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Raphaël TinarrageView profile

Research Fellow

• Topological Data Analysis
• Combinatorial Topology
• Medical Image Analysis
• +3 more

Raphaël Tinarrage is a Research Fellow at the Institute of Science and Technology Austria (ISTA), part of the Wagner group. Previously, he held a postdoctoral position at FGV EMAp (Brazil) and was an Assistant Professor at Université Paris-Saclay (2017–2020). His research focuses on Topological Data Analysis (TDA), including theoretical advancements and practical applications in medical imaging, legal document analysis, and equivariant neural networks. Education: He earned his PhD from Université Paris-Saclay and Inria, advised by Fred Chazal and Marc Glisse (2017–2020). His dissertation, Topological Inference from Measures and Vector Bundles , introduced novel methods for topological data analysis. Research Interests: Tinarrage explores topological inference (e.g., stability of persistent homology), combinatorial topology (triangulation, Lusternik–Schnirelmann category), and applications like medical image segmentation and legal text analysis. His work bridges theoretical mathematics with computational tools, emphasizing practical implementations. Key Publications (2021–2025): Recent articles include LieDetect (2025) for Lie group symmetry detection, Empirical Analysis of Brazilian Supreme Court Precedents (2025), and ZigzagNetVis (2025) for dynamic graph visualization. Preprints include Train-Free Segmentation in MRI (2024). Teaching & Advising: He taught courses on topology and TDA at FGV EMAp and Université Paris-Saclay. He advised multiple students on projects involving legal NLP and TDA applications. Labs & Tools: His PhD work is implemented in the velour Python package. Code for other projects is available on his GitHub repository.

Manish SinghView profile

Assistant Professor

• Power Systems
• Optimization
• Control Systems
• +4 more

Manish Singh is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison. His work focuses on advancing power systems and energy networks through the integration of optimization, control theory, and machine learning. He holds a PhD from Virginia Tech (2021), MS from Virginia Tech (2018), and BS from IIT Varanasi (2013), with prior industry experience in the power sector (2013–2016). Research Interests: Modeling, optimization, and control of power systems; data-driven methods for energy networks; and interdisciplinary approaches for natural gas and water systems. Notable contributions include physics-informed machine learning for voltage stability, grid-forming inverter dynamics, and topology-aware control strategies for distributed energy resources. Education: PhD in Electrical Engineering, Virginia Tech (2021) MS in Electrical Engineering, Virginia Tech (2018) BS in Electrical Engineering, Indian Institute of Technology, Varanasi (2013) Recent work emphasizes smart grid resilience, low-frequency oscillation mitigation, and self-supervised learning algorithms for system modeling. His research has been recognized with awards such as the ABB Research Award (Top 5 Finalist, 2022) and the Blackwell Graduate Research Award (Second Prize, 2022). Notable Awards: 2022 ABB Research Award Finalist 2022 Blackwell Graduate Research Award 2019 INFORMS ‘Frame that Problem’ Winner 2007 KVPY Fellowship (Government of India) Teaching Activities: Courses include Signals and Systems (E C E 330), Electric Power Systems (E C E 427), and Advanced Independent Study (E C E 699). His academic contributions extend to collaborative projects on grid-forming inverters and energy network interoperability through the UNIFI Consortium.

Eric HunterView profile

Professor

• Occupational Voice Use
• Signal Processing
• Biomechanics of Speech
• +3 more

Eric Hunter, PhD, is the Department Executive Officer (DEO) and Chair of the Department of Communication Sciences and Disorders at the University of Iowa, as well as the Harriet B. and Harold S. Brady Chair in Liberal Arts and Sciences. His research focuses on occupational voice use in educators, signal processing, biomechanics of speech articulators, and muscle mechanics. A Fellow of the Acoustical Society of America, his work bridges clinical voice disorders and interdisciplinary collaboration in health research. Previously, he served as Senior Associate Dean for Research at Michigan State University’s College of Communication Arts and Sciences and directed the Trifecta Initiative for Interdisciplinary Health Research. His NIH-funded studies prioritize teacher vocal wellness, classroom acoustics, and biophysical models of voice production. Research Interests: Voice Biomechanics Acoustic Voice Analysis Teacher Vocal Health Signal Processing Interdisciplinary Health Research Swallowing-Voice Interactions Lab & Collaborations: The Voice Biomechanics and Acoustics Lab (V-BAL) at UI unites experts in acoustics, engineering, linguistics, and clinical practice. Key projects include developing machine learning tools for voice disorder screening and understanding vocal effort in multilingual speakers. Awards: Fellow, Acoustical Society of America Advising & Grants: Leads NIH-funded projects on vocal load and fatigue. Collaborates with educators and administrators to design interventions for workplace voice preservation.

• Quantum Chemistry
• Density Functional Theory
• Reduced Density Matrix
• +1 more

Dr. Klaas Giesbertz is an Assistant Professor in Chemistry and Pharmaceutical Sciences at Vrije Universiteit Amsterdam, with joint appointment at AIMMS (Amsterdam Institute for Molecular and Life Sciences). His research focuses on theoretical quantum chemistry and density functional theory. Research Focus: Specializes in reduced density-matrix functional theory, electronic structure calculations, and quantum many-body systems. Current work develops advanced computational methods for electronic excitations and strongly correlated systems. Honors: Journal of Chemical Physics Editor's Choice Award (2013, 2012) Publications: Research demonstrates consistent innovation in density matrix methodologies, with recent emphasis on machine learning applications, exact solutions to quantum representability problems, and relativistic effects in electron correlation.

Alexander G. Ororbia IIView profile

Assistant Professor

• Predictive Processing
• Active Inference
• Spiking Neural Networks
• +7 more

Alexander G. Ororbia II is an Assistant Professor in the Department of Computer Science at Rochester Institute of Technology (RIT), where he directs the Neural Adaptive Computing (NAC) Laboratory. He is also an affiliate professor in the RIT Department of Psychology, an affiliate faculty member of RIT's Center for Applied Neuroscience, and affiliate faculty in RIT's Cognitive Science program. Ororbia received his Ph.D. in Information Science & Technology from Penn State University and a B.S.E. in Computer Science & Engineering from Bucknell University. His research focuses on developing computational models of neural information processing and synaptic plasticity, with significant contributions to predictive coding, active inference, spiking neural networks, neural-based cognitive modeling, and neurorobotic control. As Director of the NAC Laboratory, he leads research on biomimetic intelligence and brain-inspired computing, specifically developing neurocognitively plausible approaches to adaptation and memory formation in artificial neural systems. His publication trends show a strong focus on brain-inspired computing, with particular emphasis on predictive coding frameworks, active inference models, and spiking neural networks. Recent work has advanced the fields of lifelong machine learning, biomimetic intelligence, and backpropagation alternatives, with applications in robotics, computer vision, and cognitive modeling. His research bridges machine learning, computational neuroscience, and cognitive science to create more robust and adaptive artificial intelligence systems. IGERT Fellow NACME Sloan Scholar Jordan-Rednor Scholarship Bunton-Waller Scholarship Ororbia advises numerous Ph.D. and M.Sc. students working on projects related to spiking neural networks, predictive coding, active inference, and lifelong machine learning. His lab has secured research funding that supports multiple graduate students and postdoctoral researchers. The NAC Laboratory collaborates with institutions including Penn State University and has produced research published in top-tier venues including Nature Communications, Science Advances, NeurIPS, and ICRA. The NAC Laboratory, under Ororbia's direction, focuses on neurocognitively-inspired lifelong machine learning, developing novel learning algorithms and memory architectures for artificial neural systems. The lab explores both non-spiking and spiking neural networks, with applications in robotics, computer vision, and cognitive modeling. Current research directions include predictive processing implementations of cognitive architectures, mortal computation frameworks, and biologically plausible alternatives to backpropagation.

• Agricultural Robotics
• Control Systems
• Autonomous Systems
• +3 more

Girish Chowdhary is an Associate Professor in multiple departments at the University of Illinois, including Agricultural and Biological Engineering, Siebel School of Computing and Data Science, and Electrical and Computer Engineering. He holds the Donald Biggar Willett Faculty Fellowship and is affiliated with the Coordinated Science Lab, Center for Digital Agriculture, NCSA, and Carl R. Woese Institute. His research focuses on autonomous systems, agricultural robotics, and adaptive control systems, with applications in precision agriculture and environmental monitoring. He has received the AIAA Associate Fellow award (2018) and leads projects involving robot navigation, crop phenotyping, and AI-driven agricultural solutions. Chowdhary’s work integrates robotics, machine learning, and control theory to address challenges in agriculture, such as autonomous crop monitoring and weed management. His lab develops platforms like TerraSentia for high-throughput phenotyping and frameworks for under-canopy navigation. Key collaborations span engineering, biology, and computer science. Current research trends emphasize self-supervised learning for robotics, predictive control algorithms, and AI-driven decision-making in farming systems. Awards: AIAA Associate Fellow (2018) Labs: Coordinated Science Lab, Center for Digital Agriculture Grants: Focus on USDA, NSF-funded projects in agricultural automation His publications highlight advancements in robotic navigation, adaptive control systems, and AI applications for sustainable agriculture. Future work aims to scale robotic solutions for global agricultural resilience.

• Transportation Engineering
• Traffic Flow Theory
• Quantum Computing
• +7 more

Alireza Talebpour is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois. He leads the Smart City Lab and focuses on advancing transportation systems through research in automated vehicles, traffic flow theory, and quantum computing applications. His work bridges microeconomic principles with microscopic traffic modeling to enhance urban mobility solutions. Education Ph.D. Civil and Environmental Engineering, Northwestern University (2015) M.Sc. and B.Sc. Civil and Environmental Engineering, Sharif University of Technology (2009, 2007) Research Interests His research spans quantum computing for infrastructure optimization, human-automated vehicle interactions, and AI-driven traffic management. Key areas include: - Mixed traffic systems with connected/autonomous vehicles - Smart city infrastructure and policy design - Microscopic traffic simulation (e.g., TGSIM dataset) - Freight and public transit consolidation strategies Key Contributions Recent work explores: - Charging lanes for EVs and their traffic impacts - Lane-changing behavior in automated driving environments - Quantum algorithms for EV charging station placement - Safety implications of self-enforcing street designs Affiliations He chairs the Traffic Flow Theory Committee at the Transportation Research Board and collaborates on national efforts for autonomous vehicle integration. Current projects include: - Developing game-theoretic frameworks for intersection maneuvers - Enhancing traffic prediction via machine learning - Evaluating infrastructure impacts of truck platooning

• Asset Pricing
• Behavioral Finance
• Executive Compensation
• +3 more

Dr. Jing Zhao is an Associate Professor in the Finance Department at La Trobe University, where she has been since July 2010. Her research focuses on asset pricing, behavioral finance, executive compensation, corporate social responsibility, and the dynamics of financial markets. She holds a BSc from Fudan University (Shanghai, China), and both an MPhil and PhD from the Chinese University of Hong Kong (Shatin, Hong Kong). BSc in [Undisclosed Discipline], Fudan University MPhil in [Undisclosed Discipline], Chinese University of Hong Kong PhD in [Undisclosed Discipline], Chinese University of Hong Kong Her work explores how investor behavior, corporate governance structures, and regulatory mechanisms influence financial market outcomes. For instance, she investigates how executive pay disparities affect debt costs, how circuit breakers impact market stability, and how limited investor attention during crises shapes stock reactions. Her research often employs rigorous quantitative methods, leveraging data from institutions like the ARC. Her articles span topics such as ETF dynamics, systemic risk, and dividend policies. These publications reflect a consistent focus on linking microeconomic behaviors (e.g., institutional overconfidence) to macroeconomic market effects (e.g., volatility contagion). Notably, her work on ETFs and ex-dividend taxation demonstrates interdisciplinary depth, combining financial instrument analysis with tax policy implications. Recipient of the Outstanding Article Award in the Journal of Financial Research (Year unspecified) Recipient of the Best Paper Award at the Financial Markets and Corporate Governance Conference (Year unspecified) Jing Zhao actively supervises Masters and PhD students in finance-related research. She secured a 2014–2017 Australian Research Council (ARC) Discovery Projects grant to study information-based securities trading, indicating her sustained commitment to empirical research. This grant underscores her role in advancing methodologies for analyzing market microstructure and trading behaviors.

Shady GadoueView profile

Associate Professor

• Power Electronics
• Control Systems
• Electrification Solutions
• +3 more

Dr. Shady Gadoue is an Associate Professor in the School of Electronic Engineering and Computer Science at Queen Mary University of London. His roles include research leadership in the Centre for Electronics and Centre for Sustainable Engineering. He holds a BSc, MSc, and PhD from Alexandria University and Newcastle University, respectively, and is a Fellow of the Higher Education Academy (FHEA). Dr. Gadoue's research focuses on Power Electronic Converters, Control Systems, and Electrification Solutions for Low Carbon applications. Key areas include Smart Grids, Electric Transportation, and Digital Twins. He has over 23 years of academic and industrial experience, collaborating with Jaguar Land Rover, National Grid, and others. His work has resulted in over 80 IEEE publications, recognized by Stanford University as among the top 2% scientists globally in Energy Engineering (2020–2021). His recent grants include a £49,923 Innovate UK project (2022–2023) for training in power electronics. He teaches modules on electric powertrains and power systems analysis, emphasizing renewable integration and HVDC. Dr. Gadoue’s awards include IEEE’s 'high quality research' designation and top 2% global rankings. His research addresses critical challenges in energy systems, transportation electrification, and sustainable engineering.

Leonardo MorsutView profile

Assistant Professor

• Synthetic Biology
• Stem Cell Biology
• Regenerative Medicine
• +3 more

Leonardo Morsut is an Assistant Professor in the Department of Stem Cell Biology and Regenerative Medicine at the Keck School of Medicine, University of Southern California. His research focuses on synthetic biology and tissue engineering, aiming to program mammalian cells for controlled tissue morphogenesis and regeneration. He leads the Morsut Lab, which integrates computational modeling, protein engineering, and stem cell biology in an iterative framework to understand and manipulate tissue self-organization. University: University of Southern California School: Keck School of Medicine Department: Stem Cell Biology and Regenerative Medicine Academic Rank: Assistant Professor Research Interests: Dr. Morsut's work combines synthetic biology with developmental systems to engineer multicellular constructs. His lab specializes in synthetic Notch receptors, genetic circuit design, and controlling spatio-temporal patterning during tissue formation. Key areas include programmable material-to-cell pathways, juxtacrine signaling networks, and computational frameworks for morphogenesis modeling. Recent Publications Trends: His 2024-2025 studies emphasize precise spatial control of differentiation in organoids using synthetic Wnt organizers, programmable material interfaces, and dynamic cell growth feedback. Earlier works focus on transgene silencing mechanisms, mucin-driven morphogenesis, and density-dependent signaling in multicellular systems. Laboratory Mission: The Morsut Lab aims to build functional tissues through synthetic biology tools while training future regenerative medicine leaders. They utilize interdisciplinary approaches from engineering, computational biology, and developmental systems to create programmable multicellular structures.

Valentin RobuView profile

Professor

• Artificial Intelligence
• Decentralized Energy Systems
• Multi-Agent Systems
• +4 more

Valentin Robu is a Full Professor of Artificial Intelligence for Decentralized Energy Systems at Eindhoven University of Technology (TU/e). He is a Senior Researcher at CWI (National Research Institute for Mathematics and Computer Science, Amsterdam) and holds a visiting appointment at Princeton University's ECE department. His core expertise lies in multi-agent systems and distributed AI, with a focus on applying AI to energy challenges like smart grids, electric vehicle coordination, and renewable integration. He has authored over 150 peer-reviewed publications and won awards such as the 2019 UK Innovation of the Year Award and the 2018 Low Carbon Transport Award. Education and Career: Robu earned his PhD from TU Eindhoven and CWI (2009). Before his current roles, he was an Associate Professor at Heriot-Watt University (UK), a Senior Research Fellow at the University of Southampton, and held visiting roles at Harvard and MIT. He collaborates globally with institutions like MIT, Harvard, and TU Delft. Research Interests: His work addresses challenges in decentralized energy systems, including AI-driven grid optimization, game-theoretic models for renewable integration, and blockchain applications in energy trading. He focuses on ensuring grid stability, fair resource sharing, and sustainable energy access. Awards: In addition to his Innovation and Low Carbon Transport awards, he has been recognized as a 'Best Reviewer' in 2018. His research impacts UN Sustainable Development Goals related to affordable energy and climate action. Grants and Projects: He leads projects like CESI (UK Energy Systems Integration), CEDRI (India Demand Reduction), and ORCA (Offshore Robotics Hub). His work is featured in media outlets like the BBC, Economist, and World Economic Forum. Labs and Teams: He contributes to TU/e's EAISI (Eindhoven AI Systems Institute) and CWI's Intelligent and Autonomous Systems Group. Collaborations span academia and industry, including Scottish Power Energy Networks and Microsoft Research.

Luna ZagoracView profile

Research Fellow

• Cosmology
• Particle Astrophysics
• High-Performance Computing
• +3 more

Luna Zagorac is a Research Fellow at the Perimeter Institute for Theoretical Physics, focusing on particle cosmology and interdisciplinary astrophysics. Her research bridges theoretical dark matter dynamics, high-performance computing, and cosmic archaeology, including work on ultralight dark matter (ULDM) simulations and ancient Egyptian star maps. She completed her Ph.D. in Physics at Yale University (2022), advised by Nikhil Padmanabhan and Richard Easther. Education: Ph.D. in Physics, Yale University (2022); earlier work involved optical studies of blazars at Colgate University’s Foggy Bottom Observatory. Research emphasizes ULDM phenomenology, soliton dynamics, and interdisciplinary approaches linking astrophysics with archaeology. Key awards include the Leigh Page Award for Teaching Excellence (2021), NASA FINESST Fellowship (2020), and the Franke Graduate Fellowship (2019). She has contributed to developing the CHPLULTRA simulation code for ULDM and pioneered methods to map ancient Egyptian constellations using hieroglyphic sources. Teaching roles include Yale’s Physics Bootcamp and serving as a Teaching Fellow for nine courses. She actively engages in science communication via Astrobites, public lectures, and outreach initiatives like Girls’ Science Investigations. Media highlights include features in the New York Times and podcasts such as H’ad Astra Historia .

Pavel SolinView profile

Professor

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
• +3 more

Pavel Solin is a Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR), within the College of Science. His work focuses on computational mathematics, numerical analysis, and innovative approaches to mathematics and computer science education. He has developed advanced finite element methods (FEM), including hp-FEM and discontinuous Galerkin techniques, with applications in engineering, physics, and porous media modeling. His research also emphasizes integrating computational tools into education, such as self-paced programming courses in Python, SQL, and linear algebra. Dr. Solin is the creator of the Hermes2D library, a C++ framework for adaptive hp-FEM and DG solvers, and has contributed to software for multiphysics simulations. His educational efforts include experimental curricula blending mathematics with programming and STEAM principles. Despite no listed scientific awards, his work has been published in numerous journals, reflecting his expertise in both computational methods and pedagogical innovation. His articles span adaptive methods, numerical simulations of physical phenomena, and educational methodologies. He has collaborated on projects involving electromagnetic stirring, thermal conduction, and fluid dynamics. While no advising or grant details are provided, his research lab and team focus on advancing computational techniques and their practical applications in academia and industry.

Petros VoulgarisView profile

Professor

• Control Systems
• Autonomous Systems
• Cybersecurity
• +5 more

Petros Voulgaris is the Victor LaMar Lockhart Professor and Chair of the Mechanical Engineering Department at the University of Nevada, Reno (UNR), where he also directs the Founding Aerospace Program. He is affiliated with the College of Engineering and leads the Learning Autonomy and Control Systems Laboratory. His work focuses on advanced control methodologies for engineering systems, including autonomous vehicles, cyber-physical systems, and resilient networks. Research Interests: Optimal, robust, and distributed control; networked control systems; autonomous systems cybersecurity; and applications in aerospace, robotics, and power systems. His lab explores cutting-edge solutions for challenges like adversarial attacks on autonomous systems and GPS-denied navigation. Prospective Students: Openings for Ph.D. candidates and postdocs with control systems or decision-making expertise. Contact via pvoulgaris@unr.edu for application details. Labs: Learning Autonomy and Control Systems Laboratory Contact: PE Building 204C | (775) 682-5725

• Battery Systems Design
• Power Electronics
• Electric Vehicles (EVs)
• +6 more

Jorge Varela Barreras is an Honorary Research Fellow at Imperial College London (2023) and an EV and Battery Expert at CTAG since 2021. He holds a PhD in Engineering from Aalborg University (2017) and master’s degrees in Electrical Engineering and Power Electronics. His roles include IEEE Senior Member (2024), Maria Zambrano Fellow at UPC (2022), and former postdoc at Imperial College and the University of Oxford. He has over 15 years of R&D experience in battery systems, EVs, and renewable energy applications. Education: M.Sc. in Electrical Engineering (University of Vigo, 2010), M.Sc. in Power Electronics (Aalborg University, 2009), PhD in Engineering (Aalborg University, 2017). Research interests span design, control, and testing of battery systems, EVs, and energy storage. His work focuses on thermal management, battery degradation, and safety, with contributions to hybrid balancing systems and BMS optimization. Notable awards include IEEE Senior Member (2024), Honorary Research Fellowship (2023), and the STFC Batteries Early Career award (2017). He has co-authored over 40 publications, including 16 journal articles, and supervised 2 PhD students. Advising/Grants: Involved in £80M+ projects, 2 as Principal Investigator. Delivered 3 IEEE tutorials, 19 keynotes, and 20 final-year projects. Editor for eTransportation and World Electric Vehicle Journal . Labs/Teams: Collaborations with The Faraday Institution, Imperial Consultants (ICON), and industry partners like Triple Solar. Visiting researcher at RWTH Aachen, FEUP, and INESC TEC.