جست‌وجوی استاد و پژوهشگر

Renato Bruniمشاهده پروفایل

دانشیار

• Optimization
• Machine Learning
• Bioinformatics
• +۱۰ مورد دیگر

Renato Bruni is Associate Professor at the Department of Computer, Control and Management Engineering of Sapienza University of Rome , Italy. He teaches in the Management Engineering and Bioinformatics study programs. His research focuses on Optimization, Machine Learning, and Bioinformatics , with applications spanning spacecraft control, biomedical engineering, and financial portfolio management. Research Interests His scientific activity is centered on: Combinatorial Optimization Data Mining and Classification Derivative-free Optimization Computational Molecular Biology Information Reconstruction Recent Research Trends The analysis of his publications reveals a strong focus on: Power distribution network optimization Spacecraft attitude control via mathematical programming Higher education data quality and institutional heterogeneity Robust classification with limited training data Stochastic dominance in portfolio selection Physician scheduling optimization Grants and Collaborations Principal Investigator in Sapienza-funded projects (2013–2019) Member of EU H2020 RISIS 2 project (2019–2022) Collaboration with international experts: Peter L. Hammer (Rutgers), Fabio Tardella (Sapienza), and Istat (Italian National Statistical Institute)

Milli Letiziaمشاهده پروفایل

استادیار

• Data Mining
• Complex Networks
• Diffusion of Innovation
• +۷ مورد دیگر

Milli Letizia is an Assistant Professor in the Department of Computer Science at the University of Pisa, Italy. She is a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group connecting the University of Pisa, CNR-ISTI, and Scuola Normale Superiore. Her work bridges theoretical and applied aspects of network science, data mining, and computational social science. Education: PhD in Computer Science, University of Pisa (2018) Master Degree in Computer Science, University of Pisa, magna cum laude (110/110 cum laude, 2013) Bachelor Degree in Mathematics, University of Pisa (2010) Her research focuses on data mining , complex networks , diffusion of innovation , quantification , and the science of success . She investigates how information, behaviors, and diseases spread across networks, using both data-driven and simulation-based approaches. Her work integrates machine learning, network modeling, and social theory to understand spreading phenomena in real-world systems. The trend in her recent publications reveals a strong emphasis on modeling diffusion processes in complex networks, with a focus on algorithmic bias, community-aware diffusion, and opinion dynamics. She has developed influential open-source tools such as NDlib and CDLIB , which are widely used in network science for simulating diffusion and detecting communities. Her work spans disciplines including computer science, public health, and social science, demonstrating interdisciplinary impact. Scientific Service and Recognition: Program Committee Chair, 3rd and 4th International Workshop on Dynamics in Networks (DyNo) at PKDD 2017 and ASONAM 2018 Program Committee Member, NetSciX 2019, DATA ANALYTICS 2017–2019, GOODTECHS 2017, DataMod 2018 Member of Local Organizing Committee, XIII AI*IA Symposium on Artificial Intelligence (2014) She has contributed to major EU projects including SoBigData++ , HumanE-AI-Net , SBD@RT , and CIMPLEX . Although no formal advising or grant leadership is explicitly mentioned, her active research output and tool development suggest significant involvement in funded research and potential mentorship. She has taught courses on data mining, big data analytics, and databases at both undergraduate and master’s levels. Milli Letizia is affiliated with the Knowledge Discovery and Data Mining Laboratory (KDDLab) , a leading interdisciplinary research group focused on big data analytics, social mining, and network science. The lab fosters collaboration between academia and research institutions, promoting innovation in data-driven societal applications.

Johann Gamperمشاهده پروفایل

استاد

• Temporal Databases
• Time Series Analysis
• Query Processing
• +۳ مورد دیگر

Johann Gamper is a Professor and Head of the Database Systems Group at the Free University of Bozen-Bolzano. He previously served as Vice-Rector for Research (2018-2024) and Vice-Dean for Studies (2014-2018). Education: Information not provided in text. Research Focus: His core research investigates data-intensive systems with emphasis on temporal data management, time series analysis, and efficient query processing. His team combines fundamental research with system development for applications in governance, tourism, healthcare, and agriculture. Current projects include temporal RDBMS extensions, time series motif mining, and multidimensional range count approximation. Publication Trends: Recent work addresses temporal query optimization, time series classification, and generative approaches for e-commerce data. His research demonstrates consistent innovation in temporal data handling and database performance optimization. Teaching: Currently teaches Database Management Systems and advises PhD students in temporal database research.

Michael Shekelyanمشاهده پروفایل

مدرس

• Data Management
• Database Systems
• Differential Privacy
• +۵ مورد دیگر

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (2023–present), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano (2018) and a Diploma in Media Informatics from Ludwig Maximilian University of Munich (2014). Previously, he worked as a Research Associate at King's College London (2021–2023) and a Research Fellow at the University of Warwick (2018–2021). His academic journey includes roles as a Meta-Reviewer for NeurIPS and ICDT Proceedings Chair (2024), with extensive service as a reviewer for top conferences like ICML, NeurIPS, ICLR, and journals such as TKDE and IEEE T-IFS. Research Interests: Focuses on algorithms and data structures for managing large/sensitive datasets, including differential privacy, random sampling (e.g., Hidden Shuffle Method), and multidimensional data summarization (e.g., DigitHist). His work bridges theoretical guarantees with practical applications in data management and machine learning. Notable contributions include privacy-preserving top-k selection, efficient sampling over joins, and error-bounded data summaries. Publications: Over 15 peer-reviewed articles in top venues including SIGMOD, ICDE, EDBT, AISTATS, and PVLDB. Key works include 'Streaming Weighted Sampling over Join Queries' (EDBT'23), 'Sequential Random Sampling Revisited' (AISTATS'21), and 'Sparse Prefix Sums' (Information Systems'19, which won an ADBIS award). Grants/Awards: Received the ADBIS'17 Award for Sparse Prefix Sums. Active in organizing conferences and mentoring early-career researchers through reviewing roles. Current projects include a PhD studentship on privacy-preserving algorithms for medical data sharing. Labs/Teams: Leads the Privacy-Preserving Algorithms initiative at Queen Mary, collaborating with international research networks in database systems and data privacy.

Roberto Bruniمشاهده پروفایل

استاد

• Operational Semantic
• Petri Nets
• Reaction Systems
• +۵ مورد دیگر

Roberto Bruni is a Professor in the Department of Computer Science at the University of Pisa, Italy. He has been actively contributing to theoretical computer science with a focus on formal methods, operational semantics, and reaction systems. His academic service includes serving as PC member for SAS 2025 and CMSB 2025 conferences. Professor Bruni's research interests span Operational Semantic, Petri Nets, Reaction Systems, Graph Transformation, and Monoidal Category. His work bridges theoretical computer science with applications in computational biology and program analysis. He has developed significant tools like BioReSolve, a Prolog interpreter for Reaction Systems analysis, and has contributed to projects such as RemConf (Confusion removal in Petri nets) and The Link Calculus. His recent publications demonstrate a strong focus on reaction systems, program analysis, and formal verification methods. The research trend shows increasing application of theoretical computer science concepts to biological modeling and healthcare applications, particularly through enhanced reaction systems frameworks. Professor Bruni holds several institutional responsibilities including membership in the Commission Didattica del GRIN since 2020, delegation to the University Information System (SIA) for the Department of Computer Science since 2020, and membership in the Doctoral College for Smart Industry since 2018. He has been Vice-president of the Master's Degree in Data Science and Business Informatics at the University of Pisa since 2017. His teaching portfolio is extensive, covering courses in programming, algorithms, quantum computing, business process modeling, and program analysis for both undergraduate and graduate students in multiple languages (Italian and English). He has taught courses such as Programmazione e Algoritimica, Models for Programming Paradigms, Business Processes Modeling, Program Analysis, and Introduction to Quantum Computing. Professor Bruni has been involved in numerous research projects including PRA (FM4HD, DECLWARE, FOG), PRIN (ASPRA, CINA, IPODS), and several EU-funded projects (IST-FP7 ASCENS, IST-FP6 SENSORIA, CoMeta, AGILE, NAPI, GETGRATS). His previous academic positions include PhD Student at University of Pisa, International Fellow at SRI Computer Science Laboratory, and Visiting Scholar at UIUC.

Andrea Munaroمشاهده پروفایل

استادیار

• Graph Theory
• Algorithms
• Computational Complexity
• +۲ مورد دیگر

Andrea Munaro is an Assistant Professor at the Department of Mathematical, Physical and Computer Sciences , University of Parma . He holds a PhD from G-SCOP Laboratory , Univ. Grenoble Alpes , supervised by András Sebő and Matěj Stehlík. His academic journey includes postdoctoral positions at institutions such as Ulm, Koper, Fribourg, Morgantown, and Belfast. Research Interests : Andrea specializes in Graph Theory and Theoretical Computer Science , focusing on width parameters like mim-width , sim-width , and tree-independence number . His work addresses algorithmic applications on graph classes (e.g., H -free, P t -free), tackling problems such as coloring , independent packing , and domination . He explores structural properties and computational complexity of graphs with respect to k-connected subgraphs and Gallai families . Scientific Contributions : His recent publications (2025–2021) include studies on width parameters , graph algorithms , and approximation schemes for problems in K 4 -free, H -free, and P t -free graphs. His research spans algorithmic graph theory , structural graph theory , and computational complexity , with applications in contact graphs , path representations , and fixed-parameter tractability . Students : He has advised Shizhou Yang (2021–2024) at Queen's University Belfast. Teaching : Andrea teaches Algorithms and Data Structures and Programming courses at the University of Parma, previously holding appointments at Queen's University Belfast and West Virginia University . He organized conferences like GWP 2025 and served on program committees for WG 2023 .

• Embedded Systems Design
• Low-Power Design
• VLSI Architecture for Image Processing
• +۴ مورد دیگر

Pasquale Corsonello is a Professor of Electronics at the Department of Informatics, Modeling, Electronics and System Engineering (DIMES) , University of Calabria. He has held academic positions at the University of Reggio Calabria and the University of Rochester (Adjunct Associate Professor). His career spans over three decades in digital electronics, VLSI design, and low-power systems. Education: Master's in Electronic Engineering, University of Naples “Federico II” (1988) Key Roles: Editor-in-Chief (Journal of Low Power Electronics), Senior Area Editor (IEEE Transactions on Circuits and Systems II), Steering Committee Member (IEEE Transactions on VLSI Systems) Research interests include: Embedded systems and low-power design for IoT VLSI architectures for image processing and neural networks FPGA-based accelerators for real-time applications Quantum-dot cellular automata (QCA) circuits His recent publications focus on energy-efficient FPGA implementations, approximate computing for imaging, and QCA-based arithmetic circuits. He has co-authored over 180 technical articles and holds three patents. Awards : BEST ASSOCIATE EDITOR IEEE CASS AWARD (2016) GOLDEN LEAF Certificate (PRIME 2019) Multiple BEST PAPER AWARDS at ICECS, CENICS, and PRIME Top 25 Downloaded Articles (IEEE Transactions on VLSI Systems) Leadership : Coordinated PhD programs in Information and Communication Technologies Directed research units in projects exceeding €8M in funding Member of editorial boards for IEEE Transactions and Electronics journal He leads the Nanoelectronics and Microsystems research group , which integrates material characterization, IC design, and system-level electronics for applications in power systems and photovoltaics.

• Applied Electromagnetics
• Antennas and Propagation
• Microwave Imaging
• +۳ مورد دیگر

David Orlando Rodriguez Duarte is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin (Politecnico di Torino), Italy. He is a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab and actively contributes to the applied electromagnetics research group. His academic work bridges electromagnetic theory with practical medical applications, particularly in developing innovative imaging technologies for healthcare. Dr. Rodriguez Duarte is completing his Ph.D. degree with the Department of Electronics and Telecommunications at Politecnico di Torino while holding a prestigious Marie Sklodowska-Curie Fellowship. He is deeply involved in the EMERALD project, which focuses on modeling and designing microwave imaging systems for cerebrovascular diseases. The department's research group has over 10 years of experience advancing both theoretical and applied electromagnetism, with significant contributions in electromagnetic theory, wave propagation, and applications in communication systems and radar technology. His research spans multiple interconnected domains in applied electromagnetics. He specializes in antenna design specifically tailored for medical applications, with particular expertise in microwave imaging systems for neurological conditions. His work in electromagnetic field theory focuses on biomedical contexts, especially stroke monitoring and detection. He develops practical implementations that bridge theoretical concepts with clinical applications, addressing critical healthcare challenges. His research directly supports SDG Goal 3: Good health and well-being through the development of accessible diagnostic technologies. Dr. Rodriguez Duarte's scholarly output demonstrates a clear progression toward developing practical, portable microwave imaging solutions using off-the-shelf components. His recent work increasingly integrates machine learning techniques with traditional microwave sensing approaches, enhancing diagnostic accuracy for neurological conditions. There's a strong emphasis on creating real-time imaging solutions that can be deployed at the bedside for immediate patient care, particularly for time-sensitive conditions like stroke. Marie Sklodowska-Curie Fellowship for research on microwave imaging systems for cerebrovascular diseases Dr. Rodriguez Duarte serves as primary supervisor for two doctoral candidates: Alex Ramiro Masaquiza Caiza (40th cycle, 2024-present) and Martina Gugliermino (39th cycle, 2023-ongoing). Their research focuses on 'EM field study in medicine: characterization of materials for phantoms and stroke monitoring algorithms.' He has secured research funding through the EMERALD European project (2018-2021) and continues to develop grant-supported work in biomedical microwave imaging. His ERC sector alignments include PE7_6 (Communication technology), PE7_11 (Medical applications), and PE7_3 (Simulation engineering). He is an integral member of the PolitoBIOMed Lab - Biomedical Engineering Lab, where his team develops microwave imaging technologies for brain stroke monitoring. The lab focuses on creating practical implementations of electromagnetic theory for medical diagnostics, with emphasis on point-of-care applications. Their work addresses a critical healthcare need as stroke affects approximately one in six people worldwide and requires prompt diagnosis for effective treatment, making their research vital for improving patient outcomes through early detection technologies.

Augusto Ferranteمشاهده پروفایل

استاد

• Control Theory
• Quantum Computing
• Linear Algebra
• +۵ مورد دیگر

Augusto Ferrante is a Full Professor at the Department of Information Engineering, University of Padova. His research spans control theory, quantum computing, and linear algebra, with a focus on optimal control, spectral estimation, and matrix analysis. University: University of Padova Department: Information Engineering Research Interests: Ferrante's work addresses theoretical and applied problems in control systems, including quantum control, Riccati equations, and covariance matrix estimation. His contributions bridge mathematical rigor with engineering applications, particularly in spectral analysis and optimization. Publications Trends: His recent work (2014-2012) emphasizes quantum channel estimation, entropy-based spectral methods, and generalized Riccati equations, reflecting a synthesis of control theory and quantum information science. Book Chapters: He has co-authored works on modeling, estimation, and control, including a 2007 Modeling, Estimation and Control volume dedicated to Giorgio Picci.

• Geometric Deep Learning
• Compositional Learning
• Optimal Transport
• +۴ مورد دیگر

Mircea Petrache is an Assistant Professor at the Department of Mathematics, Pontifical Catholic University of Chile. His research focuses on geometric deep learning, compositional learning, optimal transport, calculus of variations, and geometric measure theory, with applications to large point configurations and mathematical analysis. He holds a PhD in Mathematics from ETH Zürich (2013) and an MSc from Scuola Normale Superiore (2008). Education: PhD in Mathematics, ETH Zürich, 2013 MSc in Mathematics, Scuola Normale Superiore, 2008 Research Interests: Petrache explores the interplay between geometric principles and machine learning, particularly in equivariant networks and compositional systems. His work on optimal transport and calculus of variations addresses energy minimization and crystallization problems in materials science. Recent projects include symmetry-based structured matrices and topological pseudodistances for data analysis. Grants: Fondecyt Regular Grant (2021–2025) on rigidity and uniformity in large point configurations. Events & Organization: Co-organized workshops such as 'Theoretical and Mathematical Aspects of Deep Learning' (2022) and 'Calculus of Variations: A New Generation' (2024). Active in conferences including NeurIPS, ICIAM, and AIM workshops on discrete geometry.

• Calculus of Variations
• Partial Differential Equations
• Geometric Analysis
• +۳ مورد دیگر

Massimiliano Morini is a Full Professor at the University of Parma, specializing in geometric analysis, partial differential equations, and calculus of variations. His research focuses on geometric flows (e.g., mean curvature flow, crystalline flows), free boundary problems, and materials science. He has contributed significantly to understanding the regularity of solutions, asymptotic behavior, and energy minimization in these contexts. Key research topics include: Mean curvature flow and its discrete approximations Isoperimetric inequalities and quantitative stability estimates Nonlocal and anisotropic geometric flows Applications to nanomagnetism and thin films He has co-organized conferences such as the 2025 Warsaw Summer School on Evolutionary PDEs and the 2024 Workshop on Kinetic Integro-Differential Equations. His work frequently involves collaborations with leading mathematicians like Giovanni Leoni, Nicola Fusco, and Antonin Chambolle.

• Statistical Methodology
• Machine Learning
• Parametric Inference
• +۷ مورد دیگر

Claudia Di Caterina is an Associate Professor of Statistics at the University of Verona's Department of Economics. Her research focuses on statistical methodology, including likelihood asymptotics, pseudo-likelihoods, anytime-valid inference, and bias reduction techniques. She holds a PhD in Statistical Sciences from the University of Padova and has conducted research at University College London and the University of Toronto. She teaches courses such as Machine Learning for Economics and Statistics for Business Analysis across various programs. Her academic roles include membership in the Economics Department Council and involvement in committees like the Comitato per le Ricerche con Dati sull'Individuo (CORDI) and Commissione Paritetica Docenti-Studenti. She contributes to research laboratories like VELE (Verona Experimental Lab in Economics) and collaborates with centers such as CRIEP and CIDE. Her publications span topics in high-dimensional data analysis, confidence sequences, and causal inference. She actively engages in public events and third mission activities, including workshops on climate risk and cyber security survey methodologies.

• Stochastic Optimal Control
• Hamilton-Jacobi-Bellman Equations
• Numerical Methods for Partial Differential Equations
• +۴ مورد دیگر

Athena Picarelli is a Full Professor at the Department of Economics at the University of Verona. She holds a PhD in Applied Mathematics from ENSTA ParisTech (2018) and completed postdoctoral fellowships at the University of Oxford (Nomura Fellowship) and Imperial College London (CFM Fellowship). Her research focuses on stochastic optimal control, Hamilton-Jacobi-Bellman equations, numerical methods for PDEs, and computational finance. She teaches courses on quantitative methods for business and economics at the undergraduate and graduate levels, as well as specialized modules on stochastic optimization and control in PhD programs. She serves on multiple institutional committees, including the PhD School Board and the QA Board of the PhD in Economics and Finance. Her academic leadership roles include coordinating the Collegio didattico for Economics and Finance programs. She is affiliated with research centers like CRIEP and CIDE, and her work includes contributions to the Verona Experimental Lab in Economics (VELE). Her research interests span control theory, numerical analysis, and financial applications of stochastic control, with a focus on robust optimization and computational methods. Her teaching responsibilities include modules such as 'Mathematics' for the Bachelor's in Economics and Business and 'Quantitative Methods for Business and Economics' for Master’s programs. She has been actively involved in academic governance, including membership in the Economics Department Council and the Research Advisory Board (RAB). Her institutional duties emphasize quality assurance and academic program development.

• Stochastic Processes
• Mathematical Finance
• Probability Theory
• +۳ مورد دیگر

Sara Svaluto Ferro is a Researcher (Ricercatrice a Tempo Determinato) at the Department of Economics, University of Verona. She holds a specialist degree in Mathematics and a PhD in Financial Mathematics from ETH Zurich. After completing her PhD, she conducted a 3-year postdoctoral research at the University of Vienna under Prof. Cuchiero's group. Her research focuses on stochastic processes, including generalized stochastic processes, signature analysis, algebraic computation in rough paths, and applications in mathematical finance, computational finance, and quantitative economics. **Education:** Laurea Specialistica in Matematica (Specialist Degree in Mathematics), ETH Zurich Doctoral Degree in Financial Mathematics, ETH Zurich Postdoctoral Research (3 years), University of Vienna **Research Interests:** Generalized stochastic processes (MSC 60G20) Signatures and data streams in Rough Analysis (MSC 60L10) Algebraic structures and computation in Rough Analysis (MSC 60L70) Stochastic models in finance (MSC 91B70) Her work bridges probability theory with applications in finance, including volatility modeling, market dynamics, and machine learning-driven financial analysis. **Affiliations & Roles:** Member of the Faculty Board of PhD in Economics and Finance Member of the Commissione Comunicazione (Communication Committee) Teaching roles in courses such as AI and Finance (Master's), Fundamentals of Financial Mathematics (Bachelor's), and Stochastic Processes in Finance (PhD) **Research Labs & Centers:** VELE (Verona Experimental Lab in Economics) Visual Research Lab CRIEP (Interdepartmental Research Center on Public Economics) CIDE (Interuniversity Center for Econometrics)

• Numerical Analysis
• Linear Algebra
• Matrix Equations
• +۴ مورد دیگر

Valeria Simoncini is a Full Professor in the Department of Mathematics at the University of Bologna. Her research focuses on numerical analysis, linear algebra, and computational mathematics, with particular emphasis on matrix equations, Krylov subspace methods, and optimization algorithms. She has contributed to advancements in iterative solvers, randomized sketching techniques, and preconditioning strategies for large-scale problems. Her work often intersects with applications in partial differential equations and scientific computing. Her research interests include the development of efficient numerical methods for solving matrix equations, such as Sylvester and Lyapunov equations, as well as optimization problems arising in control theory and data science. She explores topics like tensor decompositions, approximation techniques for matrix functions, and the analysis of numerical stability in iterative algorithms. Simoncini’s publications span over two decades, with recent contributions addressing the integration of randomized algorithms into numerical linear algebra, the acceleration of quadrature methods, and the application of low-rank approximations in high-dimensional problems. Her work frequently appears in top-tier journals such as SIAM Journal on Matrix Analysis and Applications and Numerical Linear Algebra with Applications . Despite the extensive list of publications, no specific scientific awards or grants are explicitly mentioned in the provided text. She advises students through her academic role but no specific advisee names are listed here.