Introducing Our
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Dr. Mona Diab is the Lead Responsible AI Research Scientist with Meta. She is also a full Professor of Computer Science at the George Washington University (on leave) where she directs the CARE4Lang NLP Lab. Before joining Meta, she led the Lex Conversational AI project within Amazon AWS AI. Her current focus is on Responsible AI and how to operationalize it for NLP technologies. Her interests span building robust technologies for low resource scenarios with a special interest in Arabic technologies, (mis) information propagation, computational socio-pragmatics, computational psycholinguistics, NLG evaluation metrics, Language modeling and resource creation. Mona has served the community in several capacities: Elected President of SIGLEX and SIGSemitic, and she currently serves as the elected VP for ACL SIGDAT, the board supporting EMNLP conferences. She has delivered tutorials and organized numerous workshops and panels around Arabic processing, Responsible NLP, Code Switching, etc. She is a cofounder of CADIM (Consortium on Arabic Dialect Modeling, previously known as Columbia University Arabic Dialects Modeling Group), in 2005, which served as a world renowned reference point on Arabic Language Technologies. Moreover she helped establish two research trends in NLP, namely computational approaches to Code Switching and Semantic Textual Similarity. She is also a founding member of the *SEM conference, one of the top tier conferences in NLP. Mona has published more than 250 peer reviewed articles..
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Dr. Carlos Coello is Professor with Distinction (Investigador CINVESTAV 3F) at the Center for Research and Advanced Studies of the Instituto Politécnico Nacional (CINVESTAV-IPN) in Mexico City and Visiting Professor at the Basque Center for Applied Mathematics in Spain. Has taught master's and Ph.D. level courses on evolutionary computation, evolutionary multi-objective optimization, programming languages, and engineering optimization at CINVESTAV-IPN. In addition, he has taught short courses in Spain, England, Argentina, Chile, India, Bolivia, Colombia, Slovenia, and the USA. Also, Professor Coello has presented scientific articles at major conferences specialized in evolutionary computation, like the IEEE Congress on Evolutionary Computation (CEC), the Genetic and Evolutionary Computation Conference (GECCO), Parallel Problem Solving from Nature (PPSN), and others. His work and research fall at the intersection of computer science, applied mathematics, and operations research. His main contributions have revolved around the design of biologically inspired stochastic algorithms to solve highly complex multi-objective optimization problems (mainly non-linear). He has made pioneering contributions to this area which is now known as multi-objective evolutionary optimization. For example, he proposed, along with his research group, the first genetic micro-algorithm for multi-objective optimization, which has been used in real-world applications in various countries like the United States for the design of supersonic business jets. Also, the first algorithm for multi-objective optimization based on an artificial immune system incorporating the concept of Pareto optimality, which has been a reference in specialized literature used to validate new multi-objective algorithms.
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Dr. Finale Doshi-Velez is a Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability. Dr. Doshi-Velez is head the Data to Actionable Knowledge (DtAK) group at Harvard Computer Science. We use probabilistic methods to address many decision-making scenarios involving humans and AI. Our work spans specific application domains (health and wellness) as well as broader socio-technical questions around human-AI interaction, AI accountability, and responsible and effective AI regulation.
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Dr. Huan Liu is a Regents Professor and Ira A. Fulton Professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President's Award for Innovation. He is the recipient of the ACM SIGKDD 2022 Innovation Award. His research interests are in data mining, machine learning, feature selection, social computing, social media mining, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is a co-author of a text, Social Media Mining: An Introduction, Cambridge University Press. He is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction, Editor in Chief of ACM TIST, and Field Chief Editor of Frontiers in Big Data and its Specialty Chief Editor of Data Mining and Management. He is a Fellow of ACM, AAAI, AAAS, and IEEE.
