Dr. Natalie Simpson is Professor and Associate Dean for Graduate Programs at the University at Buffalo's School of Management, Department of Operations Management and Strategy. She holds a PhD and MBA from the University of Florida, and a BFA from North Carolina School of the Arts. Her research explores emergency response systems , supply chain logistics , and educational technology , with particular focus on operational challenges in crisis management. She investigates hyper-project coordination in emergency contexts and resource allocation frameworks for incident commanders. Simpson's scholarly contributions show strong emphasis on: Modeling emergency response operations and supply chain vulnerabilities Developing pedagogical innovations for operations management education Analyzing healthcare workflow efficiency and disaster management systems Her extensive recognition includes: Decision Sciences Institute's Best Case Studies Award (2005) National Instructional Innovation Award (2004) SUNY Chancellor's Award for Excellence in Teaching (2002) Grinter Fellowship and Matherly Scholarship As Academic Director of Digital Access Education, she leads technology-enhanced learning initiatives and advises graduate programs. Administrative responsibilities include heading the Digital Access Working Group and serving on editorial boards for Decision Sciences.
Professor Charlotte Deane is a leading academic in structural bioinformatics, holding the position of Professor at the University of Oxford's Department of Statistics and Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC). She leads the Oxford Protein Informatics Group (OPIG), focusing on protein structure prediction, immunoinformatics, and AI-driven drug discovery. Her research integrates computational methods with biological insights, developing tools widely used in academia and industry. Prior roles include Head of the Department of Statistics, Deputy Head of the Mathematical, Physical and Life Sciences (MPLS) Division at Oxford, and Chief Scientist of Biologics AI at Exscientia. During the COVID-19 pandemic, she served on SAGE and as UKRI's COVID-19 Response Director. In 2022, she was awarded an MBE for her contributions to pandemic research. Her research group's work spans antibody design, T-cell receptor analysis, and small molecule discovery, with a focus on open-source software development. Current projects include advancing AI methods for protein structure prediction and therapeutic antibody engineering. Recent publications highlight innovations in computational drug design, antibody developability, and machine learning applications in structural biology.
Manuel DeLanda is a New York-based cross-disciplinary theorist and artist. He holds the rank of Professor at The European Graduate School / EGS and serves as a lecturer at Princeton University's School of Architecture and Pratt Institute's Graduate Architecture and Urban Design program. His academic career includes past roles as a Fellow at Princeton's Institute for Advanced Study (2000/01) and teaching positions at the University of Pennsylvania and Columbia University. Education: BFA from the School of Visual Arts (New York), PhD from the European Graduate School (2010). Research interests span philosophy, complexity theory, materialism, science studies, and Deleuzean thought. His work integrates interdisciplinary approaches to topics like assemblage theory, urban capitalism, morphogenesis, and the philosophy of science. Key themes include the application of mathematical concepts (topology, chaos theory) to social and historical analysis, and rethinking materialist frameworks across disciplines. Notable contributions include books such as War in the Age of Intelligent Machines , A Thousand Years of Nonlinear History , and Assemblage Theory . His lectures (e.g., on economic agglomeration, urban capitalism, and Deleuzean philosophy) reflect his commitment to bridging abstract theory with empirical analysis. Labs/Teams: No specific lab affiliations mentioned, but his work is collaborative through academic networks and interdisciplinary projects in architecture, urbanism, and philosophy.
Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.
Trey Porto is an Adjunct Professor at the University of Maryland, affiliated with the Joint Quantum Institute (JQI) and NIST. His research focuses on ultra-cold atoms, quantum optics, and quantum information science. He leads projects on Rydberg atoms, optical lattices, and quantum networking, leveraging cold atom systems to explore novel quantum phenomena and control strategies. Research areas include ultra-cold Rb/Yb mixtures for studying Bose-Einstein condensates and engineered dissipation, as well as photon-photon interactions using Rydberg-dressed polaritons. His work bridges quantum simulation, quantum computing, and precision measurement, with applications in quantum networking and many-body physics. Key achievements include the 2023 UMD Quantum Invention of the Year Award for developing photon-counting methods that preserve quantum states. Porto collaborates with groups such as RQS and JQI, contributing to advancements in subwavelength optical potentials and Floquet-engineered systems. He mentors graduate students in experimental and theoretical aspects of cold atoms and quantum technologies. Publications highlight breakthroughs in Rydberg blockade enhancement, prethermal Bose-Einstein condensation, and compact auto-alignment systems for experimental setups. His lab is based in the Physical Sciences Complex on the UMD campus, with ongoing projects exploring quantum dissipation and photon-atom hybrid systems.
Matteo Magnani is a Professor in the Division of Computing Science at the Department of Information Technology, Uppsala University. He leads the Uppsala University Information Laboratory and is a founding member of the Uppsala University Computational Social Science Lab. His research spans network science, artificial intelligence, data science, and computational social science, with a focus on social data mining and multilayer networks. PhD in Computer Science, University of Bologna, 2006 Graduated with honours in Information Sciences, University of Bologna, 2002 Studies in Computer Science at University of Marne la Vallée and Imperial College London Matteo Magnani's research interests include social network analysis, multilayer and probabilistic networks, community detection, visual analytics, and the application of AI to digital media and climate communication. His work bridges computer science and social sciences, particularly in analyzing online discourse and digital intermediaries. He has contributed significantly to the understanding of network structures, uncertainty in networks, and the ethical dimensions of algorithmic analysis. His recent publications highlight trends in fairness in community detection, visual saliency in network layouts, emotional reactions to climate visuals online, and deep learning applications in social media. Topics frequently involve YouTube, Twitter, and online public debates, using advanced network and machine learning methods. Rotary Prize for best student of the Science Faculty Best Paper Award Funniest Presentation Award Best Poster Award Pedagogical Prize from UTN Distinguished University Teacher (Sweden) Docent title (Sweden) Magnani has supervised numerous students and collaborated widely, particularly with Luca Rossi, Alexandra Segerberg, and Davide Vega. He has secured funding from major sources including VR, H2020, STINT, and MIUR. He leads active research labs focused on information systems and computational social science, fostering interdisciplinary collaboration and innovation in network-based research.
Dr. Eiko Fried is an Associate Professor at Leiden University's Faculty of Social and Behavioural Sciences, where he works at the intersection of clinical psychology, psychiatry, epidemiology, methodology, and complexity science. His research focuses on improving psychological science through open science practices and innovative measurement approaches. PhD in clinical psychology, Free University of Berlin Postdoctoral training at KU Leuven and University of Amsterdam Promoted to Associate Professor at Leiden University in 2021 Key research areas include: Psychopathology measurement and classification Network analysis in mental health research Ecological momentary assessment (EMA) methodology Open science advocacy and implementation Dynamic systems modeling in psychology Transdiagnostic approaches to mental disorders Recent publications demonstrate expertise in: Symptom network analysis across disorders Improving depression measurement standards Transdiagnostic assessment protocols Mental health data integration challenges Psychological theory construction Methodological innovations in clinical research
John Capobianco, PhD, is a Professor in the Department of Chemistry and Biochemistry at Concordia University and holds the Honorary Concordia University Research Chair in Nanoscience. His research focuses on lanthanide-doped nanoparticles, upconversion luminescence, and biomedical applications. PhD, University of Geneva Key research areas include: Nanomaterials synthesis and spectroscopy Upconversion for biomedical imaging Drug delivery systems Photodynamic therapy for cancer treatment Optical thermometry and sensing Recent publications highlight advancements in: X-ray detection via photochromic nanoparticles Lipid-coated nanoparticles for lung permeation Cooperative energy transfer in Yb3+/Eu3+ complexes Biocompatible nanomaterials for secure information storage Pr3+-doped radiosensitizers for glioblastoma therapy Scientific recognitions: Honorary Concordia University Research Chair in Nanoscience Teaching includes undergraduate and graduate courses in inorganic chemistry and spectroscopy. His work bridges fundamental material science with applied biomedical engineering, emphasizing optical properties and therapeutic applications of lanthanide-based nanomaterials.
Professor Ingrid Undeland leads the Marine research group at the Division of Food and Nutrition Science, Department of Life Sciences at Chalmers University of Technology. She is a distinguished researcher with expertise spanning marine food science, lipid chemistry, and blue biorefining, having established herself as a leading figure in sustainable seafood research and marine biotechnology. Her educational background includes food science studies from Linnaeus University and a PhD in bioscience from Chalmers University of Technology and SIK (now RISE). Between 1999-2002, she was a post-doctoral fellow at University of Massachusetts Marine Station, which significantly shaped her research trajectory in marine food science. Professor Undeland's research focuses on pioneering next-generation seafood through innovative value chains from seaweed, small pelagic fish, fish side streams, mussels, and microalgae. Her specialized expertise lies in marine lipids and proteins, particularly their stabilization, isolation from complex sources, and nutritional properties including digestibility. She has extensive experience with antioxidant strategies using plant-derived side streams or extracts and fundamental studies of fish hemoglobins as pro-oxidants. Her work also encompasses innovative technologies for biomass fractionation and nutrient recycling to build blue biorefineries, along with in vitro digestion models and general seafood analytics. Beyond marine research, she explores filamentous fungi as sustainable alternative food protein sources. The trends in her recent publications reveal a strong emphasis on valorizing marine resources through advanced processing techniques. Her work increasingly focuses on sustainable extraction methods for seaweed proteins, particularly Ulva fenestrata, and developing antioxidant strategies using berry side streams to stabilize fish proteins. There's a growing interest in understanding the nutritional properties and digestibility of alternative marine proteins, along with environmental assessments of processing technologies. Her research consistently bridges fundamental science with practical applications for creating sustainable seafood value chains. Swedish representative in Nordic Lipidforum, WEFTA, and EuCheMS Editorial board member of Journal of the Aquatic Food Product Technology Member of the National Committee for Nutrition and Food Science at the Royal Swedish Academy of Sciences Co-founder of the startup company AquaFood H-index of 47 according to Google Scholar Professor Undeland has an extensive record of academic mentorship, having supervised or currently supervising 23 PhD students, 14 postdoctoral researchers, and examining over 25 MSc students. Her research is supported by numerous grants, including the WaSeaBi Project which focuses on valorizing seafood side-streams through holistic value chain design. She collaborates with various industry partners and academic institutions across Europe. Her laboratory includes technicians Dr. Karin Larsson and Dr. Rikard Fristedt, and she leads a dynamic research team working on multiple projects related to marine biorefining and sustainable seafood development. Her research group operates within well-equipped facilities at Chalmers University, with specialized laboratories for marine food analysis, protein extraction, lipid oxidation studies, and in vitro digestion modeling. The team also maintains cultivation systems for seaweed and filamentous fungi, enabling integrated research from raw material production to final product development.
Nadia Shardt is an Associate Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). Her research focuses on interfacial thermodynamics, particularly in systems with nanoscale curvature, with applications spanning atmospheric science, biomedical cryopreservation, and industrial process optimization. She contributes to teaching courses such as TKP4580 - Chemical Engineering Specialization Project and KP3100 - Chemical Engineering . PhD in Chemical Engineering (University of Alberta, 2019) BSc in Chemical Engineering (University of Alberta, 2015) Postdoctoral researcher at ETH Zurich (2020-2022) Her work addresses fundamental challenges in phase behavior under curvature constraints, combining microfluidic experimentation , Gibbsian thermodynamic modeling , and machine learning techniques to study systems like CO 2 storage media, cloud microphysics, and food emulsions. Recent publications emphasize surface tension modeling for complex multi-component systems and cryoprotectant loading efficiency. Scientific awards include the ETH Postdoctoral Fellowship Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship Outstanding Academic Fellows Programme 2024-2028
Børge Rokseth is an Associate Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His work focuses on maritime systems, autonomous vessel control, and safety verification. He actively supervises Master's students and contributes to research on risk-informed control systems, hybrid power systems, and systems-theoretic process analysis (STPA). Research Interests: Rokseth's research spans autonomous ship systems, dynamic risk assessment, and safety verification. He explores risk-based decision-making for maritime autonomy, hazard identification in hybrid propulsion systems, and control function allocation in dynamic positioning. His work integrates systems theory, machine learning, and regulatory compliance (e.g., COLREGS) to enhance safety and environmental performance in marine operations. Publications: His recent work includes probabilistic trajectory prediction frameworks for autonomous ships, STPA-based safety analyses, and studies on decarbonization barriers in the maritime industry. These publications emphasize risk modeling, systems-theoretic approaches, and simulation-based verification. Teaching: Rokseth teaches courses such as TTK4130 - Modelling and Simulation, contributing to the education of future engineers and researchers in cybernetics and maritime systems.
Lande Liu is a Senior Lecturer in Chemical Engineering at the University of Huddersfield's School of Applied Sciences. Previously, he held a Lectureship at the University of Manchester (2010-2014), and earlier worked as an industrial consultant and research fellow at Leeds and Sheffield Universities. His academic journey began with a MEng in Chemical Engineering and a PhD in kinetic theory of aggregation from Sheffield (2004), preceded by a visiting PhD at Twente University (2002). Education: PhD in Chemical Engineering (University of Sheffield, 2004) Visiting PhD (Twente University, 2002) MEng in Chemical Engineering (Tsinghua University, 1999) BSc in Applied Mathematics (Tsinghua University, 1996) Liu's research focuses on multi-scale particle interactions (molecular to granular) using kinetic theory of aggregation, with applications spanning nanotechnology, pharmaceutical engineering, and sustainable chemical processes. His work aligns with UN Sustainable Development Goals for environmental protection and industrial innovation. Recent publications examine particle deposition in turbulent flows, enhanced heat exchanger designs, and nanofluid stabilization techniques. He teaches core chemical engineering topics including transport phenomena, unit operations, and process design. Active in collaborative research, Liu has partnered with institutions across Europe on projects involving spectroscopy, ultrasonics, and dynamic modeling. His technical expertise includes particle size analysis, tomography, and computational simulation of complex systems.
Franklin Goldsmith serves as Associate Professor of Engineering within Brown University's School of Engineering, where his research bridges fundamental chemical kinetics with practical combustion applications. His work directly impacts energy conversion technologies and emission reduction strategies through rigorous investigation of reaction mechanisms. His academic foundation includes: PhD in Chemical Engineering from Massachusetts Institute of Technology (2010) BS in Chemical Engineering from North Carolina State University (2003) BA in Chemistry from University of North Carolina at Chapel Hill (1998) Goldsmith's research program centers on radical reaction kinetics and low-temperature oxidation phenomena , employing both computational master equation modeling and experimental techniques like shock tube spectroscopy and synchrotron photoionization. His investigations into non-Boltzmann energy distributions and pressure-dependent rate coefficients have established new frameworks for understanding ignition chemistry. The Thermochemistry for Combustion Database project exemplifies his commitment to foundational data resources for the field. Analysis of his publication record reveals three dominant research thrusts: (1) detailed kinetic modeling of hydrocarbon oxidation, particularly propane systems; (2) development of computational methodologies for pressure-dependent rate estimation; and (3) fundamental studies of radical-molecule interactions. His work consistently integrates high-precision experimental validation with theoretical frameworks, as evidenced by collaborations with national laboratories. Goldsmith teaches Brown's core chemical engineering curriculum including ENGN 1120 (Reaction Kinetics and Reactor Design) and ENGN 1130 (Chemical Engineering Thermodynamics), alongside specialized graduate courses in heterogeneous catalysis (ENGN 2751) and chemically reacting flow (ENGN 2910Q). His educational approach emphasizes the connection between molecular-scale kinetics and reactor design principles. His research group maintains active collaborations with Argonne National Laboratory (Klippenstein), MIT (Green), and Sandia National Laboratories (Taatjes), focusing on multiscale informatics for complex reaction systems. Current projects investigate biomass-derived fuel combustion and catalytic partial oxidation mechanisms using spatially resolved experimental techniques.
Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).
Stefano Grivet Talocia is a Full Professor in the Department of Electronics and Telecommunications at Polytechnic University of Turin. He serves as Director of the Doctoral School, is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and holds positions on the University Committee for Research and the Commission for the Promotion of Library, Archive and Museum Heritage. He is also President of the Doctoral School Council. His educational background includes a Laurea degree (summa cum laude) in Electronic Engineering (1994) and a Ph.D. in Electronic and Communication Engineering (1998), both from Polytechnic University of Torino. From 1994 to 1996, he worked at NASA/Goddard Space Flight Center in Greenbelt, MD, USA. Professor Grivet Talocia's research focuses on passive macro-modeling of concentrated and distributed interconnect structures for Signal/Power Integrity, order reduction techniques, and modeling and simulation of fields, circuits, and their interactions. His work spans several key areas including fast simulation of transmission lines (TOPLine technique), macromodeling and model order reduction, simulation methods for fields and circuits, passivity enforcement of lumped macromodels, waveform relaxation techniques, and wavelet applications. His research has significant applications in electromagnetic compatibility and signal integrity verification of complex electronic systems. His recent publications demonstrate strong trends in model order reduction techniques applied to power integrity verification, advanced macromodeling for electromagnetic compatibility, nonlinear circuit analysis, uncertainty quantification in PCB design, and power electronics modeling. These works consistently address practical engineering challenges in high-speed electronic design with emphasis on computational efficiency and accuracy. URSI Young Scientist Award (1999) Best symposium paper (2006) Three IBM Shared University Research Awards (2007-2009) IEEE Transactions on Advanced Packaging Best Paper Award (2007) Best EPEP conference paper awards (2007, 2008) Best Associate Editor Award - IEEE Transactions (2020) Best Conference Paper Award (2020) Three Intel SRS Grants (2022-2024) IEEE Fellow (2018) Professor Grivet Talocia actively supervises PhD students working on cutting-edge topics including machine learning applications in signal integrity, model reduction techniques, and electromagnetic compatibility. He has secured significant research funding through competitive grants including PRIN projects and multiple industry-sponsored research contracts with major technology companies such as IBM, Intel, Nokia, Hitachi, and Infineon. His technology transfer activities include co-founding the spin-off IdemWorks (acquired by CST in 2016) and maintaining active collaborations with industry partners. He leads the EMC Group (Electromagnetic Compatibility) within the Department of Electronics and Telecommunications and has developed the autoCircuits web service for automated generation of circuit theory problems. His research has been recognized by inclusion in the top 2% worldwide researcher catalog (Stanford) since 2019.
