Mohsen Ghafouri is an Associate Professor at the Concordia Institute for Information Systems Engineering (Concordia University). His research focuses on cybersecurity, smart grids, and cyber-physical systems with emphasis on securing energy infrastructure against cyber-attacks. Key areas include detection and mitigation of false data injection attacks, grid resilience against load-altering threats, and secure transactive energy markets. Research interests include wide-area monitoring systems (WAMS), microgrid control, and integration of renewable energy sources. He has developed frameworks for real-time anomaly detection in power systems, blockchain-based security solutions, and machine learning approaches for cyber threat identification. His work addresses vulnerabilities in smart grid components like IEC 61850 substations and EV ecosystems. Recent publications (2024-2025) highlight advancements in securing FACTS controllers, EV charging systems, and distributed energy resources. He has proposed novel mitigation strategies using reinforcement learning, graph neural networks, and federated learning. No scientific awards or grant details are provided in the source text. No advising relationships or lab affiliations are explicitly stated.
Babak Falsafi is a Full Professor at the School of Computer and Communication Sciences (IC) at EPFL, leading the Parallel Systems Architecture Laboratory (PARSA). He is a renowned expert in computer architecture, datacenter systems, and cloud-native server design. His research focuses on post-Moore era computing, emphasizing heterogeneous architectures, energy efficiency, and scalable IT infrastructure. Falsafi is the founder of EcoCloud, an EC-sponsored industrial-academic consortium investigating sustainable information technology. He holds ACM and IEEE fellowships, a Sloan Research Fellowship, and has contributed to major projects like Optimus Prime (data transformation acceleration), AstriFlash (flash-based online service systems), and Midgard (virtual memory re-design). His work spans hardware-software co-design, memory systems, and security. Falsafi advises numerous PhD students and collaborates with industry partners such as Google and Cavium. Key achievements include pioneering scalable multiprocessor architectures, snoop filters in IBM BlueGene, and spatial memory streaming in ARM cores. His lab develops open-source tools like QFlex for server simulation. He frequently presents at top conferences (HPCA, ISCA, MICRO) and chairs workshops on post-Moore infrastructure. Teaching roles include leading courses in computer architecture and parallel systems across multiple EPFL departments (SIN, EDIC, SSC, SMA). His work addresses datacenter challenges like the 'data tax' and mitigating latency through specialized accelerators.
Alexander Mathis is an Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Brain Mind Institute (School of Life Sciences). His research bridges computational neuroscience and machine learning to decode sensorimotor behaviors and develop AI tools for behavioral analysis. Pure Mathematics MSc, Ludwig Maximilians University Munich PhD in Computational Neuroscience, Ludwig Maximilians University Munich His work focuses on understanding how the brain generates behavior through computational models and algorithms. Key contributions include the DeepLabCut toolbox, hBehaveMAE , and other frameworks for pose estimation, action segmentation, and brain-inspired AI. His group also explores proprioception, motor control, and neural coding theories. Recent publications include unsupervised hierarchical behavior modeling via masked autoencoders (ECCV 2024), synthetic basketball benchmarks (Shot7M2), and extensions of BABEL into hBABEL. These works highlight his interest in temporal hierarchies in behavior and scalable AI solutions for neuroscience. Scientific Honors Robert Bing Prize (2024) Eric Kandel Young Neuroscientists Prize (2023) Frontiers of Science Award (2023) Marie Skłodowska-Curie Postdoctoral Fellowship Studienstiftung des deutschen Volkes scholarship He advises PhD candidates in neuroscience and life sciences, leads courses on brain-like computation and software engineering for life sciences , and collaborates across AI4Science initiatives. His group actively participates in competitions, such as NeurIPS' MyoChallenge, where brain-inspired reinforcement learning algorithms have won awards.
Lina Bertling Tjernberg is a Professor at the Department of Electrical Engineering, KTH Royal Institute of Technology, and Deputy Head of the School of Electrical Engineering and Computer Science (EECS) with responsibility for research conditions and impact. She served as Director of KTH's Energy Platform during 2018-2024 and holds memberships in IVA (Swedish Royal Academy of Engineering Sciences) and the IEEE Power & Energy Society. Research Focus: Applying mathematics (statistics, optimization, life cycle assessment) to enhance reliability and predictive maintenance in electric power systems, with emphasis on future electricity grids integrating microgrids, battery storage, HVDC, nuclear/pumped/hydro/wind/solar power, hydrogen, and electrified transport. Collaborations: Engaged with Comillas Pontifical University (Madrid), Addis Ababa University, Norwegian University of Science and Technology (NTNU), and IEA Wind. Key Research Trends: Recent articles highlight advancements in microgrid control (2025), SMR nuclear energy integration (2025), AI-driven asset management (2024), hydrogen sector coupling (2024), and renewable forecasting techniques (2024). Awards: 2021 Power Woman of the Year 2022 Energy Power List (Sweden’s top 20 energy influencers) Leadership Roles: Swedish Electromobility Center (SEC) board Chair of Swedish Electrical Standards (SEK Svensk Elstandard) Member, IEEE PES ISGT Europe steering committee
Mark Yatskar is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research focuses on the intersection of natural language processing, computer vision, and fairness in machine learning. He earned his PhD from the University of Washington under advisors Luke Zettlemoyer and Ali Farhadi, and previously worked as a Young Investigator at the Allen Institute for Artificial Intelligence. Education: PhD in Computer Science, University of Washington (Advisor: Luke Zettlemoyer & Ali Farhadi) Research Interests: Yatskar's work explores how language can structure visual perception and mitigate human biases in machine learning systems. Key themes include: Natural language as a scaffold for visual intelligence Bias characterization and control in machine learning systems His lab currently investigates projects like language-guided bottlenecks, annotator cognitive heuristics, and gender bias amplification. Teaching: CIS 5300: Computational Linguistics (2021-2024) CIS 7000: Language and Vision (2020) CIS 6300: Efficient NLP (2023, 2025) Awards: Best Paper Award at EMNLP (Gender Bias Amplification Research) Advising & Grants: Yatskar advises a team of PhD/Master's students and actively seeks motivated researchers. His group has explored funding in areas like interpretable AI, multimodal reasoning, and dataset bias mitigation. Labs/Teams: Leads the Penn NLP & Vision Lab, focusing on projects like MolMo/PixMo open models, ViUniT visual unit tests, and bias mitigation frameworks.
Javier Alonso-Mora is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Learning & Autonomous Control. His research focuses on autonomous systems, robotics, motion planning, and transportation logistics, with applications in mobile manipulation, dynamic environments, and urban mobility. He leads key projects such as INTERACT (Intuitive Interaction for Robots among Humans) and ACT (Perceptive Acting Under Uncertainty), exploring human-robot interaction, autonomous vehicles, and healthcare robotics. Notable achievements include an ERC Starting Grant (2022) and a Veni Grant (2017). His work addresses challenges in robot navigation, control systems, and fleet optimization, with contributions to both theoretical advancements and practical implementations. Projects like TRiLOGy focus on sustainable water transportation, while HARMONY advances assistive robotics in healthcare. Alonso-Mora’s research leverages geometric fabrics for motion planning, probabilistic modeling for dynamic environments, and multi-agent coordination. He collaborates internationally and contributes to open-source frameworks for robotics. His recent publications emphasize safety-aware control, instance-aware semantic mapping, and adaptive systems for cluttered environments.
Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.
Patrick Slade is an Assistant Professor of Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS). His lab, the Slade Lab, focuses on developing assistive devices to enhance mobility through the integration of biomechanics, robotics, and human-centered artificial intelligence. Key research areas include exoskeletons, prosthetics, wearable sensors for health tracking, and navigation aids for visually impaired individuals. Research Interests: The lab emphasizes translating research into practical solutions, such as personalized exoskeletons and robotic systems to improve mobility. Recent work includes optimizing human-robot interaction algorithms and publishing in high-impact journals like Nature . Collaborations with labs like the Biodesign Lab and BIONICs Lab highlight cross-disciplinary efforts. Publications: Over 15 articles since 2017 span topics like exoskeleton design, energy expenditure modeling, and Bayesian reinforcement learning. Notable contributions include a 2022 Nature paper on personalized exoskeleton assistance and a 2021 study on navigation aids for impaired vision. Awards & Grants: Students in his group have received prestigious NSF GRFP fellowships and conference awards, reflecting the lab's emphasis on innovation. The lab actively engages in grant-funded projects to advance assistive technology. Lab & Team: The Slade Lab opened at Harvard in 2023 and includes PhD students and postdocs working on devices like robotic exoskeletons and health-tracking systems. Future work focuses on scalable solutions for mobility challenges through interdisciplinary approaches.
Catherine Pinel is a Research Director (DR2) at the Institute for Research on Catalysis and Environment of Lyon (IRCELYON, UMR 5256), a joint research unit of the French National Center for Scientific Research (CNRS) and Claude Bernard University Lyon 1. She has held this senior research position since October 2008, following progression from CR1 (1998-2008) and CR2 (1994-1998) roles at the same institution. Her academic journey includes postdoctoral research at Cambridge University (1992-1993) under Professor S.V. Ley and with Professor M. Lemaire (1993-1994). Her educational foundation includes: Diplôme Universitaire de Technologie in Chemistry from Paris XI University (1986) Engineering Degree from École Nationale Supérieure de Chimie de Paris (1989) Advanced Studies Diploma in Organic Chemistry from Paris VI University (1989) PhD in Organic Chemistry from Paris VI University (1992) on chiral ruthenium complexes and enantioselective reductions Habilitation à diriger des recherches from Lyon I University (1999) on catalysis and fine chemistry Dr. Pinel's research pioneers sustainable catalytic processes with emphasis on biomass valorization, green chemistry, and heterogeneous catalysis. Her work bridges fundamental catalyst design with industrial applications, particularly in hydrogenation, oxidation, and biorefinery processes. Current focus areas include catalytic conversion of biomass-derived platform molecules (glucose, succinic acid, polyols), development of bimetallic catalysts, and valorization of hemicellulose streams. Her approach integrates advanced catalyst characterization with reaction engineering to create environmentally benign chemical transformations. Analysis of her recent publication trajectory (2019-2025) reveals evolving expertise from fundamental organometallic chemistry toward applied sustainable catalysis. Key trends include increasing focus on biomass-derived feedstocks (glucose, succinic acid, polyols), development of metal-carbide/nitride catalysts, and optimization of aqueous-phase reactions for industrial biorefineries. Her work demonstrates strong interdisciplinary collaboration across catalysis, materials science, and green chemistry, with growing emphasis on circular economy principles and renewable chemical production. No scientific awards were explicitly mentioned in the source materials. Dr. Pinel actively contributes to academic training through graduate instruction at University of Lyon (Master 2 'Catalysis and Physical Chemistry' since 2007) and University of Savoie (M2 courses in New Catalysts in Organic Chemistry since 1999 and Coordination Chemistry since 2012). While specific grant details aren't provided, her extensive publication record across high-impact journals indicates sustained research funding. She maintains collaborative networks within IRCELYON and internationally, particularly in biomass conversion and catalyst characterization. As a core researcher at IRCELYON (celebrating 60 years of catalysis research in 2021), she operates within a world-class facility housing specialized equipment for catalyst synthesis, characterization (including in situ techniques), and testing. Her work aligns with the institute's focus on sustainable catalysis for energy transition, air/water depollution, and biomass valorization, contributing to France's strategic research priorities in green chemistry.
Richard Franz Löscher is a researcher at the Institute of Applied Mathematics specializing in numerical methods for optimal control problems. He holds a BSc, Diplom-Ingenieur (Dipl.-Ing.), and Doctorate in Technical Sciences (Dr.techn.), demonstrating extensive technical education and engineering expertise. His educational qualifications include: BSc Diploma in Engineering (Dipl.-Ing.) Doctorate in Technical Sciences (Dr.techn.) Löscher's research centers on numerical mathematics with deep specialization in finite element methodologies for distributed optimal control problems governed by partial differential equations. His work addresses elliptic, parabolic, and hyperbolic control systems through regularization techniques, adaptive mesh refinement, and robust solver development. Key innovations include variable energy regularization frameworks, mass-lumping discretizations, and complexity-optimal space-time finite element systems that significantly advance computational efficiency and error estimation in constrained control environments. Analysis of his 2024-2026 publications reveals a cohesive research trajectory focused on overcoming computational bottlenecks in optimal control. His work consistently bridges theoretical rigor with practical implementation, emphasizing discretization schemes that balance accuracy and computational cost while addressing state/control constraints across diverse PDE systems. No scientific awards or honors were documented in available sources. Löscher maintains active research collaborations with prominent figures including Ulrich Langer and Olaf Steinbach, evidenced by co-authored publications and peer-review activities for journals like Journal of Computational and Applied Mathematics. His external research engagement includes a July 2024 visit to Delft University of Technology, highlighting international academic partnerships.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University, directing the Interactive Perception and Robot Learning Lab. Previously, she was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems (2012-2017). She holds a PhD from KTH Royal Institute of Technology (Stockholm) and degrees from Chalmers University and TU Dresden. Her research focuses on perception, learning, and real-time multi-modal methods for autonomous robotic manipulation and grasping, aiming to bridge principles of human sensorimotor coordination with robotic implementation. Education: PhD in Robotics (KTH), MSc in Art & Technology (Chalmers), Diploma in Computer Science (TU Dresden) Research interests include developing goal-directed, real-time robotic systems capable of meaningful feedback for execution and learning. Key areas are dexterous manipulation, imitation learning, and cross-embodiment policy transfer. Notable contributions include the TidyBot platform and work on force-aware surgical robotics. Awards include the 2019 IEEE ICRA Best Paper Award, 2019 IEEE RA Early Career Award, and 2020 RSS Early Career Award. Her lab explores intersections of robotics, ML, and computer vision. Advising: Actively mentoring students/postdocs in manipulation, perception, and learning. Grants and collaborations span NSF, Stanford AI Lab, and industry partnerships. Future work emphasizes robust real-world deployment and human-robot collaboration. Labs/Teams: Leads the Interactive Perception and Robot Learning Lab, contributing to Stanford’s AI ecosystem. Previously managed the MPI AMD group, fostering interdisciplinary research in autonomous systems.
Yao Yang is an Assistant Professor in the Department of Chemistry and Chemical Biology at Cornell University's College of Arts and Sciences. His research focuses on developing multimodal operando electron microscopy and synchrotron X-ray methods to probe electrochemical dynamics at solid-liquid interfaces for energy materials. PhD, Cornell University (2021) Miller Postdoctoral Fellow, UC Berkeley (2021-2024) Research interests span fundamental electrochemistry and energy material interfaces, particularly CO2 reduction, clean H2 production, and rechargeable batteries. The Yang group specializes in operando electrochemical liquid-cell scanning transmission electron microscopy (EC-STEM) and correlative synchrotron X-ray methods at Cornell Center for Materials Research (CCMR) and Cornell High Energy Synchrotron Source (CHESS). Recent publications highlight atomic-scale imaging of catalyst dynamics, Tafel slope analysis, and epitaxial growth techniques for enhanced electrocatalysts. Articles demonstrate interdisciplinary approaches combining electrochemistry, nanoscience, and advanced characterization. Scientific Awards: 2025 ACS Materials and Interfaces Outstanding Presentations by Young Investigators Award 2024 Journal of Materials Research Distinguished Invited Speaker Miller Postdoctoral Fellowship (2021-2024) 2023 Best Early Career Presentation at MRS Spring 2022 ACS AC/DC Rising Stars in Analytical Chemistry Contact: yaoyang@cornell.edu
Virginia Leavell serves as Assistant Professor in Organisational Theory and Information Systems at Cambridge Judge Business School, University of Cambridge. Her research investigates how organisations anticipate technological change through digital prediction systems, AI, and infrastructure transformation, drawing on her decade-long background in political organising for non-profits and labour groups across the US and Thailand. Her academic credentials include: BA in Interdisciplinary Studies, Georgetown University MA in Sociology, University of California Santa Barbara PhD in Technology Management, University of California Santa Barbara Leavell's scholarly work centers on organisational anticipation of technological futures, employing ethnographic and social network analysis methods to examine digital transformation in critical infrastructure contexts. She explores how predictive technologies reshape organisational structures and actions before technological change fully materializes, with particular focus on water systems, urban planning, and labor industries where digital twins and simulation technologies create new governance challenges. Analysis of her 2017-2024 publications reveals consistent examination of prediction performativity across sectors, demonstrating how digital models influence physical realities in infrastructure management. Her research trajectory shows increasing emphasis on sociomaterial dimensions of anticipatory organising, with recent work interrogating the equalization of human and technological agency in digital transformation processes. No scientific awards were specified in source materials. Leavell actively contributes to the Organisational Theory and Information Systems subject group at Cambridge Judge Business School, which engages cross-disciplinary leadership themes. Information regarding student advising and research grants remains unspecified in available documentation.
Nima Mesgarani is an Associate Professor of Electrical Engineering at Columbia Engineering, Columbia University, affiliated with the Sense, Collect and Move Data Committee. His research bridges engineering and neuroscience through reverse-engineering neural signal processing mechanisms, leading to advancements in brain-machine interfaces, neural prosthetics, and speech processing algorithms. He received his PhD in Electrical Engineering from the University of Maryland and completed postdoctoral training at Johns Hopkins University's Center for Language and Speech Processing and UC San Francisco's Neurosurgery Department. Research Focus Professor Mesgarani's lab integrates computational neuroscience and engineering to study acoustic signal processing. Key areas include: Neural decoding of speech and auditory attention in multi-talker environments Development of brain-controlled hearing technologies Novel speech separation and synthesis algorithms inspired by cortical processing Cross-modal learning between auditory and visual systems Applications of large language models in neural signal interpretation Publication Trends Analysis of his 15 most recent articles (2025) reveals dominant themes: neural decoding techniques using intracranial EEG, brain-inspired speech separation models (e.g., Mamba architectures), applications of large language models in auditory neuroscience, cross-modal distillation methods, and clinical translation of audio processing algorithms. A strong emphasis emerges on real-time brain-computer interfaces and noise-robust speech processing. Laboratory and Collaborations Mesgarani directs an interdisciplinary lab developing neurotechnology for hearing restoration. His team collaborates with neurosurgery departments and speech processing centers, focusing on translating theoretical models into clinical brain-machine interfaces. The lab's work has yielded patents for brain-informed speech separation systems and attention-decoding frameworks.
Steven Rogak is a Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science. He holds a P.Eng. license and degrees including a B.A.Sc. in Mechanical Engineering from UBC, and M.Sc. and Ph.D. from Caltech. P.Eng., University of British Columbia B.A.Sc., University of British Columbia M.Sc., Ph.D., California Institute of Technology His research focuses on aerosol science, particularly solid nanoparticles from combustion processes, their climate and health impacts, and mitigation strategies. Key areas include: Soot morphology and transport properties Engine emission reduction via fuel injectors Indoor air filtration systems Membrane-based energy exchangers Atmospheric particulate analysis The 15 most recent articles span experimental and theoretical studies on soot characterization, membrane technologies, and aerosol dynamics, with applications in climate modeling, healthcare ventilation, and sustainable materials. Collaborations include Westport Innovations and interdisciplinary teams. Rogak leads the Aerosol Laboratory at UBC, where he applies fluid mechanics and heat transfer fundamentals to address environmental and health challenges. He emphasizes experimental rigor and welcomes graduate students with expertise in these areas.
