Akhil Arora is a PhD student affiliated with the Data Science Lab (dlab) at EPFL and an external research collaborator of the Wikimedia Foundation. Prior to this, Akhil spent close to five years in industry working with the research labs of Xerox and American Express as a Research Scientist. He graduated from the Computer Science department of IIT Kanpur in June, 2013. Akhil’s research interests include large-scale data management, graph mining, and machine learning. He is a recipient of the prestigious “EDIC Doctoral Fellowship” for the academic year 2018-19, and the Most Reproducible Paper Award at the ACM SIGMOD Conference, 2018. He has published his research in prestigious data management conferences, served as a program committee member, and co-organized workshops in these conferences. Additional details are available on his website: https://dlab.epfl.ch/people/aarora/.
Lucas Clarté comes from France. He did his undergraduate studies at Ecole Polytechnique where he majored in applied mathematics. After that, he studied machine learning at ENS Paris-Saclay and theoretical physics at ENS Paris. He is currently pursuing a PhD under the supervision of Prof. Lenka Zdeborova in the « Statistical Physics of Computation Lab ». More specifically, he is interested in high dimensional statistics and the theory of neural networks.
Michele Vidulis was born in Brescia, Italy, and studied Mathematical Engineering at Politecnico di Milano. He moved to Lausanne for a double master’s degree program in Computational Science and Engineering, and then decided to stay at EPFL for a PhD. He is now a first year student in Computer Science. He does research on properties of elastic knots in Prof. Mark Pauly’s Geometric Computing Laboratory, a topic that blends mathematics, physics, and computer science. His interests include deployable structures and computational design.
Stefan is a 5th year PhD student at the Processor Architecture Laboratory (LAP), supervised by Prof. Paolo Ienne. His research focuses on FPGA interconnect and, in particular, on developing methods for comprehensive architectural exploration, intended to help designing interconnect architectures capable of addressing future challenges.
Stefan was a recipient of the Michal Servit Memorial Award for the best paper in the area of design algorithms, methods, and CAD tools at FPL’20 and FPL’21.
Prior to joining LAP, he got a bachelor degree in Electrical and Computer Engineering from the University of Novi Sad, in his home town of Novi Sad, Serbia.
Sandra is a fifth year PhD student in the Security and Privacy Engineering (SPRING) lab, advised by Prof. Carmela Troncoso. She comes from India, and did her schooling in Oman. She obtained her bachelors from the National University of Singapore (NUS) and her masters from ETH Zurich. Her research interests lie mainly in the areas of network security, web security, and privacy.
Manoel is a 3rd year Ph.D. student at EPFL working at dlab with Prof. Robert West. His research uses a diverse methodological toolkit to characterize troublesome online phenomena and to assess how moderation policies and recommendation algorithms can improve our online information ecosystem. He received his B.S./M.S. in CS from UFMG (Universidade Federal de Minas Gerais) in Brazil.
Tugrulcan is a 5th year Ph.D. student in Distributed Information Systems (LSIR) lab, advised by Prof. Karl Aberer. His research focuses on social media manipulation. He uses data mining techniques to understand and analyze the strategies adversaries employ to disrupt public discussion on social media. His recent work “Ephemeral Astroturfing Attack: The Case of Fake Twitter” reported that at least 20% of global Twitter trends are fake is published by IEEE Euro S&P and well-received by the international media. He received his B.S. in CS from Bilkent University in Ankara, Turkey.
Amirkeivan was born in Iran where he received his B.Sc from Sharif University of Technology in Computer Engineering. He then directly started his Ph.D. in Computer Science at EPFL and is now in his 2nd year, working in the Machine Learning and Optimization (MLO) laboratory under supervision of Prof. Martin Jaggi. In his research, he is interested in obtaining a deeper understanding of neural networks training and finding ways to make it more efficient.