Lieven Vandenberghe is a Professor in the Electrical and Computer Engineering Department and Department of Mathematics at the University of California, Los Angeles (UCLA). His research focuses on convex optimization, semidefinite programming, and applications in signal processing, system identification, and control theory. Books: Co-author of Convex Optimization (2004) and Introduction to Applied Linear Algebra (2018) Courses: Teaches graduate-level courses in linear programming, convex optimization, and numerical computing (ECE236A/B/C, ECE133A/B) Software: Developer of CVXOPT, CHOMPACK, and SMCP for optimization algorithms His research group has produced significant work in sparse matrix computations, operator splitting methods, and applications to machine learning and control systems. Publications span topics like Bregman splitting, proximal gradient methods, and semidefinite programming for signal processing. His advisees include PhD students in optimization and postdoctoral researchers in applied mathematics.
Fatme Yuseinova Rashidova is a Senior Lecturer at the Technical University - Gabrovo , affiliated with the College of Engineering and the Department of Automation, Information and Control Technology . Her work bridges computer science , educational technology , and systems engineering . Research Focus: Automated scheduling, distance learning platforms, cybersecurity in Industry 4.0, and AI applications in education. Publications: 29 scientific works, including a 2025 textbook on internet-based systems and recent papers on timetabling optimization, SWOT analysis in education, and AI challenges. Projects: Participated in 14 research initiatives, including electronic voting systems, smart medicine dispensers, and cybersecurity frameworks for industrial environments. Contributions: Developed centralized timetabling systems, virtual admission environments, and online testing platforms. Her projects emphasize reducing administrative burdens, improving accuracy in educational processes, and enhancing student satisfaction.
Professor David Millard is a Professor of Computer Science at the University of Southampton's Electronics and Computer Science department. He serves as Head of the ECS Education Group, leading a team of 15 Teaching Fellows and Senior Fellows to improve teaching and learning across the department. Previously, he was Director of Admissions for ECS from 2012 to 2022. David is a founding member of the Web and Internet Science research group and has been active in the international hypermedia community for over twenty-five years. His professional affiliations include: Chair of the ACM Hypertext steering committee SIGWEB Liaison for ACM Web Science Former vice-chair of ACM SIGWEB (2015-2019) Editorial board member of the New Review of Hypermedia David Millard's research focuses on the intersection of hypertext, digital narratives, and mixed reality games, with particular emphasis on locative literature. His work has helped define 'Sculptural Hypertext' - hypertext governed by rules and constraints rather than navigational links - which is highly suitable for designing narrative structures within digital games. He has been a pioneer in locative narratives, interactive digital narrative games experienced on location-aware smart devices, with work dating back to 2002 at Chawton House. His recent research focuses on understanding the locative narrative design space and proposing the 'Balance of Attention' as the primary poetic of locative narrative. His research has been recognized with multiple awards including the Engelbart Award in 2013 and 2016 for his work on location-based storytelling and sculptural hypertext. His interdisciplinary StoryPlaces project explores the poetics of location-based narratives through digital storytelling deployments in Southampton, Bournemouth, and Crystal Palace. David has authored more than 230 peer-reviewed articles across Technology Enhanced Learning, Web Science, and Hypertext and Narrative, with over 4800 citations and an h-index of 35. His work has been published in prestigious journals including Springer Multimedia Tools and Applications, Elsevier Computers in Education, IEEE Intelligent Systems, PloS one, ACM Transactions on Information Systems, and ACM Transactions on Learning Technologies. He leads a team of PhD students focused on Interactive Narratives and Mixed Reality Spaces and has graduated 34 postgraduate students. His students have pursued careers in academia at institutions like Oxford, Winchester, Bournemouth, and East Anglia, as well as in industry at major technology firms including IBM and Google. David has been instrumental in Open Educational Resources development, creating EdShare in 2010 - an OER platform based on EPrints that now hosts over 20,000 learning items. He has also developed innovative teaching approaches, including modules on Multimedia Systems (which won the Vice-Chancellor's Teaching award), Programming 1, Social Media and Network Science, and Games Design and Development.
Dr. Srishti Banerji is an Assistant Professor in the Department of Civil and Environmental Engineering at Utah State University and Director of the Systems, Materials, and Structural Health (SMASH) Lab. She leads research on advanced construction materials, structural resilience under extreme loads (particularly fire), sustainable infrastructure, and structural health monitoring. Her group focuses on experimental testing, numerical simulations, and developing design solutions for civil infrastructure. Education: PhD in Civil (Structural) Engineering, Michigan State University (2021) MS in Civil (Structural) Engineering, Concordia University (2016) BS in Civil Engineering, National Institute of Technology Silchar (2013) Research Focus: Her work spans: 1) Characterization of high-performance/sustainable materials (e.g., UHPC, recycled glass pozzolan), 2) Structural behavior under fire exposure, 3) Integration of electric charging systems in concrete pavements, 4) Non-destructive testing and structural health monitoring, and 5) Retrofitting techniques for infrastructure strengthening. She employs machine learning, thermo-mechanical modeling, and full-scale experimentation. Publication Trends: Her 13+ journal articles primarily analyze fire resistance of concrete/timber structures, UHPC material properties at high temperatures, sensor-based infrastructure monitoring, and sustainable material development. Recent works increasingly incorporate machine learning and electrification concepts. Awards & Honors: Teacher of the Year (USU, 2025) ASCE ExCEEd Faculty Teaching Fellowship (2023) Top Cited Article Award, Fire and Materials Journal (2023) SHMII-11 Early Career Grant (2022) NSERC Scholarship (2015) Best Conference Paper (SEC 2016) Current Projects & Teams: She leads 5+ funded projects including fire performance of polymer concrete, self-healing concrete for bridges, and Utah-sourced UHPC development. Mentees include 3 PhD students (Abdullah Al Sarfin, Mehrnoosh Nazari, Mahmoud Ali) and alumni working on sustainable materials and additive manufacturing.
Dr. Shakhawat Hossain is a Professor of Statistics at the University of Winnipeg, serving as Chair starting July 2025. He holds adjunct positions at the University of Manitoba and University of Regina. His academic journey includes a PhD from the University of Windsor (2008), postdoctoral training at the University of Alberta's School of Public Health (2008–2010), and prior faculty roles at Alabama A & M University. He specializes in advanced statistical methodologies with applications in health sciences and epidemiology. Dr. Hossain's education includes: Ph.D. in Statistics, University of Windsor M.Sc. in Statistics, University of Alberta M.Sc. in Mathematics, Jahangirnagar University, Bangladesh B.Sc. (Hons.) in Mathematics, Jahangirnagar University, Bangladesh His research focuses on shrinkage estimation techniques , longitudinal data analysis , survival analysis , and health services research . He actively applies these methods to study dengue transmission dynamics, neuroimaging correlates of developmental disorders, and clinical outcomes in pediatric populations. His work bridges theoretical statistical innovation with real-world public health challenges. Dr. Hossain currently holds an NSERC Discovery Grant supporting student research. His 2023-2024 publications emphasize spatial epidemiology, advanced survival models, and dengue fever dynamics. He serves as Associate Editor of the Journal of Statistical Computation and Simulation . His advisory and grant activities include mentoring students in statistical modeling and securing funding for interdisciplinary health projects. While specific lab affiliations are not explicitly stated, his collaborations span departments in statistics, public health, and biomedical sciences.
Yuan Gao is an Assistant Professor of Mathematics at Purdue University's Department of Mathematics (College of Science). His research focuses on analysis and computations of PDEs in materials science, biology, and microfluidics, with recent emphasis on optimal control, Hamilton-Jacobi equations, and non-equilibrium chemical reactions. His work is supported by NSF awards DMS-2204288 and DMS-2440651. Previously, he held the William W. Elliott Assistant Research Professor position at Duke University (2019-2021). Research interests include PDE analysis in materials science (crystal growth, dislocation dynamics), numerical methods for interface dynamics, applied stochastic analysis (Langevin dynamics, transition path theory), and mean-field games for fluid systems. He organizes the PSU-Purdue-UMD Joint Seminar on Mathematical Data Science. Key publications span topics like dislocation evolution, Wasserstein gradient flows, and stochastic algorithms for rare events. Awards include NSF CAREER funding recognizing his contributions to mathematical analysis of non-equilibrium systems.
Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .
Alfio Grillo is a Full Professor at the Department of Mathematical Sciences (DISMA) of Politecnico di Torino, with research interests in biomechanics, continuum mechanics, and mathematical physics. His expertise spans classical mechanics and multiscale modeling of biological tissues. Research Focus: Grillo's work integrates analytical mechanics with nonholonomic constraints, fractional calculus applications, and multiscale modeling of growth/remodeling phenomena in biological systems. Recent articles emphasize poroelasticity, viscoelastic composites, and bi-phasic material behavior. Scientific Contributions: Editorial roles in leading journals since 2014 Member of INdAM-GNFM since 2009 Recipient of National Scientific Qualification in 2017 €128,609 PRIN grant for multiscale biological modeling Academic Leadership: Supervises PhD students in Civil Engineering, Mathematics, and Mathematical Engineering. Teaches advanced courses in Differential Varieties, Variational Methods, and Porous Media Mechanics.
Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.
Paula Schneider serves as Teacher for Special Tasks at the University of Art and Design Offenbach am Main , heading their book and paper workshop since 2020. She studied Book Art at Burg Giebichenstein Halle (2013-2019) and previously taught at Bezalel Academy (2017-2020). Her work bridges bookbinding , literary experimentation , and archival research through installations, artist books, and public interventions. Education : 2019: Diploma in Book Art, Burg Giebichenstein University of Halle Teaching : 2020–present: Lecturer for special tasks at HfG Offenbach 2017–2020: Annual courses at Bezalel Academy for Arts and Design, Jerusalem Screen printing tutor during studies Her artistic research explores narrative structures , time perception , and memory systems through works like Inner Monologue (2018) and Mio Mnemotop (2021). Recent projects include Night Frost (2024) examining textual accumulation and Cloudburst Working on the Textberg (2024) using mining metaphors for textual analysis. She examines book formats (e.g., leporello, monotypes, screen printing) to interrogate knowledge preservation (Rabaraababaraba, 2015) and urban observation (Praise of the Gate, 2023). Notable exhibitions include Because We Are Young (2021) analyzing GDR industrial history and Between (2019) at Halle/Leipzig Airport, which required mobile audience engagement with audio pieces about airport soundscapes. Her works have been featured in Text3 and Spin n errei tours. Contact: schneider@hfg-offenbach.de | Workshop: +49 69.80059-245 | Office: +49 69.80059-106
Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.
Qiang Li is a SUNY Empire Innovation Professor in the Department of Physics and Astronomy at Stony Brook University and holds a joint appointment at Brookhaven National Laboratory (BNL) as the leader of the Advanced Energy Materials Group. His roles span both research and academia, with a focus on quantum materials and their applications in energy and quantum information science. University: Stony Brook University Affiliations: Brookhaven National Laboratory, Department of Physics and Astronomy His research bridges fundamental and applied studies of topological quantum materials , superconductivity , thermoelectrics , and quantum information science . Key areas include synthesizing single crystals and thin films, exploring low-temperature transport properties, and developing scalable methods for superconducting and thermoelectric devices. Recent work highlights light-induced symmetry switching in Weyl semimetals and topological phase transitions. His publications emphasize quantum materials , particularly Dirac/Weyl semimetals , iron-based superconductors , and topological insulators , with techniques ranging from terahertz spectroscopy to phononic control of quantum states. Collaborations with institutions like Ames Laboratory and the University of Alabama at Birmingham are notable. Scientific Awards: Brookhaven Science and Technology Award (2019) R&D 100 Award for aFCL (2015) Fellow of American Physical Society (2013) New York State Leader in Superconductivity (2011) Qiang Li advises PhD students such as Pedro Lozano and leads the Quantum Materials Laboratory at Stony Brook, which integrates theory, AI-driven design, and experimental synthesis for quantum applications. His work is supported by the U.S. Department of Energy's Office of Basic Energy Science.
Michael Fink is a researcher at the Chair of Automatic Control Engineering , Technical University of Munich . He holds an M.Sc. in Electrical Engineering and Information Technology (2020) and a B.Eng. in the same field from Technical University Munich and University of Applied Sciences Landshut (2018), respectively. Research Interests : Model Predictive Control (MPC) with focus on stochastic and robust variants Optimal control strategies for autonomous driving and vertical farming Constraint violation probability minimization in dynamic systems Publications span topics in: Time-optimal MPC for linear systems Stochastic and robust MPC frameworks Learning-based control for greenhouse climate systems Vertical farming optimization Contact: michael.fink@tum.de
Dr. Ben Lecorps serves as a Senior Lecturer at the Bristol Veterinary School, University of Bristol, specializing in animal welfare science with a focus on emotional states and cognitive processes in farm animals. His expertise directly addresses welfare assessment in dairy systems and laboratory settings through innovative methodological approaches. Dr. Lecorps holds a PhD, MSc, and BSc in relevant disciplines, forming the foundation for his research in animal sentience. His educational trajectory supports his interdisciplinary approach combining veterinary science, behavioral biology, and ethics. His research program centers on Animal Welfare , Animal Emotions , and Animal Cognition , with particular emphasis on dairy cattle , pain assessment , and affective state measurement . He pioneers cognitive bias testing (e.g., judgment bias paradigms) and physiological monitoring to quantify emotions like pessimism and anhedonia in calves and heifers. Current investigations span disbudding pain management, hunger effects on cognition, and human-animal relationship impacts, bridging fundamental neuroscience with practical on-farm applications. Analysis of his 15 most recent publications (2021-2025) reveals three dominant research strands: (1) development of welfare assessment tools (conditioned place preference/aversion tests), (2) dairy-specific welfare challenges (disbudding, regrouping, transportation), and (3) policy implications of welfare science (EU regulations, organic standards). His work consistently integrates behavioral, cognitive, and physiological metrics to advance evidence-based welfare frameworks. Dr. Lecorps actively supervises student research projects and theses within the Bristol Veterinary School. While specific grant details aren't public, his output suggests sustained funding for experimental welfare studies involving dairy cattle, pigs, and poultry systems. His collaborative network includes key figures like Professor Michael Mendl (Animal Behaviour and Welfare) and Dr. Suzanne Held (Behavioural Biology), forming a cohesive research group focused on affective neuroscience in farm animals. The Langford-based research team employs advanced methodologies including non-linear heart rate variability analysis, cognitive testing arenas, and behavioral observation protocols. Current projects investigate pain-emotion interactions in calves, social learning in poultry, and regulatory effectiveness for transport welfare, positioning the group at the forefront of translational animal welfare science.
Adilson Motter is the Charles E. and Emma H. Morrison Professor of Physics and Astronomy and (by courtesy) Engineering Sciences and Applied Mathematics at Northwestern University. He serves as Director of the Center for Network Dynamics (CND) and has been a faculty member since March 2006. His academic appointments include affiliations with the Chemistry of Life Processes Institute (CLP), Molecular Biophysics Program, NSF-Simons National Institute for Theory and Mathematics in Biology (NITMB), Paula M. Trienens Institute for Sustainability and Energy, Graduate Program in Applied Physics, Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Institute for Quantum Information Research and Engineering (INQUIRE), and Northwestern Institute on Complex Systems (NICO). Professor Motter received his Ph.D. in 2002 from UNICAMP (University of Campinas), Brazil, where he worked with Professor Patricio S. Letelier. Prior to joining Northwestern, he held positions as Guest Scientist at the Max Planck Institute for the Physics of Complex Systems in Germany and as Director's Funded Postdoctoral Fellow at the Center for Nonlinear Studies at Los Alamos National Laboratory. Professor Motter's research focuses on the dynamical behavior and control of complex systems and networks. His work spans theoretical and computational approaches to understanding phenomena in physical, biological, and engineered systems. Key research areas include: Cascading dynamics and network resilience Spontaneous synchronization and symmetry phenomena Network control theory and applications Quantum networks and information transfer Machine learning applications to network science Data-driven discovery in complex systems Applications to quantitative biology, biomedical research, renewable energy, smart power grids, microfluidics, and metamaterials Analysis of Professor Motter's recent publications reveals a strong interdisciplinary focus spanning physics, engineering, biology, and computer science. His work demonstrates consistent innovation in network science, with recent contributions advancing quantum networking architectures, understanding power grid limitations for electric vehicle integration, developing machine learning approaches for genetic analysis, and exploring fundamental synchronization phenomena. A notable trend is the increasing application of his theoretical frameworks to real-world challenges in energy systems, biomedical research, and quantum information technology. Professor Motter has received numerous prestigious awards and honors: Alfred P. Sloan Research Fellowship (2009) Weinberg Award for Excellence in Mentoring Undergraduate Research (2009) Northwestern-Argonne Early Career Investigator Award for Energy Research (2010) NSF Faculty Early Career Development (CAREER) Award (2011) Erdös-Rényi Prize in Network Science (2013) Fellow of the American Physical Society (2013) Simons Foundation Fellowship in Theoretical Physics (2015) Fellow of the American Association for the Advancement of Science (2015) Scialog Fellow (2015) Outstanding Referee, American Physical Society (2016) Fellow of the Network Science Society (2020) Senior Scientific Award, Complex Systems Society (2022) Professor Motter has demonstrated exceptional commitment to mentoring, as evidenced by the Weinberg Award for Excellence in Mentoring Undergraduate Research. His research group has received significant funding through multiple NSF grants, including his CAREER award, and collaborations with Argonne National Laboratory. Current research directions include mechanical metamaterial networks, quantum network science, and other areas of complex systems. The group has been actively recruiting postdoctoral researchers and has seen students recognized with awards and research grants. As Director of the Center for Network Dynamics (established September 2023), Professor Motter leads a multidisciplinary team exploring network phenomena across various domains. The Center has hosted significant events including the 'Brain Architecture and Computing 2024' workshop and is organizing the 2025 CDC Workshop on Neurocomputation and Dynamics in Rio de Janeiro. The Motter Group maintains active collaborations with experimentalists and researchers from diverse disciplines, facilitating the translation of theoretical insights into practical applications.
