Perry Anderson is a Research Professor of History at the University of California, Los Angeles (UCLA). His work focuses on state structures, political ideas, and European integration, with publications spanning classical, feudal, and modern systems of power. He has taught across continents, held residencies at European institutes of advanced study, and lectured at prestigious institutions including the Collège de France. His research interests include: Comparative historical analysis of state structures Political philosophy (justice, equality, hegemony) European Union history and legal evolution Modernity and postmodernity discourses Anderson's recent lectures at the Collège de France (2022) examined the interplay of law, sovereignty, and European integration, culminating in critical reflections on the Ukraine war's impact on European construction.
Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.
Anuj Pathania serves as an Assistant Professor in the Parallel Computing Systems (PCS) group within the Informatics Institute at the University of Amsterdam's Faculty of Science. His research pioneers sustainable computing systems operating under severe power, thermal, and reliability constraints, with significant contributions to energy-efficient hardware design and embedded systems. Education: PhD in Computer Science (2018), Karlsruhe Institute of Technology MSc in Computer Science (2012), National University of Singapore B.Tech in Computer Science (2009), Maharaja Agrasen Institute of Technology Pathania's research centers on low-power design and sustainable systems for constrained environments, with particular expertise in thermal management of 3D-stacked architectures and energy-efficient machine learning inference . His work bridges electronic design automation with real-world reliability challenges, developing novel power budgeting techniques like T-TSP that incorporate transient temperature effects ignored by conventional methods. Current projects include EU-funded initiatives on energy labeling for digital services, addressing ecological impacts through technological, behavioral, and legal frameworks. His publication trajectory reveals a strategic evolution toward zero-waste computing , with recent work (2023-2025) focusing on hardware-software co-design for edge AI, energy modeling across computing continua, and parameter-efficient neural adaptation. Key themes include thermal-aware scheduling for S-NUCA many-cores, cooperative processor utilization in heterogeneous systems, and sustainability metrics for digital services. Scientific Recognition: Best Paper Award Nomination at IEEE Computer Society Annual Symposium on VLSI 2023 for 3D-TTP power budgeting technique Pathania actively mentors 4 PhD students (Ehsan Aghapour, Saeedeh Baneshi, Sudam Wasala, Yixian Shen) and has successfully supervised 5 Master's theses (including Cum Laude defenses by Joris op ten Berg and Jurre Wolff). His research is supported by major grants including Energy Labels for Ecologically Sustainable Digital Services (2023-2024) and Towards Zero-Waste Computing (2021-2025), developing simulation frameworks like HotSniper and CoMeT for thermal analysis. The PCS group maintains strong industry collaborations with ARM and NVIDIA, particularly through tools like ARM-CO-UP for heterogeneous processor utilization.
Dr. Susanne Ehrich (born 1977) is a Research Fellow and academic coordinator at the Institute of History , University of Regensburg , since 2012. As academic coordinator of the Forum Mittelalter since 2007, she specializes in interdisciplinary medieval research with focus on apocalyptic commentaries , German religious poetry , and text-image analysis . She co-edits the Forum Mittelalter-Studien series. Education: German Studies, Catholic Theology, Romance Studies (Regensburg & Pisa, 2002); First State Examination for secondary school teaching; German as a Foreign Language Certificate Research Interests center on medieval eschatological frameworks, vernacular biblical poetry, and the interplay between textual and visual media in apocalyptic traditions. Her work examines how religious texts were adapted across Teutonic Order communities and urban contexts. Publications focus on Middle High German apocalypse manuscripts, urban cultural analysis, and interdisciplinary methodologies. Key trends include text-image relationships , didactic elements in religious poetry , and eschatological historiography . Scientific Awards: Doctoral grant from the Cusanuswerk (Bishop's Study Fund, 2003–2007) Editorial Roles: Co-editor of Forum Mittelalter-Studien series since 2009; collaborative projects with Prof. Dr. Jörg Oberste and other medievalists.
Chris Monico is an Associate Professor in the Department of Mathematics & Statistics at Texas Tech University . He has been a faculty member there since 2003, following post-doctoral research at the University of Notre Dame. Education B.S. in Mathematics – Monmouth University M.S. in Mathematics – University of Notre Dame Ph.D. in Mathematics – University of Notre Dame Research Focus Monico’s scholarship centers on the intersection of cryptology , computational algebra , and number theory . A significant recent thrust has been the application of machine-learning techniques to mathematical finance , evidenced by work on random-forest models for option pricing and high-frequency trading risk metrics. Parallel lines of inquiry include post-quantum cryptographic schemes built on tropical algebra and semigroup actions, as well as classical problems in Ramsey theory and combinatorial semigroups . Publication Trends Between 2015 and 2025 Monico has published prolifically, with a clear shift around 2020 toward mathematical finance and machine-learning applications , alongside continued output in algebraic cryptanalysis and combinatorics . His 2024–2025 articles emphasize data-driven models in trading, whereas 2020–2021 works concentrate on cryptanalyses of tropical and group-based key-exchange systems. Earlier contributions focus on computational number theory and semigroup-based cryptography. Contact Information Email: c.monico@ttu.edu Phone: 806-834-4144 Office: Department of Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409-1042 Advising & Grants No specific doctoral or master’s students, funded grants, or named awards are detailed in the provided text. Laboratory or Research Group The text does not mention any dedicated laboratory or research group.
Louise Reardon is Professor of Governance and Public Policy at the University of Birmingham's School of Government, Department of Public Administration and Policy. She serves as School Deputy Director of Research (Impact) and is Thematic Research Lead for Transport at UK Parliament. Her internationally recognized expertise spans transport policy and governance, with research focusing on institutional collaboration across scales to advance local transport goals, technological innovation, and sustainability. Louise earned her PhD in Political Science from the University of Sheffield (2014), MA in Governance and Public Policy from the University of Sheffield (2009), and BA (hons) in Philosophy, Politics and Economics from the University of Durham (2008). She holds a Post-Graduate Certificate in Higher Education (2018) and is a Senior Fellow of the Higher Education Academy (2020). Her research interests center on transport policy and governance, with specific focus on multilevel governance, agenda setting, policy implementation, smart mobility transitions, urban governance, and wellbeing. As a political scientist, she advances theoretical understanding of policy processes through transport analysis, examining how local dynamics influence issue recognition, agenda setting, policy change, and approaches to wicked problems. Her work consistently bridges academic theory with practical policy application. Louise's research portfolio demonstrates a clear trajectory toward increasingly complex, interdisciplinary climate-related transport research. Her recent publications show strong emphasis on net-zero transitions, smart city governance, and pandemic recovery implications for transport systems, with growing international collaboration particularly with Indian and Australian institutions. The trend indicates deepening engagement with practical policy implementation challenges alongside theoretical contributions. Senior Fellow of the Higher Education Academy, 2020 Louise regularly provides scientific advice to diverse stakeholders and serves on the UK Department for Transport's Transport Research Innovation Grant advisory board. She co-edited the Handbook of Transportation and Public Policy (2025) and previously co-chaired the Governance and Decision-Making Processes Special Interest Group for the World Conference on Transport Research Society. She serves on editorial boards for Research in Transportation Business & Management and Local Government Studies. As an educator, Louise led the College of Social Science's first Degree Apprenticeship programme and specializes in blended teaching approaches. She has supervised PhD students on evidence use in local policymaking, center-local government relations, and walking's role in climate change agendas. She actively seeks new doctoral students interested in multi-level governance, policy change, sustainable transitions, and urban governance topics.
Dengfeng Sun is a Professor and Associate Head of the Gambaro Graduate Program in the School of Aeronautics and Astronautics at Purdue University. His research focuses on distributed control systems, autonomy, resilient networks, and air traffic management. Sun holds a B.Eng. from Tsinghua University, an M.S. from The Ohio State University, and a Ph.D. from UC Berkeley. His work spans advanced air mobility, UAV trajectory planning, and stochastic optimization for large-scale systems. Key contributions include resilient UAV traffic control, distributed state estimation algorithms, and fault detection methods for navigation systems. Sun's research has been published in top journals like IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part E. Education: B.Eng., Tsinghua University (2000) M.S., Ohio State University (2002) Ph.D., UC Berkeley (2008) He advises on cutting-edge projects integrating robotics, autonomous systems, and cloud-based traffic modeling. His lab develops solutions for urban air mobility, emergency medical UAV networks, and next-generation air traffic control systems. Notable collaborations include work with NASA and industry partners on continuous descent approach procedures and metroplex routing paradigms. Sun's work bridges theoretical control systems with practical applications in aviation and infrastructure optimization.
Yongle Zhang is an Assistant Professor in the Department of Computer Science at Purdue University, joining in Spring 2021. His research focuses on systems software, particularly improving reliability and availability in complex distributed systems through failure detection and diagnosis. He holds a Ph.D. from the University of Toronto and has prior degrees from Shandong University and the Chinese Academy of Sciences. **Education:** Ph.D., University of Toronto, Computer Engineering (2020) Master, Institute of Computing Technology, Chinese Academy of Sciences (2013) Bachelor, Shandong University, Computer Science (2010) **Research Interests:** His work addresses challenges in distributed systems, including root cause diagnosis in cloud environments, diagnosable software design, and concurrency bugs in persistent memory applications. Recent projects include analyzing live debugging activities in production systems and detecting cross-system interaction failures. **Awards & Grants:** SIGOPS Dennis M. Ritchie Thesis Award (2021) Meta 2022 Systems Research Award NSF Core Grant (2021) **Advising & Labs:** Advises PhD and Master’s students in distributed systems research (e.g., Shangshu Qian, Panchapakesan Chitra Sruthi). Leads a lab focused on production system reliability, with collaborations on cloud infrastructure and failure analysis tools.
Konstantin Makarychev is a Professor of Computer Science and Associate Chair for Graduate Studies at Northwestern University's McCormick School of Engineering. His research focuses on designing efficient algorithms for computationally hard problems, with an emphasis on approximation algorithms, beyond worst-case analysis, and applications of high-dimensional geometry. Before joining Northwestern, he was a researcher at Microsoft and IBM Research Labs, and earned his Ph.D. from Princeton University in 2007 under Moses Charikar. He holds a B.S. in Mechanics and Mathematics from Moscow State University and an M.S. from the Department of Mathematics at Moscow State University. His academic career includes roles at Microsoft Research, IBM Research, and teaching positions at the University of Washington. Research interests include approximation algorithms for constraint satisfaction problems, clustering algorithms, and algorithmic approaches to machine learning. He has published extensively in top conferences like SODA, ICML, and STOC, and his work often bridges theoretical computer science with practical applications in data storage and bioinformatics. Awards: IBM A-Level Accomplishment (2011), IBM Pat Goldberg Best Paper Award (2009), IBM PhD Fellowship (2006–2007). Grants: NSF Award CCF-1955351 (2020–2025), participation in IDEAL Institute (2019–2022). Teaching: Courses include Design and Analysis of Algorithms, Approximation Algorithms, and Advanced Algorithm Design. His work on correlation clustering, explainable k-means, and DNA data storage has led to impactful contributions in both theory and practical applications.
Dr. Sie Teng Soh is an Associate Professor at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences. With qualifications including a PhD from Louisiana State University, he specializes in computer networks, wireless systems, and algorithm design. Research focuses on: Network topology optimization for UAV systems Energy-efficient IoT task scheduling Reliable wireless communication protocols Game-theoretic network management Green computing in software-defined networks Publication trends show advancing work in UAV network optimization, with recent articles addressing max-min rate optimization, energy harvesting in IIoT, and machine learning approaches for coverage prediction. His research consistently addresses practical challenges in wireless network deployment under real-world constraints. Teaching areas include advanced courses in network reliability and traffic engineering. Professional service includes editorial roles for IEEE Transactions on Parallel and Distributed Systems and program committee memberships for major conferences including FAST and EuroSys.
Nicolas Cambier is a Visiting Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Artificial Intelligence department and the Network Institute. His research focuses on swarm robotics, collective behavior, and evolutionary systems. He explores topics like emergent communication, modular robotics, and prosociality in robotic swarms. Key areas include task-driven language evolution, adaptive decision-making, and environmental interaction in constrained environments. His work bridges theoretical models with practical implementations, emphasizing self-organization and cultural evolution in synthetic systems. Recent contributions address challenges in heterogeneous swarms, skill acquisition in modular robots, and decision-making without prior knowledge. He collaborates widely, with publications in IEEE Robotics and Automation Letters, Nature Communications, and top conferences like GECCO and Distributed Autonomous Robotic Systems. Research interests span robotics, artificial intelligence, and evolutionary computation, with applications to both theoretical frameworks and real-world robotic systems. His studies often involve agent-based simulations and embodied evolution approaches to understand complex collective phenomena.
Prof. Dr. Sören Laue is a Professor of Machine Learning at the University of Hamburg's Department of Informatics. His research focuses on optimization algorithms, machine learning frameworks, and high-performance computing. He leads the Machine Learning research group and developed the GENO optimization framework and the Matrix Calculus toolset. His work emphasizes GPU acceleration, tensor operations, and scalable solutions for classical machine learning problems. Projects: GENO solver (Python-based optimization), Matrix Calculus (derivative computation), and SQL-based tensor operations. Key Research Themes: Optimization frameworks, GPU computing, neural network scalability, and algorithm design. Selected recent publications highlight contributions to tensor calculus benchmarks, GPU-optimized machine learning pipelines, and novel optimization methods. His work bridges theoretical foundations and practical software tools for the machine learning community.
Farokh B. Bastani is a Professor of Computer Science at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. from the University of California, Berkeley. His research focuses on AI-driven software synthesis, embedded real-time systems, formal methods, high-assurance autonomous systems, and fault-tolerant distributed systems. He leads research in the NSF Industrial/University Cooperative Research Center (IUCRC). Education: Ph.D., Computer Science, UC Berkeley His work emphasizes software reliability, safety assurance, and modular parallel programming. Research outputs include journal and conference publications, though specific titles are not listed here. The awards section appears incomplete (404 error noted). Labs/Teams: Active involvement with the NSF IUCRC program. No advising records or grant details provided in the text.
Ronald D. Haynes is a Full Professor and Chair of Scientific Computing Graduate Programs in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He leads research in numerical methods for PDEs and industrial-scale optimization problems. His work develops advanced domain decomposition techniques, adaptive mesh methods, and parallel computing approaches for solving complex physical systems. Applications include modeling pitting corrosion of materials, predicting rock strength for drilling optimization, and simulating multiphase fluid flows in porous media. Recent publications demonstrate innovations in mesh adaptation, parallel algorithms, and machine learning applications for industrial problems. Collaborative projects have addressed reservoir simulation, drill bit analysis, and corrosion prediction through integrated computational approaches. Professor Haynes has received the President's Award for Outstanding Research (2018) and Dean of Science Distinguished Teaching Award (2017). He serves as Co-editor-in-chief of the CAIMS Mathematics in Science and Industry Journal and was President-Elect of the Canadian Applied and Industrial Mathematics Society (2023-2025). He maintains active doctoral supervision with current research groups focusing on domain decomposition methods, closest point algorithms, and optimization techniques. Industry partnerships include projects with ExxonMobil and Global Maritime addressing drilling optimization and mooring design challenges.
Stephen Pankavich is a Professor and Department Head in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. He holds a PhD in Mathematical Sciences from Carnegie Mellon University, with research focused on partial differential equations, kinetic theory, and mathematical biology. His work bridges theoretical analysis and computational methods, addressing challenges in plasma dynamics, epidemiological modeling, and multiscale systems. Education: PhD, Mathematical Sciences, Carnegie Mellon University (2005) MS, Mathematical Sciences, Carnegie Mellon University (2001) BS, Mathematical Sciences, Carnegie Mellon University (2000) Research interests include the analytical and numerical study of collisionless plasmas, HIV dynamics, and epidemiological models. He has received awards such as the W.M. Keck Mentorship Award and the Colorado School of Mines Alumni Teaching Award. His articles explore topics like plasma decay rates, HIV therapy models, and particle-tracking algorithms. He has advised over 20 graduate and undergraduate students, contributing to impactful research in applied mathematics and computational science.
