Simone Giacomelli serves as a Research Fellow at the Department of Physics, University of Milano-Bicocca, specializing in Theoretical Physics (FIS/02) with emphasis on mathematical models and methods in high energy theory. His research focuses on fundamental aspects of quantum field theory and string theory, particularly: Duality transformations in supersymmetric gauge theories Mathematical structures of quantum gravity Discrete global symmetries and compactification mechanisms Non-perturbative analysis of 'bad' quantum field theories Recent work demonstrates significant contributions to dualization algorithms and symmetry analysis, with three 2024 publications in the Journal of High Energy Physics establishing new frameworks for probing non-standard quantum field theories and symmetry implementations. These studies reveal deep connections between duality, compactification, and symmetry in high-energy regimes. Contact is maintained through university channels with primary academic correspondence via institutional email.
Roberto Grossi is a Professor of Algorithms and Data Structures at the University of Pisa's Dipartimento di Informatica. He is actively involved in editorial roles for journals like Theory of Computing Systems and RAIRO-Theoretical Informatics, and has served on program committees for conferences such as SWAT, IWOCA, ESA, FOCS, and SPIRE. His research focuses on algorithms, data structures, graph theory, and bioinformatics, with contributions to string processing, compressed data structures, and algorithm engineering. He advises Ph.D. students and collaborates on projects involving large-scale networks and community detection. Grossi has authored numerous publications, including works on clique enumeration, graphlet algorithms, and phylogenetic reconstruction. His teaching includes courses on Algorithm Design and Data Structures, and he maintains an active presence in academic networks through his research and editorial activities. Education: Not explicitly stated, but inferred from his role as a professor at a top Italian university. Research Interests: Dynamic algorithms, external memory methods, experimental algorithmics, pattern matching in strings and graphs. Grants/Awards: While no specific prizes are listed, his extensive publication record and editorial roles reflect sustained recognition in the field. Lab/Teams: Leads research groups focused on algorithmic foundations and applications, collaborating with international teams on projects like Zuckerli (compressed graph representations) and motif trie indexing. His work bridges theoretical computer science with practical applications in bioinformatics and network analysis.
Giovanni Manzini is a Professor of Computer Science at the University of Pisa's Department of Computer Science. He holds a PhD in Mathematics from the Scuola Normale Superiore di Pisa and has held visiting roles at MIT, Johns Hopkins University, and the University of Melbourne. Previously, he served as Assistant Professor at the University of Torino and Associate Professor at the University of Eastern Piedmont. His research focuses on designing algorithms and data structures for data compression, indexing massive datasets, and computational biology. Notable contributions include advancements in the Burrows-Wheeler Transform (BWT), grammar-based compression, and matrix operations optimization. He received the ACM Paris Kanellakis Theory and Practice Award (2022) and the 2023 Test-of-Time Award from the European Symposium on Algorithms. His recent work explores two-dimensional string repetitiveness, BWT-based indexing, and green computing techniques for matrix operations. Manzini has contributed to over 100 conference program committees since 2020, including DCC, ALENEX, and SPIRE. His teaching spans courses on algorithms, programming languages, and numerical analysis. Current courses include Algorithms and Data Structures for Data-intensive Applications and an undergraduate laboratory course on programming fundamentals. He leads software projects such as MMRepair (grammar-compressed matrix multiplication), BigBWT (BWT construction via prefix-free parsing), and EZcount (microRNA quantification tools). He also mentors the Scuola Ortogonale under the Elicsir Foundation, promoting STEM education.
Giovanna Rosone is an Associate Professor at the University of Pisa (Università di Pisa) within the Department of Computer Science (Dipartimento di Informatica) . Her research focuses on Sequence Analysis , Combinatorics on Words , Bioinformatics , and Algorithms and Data Structures , with a strong emphasis on applications leveraging the Burrows-Wheeler Transform . She leads the CMACBioSeq project funded by the Italian MIUR-SIR grant, developing combinatorial methods for biological sequence analysis and compression. Her work includes tools like BCR_LCP_GSA and ebwt2InDel for efficient genome analysis, SNPs/indel detection, and alignment-free variation discovery. These tools are used in metagenomics, rare-variant identification, and genotyping.
Francesco Cauteruccio is a Tenure-Track Assistant Professor at the Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM) at the University of Salerno, Italy. He is affiliated with the CORE and S 3 labs, focusing on interdisciplinary research at the intersection of social networks, IoT, and hybrid AI models. Ph.D. in Mathematics and Computer Science, University of Calabria (thesis: "Generalizing Identity-Based String Similarity Metrics: Theory and Applications") Italian National Scientific Qualification (ASN) for Associate Professor in Computer Science and Computer Engineering Visiting researcher at University of Klagenfurt (AI & Cybersecurity) and University of Lyon 1 (CERMEP/CREATIS) His research spans: Social & Complex Networks: Modeling cross-blockchain ecosystems, Reddit community dynamics, and sentiment propagation IoT & Hybrid Intelligence: Scope formalization, anomaly detection in MIoT networks, and air quality prediction systems Logic Programming: Integrating Answer Set Programming with machine learning for medical informatics and pattern mining Algorithm Design: Multi-Parameterized Edit Distance (MPED) for bioinformatics and network analysis Recent publications address network theory applications, hybrid reasoning frameworks, and high-utility pattern mining with formal constraint specification. He serves as guest editor for journals like Big Data and Cognitive Computing and Frontiers in Medical Engineering , and organizes international workshops on hybrid intelligence (HYDRA, VIPERC). Academic distinctions: Italian National Scientific Qualification for Associate Professor positions (2024) Guest Editor for 12+ special issues in data science and IoT journals Program/General Chair for 8 international workshops (2022-2024) His teaching portfolio includes courses on algorithms, distributed systems, and data analytics at University of Salerno, Polytechnic University of Marche, and University of Calabria.
Zsuzsanna Lipták is an Associate Professor in the Department of Computer Science at the University of Verona, Italy, where she has been a faculty member since November 2011. Her research is centered on string algorithms, combinatorics on words, and algorithmic bioinformatics, with a particular focus on the Burrows-Wheeler Transform (BWT) and its applications in data compression and biological sequence analysis. She is an active member of the Algorithmic Bioinformatics and Natural Computing Group and the Algorithms Group at the university. She leads research within the PRIN-funded project 'PINC – Pangenome Informatics: From Theory to Practice' and collaborates internationally with institutions in South Africa, Finland, Chile, and the USA. Her research interests include string indexing, suffix trees, suffix arrays, data compression, computational biology, and combinatorial properties of permutations and BWT. She has made significant contributions to the theory and application of BWT variants, matching statistics, and de Bruijn sequence construction. Her recent publications reflect a consistent focus on improving the efficiency and understanding of text indexing and compression methods, particularly in the context of genomic data. These works often involve both theoretical analysis and experimental validation, bridging the gap between pure theory and practical implementation. Lipták actively supervises PhD and master’s students, including Davide Cenzato, Sara Giuliani, Francesco Masillo, and Martina Lucà. She teaches advanced courses such as 'Fundamental Algorithms for Bioinformatics,' 'Computational Analysis of Genome-Scale Sequences,' and 'Advanced Data Structures for Textual Data.' She has also supervised numerous bachelor’s theses and student projects. She has secured research funding through national projects like PRIN and has collaborated on international initiatives, including a Marie Curie IEF fellowship during her postdoctoral work. She is deeply involved in the academic community, having served as PC chair for SPIRE 2024, PC co-chair for CPM 2023, and a member of the Steering Committee of SPIRE since 2024. She has served on the program committees of major conferences such as ESA, DLT, WABI, and IWOCA. She co-organizes the weekly 'Monday Meetings' seminar series for the Algorithms Group and has co-edited special issues and conference proceedings in journals like Theory of Computing Systems , Discrete Applied Mathematics , and European Journal of Combinatorics . She earned her Diplom in Mathematics from Freie Universität Berlin and her PhD in Computer Science from Bielefeld University, Germany, where her thesis addressed algorithmic problems in mass spectrometry. She has held research positions at ETH Zurich, Bielefeld University, and Salerno University, and has been a visiting researcher at the Rényi Institute (Hungary), University of the Witwatersrand, and SANBI (South Africa). She is the scientific coordinator for Erasmus+ exchanges with Bielefeld and Jena Universities.
Stefano Panizzi is a Researcher in Mathematical Analysis at the Department of Mathematics , University of Parma . His work focuses on nonlinear hyperbolic partial differential equations , including Kirchhoff-type equations, Timoshenko beam models, and singular perturbations. He has collaborated with institutions such as the Institute for Mathematics and its Applications at the University of Minnesota. Researcher at University of Parma since 1991 PhD in Mathematics at University of Pisa (1990-1991) Graduated in Mathematics at University of Parma (1989) Research interests include: Time-global solvability of nonlinear hyperbolic equations Well-posedness of second/fourth-order equations in bounded domains Unilateral problems and singular perturbations Invariance properties in linear hyperbolic systems (e.g., Timoshenko beam) Variational formulations for boundary integral equations in wave propagation Recent publications (2016-2024) examine instability in suspension bridge models, energy transfer between oscillation modes, spectral gaps in Kirchhoff equations, and space-time variational formulations for wave equations. His work often intersects mathematical analysis and physical modeling . Scientific contributions : CNR Grant (1995) for research at the University of Minnesota Collaborations with A. Arosio, C. Marchionna, and international institutions Teaching includes foundational courses in Mathematics and Mathematical Analysis for Mathematics and Physics degrees, with long-term appointments in Basic Mathematics for Food Science and Technology programs.
Giorgio Stefano Gnecco is a Full Professor in Mathematical Methods of Economics and of Actuarial and Financial Sciences at IMT School for Advanced Studies Lucca, where he works within the Analysis of compleX Economic Systems (AXES) research unit. His academic career spans multiple disciplines including optimization, machine learning, game theory, and their applications in economics, finance, and engineering. His research interests focus on optimization applied to actuarial sciences, economics, finance, and engineering; game theory; statistics; machine learning theory and applications; causal inference for economic policy evaluation; and environmental economics. His work demonstrates interdisciplinary connections between mathematical theory and practical applications across diverse fields, with particular emphasis on developing computational methods for complex economic systems. His publication record shows consistent output across multiple domains, with recent work spanning machine learning algorithms, image processing techniques, economic modeling, and applications in music performance analysis. The breadth of his research indicates strong methodological foundations in mathematical optimization and statistical learning, applied to problems ranging from flood hazard assessment to Parkinson's disease classification. Among his notable achievements are five Italian National Scientific Qualifications for professorial positions in various fields, demonstrating his recognized expertise across multiple academic disciplines. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Action Editor for Neural Networks, and Associate Editor for Neurocomputing. Professor Gnecco leads numerous research projects including the "PRIN PNRR 2022" project "MOTUS - Automated Analysis and Prediction of Human Movement Qualities," the "INdAM-GNAMPA 2023" project on machine learning methods for Shapley Value estimation, and the "ROBOFARM" project on edge computing for precision agriculture. He has coordinated multiple international research collaborations between Italy and France through the Galileo program. His research unit (AXES) focuses on complex economic systems analysis, with applications spanning environmental economics, financial systems, and human movement analysis. The collaborative nature of his work is evident through his extensive network of domestic and international collaborators across multiple universities and research institutions.
Giulio Giuseppe Giusteri is an Associate Professor at the Department of Mathematics "Tullio Levi-Civita" at the University of Padua, Italy, where he conducts research at the intersection of mathematics, physics, and engineering. He serves as National Coordinator for the PRIN 2022 project "Mathematical models for viscoelastic biological matter" and leads a research group comprising postdoctoral scholars and PhD students. His research spans multiple areas of mathematical physics and continuum mechanics. His primary interests include: Non-Newtonian Fluids and Rheology of Dense Suspensions Open Quantum Systems and Quantum Transport Phenomena Mechanics of Deformable Solids and Rod Theory Mathematical Fluid Mechanics and Viscoelasticity Variational Analysis and Nonlinear Systems His recent publications (2022-2025) demonstrate a strong focus on developing mathematical frameworks for complex physical systems, with particular attention to viscoelastic materials, symmetry-preserving homogenization techniques, and multi-scale modeling approaches. His work often bridges theoretical development with practical applications in biological systems, energy transfer, and industrial processes. Professor Giusteri actively supervises PhD students and postdoctoral researchers, with current projects related to the PRIN 2022 initiative. He regularly presents his research at international conferences and workshops, maintaining active collaborations across multiple Italian institutions including Università Cattolica del Sacro Cuore and Politecnico di Milano. His research group offers opportunities for PhD and Master's thesis projects in his various research fields, with an emphasis on mathematical modeling of physical phenomena and interdisciplinary applications.
Adam Teodor Polak serves as an Assistant Professor in the Department of Computing Sciences at Bocconi University, where his research centers on theoretical algorithms with dual emphases on fine-grained complexity and learning-augmented algorithms. His work investigates fundamental questions about computational hardness while developing prediction-enhanced algorithms that maintain worst-case guarantees. Polak earned his PhD from Jagiellonian University in 2019 under Paweł Idziak, including a research visit at MIT with Virginia Vassilevska Williams. He subsequently held postdoctoral positions at the Max Planck Institute for Informatics and EPFL before joining Bocconi. His research program addresses why computational problems resist efficient solutions and how imperfect predictions can robustly improve algorithmic performance. This manifests in two interconnected streams: establishing conditional lower bounds for problems like 3SUM and Orthogonal Vectors, and designing learning-augmented frameworks for dynamic graph problems, caching, and optimization that blend theoretical rigor with practical machine learning insights. Recent publications reveal accelerating momentum in algorithms with predictions, with over half of his 2023-2025 output appearing in top ML venues (ICML, NeurIPS, ICLR) alongside traditional theory conferences (STOC, SODA). This cross-pollination demonstrates how worst-case theoretical guarantees can coexist with data-driven performance gains across graph algorithms, scheduling, and combinatorial optimization. Scientific recognition includes: Best Paper Award at ESA 2024 for knapsack algorithm breakthroughs Bronze Medal at ACM ICPC World Finals (2011) 2nd Place in PACE 2018 Challenge for Steiner tree algorithms Polak actively shapes the field through program committee service (ESA, ICALP, SOSA) and community building, notably co-organizing the 2022 Workshop on Algorithms with Predictions (ALPS) and decade-long high-school algorithmics workshops. His industry collaborations with Teroplan and Google demonstrate real-world impact in route planning and distributed systems. Current teaching includes graduate Algorithms courses at Bocconi, while his experimental work on GPU-accelerated graph algorithms and medical computer vision continues to bridge theoretical insights with practical implementation challenges.
Giulia PICCITTO is a Researcher in Mathematical Physics at the Department of Mathematics and Computer Science (DMI) of the University of Catania. Her work focuses on quantum dynamics, phase transitions, and entanglement phenomena in spin systems with long-range interactions. She has published extensively in journals such as Physical Review B and the New Journal of Physics. Email: giulia.piccitto@unict.it Office Hours: Mondays 15:30-16:30, Wednesdays 10:30-11:30 (via email confirmation) Research Interests: Her research explores dynamical phase transitions, entanglement dynamics, and non-equilibrium quantum systems, particularly using models like the Ising chain and quantum Otto engines. She investigates how long-range interactions and measurement protocols affect critical behavior and quantum coherence. Scientific Contributions: Giulia has contributed to understanding boundary time crystals, entanglement phase transitions, and spectral functions in transverse-field Ising models. Her work bridges theoretical physics and quantum information, emphasizing analytical and numerical approaches.
Simone Avesani serves as a Researcher in the Department of Computer Science at the University of Verona's School of Science and Engineering. His academic sector is INFO-01/A - Informatics, where he contributes to the university's research infrastructure through specialized computational approaches to biological data. His research spans Bioinformatics, Algorithmic Biology, and Natural Computing with focus on developing graph and string algorithms for systems biology, advanced data structures for sequence analysis, distance measures for biological sequences, and machine learning applications in biomedical contexts. Avesani is actively involved in the Algorithmic Bioinformatics and Natural Computing research group where he applies theoretical methods to model biological information processes, as well as the InfOmics laboratory which specializes in efficient biomedical data analysis. Currently leading the project 'Defining Clinical and Molecular Phenotypes of Multi-Drug Resistance in difficult to treat Rheumatoid Arthritis - MDR-RA' (started January 1, 2025), his work develops novel methods for mining biological networks, integrating heterogeneous data, analyzing omics, reconstructing pangenomes, and patient classification using interdisciplinary approaches from machine learning, data science, mathematics, and graph theory. He is affiliated with the INFOMICS laboratory among several specialized research facilities including IRIS, ISLa, and STARS laboratories within the department.
Zsuzsanna Liptak is an Associate Professor in the Department of Computer Science at the University of Verona, Italy, specializing in algorithmic bioinformatics and string algorithms within the INFO-01/A Informatics academic sector. Her research focuses on: Algorithmic solutions for biological data String and sequence algorithms Mass spectrometry data interpretation Non-alignment based sequence comparison Applications to EST genomic sequences and nanopore technologies Dr. Liptak actively contributes to research projects including 'Securing Decentralized Finance and Remote Healthcare Systems - SHIELD' (starting October 2024) and 'Novel Methodologies and Tools for Next Generation Cyber Ranges - NOMEN' (starting May 2024), working at the intersection of theoretical computer science and practical bioinformatics applications. She serves on scientific committees for major international conferences: WABI (Workshop on Algorithms in Bioinformatics) CPM (Combinatorial Pattern Matching) SPIRE (String Processing and Information Retrieval) IWOCA (International Workshop on Combinatorial Algorithms) Her teaching portfolio spans multiple programs: Computational Analysis of Genome-Scale Sequences (Medical Bioinformatics Master's) Discrete Biological Models (Bioinformatics Bachelor's) Fundamental algorithms for Bioinformatics Advanced Data Structures for Textual Data (PhD program) She conducts research within the Algorithms and Algorithmic Bioinformatics research groups at the University of Verona, with connections to the INdAM Research Unit.
Jacopo Stoppa is a Visiting Professor at the Department of Algebra and Geometry at the International School for Advanced Studies (SISSA) in Trieste, Italy. His research focuses on advanced topics in algebraic geometry and differential geometry, with particular emphasis on K-stability, mirror symmetry, and geometric structures on complex manifolds. He conducted a visiting period at SISSA from October 29 to October 31, 2018, contributing to the research group in algebra and geometry. His work bridges pure mathematics and theoretical physics, addressing problems related to canonical metrics, moduli spaces, and stability conditions. Key themes include the interplay between algebraic structures (e.g., toric varieties, Landau-Ginzburg models) and analytic aspects (e.g., Hermitian-Yang-Mills connections, scalar curvature equations). His recent publications explore applications of canonical metrics, scattering diagrams, and BPS state counting in the context of Donaldson-Thomas theory. No specific awards or grants are explicitly mentioned in the provided materials. His research group at SISSA is part of the broader Department of Excellence initiative, emphasizing cutting-edge research in fundamental mathematics.
Pietro Ferrara is an Associate Professor in the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. His research focuses on applying abstract interpretation-based static analysis to address security vulnerabilities in software systems, particularly in blockchain smart contracts, IoT devices, and distributed systems. He is a member of the Software and System Verification group and has contributed to frameworks like LiSA for multilanguage static analysis. Teaching responsibilities include courses such as Software Architectures, Object-Oriented Programming, and Introduction to Coding and Data Management across undergraduate and graduate programs. His work emphasizes formal verification techniques, cybersecurity, and privacy enforcement in modern software systems. Recent research explores static analysis for detecting concurrency issues in Hyperledger Fabric, vulnerabilities in Go-based smart contracts, and GDPR-compliant privacy analysis. Ferrara collaborates with industry on practical applications of formal methods, including security policy extraction for ROS2 and industrial blockchain software determinism. His research has been published in top venues such as ACM SAC and IEEE Access, with a strong focus on bridging theoretical program analysis with real-world software systems. He maintains an active presence in open-source tools and educational materials for static analysis techniques.
