جست‌وجوی استاد و پژوهشگر

• Logic
• Epistemology
• Quantum Logic
• +۷ مورد دیگر

Sonja Smets is a full professor of Logic and Epistemology at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, holding a double affiliation between the Faculty of Science and the Faculty of Humanities (Department of Philosophy), with each faculty accounting for 50% of her employment. She joined the ILLC in 2012 and served as its scientific director from June 2016 until July 2021. Previously, she held a Rosalind Franklin Fellowship at the University of Groningen (2008-2012) and conducted postdoctoral research at the Center for Logic and Philosophy of Science in Brussels. Her research spans multiple domains at the intersection of logic, philosophy, and computer science. Smets has made significant contributions to quantum logic, developing logical frameworks for understanding quantum information and computation. She has pioneered work in dynamic epistemic logic applied to social networks, belief revision, and information flow. Her recent work increasingly addresses the philosophical and logical foundations of artificial intelligence, particularly focusing on AI ethics, fairness, and the societal impact of algorithmic decision-making systems. Smets has received numerous prestigious awards recognizing her interdisciplinary contributions, including the Hermann Lotze Prize (2015) and the Birkhoff-Von Neumann Prize (2012). She has secured major research funding through a VIDI award from the Netherlands Science Foundation (2009) and an ERC starters grant for the LogiCIC project (2011-2016). Currently, she serves as co-PI of the University-wide Research Priority Area on Human(e) AI (2019-2023) at the University of Amsterdam, which examines societal consequences of AI development. Corresponding member of Academie Internationale de Philosophie des Sciences (2021) Fellow of Asia-Pacific Artificial Intelligence Association (2021) Member of Academia Europaea (2019) Hermann Lotze Prize (2015) Birkhoff-Von Neumann Prize (2012) Rosalind Franklin Fellowship VIDI award from Netherlands Science Foundation ERC starters grant Smets has been instrumental in building research communities, co-organizing numerous workshops and conferences including the LogiCIC workshops, Quantum Logic workshops, and recent events on Reasoning about Responsible Agency in AI. She serves on multiple editorial boards including the Journal of Logic and Computation, Synthese, and Foundations of Science, and has held leadership positions in professional organizations such as FoLLI (Association for Logic, Language and Information) where she serves as vice-president. Her research group at the ILLC bridges formal logic with practical applications in AI, quantum computing, and social network analysis.

Rupak Majumdarمشاهده پروفایل

پژوهشگر

• Formal Verification
• Computer-Aided Verification
• Reactive Systems
• +۱۰ مورد دیگر

Rupak Majumdar is a Scientific Director at the Max Planck Institute for Software Systems (MPI-SWS), with a distinguished career in formal verification, control systems, and programming languages. His research spans reactive, real-time, hybrid, and probabilistic systems, focusing on verification and synthesis problems in distributed and concurrent environments. Majumdar earned his B.Tech. in Computer Science from IIT Kanpur (where he received the President’s Gold Medal) and his Ph.D. from UC Berkeley (awarded the Leon O. Chua Award). His work has been recognized through prestigious honors including an NSF CAREER Award, Sloan Fellowship, ERC Synergy Grant, and Most Influential Paper Awards from PLDI and POPL. Research Areas: Formal Verification, Hybrid Systems, Stochastic Systems, Programming Languages, Automata Theory Leadership: Scientific Director, MPI-SWS; ISEC 2026 PC Chair His publications address critical problems in software verification, with recent work focusing on probabilistic systems, distributed protocol testing, and reinforcement learning for formal methods. Awards and grants reflect his impact on computer science and software engineering. Current students include Mahmoud Salamati, Ashwani Anand, V.R. Sathiyanarayana, and Mohammad Khoshechin, while graduated advisees hold positions at institutions like Amazon, Google, and academic research centers. Scientific Awards: President’s Gold Medal (IIT Kanpur) Leon O. Chua Award (UC Berkeley) NSF CAREER Award Sloan Foundation Fellowship ERC Synergy Award Distinguished Alumnus Award (IIT Kanpur) Most Influential Paper Awards (PLDI, POPL) Best Paper Awards (SIGBED, EAPLS, SIGDA)

Mor Naamanمشاهده پروفایل

استاد

• AI-Mediated Communication
• Computational Social Science
• Data Science
• +۵ مورد دیگر

Mor Naaman is the Don and Mibs Follett Professor of Information Science at Cornell Tech, where he leads the Social Technologies research group and serves as Associate Dean for Faculty Affairs at the Jacobs Technion-Cornell Institute. His career spans academia, industry research (Yahoo! Research Berkeley), and entrepreneurship (startup co-founder). Cornell Tech, 2012-present Rutgers School of Communication and Information, 2008-2012 Yahoo! Research Berkeley, 2005-2008 Stanford University, Ph.D. in Computer Science His research focuses on Technology, Media and Democracy , particularly AI-mediated communication's societal impact and information ecosystem trustworthiness. The lab employs multidisciplinary methods (machine learning, experiments, qualitative analysis) to address misinformation, algorithmic bias, and digital governance challenges. Recent work examines AI-generated content moderation Visual misinformation tracking Language model cultural bias Workplace AI communication dynamics Political misinformation campaigns Notable scientific awards include NSF Early Faculty CAREER Award Microsoft Research Award LinkedIn-Bowers CIS Strategic Partnership award Vannevar Bush Best Paper Awards (JCDL 2007, JCDL 2004) Best Paper Honorable Mentions (CHI 2023, CHI 2016) As an advisor , he has mentored numerous PhD students in areas like computational social science and digital democracy. His research is supported by grants from National Science Foundation Microsoft Meta (Facebook) Google The Social Technologies Lab (founded 2012) examines AI-Mediated Communication implications Healthy information ecosystems Trust, credibility and misinformation Collaborative fact-checking systems

Atılım Güneş Baydinمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Generative Modeling
• Probabilistic Programming
• +۴ مورد دیگر

Dr. Atılım Güneş Baydin is an Assistant Professor in Machine Learning at the University of Oxford , where he leads the Oxford AI for Science Lab . He holds dual appointments in the Department of Computer Science and Department of Engineering Science , and is affiliated with Jesus College , Kellogg College , and the European Lab for Learning and Intelligent Systems (ELLIS) . Academic Roles : Departmental Lecturer (Assistant Professor) in Machine Learning, Senior Researcher in Engineering Science Research Groups : Torr Vision Group, Oxford Applied and Theoretical Machine Learning Group (OATML) His research focuses on probabilistic programming , differentiable programming , and generative modeling , with applications in scientific discovery and space missions. He develops methods for large-scale simulator inference and deep learning for space weather , collaborating with NASA , ESA , and Lawrence Berkeley National Lab . Notable Contributions : Co-organized 7+ NeurIPS workshops on ML for Physical Sciences Led the development of PyProb and DiffSharp probabilistic programming frameworks Served on editorial board of ACM Transactions on AI for Science

Tamir Hazanمشاهده پروفایل

دانشیار

• Machine Learning
• Theoretical Machine Learning
• Computer Vision
• +۶ مورد دیگر

Associate Professor Tamir Hazan is a faculty member at Technion - Israel Institute of Technology, where he joined in 2015. His research focuses on theoretical and practical aspects of machine learning, with applications spanning computer vision, natural language processing, and computational biology. His work bridges mathematical foundations with real-world problem solving in complex systems. Professor Hazan received his Ph.D. from the Hebrew University in 2009. His academic trajectory has established him as a leading researcher in machine learning theory and its applications, with a particular emphasis on developing mathematically rigorous approaches to modern AI challenges. Professor Hazan's research centers on mathematically founded solutions to problems demonstrating non-traditional statistical behavior. His work encompasses perturbation models for efficient learning of high-dimensional statistics, deep learning of infinite networks, and primal-dual optimization for high-dimensional inference problems. His research program spans three major interconnected areas: attention models that improve prediction interpretability, perturbation frameworks that integrate optimization and sampling through extreme value statistics, and convex duality approaches to message-passing in graphical models. His work demonstrates both theoretical depth and practical relevance across multiple domains. Analysis of Professor Hazan's recent publications reveals an evolving research trajectory with increasing emphasis on interpretable machine learning, causal modeling, and applications in medical imaging and behavioral science. His work consistently bridges theoretical foundations with practical implementations, with recent publications showing strong connections between perturbation theory, attention mechanisms, and optimization frameworks. The interdisciplinary nature of his research is evident in applications ranging from pedestrian navigation using smartphone sensors to video-text matching systems and medical image analysis. Professor Hazan has mentored numerous students throughout his career, including: Alex Schwing, now Assistant Professor at UIUC Alon Cohen, now Associate Professor at Tel Aviv University Idan Schwartz, currently Postdoc at Tel Aviv University Current Ph.D. students: Guy Lorberbom, Itai Gat, and Hedda Cohen Multiple M.Sc. students including Adi Manos, Ram Yazdi, and others Professor Hazan's research group maintains an active program with several key focus areas: Attention models for interpretable and improved prediction processes in visual question answering and multimodal applications Perturbation models that enable efficient statistical reasoning in complex systems with exponential configuration spaces Markov random fields, convex duality, and message-passing algorithms for structured prediction and distributed computing

Laura Cantiniمشاهده پروفایل

پژوهشگر

• Single-cell genomics
• Multi-omics data integration
• Machine learning in biological systems
• +۳ مورد دیگر

Laura Cantini is a CNRS tenured Researcher and G5 junior group leader at Institut Pasteur , Paris, with a Chair position at Institut Prairie . She specializes in machine learning for integrative genomics , focusing on single-cell multi-omics data integration and AI-driven biological network inference . Her work addresses challenges in rare disease modeling, cancer systems biology, and spatial transcriptomics analysis. Academic Roles : G5 group leader (Institut Pasteur, 2023-), Chair (Institut Prairie, 2021-), CNRS tenured researcher (2018-) Research Themes : Foundation models, Optimal Transport, Molecular network reconstruction, Cellular heterogeneity, Multi-scale biology Her recent publications highlight Nature Communications and iScience contributions in single-cell integration (scConfluence, scPRINT), T cell immunology, and spatial transcriptomics. She organizes workshops on AI in Structural Biology , Genomics , and Neurosciences at Institut Pasteur. Awards include the CNRS Bronze Medal (2024) , Sanofi iTech Award (2020) , and L’Oréal-UNESCO Fellowship (2018) . Grants : ERC StG Multiview-CELL (2024), PEPR CELL-ID (2024), ANR JCJC scMOmix (2020) Leadership : PI of machine learning for Integrative genomics lab (Institut Pasteur), key organizer of AI symposiums at Institut Pasteur

Lorenzo Seidenariمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Generative AI
• +۴ مورد دیگر

Lorenzo Seidenari is an Associate Professor of Computer Engineering at the Department of Information Engineering, University of Florence . He earned his PhD in Computer Engineering (2012) and MEng in Artificial Intelligence (2008) from the University of Florence. As a member of the UNIMORE ELLIS Unit, he contributes to European AI excellence, and co-founded the startup Small Pixels for video processing solutions. PhD in Computer Engineering, University of Florence (2012) MEng in Artificial Intelligence, University of Florence (2008) His research focuses on deep learning applications in computer vision and robotics , particularly generative AI , model uncertainty , and robustness . Recent work includes trajectory forecasting for autonomous systems, 360° video streaming optimization, and synthetic data generation. Key projects like WILD.AI and IMRA-SD address real-world challenges in biodiversity monitoring and multi-agent prediction. Recent publications highlight trends in generative adversarial networks (GANs) , spatio-temporal modeling , and autonomous driving applications. He has received the ELLIS Scholar award and holds habilitations from MIUR for associate professorship in both Computer Science (01/B1) and Computer Engineering (09/H1). As a conference area chair for ACM MM and BMVC, he actively shapes AI research discourse.

• Computer Vision
• Natural Language Processing
• Sign Language Technologies
• +۵ مورد دیگر

Gül Varol is a permanent researcher (equivalent to Associate Professor) at École des Ponts ParisTech, where she is part of the IMAGINE group within the Laboratoire d'Informatique Gaspard-Monge (LIGM). She holds additional affiliations as an ELLIS Scholar and a Guest Scientist at the Max Planck Institute (MPI). Her academic journey includes a postdoctoral position at the University of Oxford's Visual Geometry Group (VGG) under Andrew Zisserman, and a PhD from the WILLOW team at Inria Paris and École Normale Supérieure. Her educational background includes BS and MS degrees from Boğaziçi University, followed by doctoral studies at Inria Paris and École Normale Supérieure. During her PhD, she spent research periods at MPI, Adobe, and Google, gaining diverse industry and academic experience. Dr. Varol's research focuses on the intersection of computer vision and language processing, with particular expertise in video representation learning, human motion synthesis, and sign language technologies. Her work bridges theoretical advances with practical applications, especially in accessibility technologies like audio description for visually impaired audiences and sign language recognition systems. She has pioneered approaches for text-driven 3D human motion generation, sign language translation with contextual cues, and film-grammar-aware audio description generation. Her research often leverages large language models and vision-language models to solve complex multimodal problems without requiring extensive training data. Analysis of her recent publications reveals a clear trajectory toward more sophisticated multimodal systems that integrate temporal understanding, contextual awareness, and fine-grained control. Her work increasingly focuses on practical applications in accessibility, with significant contributions to sign language technologies and audio description systems. She has demonstrated leadership in developing large-scale datasets like BOBSL (BBC-Oxford British Sign Language Dataset) and advancing methods for zero-shot and training-free approaches that can be deployed without extensive fine-tuning. Her scientific achievements have been recognized with several prestigious awards: ELLIS 2020 PhD Award AFRIF 2019 PhD thesis award from the French association for pattern recognition Google Research Scholar award (2023) Best application paper award at ACCV'20 Dr. Varol has received significant research funding including an ANR JCJC project "CorVis" and research gifts from Google and Adobe. She actively mentors students and researchers, with prospective students encouraged to apply through her lab's application form. Her leadership in the academic community is evident through her service as Program Chair for ECCV'24, Senior Area Chair for CVPR 2025, and associate editor for the International Journal of Computer Vision (IJCV). She is a key member of the IMAGINE research group at École des Ponts ParisTech, which focuses on image analysis and computer vision. Her collaborative work extends to multiple institutions including MPI, Oxford, and various industry research labs. She has co-organized numerous workshops on specialized topics including sign language recognition, vision transformers, and foundation models for 3D humans, fostering community building in these emerging research areas.

Mihai Duguleanăمشاهده پروفایل

استاد

• Smart mobility
• Autonomous vehicles
• Robotics
• +۱۰ مورد دیگر

Mihai Duguleană is a Professor at the Department of Automotive and Transport Engineering, Transilvania University of Brasov , Faculty of Mechanical Engineering. His research spans interdisciplinary domains including robotics, smart mobility, and digital cultural heritage preservation. Key research areas include: AI and neural networks for autonomous vehicle navigation Blockchain applications in augmented reality and property rights EOG-based bio-potential interfaces for robotics Digital transformation of cultural heritage Economic modeling in technological contexts Recent publications demonstrate a trajectory from foundational robotics research (2012 reinforcement learning for manipulators) to emerging NFT-based AR solutions (2021) and virtual assistant development (2020), with consistent focus on AI integration and multimodal interaction.

Zoran Tomljanovićمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Damping Optimization in Mechanical Systems
• Control Theory
• +۱ مورد دیگر

Zoran Tomljanović is an Associate Professor and Vice-Dean for Teaching and Students at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His research focuses on numerical linear algebra, damping optimization in mechanical systems, control theory, and matrix equations, with significant contributions to model reduction and parametric system optimization. His work includes novel approaches to $H_{\infty}$ norm analysis for multi-agent systems, dimension reduction techniques for damped vibrational systems, and parametric dominant pole algorithms for semi-active damping optimization. These methods have been applied to problems in mechanical engineering, synchronization, and high-dimensional data partitioning. Key projects led by Tomljanović include Accelerated solution of optimal damping problems (DAAD 2021–2022), Vibration Reduction in Mechanical Systems (Croatian Science Foundation 2020–2023), and Robustness optimization of damped mechanical systems (DAAD 2017–2018). He has also contributed to educational initiatives through publications like Metode optimizacije (2014), a textbook on optimization methods. Professional activities include co-organizing the 8th Croatian Mathematical Congress (2024), Winter School on Model Reduction (2024), and multiple international workshops on optimal control and model reduction. Teaching responsibilities include courses on linear algebra and control theory applications.

Nicolas Audebertمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Representation Learning
• +۵ مورد دیگر

Nicolas Audebert is a Computer Vision and Machine Learning researcher working as a junior research director at the French National Institute of Geographic and Forest Information (IGN) in the LASTIG laboratory, STRUDEL team. He is currently on leave from his position as Associate Professor of Computer Science at the Conservatoire national des arts et métiers (Cnam) where he was part of the Vertigo team. His research spans computer vision, machine learning, and Earth Observation with applications in remote sensing and video games. Dr. Audebert earned his PhD in Computer Science from ONERA and IRISA in 2018, followed by an MEng in Computer Science from Supélec and an MSc in Human-Computer Interaction from Université Paris-Sud in 2015. In May 2025, he successfully defended his habilitation à diriger des recherches (HDR) titled "Learning representations from observations". His research focuses on representation learning , where he develops methods to create abstract representations of raw data that allow computers to manipulate high-level concepts numerically. In Earth Observation , he processes and makes sense of large volumes of satellite data for land cover mapping, change detection, and image interpretation. His work in machine learning for games explores how to use reinforcement learning to generate diverse and challenging AI in video games. Additional research interests include generative models, multimodal learning, and domain adaptation techniques. His recent publications demonstrate expertise in cross-sensor learning, super-resolution of satellite imagery, diffusion models for Earth Observation, and robust image retrieval systems. His work bridges theoretical advances in deep learning with practical applications in geospatial analysis, with a particular focus on developing methods that work across different sensor types and environmental conditions. Outstanding Reviewer for ECCV 2024 Outstanding Reviewer for BMVC 2021 Outstanding Reviewer for ICCV 2021 Best Benchmarking Contribution Award at GEOBIA 2016 2nd best student paper award at JURSE 2017 Google Research Scholar Program gift Dr. Audebert currently advises four PhD students: Maxime Merizette (semantic segmentation of 3D point clouds), Georges Le Bellier (domain adaptation for Earth Observation), Léo Géré (generative models for music), and Aimi Okabayashi (super-resolution of satellite image time series). He has successfully supervised two PhD students to completion: Perla Doubinsky (controlling generative models) and Elias Ramzi (robust image retrieval), whose thesis won the AFRIF PhD award 2024. He has mentored numerous MSc students on diverse topics including flood detection, procedural generation of video game levels, and deep learning for communication systems. He leads the MAGE project (2022-2026), funded by the Agence Nationale de la Recherche, which investigates using procedural generation and modern rendering engines to create labeled synthetic data for Earth Observation models, particularly for disaster mapping applications. He also leads the SESURE project (2021-2023) focused on super-resolution of Sentinel-2 time series, and previously led the RL-Games project (2020-2022) exploring reinforcement learning applications for video games.

Fan Wangمشاهده پروفایل

استاد مدعو

• Neuroscience
• Neural Circuits
• Pain Research
• +۴ مورد دیگر

Fan Wang is an Adjunct Professor in the Department of Neurobiology at Duke University since 2021. She is also a Faculty Network Member of the Duke Institute for Brain Sciences (2008–Present) and an Affiliate of the Duke Regeneration Center (2021–Present). Her research focuses on the neural circuit basis of sensory perception, including active touch sensation , pain processing , and anesthesia-induced unconsciousness . Using genetic, viral, electrophysiological, and in vivo imaging techniques, her lab investigates tactile motor control, pain suppression mechanisms, and consciousness modulation. Ph.D., Columbia University (1998) Research trends in recent publications span neural circuit mapping , AI-driven drug design , and medical imaging innovations . Key areas include anesthesia mechanisms , pain suppression pathways , 3D dental modeling , and GABAergic neuron function in the brain stem. Collaborative work extends to computational toxicology , graph neural networks , and green pesticide design using machine learning. Scientific recognition includes the AAAS Fellowship (2014) and the Sloan Research Fellowship in Neuroscience (2004). Media coverage highlights breakthroughs such as the discovery of a potent pain-suppression center in the brain (2020) and advancements in cortical parcellation (2022). Her grants include multiple National Institutes of Health awards (1997–2022) and support from the W.M. Keck Foundation (2016–2018). Labs and teams at Duke, including the Duke Institute for Brain Sciences , collaborate on projects spanning neuroanatomy , computational biology , and environmental toxicology . Current work integrates AI tools for cortical parcellation, protein-ligand affinity prediction , and multi-agent reinforcement learning for biomedical applications.

• Medical Physics
• Radiation Oncology
• Brachytherapy
• +۵ مورد دیگر

Emily Draeger is an Assistant Professor of Therapeutic Radiology at Yale School of Medicine , with a concurrent role as a Medical Physicist at Yale-New Haven Hospital. She holds a PhD in Experimental High Energy/Nuclear Physics from Illinois Institute of Technology and completed a postdoctoral fellowship at the University of Maryland, focusing on prompt gamma proton range verification, which resulted in a patent and multiple awards. Research Interests include Brachytherapy applications, particularly modulated shielding (MOSH) for cervical cancer Stereotactic radiosurgery (Gamma Knife) with inverse planning Monte Carlo dose modeling for radiation therapy Portal dosimetry and AAPM TG-218 compliance Scientific Awards Abell Foundation Award for Top Biotechnology Innovation (2016) AAPM Science Council Award for Top Abstract (2016) Best Oral Presentation Award from COMP/CARO/CAMRT (2018) Professional Activities span membership in the Radiation Research Society, editorial roles at journals like Journal of Applied Clinical Medical Physics , and leadership in organizations such as the Connecticut Area Medical Physics Society. Her article trends emphasize dosimetry, brachytherapy innovation, and proton therapy verification using Compton cameras.

Daniel Gutzmannمشاهده پروفایل

استاد

• Semantics
• Pragmatics
• Linguistics
• +۳ مورد دیگر

Professor Daniel Gutzmann, holder of the Heisenberg Professorship in "German Linguistics: German Language System" at Ruhr University Bochum since 2022, specializes in semantics and pragmatics with a focus on expressive language and conversational context. His work explores how linguistic meaning is shaped by context, including non-verbal cues and multi-party interactions. PhD from Goethe University Frankfurt am Main (2013) Habilitation at University of Cologne (2018) Master's in German philology, philosophy, and Indology from Johannes Gutenberg University Mainz (2008) Key research areas include: Formal semantics/pragmatics of Germanic languages Expressive modifiers and their syntactic anomalies Contextual modeling in multi-party conversations Pragmaticalization and use-conditional meaning His publications span topics from punctuation as pragmatic markers to multidimensional semantics. Current projects combine formal linguistic frameworks with discourse/conversational analysis to model contextual dynamics. Scientific recognition includes: Heisenberg Professorship (2022) for outstanding researchers preparing for leadership roles

Rebecca Toddمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Human Cognition and Emotion
• Neurophenomenology
• +۶ مورد دیگر

Rebecca Todd is an Associate Professor in UBC's Department of Psychology within the Faculty of Arts, and a member of the Centre for Brain Health. She specializes in cognitive neuroscience with a focus on the interaction between emotion and cognition in health and psychopathology. Her research employs brain imaging methods and laboratory experiments to investigate how we process affective salience and how this influences perception, learning, and memory. Dr. Todd earned her PhD in Developmental Science and Neuroscience from the University of Toronto in 2008. Prior to her academic career in neuroscience, she was a contemporary dance choreographer and journalist, which has informed her unique interdisciplinary approach to studying cognition through movement and interaction. Her research program focuses on neurocognitive processes underlying the interaction between human emotion and cognition. She investigates how we process affective salience (emotional/motivational importance) of objects and events, and how this influences perception, learning, and memory. Her work examines individual differences in how we filter the world to perceive specific categories of salient events (threatening vs. rewarding), how such filters develop over time, and their consequences for emotional health. Specific programs include investigation of neurocognitive processes underlying effects of acute stress on attention, learning, and memory, and attentional biases predicting treatment outcomes in depression. Dr. Todd's recent publications show an evolving research trajectory that increasingly integrates participatory sensemaking, ecological approaches to cognition, and multidisciplinary perspectives. Her work spans cognitive neuroscience, clinical applications, dance cognition, and embodied approaches to understanding how we interact with our environment. The breadth of her research demonstrates a commitment to understanding cognition as embedded in real-world contexts rather than isolated laboratory settings. Michael Smith Foundation for Health Research Scholar Award (2017) CIHR New Investigator Award (2016) Dr. Todd leads the Motivated Cognition Lab at UBC, where she employs a multi-method approach including EEG, fMRI, psychophysiology, eye tracking, and genotyping to assess how common genetic variations interact with life experience to shape brain activity. Her lab investigates how these interactions influence emotional filters for perceiving and remembering. She is affiliated with MATRIX-N: Multidisciplinary Alliance for Translational Research and Innovation in Neuropsychiatry, reflecting her commitment to interdisciplinary collaboration. While she is not currently accepting graduate students, her research continues to advance understanding of the neurocognitive basis of emotional experience and its impact on cognition.