DMITRY USTALOVDmitry Ustalov is a post-doctoral researcher at the Data and Web Science Group, University of Mannheim, Germany. His research is focused on Computational Lexical Semantics and Crowdsourcing. In 2018 he defended his Kandidat Nauk (PhD) thesis which he worked on at the Krasovskii Institute of Mathematics and Mechanics, Russia. Dmitry's research is published in the premier international scientific conferences, such as ACL, EACL, and EMNLP. He serves as a reviewer for ACL, EMNLP, *SEM, EKAW, and other prestigious events.
E-mail: dmitry (at) informatik (dot) uni-mannheim (dot) de
Webpage: https://www.uni-mannheim.de/dws/people/researchers/postdoctoral-research-fellows/dr-dmitry-ustalov/
SWAPNA SOMASUNDARANSwapna is a Research Scientist at Educational Testing Service. Her main research areas are sentiment analysis and discourse modeling, and some of her work has specifically focused on extracting, representing and exploiting graphs from the discourse (e.g. opinion graphs and lexical chains). Prior to ETS, Swapna was a Research Scientist at Siemens Corporate Research, where she focused on relationship mining on biomedical entities. Swapna coorganized TextGraphs in 2010 and 2017. She has also served as area chair (NAACL 2018, COLING 2016), SRW Faculty Advisor (SRW 2018) handbook chair (EMNLP 2016), and program committee member/ reviewer for several journals, conferences and workshops (e.g. CL, ACL, NAACL, EACL, EMNLP, COLING, LREC, AIED, IJCNLP, SAAIP, TAFC, BEA, LSDSem).
E-mail: ssomasundaran (at) ets (dot) org
Webpage: http://www.somasundaran.net/
PETER JANSEN Peter Jansen is an Assistant Professor in the School of Information at the University of Arizona. Dr. Jansen is a broadly interdisciplinary artificial intelligence researcher specializing in natural language processing and methods inspired by cognition and the brain. He applies these to application areas in science and health care. A central focus of his research is on how we can teach computers question answering in the form of passing standardized science exams, as written. In particular, he focuses on methods of automated inference that combine information together and generate explanations for why the answer is correct, largely using graph-based methods.
E-mail: pajansen (at ) email (dot) arizona (dot) edu
Webpage: http://www.cognitiveai.org
GORAN GLAVAŠGoran is an Assistant Professor at the Data and Web Science group, School of Business Informatics and Mathematics, University of Mannheim. Prior to joining University of Mannheim, Goran obtained his Ph.D. and worked at the Text Analysis and Knowledge Engineering Lab (TakeLab), University of Zagreb. His research efforts have mainly focused on graph-based event extraction and retrieval, lexical and computational semantics, NLP applications for social sciences. Goran has co-organized the TextGraphs-11 workshop and has served as a program committee member / reviewer for several journals and conferences (Information Sciences, Web Semantics, Natural Language Engineering, Artificial Intelligence Review, Expert Systems with Applications, ACL, EMNLP, EACL, AAAI, RANLP).
E-mail: goran (at) informatik (dot) uni-mannheim (dot) de
Webpage: https://www.uni-mannheim.de/dws/people/professors/prof-dr-goran-glavas/
MARTIN RIEDLMartin is a postdoctoral researcher at the “Theoretical Computational Linguistics” group at the University of Stuttgart. Prior to that position, he obtained his Ph.D. at the University of Darmstadt and was a postdoctoral researcher at the University of Hamburg. His main focus of research is the development of statistical semantic methods, with the special focus on unsupervised methods. Martin has organized the TextGraphs-10 and TextGraphs-11 workshop and has been program committee of several journals, conferences and workshops e.g. NLE, ACL, EMNLP, NAACL, COLING, IJCNLP, LREC, RANLP, NLDB and TextGraphs.
E-mail: martin.riedl (at) ims (dot) uni-stuttgart (dot) de
Webpage: http://www.ims.uni-stuttgart.de/institut/mitarbeiter/riedlmn/
MIHAI SURDEANUMihai Surdeanu is an Associate Professor in the Computer Science department at University of Arizona. Dr. Surdeanu earned a PhD degree in Computer Science from Southern Methodist University, Dallas, TX. He has 15+ years of experience in building systems driven by natural language processing (NLP) and machine learning. His experience spans both academia (Stanford University, University of Arizona) and industry (Yahoo! Research and two NLP-centric startups). During his career he published more than 90 peer-reviewed articles, including four articles that were among the top three most cited articles at their respective venues. His work was funded by several government organizations (DARPA, NSF, NIH), as well as private foundations (the Allen Institute for Artificial Intelligence, the Bill & Melinda Gates Foundation). Dr. Surdeanu's current work focuses on using machine reading to extract structure from free text, and using this structure to construct causal models that can be used to understand, explain, and predict.
E-mail: msurdeanu (at) email (dot) arizona (dot) edu
Webpage: http://uacc.arizona.edu/profile/mihai-surdeanu
MICHALIS VAZIRGIANNISDr. Vazirgiannis is a Professor at LIX, Ecole Polytechnique in France and leads the Data Science and Mining group. He holds a degree in Physics and a PhD in Informatics from Athens University(Greece) and a Master degree in AI from HerioWatt Univ Edinburgh. He has conducted research in GMD-IPSI, Max Planck MPI (Germany), in INRIA/FUTURS (Paris). He has been a teaching in AUEB (Greece), Ecole Polytechnique, Telecom-Paristech, ENS (France), Tsinghua, Jiaotong Shanghai (China) and in Deusto University (Spain). His current research interests are on machine learning and combinatorial methods for Graph analysis (including community detection, graph clustering and embeddings, influence maximization), Text mining including Graph of Words, word embeddings with applications to web advertising and marketing, event detection and summarization. He has active cooperation with industrial partners in the area of data analytics and machine learning for large scale data repositories in different application domains. He has supervised fifteen completed PhD theses. He has published three books and more than a 160 papers in international refereed journals and conferences. He has organized large scale conferences in the area of Data Mining and Machine Learning (such as ECML/PKDD) while he participates in the senior PC of AI and ML conferences – such as AAAI and IJCAI, He has received the ERCIM and the Marie Curie EU fellowships, the Tencent “Rhino-Bird International Academic Expert Award” in 2017 and since 2015 he leads the AXA Data Science chair.
E-mail: mvazirg (at) lix (dot) polytechnique (dot) fr
Webpage: http://www.lix.polytechnique.fr/Labo/Michalis.Vazirgiannis/