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Adriana Iamnitchi
Adriana Iamnitchi
Adriana Iamnitchi is Professor of Computer Science and Engineering at University of South Florida. Her research lies at the intersection of distributed systems and social computing. The overarching goal of her current work is empirical analysis of phenomena in online social environments and designing solutions for modeling them. Her work has been funded by the National Science Foundation, Office for Naval Research, and DARPA. She has served on the Technical Program Committee of numerous conferences in distributed systems and online social networks, such as HPDC, WWW, and ICDCS. Iamnitchi has been Associate Editor for IEEE Transactions on Parallel and Distributed Systems and Elsevier Online Social Networks and Media. She holds a PhD in Computer Science from The University of Chicago and is an ACM Distinguished Member, IEEE Senior Member, and recipient of the National Science Foundation CAREER award.
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Francesco Fabbri
Francesco Fabbri
Francesco Fabbri is a 2nd year Ph.D. student at UPF, Barcelona, working mainly on Algorithmic Fairness, with a particular focus on Recommender Systems. He is supported by the Vicente López scholarship, awarded by Eurecat, Research Center in Catalunya and he is luckily advised by Dr. Francesco Bonchi and Prof. Carlos Castillo. Prior to start his PhD, he has been awarded with a full grant to participate the 8 weeks study program in Artificial Intelligence at Pi School, Rome, Italy. He received his Master’s Degree (Honours) in Data Science and Bachelor of Science in Statistics from Sapienza, University of Rome. During his study he has been awarded with a research scholarship at DIAG (Department of Computer, Control and Management Engineering), concerning the development and application of data analysis techniques in bibliometrics.
Ryan Gallagher
Ryan Gallagher
Ryan Gallagher is a network science PhD candidate at Northeastern University. As a member of the Communication Media and Marginalization (wCoMM) Lab at Northeastern's Network Science Institute, he studies how individuals use online communication networks to amplify their voices, and how that amplification resonates through online media ecologies. To do so, his research makes advances in network science and text-as-data methodology to develop new approaches for measuring the complexities of polarization, misinformation, and the networked public sphere. Ryan interned with Facebook Core Data Science and their Political Organizations & Society team, where he developed methods for identifying inauthentic coordinated information operations, and spent two summers as a visiting research assistant at the University of Southern California's Information Sciences Institute. He holds an MS in mathematics from the University of Vermont, where he worked with the Computational Story Lab at the Vermont Complex Systems Center, and a BA in mathematics from the University of Connecticut.
Giovanni Briganti
Giovanni Briganti
Giovanni Briganti is a medical doctor (MD, PhD) specialized in psychiatry. He is a postdoctoral research fellow at Harvard University, in the Richard J. McNally laboratory. He is interested in studying mental disorders through the lens of machine learning. He works as a physician at the Department of Psychiatry of the Brussels Teaching Hospital - Brugmann (CHU Bruxelles Brugmann). He leads the AI4Health group at AI4Belgium, the Belgian government’s initiative for Artificial Intelligence. He is also a Professor at the École Supérieure de la Santé (Lausanne, Switzerland), where he teaches human physiology to undergraduate students in biomedical science laboratory technology. He is guest lecturer of Artificial Intelligence in Medicine at Université libre de Bruxelles and University of Mons.
Teague Henry
Teague Henry
Teague Henry is an assistant professor at University of Virginia, where he investigates network methods applied to brain, behavioral and social data. Teague's research focuses on how we can discover, infer qualities of, and manipulate complex psychological and social systems. His work has covered the gamut from inferring latent groups within social networks to application of control methods to psychological systems. Currently, he is interested in methodologies to control complex processes operating on networks (i.e. disease processes, brain activity).
Kristen Altenburger
Kristen Altenburger
Kristen Altenburger is a Research Scientist in tech and a Non-Resident Fellow with the Regulation, Evaluation, and Governance Lab (RegLab) at Stanford Law School. Her research focuses on developing statistical methods for characterizing social structures in networks and focuses on promoting equitable digital systems that feature complex cultural and political considerations. She received her PhD (January 2020) in Computational Social Science in the Management Science & Engineering Department at Stanford University advised by Johan Ugander. Her graduate work was supported in part by a National Defense Science and Engineering Graduate Fellowship. She received her BS in Mathematics from Ohio University in 2012 where she was also a Barry M. Goldwater Scholar, completed a research fellowship at Stanford Law School in 2012-2014, and received her AM in Statistics from Harvard University in 2015. She was previously a Member of Technical Staff in the Data Science and Cyber Analytics Department at Sandia National Laboratories and was a 2016 SPOT Award recipient based on her research. During the summer of 2017, she was the first intern for the Social Science & Algorithm team at Netflix.
Naomi Arnold
Naomi Arnold
Naomi Arnold is a final year PhD student in the Networks group at Queen Mary University of London, where she studies how complex networks evolve in time. To this end, she has developed methodologies for fitting network formation models and tools for studying networks at differing timescales. This is currently being applied to a wide variety of temporal network use cases including online social network analysis, word semantics evolution and cryptocurrency transactions. Prior to her PhD Naomi worked at the British Antarctic Survey studying the recovery of historically overexploited ecosystems, alongside her mathematics degree at the University of Cambridge.
Eszter Bokányi
Eszter Bokányi
Eszter Bokányi is currently a part-time research fellow at Corvinus University of Budapest, CIAS NETI Lab, external fellow at ANET Research Group, CERS, Budapest, and part-time postdoc in the POPNET project at the University of Amsterdam investigating a population-scale longitudinal social network of the Netherlands. Her background is in physics, from which she graduated with a PhD from statistical physics at Eötvös Loránd University in Budapest. The topic of her thesis was already rather connected to computational social science, having investigated a huge database of geolocated Twitter messages. Nowadays, she's still mostly interested in spatial social networks and their connection to human mobility, as well as dynamical phenomena such as the spreading of innovations over these social networks.
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