This Award was established by the Gerla family in 2019 in memory of Dr. Mario Gerla, pioneer in computer networking, professor of Computer Science at UCLA and ISSNAF founding member.
We are very excited to announce and congratulate the top-notch finalists of the 2023 edition: Alessandro Achille, Giulia Guidi, and Giovanni Paolini
Watch them present their outstanding work at the Symposium on October 18, 2023 to the Jury chaired by Prof. Elisa Bertino. https://youtu.be/K0p4SqA-rj0?si=jBiCMe6j7uVgrP1M
“I am an Applied Scientist working in computer vision and deep learning at Amazon AI (Pasadena) and Caltech (visiting scholar). I graduated in 2019 from the Computer Science Department of UCLA, working with Prof. Stefano Soatto in the Vision Lab. During my PhD I have also been a research scientist intern at Deep Mind and Amazon AI. My research interests include representation learning, information theory, multi-task learning and variational inference.
Before coming to UCLA, I obtained a Master in Pure Math at the Scuola Normale Superiore and the University of Pisa, where I studied model theory, algebraic topology, and their intersection with Prof. Alessandro Berarducci, with particular focus on definable groups in o-minimal theories. During that period, I have also been a visiting student at the University of Leeds Math department.” [from Alessandro Achille]
Giulia Guidi is an Assistant Professor of Computer Science at Cornell University in the Bowsers College of Computing and Information Sciences (CIS) and is a member of the Graduate Field of Computational Biology and Applied Mathematics in addition to Computer Science. Professor Guidi’s work focuses on high-performance computing for large-scale computational sciences. Guidi received her Ph.D. in Computer Science from the University of California Berkeley in 2022. Professor Guidi is part of the Performance and Algorithms Research Group in the Applied Math and Computational Sciences Division at Lawrence Berkeley National Laboratory in Berkeley, California, where she is currently Affiliate Faculty.
Giulia's research focuses on methodologies for writing highly parallel code for irregular computation without sacrificing productivity, as well as designing software infrastructures and systems to speed up data processing and make HPC more accessible. The challenge with irregular science applications, which are characterized by their unstructured and irregular nature, is parallelism in distributed memory, which is essential for analyzing the massive volumes of data that science generates today. In this context, Giulia uses graph structures, common in genomics, to harness the power of sparse matrices as core data structures. Computations are modeled as operations between matrices, such as sparse matrix multiplication, which are adapted to application-specific needs using semiring abstraction. By introducing a novel approach to combining graph structures and sparse matrices, computation time is significantly reduced, allowing large volumes of data to be processed within minutes. Giulia’s research also addresses the design of system-level methodologies that support not only highly scalable irregular computation but also high system utilization to support the design of future large-scale systems. This research is critical to meeting the growing need for efficient parallel computing in the computational sciences, enabling faster and higher-quality discoveries in genomics and related areas.
Giulia collaborates with several academic and industry research partners, including IBM Research, Intel, NVIDIA, the University of Trento, Simula Research Laboratory, and the University of California at Berkeley. Giulia’s research is currently funded by the National Science Foundation (NSF) Center for Research on Programmable Plant Systems, Cornell, and Intel. Giulia is a member of several program committees of various parallel computing and high-performance computing conferences, such as Supercomputing, IPDPS, IEEE Cluster, PPoPP, and EurPar. Giulia teaches courses on high-performance computing and parallel programming at Cornell University.
I am a mathematician and machine learning scientist. I earned my Ph.D. from Scuola Normale Superiore of Pisa, working at the intersection of combinatorics, topology, and group theory. During my studies, I ventured into machine learning, and this led me to a role as an applied scientist at Amazon Web Services (AWS), based in a lab located at Caltech. Over my four years at AWS, I got increasingly involved in natural language processing and in the development of language models. Now, I've taken on a new chapter as a faculty member at the University of Bologna, where my aim is to fuse my passion for mathematics and machine learning, exploring the fascinating crossroads between these two fields.
My research spans Generative AI, natural language processing, and abstract mathematics. While the impact of AI on natural language domains is well-established, I am especially enthusiastic about its potential in pure mathematics. I believe Generative AI can create new perspectives and even aid in solving long-standing mathematical conjectures. A notable part of my work involved using Generative AI to produce structured outputs, such as graphs, instead of simple textual data. This was explored in my paper "Structured Prediction as Translation between Augmented Natural Languages," where I was the primary author, collaborating with colleagues from AWS. Additionally, I have worked on the alignment of language models, an emerging area aimed at tuning generative models to make them better assistants to humans. I foresee alignment as crucial in developing Generative AI systems capable of helping with advanced mathematical research. In the realm of pure mathematics, I've been actively working on significant problems in combinatorics, topology, and group theory. In particular, I pioneered a new combinatorial approach to a long-standing topological problem, called the K(π,1) conjecture, leading to a solution of the so-called "affine" case of this conjecture. With my expertise in pure mathematics, I believe I am well-positioned to develop new ways to bring AI into the picture to expedite mathematical research breakthroughs.
I possess a unique blend of experience, spanning academia and industry, and grounded in both pure mathematics and machine learning. I intend to continue nurturing my multidisciplinary interests. I'm keen on leveraging my diverse skill set to drive innovations, particularly by employing cutting-edge AI techniques to advance mathematics.
In addition to research, I am passionate about promoting and disseminating mathematics and computer science. I conduct lectures for high-school students interested in math or informatics olympiads and actively engage in the organization of various such competitions. Additionally, I authored a book on elementary mathematics named "La matematica delle olimpiadi", with a second edition released in 2022.