus on semantic resource development, evolving from early WordNet extensions (2003) to contemporary open knowledge extraction frameworks (2015). This trajectory demonstrates consistent innovation in transforming unstructured text into structured knowledge representations for multilingual applications. Major scientific recognition includes: Marco Cadoli 2007 AI*IA Prize for best doctoral thesis in AI Marco Somalvico 2013 AI*IA Prize for best young AI researcher ERC Starting Grant (2011-2016) for multilingual word sense disambiguation Google Focused Research Award on Natural Language Understanding Navigli directs significant research initiatives funded by competitive grants, including his ERC project and Google collaboration, while providing academic leadership through area chair roles at ACL, WWW, and *SEM conferences. His service as senior program committee member for IJCAI and editorial board positions underscores substantial community impact.
Vivek Srikumar is an Associate Professor in the Kahlert School of Computing at the University of Utah, co-leading the Utah NLP group and affiliated with the Utah Center for Data Science. His research focuses on Machine Learning and Natural Language Processing, particularly in structured prediction, bias mitigation, and healthcare NLP applications. He teaches Machine Learning (CS 6350/DS 4350) and has been supported by NSF, NIH, and corporate grants from Intel, Google, and others. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2013) Postdoctoral Researcher at Stanford University's NLP Group (2013-2014) Visiting Researcher at Allen Institute for Artificial Intelligence (2022 sabbatical) Research Interests: Srikumar explores text understanding, structured learning, and robust AI systems. His work addresses challenges in table-based reasoning, adversarial robustness, and ethical AI. He develops methods to ensure models use appropriate evidence and mitigate biases in representations. Grants & Collaborations: Supported by NSF, NIH, BSF, and industry partnerships with Intel, Google, Verisk, Bloomberg, and Nvidia. Notable projects include table QA systems (TempTabQA), bias mitigation (OSCaR/VERB), and crisis counseling NLP tools (ClientBot). Advising: Supervised over 30 students, including 15+ Ph.D./M.S. alumni now in academia and industry (e.g., Google, Amazon, Microsoft). Current advisees focus on multimodal reasoning, healthcare NLP, and AI ethics. Labs/Teams: Utah NLP Group and Utah Center for Data Science. Active in reproducibility efforts (LogFlux) and open-source tools (CogCompNLP/Pylon frameworks).
Pascale Trevisiol Okamura is an Associate Professor in Language Sciences and Language Teaching at Sorbonne Nouvelle University, affiliated with the DILTEC research team (EA 2288). Her primary role includes teaching language acquisition and foreign language didactics within the UFR of Literature, Linguistics, and Didactics (LLD). She co-heads the Master 2 program in Language Teaching (FLE/FLS) and holds administrative roles in departmental councils and educational committees. Education Background: Lecturer at Sorbonne Nouvelle University since 2015 Previously taught at Université de Poitiers (2010–2015) and Université Paris 8 (2007–2010) French teaching assistant at Hokkaido University, Japan Research Focus: Specializes in third language acquisition (L3), crosslinguistic influence, and the interface between language acquisition and didactics. Key themes include: Discourse construction in L3 French Input processing and initial language exposure Development of teaching materials for FLE Plurilingual practices among language teachers Current projects involve multilingualism in primary education, Tamil-speaking learners' L3 French acquisition, and reflexive training in research. Teaching & Supervision: Co-supervises doctoral research on topics such as L3 French acquisition in Chinese contexts, translinguistic discourse influences, and plurilingual teacher practices. Teaches advanced courses on language acquisition theories and FLE methodology. Labs/Teams: Member of the Second Language Acquisition Network (ReAL2) and part of the DILTEC team, focusing on language didactics and multilingualism research.
