Nancy Lynch is the NEC Professor of Software Science and Engineering in MIT's Department of Electrical Engineering and Computer Science. She heads the Theory of Distributed Systems Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). Her affiliations include MIT, University of Southern California, Brooklyn College, University of Washington, and Georgia Tech. Her research spans: Distributed computing theory and algorithms Formal modeling and verification of real-time systems Wireless network protocols Biological distributed algorithms (ant colonies, neural networks) Nanobot systems for medical applications Recent publications show a strong interdisciplinary focus, with work combining theoretical computer science with neuroscience and biology. Her group maintains an active publication record in top-tier conferences and journals. Professor Lynch actively advises graduate students and teaches courses including Distributed Algorithms at MIT. Her lab develops open-source tools like ParSwarm for evaluating distributed algorithms.
Dr. Angelo Valleriani serves as Group Leader for Stochastic Processes in Complex and Biological Systems at the Max Planck Institute of Colloids and Interfaces in Potsdam, Germany, and coordinates the International Max Planck Research School (IMPRS) on Multiscale Bio-Systems. His research bridges theoretical physics and biological applications with a focus on quantitative modeling of complex biological phenomena. Dr. Valleriani earned his PhD in High Energy Physics from SISSA in Trieste, Italy (1996), following a Laurea Degree in Theoretical Physics from the University of Bologna (1992) with full marks and honors. His academic journey includes Visiting Scientist positions at Max Planck Institutes in Golm and Dresden before becoming a Group Leader in November 2000. His research interests encompass: Stochastic modeling of biological processes RNA biology and translational control mechanisms mRNA and tRNA turnover dynamics Population genetics and evolutionary biology Biostatistical data analysis Dr. Valleriani's recent publications (2022-2024) reveal a strong emphasis on computational approaches to biological problems, particularly in ribosome dynamics, protein synthesis regulation, and cellular remodeling processes. His work demonstrates sophisticated integration of mathematical modeling with experimental biology. He maintains active collaborations with researchers from multiple institutions including the University of Potsdam (Silke Leimkühler, Carsten Beta, Stefanie Barbirz), University of Cambridge (Davide Chiarugi), Weizmann Institute (Ziv Reich, Ruti Kapon), DRFZ Berlin (Ria Baumgrass), and others. The research group actively recruits MSc students from physics, mathematics, engineering, and bioinformatics backgrounds for challenging thesis projects in computational biology and biophysics.
Dylan Agius is a Research Fellow at Deakin University's School of Engineering, part of the Faculty of Science Engineering and Built Environment. Based at the Melbourne Burwood Campus, his research focuses on advanced computational modeling of material behavior with particular emphasis on additive manufacturing processes and crystal plasticity. Dr. Agius's research interests span multiple areas of materials science and mechanical engineering: Additive Manufacturing (particularly electron beam powder bed fusion and selective laser melting) Crystal Plasticity Modeling and Finite Element Analysis Microstructure Evolution and Characterization Residual Stress Analysis in Welded and Additively Manufactured Components Mechanical Behavior of Titanium and Stainless Steel Alloys Creep and Fatigue Deformation Mechanisms His publication record demonstrates a strong focus on integrating experimental characterization with computational modeling to understand and predict material behavior. Recent work has particularly emphasized the relationship between microstructure and mechanical properties in additively manufactured metals, with applications to aerospace and safety-critical components. His research often combines advanced techniques like electron backscatter diffraction with sophisticated modeling approaches to capture material behavior at multiple scales. Dr. Agius has published extensively in high-impact journals such as International Journal of Plasticity, Materials Science and Engineering: A, and Additive Manufacturing. His research has been cited extensively, with several papers exceeding 50 citations. His collaborative research involves working with experts in materials characterization, mechanical testing, and computational modeling. Current projects appear to focus on optimizing additive manufacturing processes through computational prediction of microstructure and properties, as well as developing more accurate models for predicting deformation behavior in complex loading scenarios.
Gang (Gary) Tan is a Professor in the Computer Science and Engineering Department at Pennsylvania State University, co-directing the Institute for Networking and Security Research (INSR). His research bridges computer security, formal methods, and programming languages to develop practical solutions for software vulnerabilities and AI fairness. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University Dr. Tan specializes in applying compiler techniques and formal verification to security challenges, with seminal work on cache side-channel attacks and fairness in machine learning. His Security of Software (SOS) Group develops frameworks that integrate theoretical guarantees into real-world systems, emphasizing measurable security outcomes and ethical AI. Recent projects focus on quantifying bias in neural networks and mitigating speculative execution vulnerabilities. Analysis of his 2021-2025 publications reveals a strategic pivot toward AI security, where he pioneers methods for fairness testing (e.g., information-theoretic debugging) and repair (e.g., NeuFair). Concurrently, his security work evolves from foundational side-channel research (SpecSafe, 2021) toward hardware-software co-design solutions, demonstrating consistent innovation across theoretical and applied domains. Scientific Awards: James F. Will Career Development Professorship NSF CAREER Award Google Research Award (two instances) Distinguished Reviewer Award at 2018 IEEE Symposium on Security and Privacy Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Best Paper Award at PLDI 2024 Dr. Tan leads the SOS Group with funding from NSF (including CAREER), DARPA (ISAT study group membership), and industry partners like Google. His grants support interdisciplinary projects spanning secure compilation, fairness engineering, and hardware security, while his teaching excellence award reflects commitment to pedagogy in core systems courses. He co-directs Penn State's Institute for Networking and Security Research (INSR), fostering collaboration between systems, security, and AI researchers. The SOS Group maintains active partnerships with industry security teams and contributes to open-source tools for vulnerability detection, with recent work expanding into fairness certification for machine learning pipelines.
Laurent Signac is an Associate Professor in Automatic Control and Systems at ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers), part of the University of Poitiers. He maintains dual affiliations with the LIAS laboratory at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil, contributing to the Automatic Control, Data Engineering, and Real Time research teams. His research spans automatic control, neural networks, and artificial intelligence with applications in industrial fault detection, robotics, and algorithmic problem-solving. Key contributions include neural network-based stator fault diagnosis in induction motors, fractional-order system identification, and explorations of algorithmic survival mechanisms. His work bridges theoretical control systems with practical engineering solutions across electrical engineering and computer science domains. Analysis of his publication timeline reveals consistent innovation in neural system identification (2001-2009), evolving toward interdisciplinary applications in cryptography (2013), light diffusion modeling (2014), and computational thinking education (2017). His research demonstrates sustained integration of control theory with neural computation across industrial, biological, and educational contexts. As a core member of LIAS laboratory's Automatic Control team, he participates in France's national research ecosystem focused on signal processing and real-time systems. The laboratory's dual-campus structure facilitates collaboration between ENSIP's engineering programs and ISAE-ENSMA's aerospace expertise, positioning his work at the intersection of academic research and industrial application.
