Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.
Roles and Affiliations: Full Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). Research Advisor to Xiaosen Zheng and Kankan Zhou. Serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) , Program Co-Chair of EMNLP 2019, and Editorial Board Member of Computational Linguistics (2015-2017). Education: PhD in Computer Science, University of Illinois at Urbana-Champaign (2008) B.S. and M.S. in Computer Science, Stanford University Research Focus: Specializes in natural language processing (NLP), text mining, machine learning, and data mining. Current interests include question answering, social media content analysis, and combating misinformation. Explores topics like counterfactual syntax for cross-lingual understanding, interventional training for robust NLU, and bias detection in vision-language models. Publications: Over 100+ peer-reviewed papers across top conferences (ACL, EMNLP, NAACL) and journals. Recent work emphasizes multimodal analysis, hate speech detection in memes, and robustness improvements for large language models. Key themes include cross-lingual systems, knowledge base question answering, and misinformation mitigation. Grants & Advising: Supervises PhD/Master’s students in cutting-edge NLP research. Leads projects on model memorization studies, hate meme classification, and interventional training frameworks. Active in organizing conferences and editorial roles. Teaching: Teaches courses in software foundations and programming fundamentals, bridging theory and practical NLP applications.
Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .
Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD and has been affiliated with the Department of Computer Science since 2011, following a 15-year career at Kodak Research. His research spans computer vision, natural language processing, machine learning, data mining, computational social science, and digital health. Luo is an ACM Fellow, AAAI Fellow, IEEE Fellow, SPIE Fellow, and IAPR Fellow. Education: PhD (specific discipline not explicitly stated in text). Research interests include computer vision, machine learning, data mining, social media analysis, biomedical informatics, human-computer interaction, and ubiquitous computing. He co-authored the book Deep Neural Network for Medical Image Computing: Principles and Applications (Elsevier, 2022). His work has led to nearly 600 technical papers and 90+ U.S. patents. Research Trends: Recent work focuses on large language models (LLMs), multimodal systems, AI-driven social media analysis, and healthcare applications. Key areas include bias analysis in political simulations, video understanding, and benchmark development for AI-generated content evaluation. His research bridges theoretical advancements with practical applications in healthcare, social sciences, and multimedia systems. Awards: ACM SIGMM Technical Achievement Award (2021), IEEE Region 1 Technological Innovation Award (2018), Eastman Innovation Award (2004), and multiple best-paper recognitions at top conferences. Service & Leadership: Served as program co-chair for ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017. Currently Editor-in-Chief of IEEE Transactions on Multimedia (2020–2022). Editorial board roles include several IEEE Transactions journals and conferences. Labs & Teams: Leads research groups in computer vision and multimodal computing at the University of Rochester, collaborating on projects like UroSAM (kidney stone classification) and computational social science initiatives.
Shujun Li is a Professor of Cyber Security and Head of the Cyber Security Research Group at the School of Computing, University of Kent. He also holds a Visiting Professorship at the Department of Computer Science, University of Surrey. His research focuses on cyber security, privacy, AI applications, and human-centric computing. He leads the Institute of Cyber Security for Society (iCSS), a university-wide interdisciplinary research centre. Education: PhD in Information and Communication Engineering (Xi'an Jiaotong University, 2003), followed by postdoctoral research at City University of Hong Kong, Humboldt Research Fellowship at FernUniversität in Hagen, and a 5-year Zukunftskolleg Research Fellowship at Universität Konstanz. Research interests include cyber security (usable security, digital forensics, misinformation), AI safety, human factors, and socio-technical systems. He has published over 100 papers, with awards including the IEEE Guillemin-Cauer Best Paper Award and EPSRC recognition. Awards: Includes IEEE Transactions Best Paper Awards, EPSRC peer review recognition, and multiple conference best paper awards. Active in interdisciplinary projects like MACRO (cyber risks in mobility systems) and ACCEPT (reducing human-related cyber risks). Labs/Teams: Directs iCSS, co-founded Kent & Medway Cyber Cluster, and leads the Kent Interdisciplinary Research Centre in Cyber Security (KirCCS). Collaborates with industry and government agencies on cyber resilience and AI ethics.
Professor Marilyn A. Walker is a leading academic in Natural Language Processing and Dialogue Systems at the University of California Santa Cruz , with significant contributions to conversational agents, personality modeling, and narrative analysis. She has held visiting roles at Google Research and leadership positions at University of Sheffield and AT&T Labs. Education : Ph.D. in Computer and Information Science (University of Pennsylvania, 1993), M.A. in Linguistics (University of Pennsylvania, 1993), M.S. in Computer Science (Stanford, 1988), B.A. in Computer and Information Science (UC Santa Cruz, 1984). Research Interests include Natural Language Processing , Conversational Agents , Dialogue Systems , and Personality Modeling . Her work bridges machine learning with linguistic theory to enhance dialogue adaptivity and expressive language generation. Scientific Awards include ACL Fellow (2016) Best Paper Awards at SIGDIAL 2016 and 2014 Royal Society Wolfson Research Merit Award (2003-2009) Grants exceed $2.5M, including NSF awards for projects like Interactive Dialog Agents for Social Language Development (2017) and Processing Opinion Sharing Dialog in Social Media (2011). She has also received corporate funding from Amazon , Fujitsu , and Hitachi .
William Hobbs is the Lois and Mel Tukman Assistant Professor in the Department of Psychology at Cornell University , affiliated with the College of Human Ecology. His research intersects politics and health , focusing on social spillover effects of government actions and adaptation to life changes through computational social science methods. Teaches Data Science for Social Scientists I & II (HD/Psych 2930/2940) Co-teaches graduate course Text and Networks in Social Science Research (HD/Soc/Info 6610, Govt 6619) Research strengths include causal inference , representative sampling , and machine learning applications for small training sets. His work has been featured in The Atlantic , Science Magazine , and other major outlets. Current Data Science Lab projects analyze: Political polarization in social media Health behavior networks Government policy feedback Content moderation systems Lab hires Cornell undergraduates with R/Python experience for data management tasks through HD 4010 research credit.
Kalina Bontcheva is a Senior Researcher in the Natural Language Processing Group within the Department of Computer Science at the University of Sheffield. She holds an EPSRC Career Acceleration Fellowship (working part-time since October 2015) focused on personalized summarization of social media content. Her research spans multiple EU-funded projects including PHEME (computing veracity of social media), TrendMiner, DecarboNet, and uComp, with significant contributions to the GATE (General Architecture for Text Engineering) open-source NLP infrastructure since 1999. Dr. Bontcheva's research interests focus on the intersection of natural language processing and social media analysis. Her work encompasses NLP for social media, semantic search, information extraction from social platforms, crowdsourcing of NLP corpora, collaborative text annotation, semantic technologies, and text mining and analytics. She has particular expertise in developing methods for personalized, abstractive multi-document summarization across different social media platforms, addressing the challenges of noisy, jargon-filled and dynamic content. Her interdisciplinary approach combines machine learning, semantic technologies, and social dimension analysis to create systems that adapt to individual users' information seeking goals. Analysis of her recent publications reveals a strong focus on social media processing challenges, with emphasis on Twitter analysis, temporal expression recognition, and handling noisy text. Her work consistently addresses the unique characteristics of social media content and develops specialized techniques for information extraction, sentiment analysis, and user geolocation within these platforms. The GATE framework serves as the foundation for much of her tool development, demonstrating her commitment to creating reusable, open-source NLP infrastructure. Her most significant award is the EPSRC Career Acceleration Fellowship, which supports her work on personalized social media summarization. This prestigious fellowship includes a substantial budget of £560k and involves collaborations with industry partners including The Press Association, British Telecom, and Fizzback. Dr. Bontcheva has led numerous major research projects throughout her career. She was Principal Investigator on three EU-funded projects (MUSING, TAO, and ServiceFinder) between 2006-2009, coordinating the TAO consortium with seven partner institutions. She currently leads the PHEME EU project and serves as PI for TrendMiner and DecarboNet European projects, while also contributing as Co-I on the uComp project. Her project portfolio demonstrates consistent success in securing competitive research funding across multiple domains within NLP and semantic technologies. She works within the Natural Language Processing Group at the University of Sheffield, which has been central to the development of the GATE infrastructure. Her work connects with various initiatives including the GATE Cloud platform and the TextVRE project for e-humanities textual studies. She has established collaborations with organizations including the Press Association, British Telecom, Oxford Internet Institute, and Sheffield's Department of Journalism to ensure her research addresses real-world needs across different user communities.
Carlos Cifuentes is an Associate Professor in Human-Robot Interaction at the Bristol Robotics Laboratory (BRL) , part of the University of the West of England (UWE Bristol) . He also serves as the Deputy Director of the VIVO Hub , a £13.4M UK-funded research initiative (2024-2030) focused on robotics for rehabilitation and healthcare. His research spans Human-Robot Interaction , Rehabilitation Robotics , Healthcare Robotics , and Socially Assistive Robotics , with applications for conditions such as cardiac diseases , post-stroke recovery , spinal cord injuries , cerebral palsy , Parkinson's disease , musculoskeletal disorders , and autism spectrum disorder (ASD) . Carlos has over 15 years of experience and 200+ publications in robotics for rehabilitation and assistive technologies. His recent work explores smart walkers with multimodal feedback, soft prosthetics using polymeric optical fiber sensors , and machine learning models for fatigue and stress estimation. He also investigates inclusive design for social robots in global contexts , including a CASTOR Robot for ASD therapy and collaborative design processes with Colombian communities. Carlos serves as an Associate Editor for IEEE Robotics and Automation Magazine (since 2021), ICRA , and IROS (since 2023). He leads projects integrating wearable sensors , deep learning , and haptic feedback to enhance mobility, autonomy, and quality of life for individuals with disabilities. As Deputy Director of the VIVO Hub, he focuses on long-term deployment of robotic systems in real-world healthcare settings, emphasizing collaboration with clinicians , caregivers , and neurodiverse communities . His work bridges robotics , biomedical engineering , and human-centered design .
Paul Firbas is an Associate Professor in the Department of Hispanic Languages and Literature at Stony Brook University, with affiliations in the Department of History. He holds a Ph.D. from Princeton University (2001), an M.A. from the University of Notre Dame (1995), and a B.A. from Universidad Católica del Perú (1992). Previously, he was an Assistant Professor at Princeton University (2001-2007) and a Visiting Professor at Columbia University (Spring 2007). Education: Ph.D., Princeton University, 2001; M.A., University of Notre Dame, 1995; B.A., Universidad Católica del Perú, 1992 His research focuses on Epic Poetry , Colonial Latin American Literature , Textual Criticism , Colonial Geopolitics , and Digital Humanities . He has extensively studied Spanish-American texts from the colonial period, particularly those discussing transatlantic South America's geography, information circulation, and moral geography. His work bridges literary analysis with historical documentation, emphasizing the role of epic poetry in shaping colonial identity. Recent publications include a 2025 digital humanities project on Magellanic route information flows, the 2023 two-volume edition of Lima's early news-sheets, and critical studies on 18th-century Lima's satire (2009), 16th-17th century Peruvian poetry (2019), and 2006 critical edition of 'Armas antárticas' . These works highlight transatlantic networks, geopolitical shifts, and literary responses to colonial crises. He has co-edited multiple volumes, including 'La biblioteca del Inca Garcilaso' (2016) and 'La forma inicial' (2015), a conversation book with Ricardo Piglia. His research also intersects with documentary film studies, as seen in 'Conversaciones en Princeton' (1998), co-edited with Pedro Meira Monteiro.
Arjun Mukherjee is a Lecturer at the Department of Computer Science , University of Houston , where he teaches courses in Machine Learning , Data Mining , Natural Language Processing , and Data Structures . His research focuses on Bayesian Inference , Data Mining , Natural Language Processing , Sentiment Analysis , Opinion Spam , and Web Mining , with a strong emphasis on deception detection and social media analysis. His recent publications explore advanced techniques in LLM-generated content detection synthetic data applications cross-domain deception modeling temporal user behavior analysis , reflecting his commitment to addressing modern challenges in digital content authenticity and machine learning robustness. Dr. Mukherjee has developed educational materials for graduate-level courses, including a well-structured Machine Learning course (COSC 6342) covering probabilistic inference, supervised/unsupervised learning, and neural networks. He earned his Ph.D. from the University of Illinois at Chicago in 2014, with a thesis titled Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications .
Swapna Gottipati is a Full-time Associate Professor of Information Systems (Education) and Associate Dean (Undergraduate Education) at the School of Computing and Information Systems , Singapore Management University . Holding a PhD from SMU (2014) , she specializes in text analytics, education technology, and digital business. Current focus areas: Artificial Intelligence, Data Science, Technology-Enhanced Learning Interdisciplinary applications: Health Informatics, Social Media Analytics Research Interests span text analytics, opinion mining, curriculum analytics, and digital transformation. She applies machine learning and data mining to educational systems, business processes, and health domains. Scientific Contributions: 2016 Best Paper Award (AIS SIG-ED) 2016 MOE TRF Grant 2017-2020 Conference Best Paper Nominations 2013 Best Paper Finalist (CIKM) Academic Leadership includes: Director of Undergraduate Education Co-developer of SMU-X pedagogy Founder of MyCompetencies mobile platform Organizer of curriculum analytics workshops Grant Projects include the Learning Analytics on Qualitative Student Feedback system (MOE TRF 2016) and ICDL-funded digital literacy studies. She has delivered 12+ invited talks on digital transformation in education and industry.
Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.
Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.
Fosca Giannotti is a Full Professor at Scuola Normale Superiore in Pisa, Italy, and leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR. Founded in 1994, the Pisa KDD Lab is one of the earliest research labs focused on data mining. Giannotti is a pioneering scientist in mobility data mining, social network analysis, and privacy-preserving data mining. Her educational background includes a Master Degree in Computer Science from the University of Pisa (1982) with 110/100 cum laude. She has held numerous visiting positions including at MCC in Austin, CWI Amsterdam, UCLA, and the Barabasi Lab at Northeastern University. Giannotti's research focuses on social mining from big data, encompassing smart cities, human dynamics, social and economic networks, ethics and trust, and diffusion of innovations. She has authored more than 300 papers and coordinated tens of European projects and industrial collaborations. Her current work increasingly centers on Explainable AI (XAI), as evidenced by her prestigious ERC Advanced Grant for the XAI project focused on "Science and technology for the explanation of AI decision making." Her recent publications reveal a strong emphasis on trustworthy AI, with research spanning privacy-preserving techniques, fairness in machine learning, human-AI collaboration frameworks, and medical applications of explainable AI. The breadth of her work demonstrates how data mining principles are being applied across diverse domains from social sciences to healthcare. ERC Advanced Grant for XAI project Premio Internazionale Tecnovisionarie 2021 Intelligenza Artificiale Giannotti has coordinated numerous significant projects including SoBigData (the European research infrastructure on Big Data Analytics and Social Mining), XAI, TAILOR (Foundations of Trustworthy AI), HumanE-AI-Net, and AI4EU. As former coordinator of SoBigData, she led an ecosystem of ten cutting-edge European research centers providing an open platform for interdisciplinary data science. She leads the Pisa KDD Lab, which serves as a hub for research on knowledge discovery and data mining. The lab has been instrumental in developing techniques for mobility data analysis, social network mining, and privacy-preserving data analytics, with applications ranging from smart cities to pandemic response.
