David Whitney is a Professor of Psychology at the University of California, Berkeley , with affiliations in Cognitive Science and Neuroscience . His research focuses on visual perception, particularly how humans process information in cluttered environments through mechanisms like ensemble perception , serial dependence , and perceptual crowding . He employs techniques such as psychophysics, fMRI, and TMS to study these phenomena. Whitney's recent work examines serial dependence in schizophrenia, emotion perception in crowds , and medical image analysis for dermatology and radiology. His studies reveal how the brain uses dynamic predictive templates and motion cues to stabilize perception despite neural processing delays. Publications span Current Biology , Nature Reviews Psychology , and PLoS ONE , with a strong emphasis on interdisciplinary applications of perceptual science. Scientific contributions include foundational studies on visuomotor control , blind spot filling-in , and holistic face processing . His lab investigates perceptual stability across eye movements, spatial localization, and cross-modal interactions. Whitney has received grants such as NIH EY018216 to support his research on motion-dependent visual coding. Detailed information about his work is available on the Whitney Lab website .
Aurora Maccarone serves as an RAEng Research Fellow within the Institute of Photonics and Quantum Sciences at Heriot-Watt University's School of Engineering & Physical Sciences. Her work focuses on advanced photonics applications for challenging environments, particularly underwater and obscured conditions. Her research expertise spans: Single-photon LiDAR systems for underwater 3D imaging Photon-counting detector arrays for depth profiling Real-time reconstruction algorithms for obscurant-penetrating imaging Quantum sensing applications in marine environments Recent publications demonstrate consistent innovation in single-photon imaging techniques, with emphasis on underwater applications (2023-2024) and obscurant penetration (2022). Her work shows strong interdisciplinary connections between optical engineering, computational imaging, and environmental sensing. Key publications reveal growing impact in underwater LiDAR technology, with multiple high-citation papers on photon-efficient imaging systems. Dr. Maccarone actively supervises PhD students and has created significant research datasets. Her collaborations span international institutions, with notable contributions to sensor hardware development and computational imaging algorithms. Recent work shows increasing integration of machine learning techniques with traditional photon-counting approaches.
Dr. Kim Yong-Joe is an Associate Professor in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University. He serves as the Director of the Acoustics and Signal Processing Laboratory (ASPL), founded in 2009. His research focuses on acoustics, applied signal processing, nonlinear acoustics, biomedical acoustics, noise and vibration control, and structural dynamics. He has received notable awards including the 2014 Department of Mechanical Engineering Graduate Teaching Award and the 2014 Pioneer Natural Resources Faculty Fellow II. His lab specializes in wave propagation analysis, ultrasonic structural health monitoring, and acoustophoresis in microfluidic systems. He has collaborated with sponsors like the National Science Foundation, Qatar National Research Fund, and Samsung Techwin. His research has led to advancements in noise reduction technologies, biomedical diagnostics, and nondestructive evaluation methods.
Meng Li is the Noah Harding Associate Professor of Statistics at Rice University's School of Engineering. He specializes in Bayesian analysis, machine learning, and statistical theory. His research bridges methodological development and applications in biomedical sciences, materials informatics, and neuroimaging. Li holds a Ph.D. from North Carolina State University and a B.S. from Sun Yat-sen University. He has been recognized with awards including the 2020 Rice Engineering Excellence Award and the Ralph E. Powe Junior Faculty Enhancement Award. Li's research focuses on probabilistic modeling of complex data such as images, functional data, and networks. His funded projects include AI frameworks for pancreatic cancer biomarkers and Bayesian spatiotemporal modeling of marine ecosystems. He collaborates with institutions like Houston Methodist and Baylor College of Medicine on medical applications. His teaching includes advanced courses like Bayesian Statistics and Advanced Bayesian Inference. He advises over 30 students, many of whom have pursued academic and industry roles. Li serves as an associate editor for Bayesian Analysis and the new ACM Transactions on Probabilistic Machine Learning.
Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.
Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration
Yuxin Chen is a Professor at the University of Pennsylvania , holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory , with a focus on theoretical foundations and practical algorithms for machine learning. Education : Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008). Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization , sample complexity analysis , low-dimensional adaptation , and generative modeling . His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis. Scientific Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024) Alfred P. Sloan Fellowship (2022) NSF Career Award (2022) Google Research Scholar Award (2022) IEEE Transactions on Power Electronics Prize Paper Award (2024) Advising and Grants : He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF , Amazon , and Google , with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms .
Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.
Brett Laursen is a Professor of Psychology at Florida Atlantic University's Charles E. Schmidt College of Science, with additional Docent Professor appointments in Educational Psychology at the University of Helsinki and in Social Developmental Psychology at the University of Jyväskylä in Finland. His research focuses on developmental psychology, particularly adolescent development, peer relationships, and social dynamics. Dr. Laursen earned his Ph.D. and M.A. in Child Psychology from the Institute of Child Development at the University of Minnesota and his B.A. in Psychology from Nebraska Wesleyan University. He also holds an Honorary Ph.D. from Örebro University in Sweden. His research program examines influence within close relationships, with particular focus on peer relationships during childhood and adolescence. Current projects include longitudinal studies of elementary and middle school children in Florida, Lithuanian youth transitioning from middle to secondary school, and child characteristics affecting parent engagement in literacy activities. His work consistently explores how social dynamics shape development, with special attention to friendship formation, dissolution, and influence processes. Analysis of his recent publications reveals a strong focus on peer relationships, social status, and developmental transitions. His research employs sophisticated longitudinal and genetically informed designs to examine how social contexts shape development across childhood and adolescence, with particular emphasis on the mechanisms through which peer influence operates. Fellow, American Psychological Association (Division 7, Developmental; Division 8, Social) Fellow and Charter Member, Association for Psychological Science Fellow, International Society for the Study of Behavioural Development Distinguished Alumnus, College of Education and Human Development, University of Minnesota Florida Atlantic University Scholar of the Year (2023-24 and 2016-17) Dr. Laursen has mentored numerous doctoral and master's students who have gone on to successful careers in academia and research. His research has been supported by major funding sources including the US National Institute of Child Health and Human Development, the US National Institute of Mental Health, the US National Science Foundation, Trygfonden, the Jacobs Foundation, and the European Social Fund. As Editor-in-Chief of Merrill Palmer Quarterly and Founding Editor of Cambridge Elements in Research Methods for Developmental Science, he plays a significant role in shaping the field's scholarly discourse. The Laursen Lab operates as a collaborative research team involving current students, alumni, and international collaborators including Professors Goda Kaniušonytė and Rita Žukauskienė of Mykolas Romeris University in Lithuania. The lab regularly presents research at major conferences including the Society for Research on Child Development.
Sanne Cottaar is a researcher at the Department of Earth Sciences, University of Cambridge, specializing in seismology and deep Earth structure. Her work integrates seismic waveform analysis, mineral physics, and geodynamic modeling to investigate mantle plumes, ultra-low velocity zones (ULVZs), and core-mantle boundary dynamics. Key research areas include: Seismic imaging of deep Earth heterogeneity Core-mantle boundary and mantle transition zone structure Multidisciplinary approaches with mineral physics and geodynamics Development of seismic tools like BurnMan for thermodynamic modeling Public engagement through educational initiatives such as Deep Earth Explorers Her recent publications focus on mapping ULVZs using Sdiff and Pdiff waves, resolving mantle plume origins, and benchmarking seismic methods against geodynamic constraints. She actively supervises doctoral projects in seismology and deep Earth dynamics.
Brice Kuhl is a Professor in the Department of Psychology at the University of Oregon , where he leads the Kuhl Lab. His research focuses on the cognitive neuroscience of memory formation, retrieval, and forgetting , utilizing advanced neuroimaging techniques like fMRI and EEG combined with machine learning algorithms to analyze distributed neural activity patterns. His work explores mechanisms of memory interference resolution , forgetting , and neural representation transformation . Recent publications emphasize spaced learning , temporal memory dynamics , and memory-cognitive control interactions . The lab has received attention for decoding perceptual content from neural activity and reconstructing face images based on brain states. Current lab members include graduate students Anisha Babu , Tongle Cai , and America Romero , alongside postdocs like Soroush Mirjalili and Yoonjung Lee . The lab frequently presents at conferences like CNS and SFN , and maintains active collaborations in memory research and neuroimaging methodology . Notable projects include investigations into hippocampal pattern differentiation and parietal cortex roles in memory . The lab also contributes to open science initiatives with publicly available experimental codes and data .
Paul Pu Liang is an Assistant Professor at the Massachusetts Institute of Technology (MIT) Media Lab and Department of Electrical Engineering and Computer Science (EECS). He directs the Multisensory Intelligence research group, focusing on building AI systems that integrate diverse sensory inputs to enhance human-AI symbiosis. His work spans theoretical foundations, large-scale resources, and neural architectures for multisensory learning. Education: PhD in Machine Learning (Carnegie Mellon University), MS in Machine Learning (Carnegie Mellon), BS with University Honors in Computer Science and Neural Computation (Carnegie Mellon) Research Interests: Multimodal machine learning, human-AI interaction, clinical AI, generative models, and responsible deployment of AI systems Key Contributions: MultiBench, HEMM evaluation framework, CLIMB clinical data foundations, and multimodal transformer architectures Recent publications emphasize multimodal foundation models , clinical applications , and socially responsible AI . His work has been recognized with multiple best paper awards and fellowships from Siebel, Facebook, and other institutions. Scientific Awards Siebel Scholars Award Waibel Presidential Fellowship Facebook PhD Fellowship Center for ML and Health Fellowship Rising Stars in Data Science Four best paper awards Paul teaches courses on machine learning and multimodal AI at MIT and CMU. He mentors students across multiple programs including Media Arts & Sciences, EECS, and IDSS, with former advisees now at institutions like OpenAI, UC Berkeley, and Princeton.
Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, a Faculty Member at the Vector Institute, and a Canada CIFAR AI Chair. He leads The Salon, a research group focused on statistics, algorithms, machine learning, and optimization. His work bridges theoretical foundations with practical applications in data privacy and robustness, contributing to both academic research and real-world deployments that impact millions of users. Dr. Kamath earned his PhD and SM degrees in Electrical Engineering and Computer Science at MIT, where he was advised by Costis Daskalakis. Prior to MIT, he graduated from Cornell University in May 2012 with a degree in Computer Science and Electrical and Computer Engineering, where he worked with Bobby Kleinberg. His academic journey reflects a strong foundation in both theoretical computer science and practical applications. His research focuses on developing solutions for trustworthy and reliable machine learning and statistics, with particular emphasis on data privacy and robustness. He addresses fundamental problems in these areas, revitalizing statistical toolkits for the modern data era where privacy preservation is paramount. His work spans theoretical foundations of differential privacy to practical applications that have been deployed at scale, including contributions to systems that protect the sensitive information of hundreds of millions of individuals. Analysis of his recent publications reveals a strong trend toward addressing the interplay between privacy, robustness, and machine learning performance. His work increasingly examines practical deployment challenges while maintaining theoretical rigor, with growing emphasis on diffusion models, generative AI, and the legal implications of AI systems. There's a clear trajectory from foundational privacy theory toward addressing real-world implementation challenges across diverse application domains. Canada CIFAR AI Chair Ontario Early Researcher Award 2024 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies STOC Best Student Presentation Award ICML 2024 Best Paper Award Microsoft Research Fellow at the Simons Institute for the Theory of Computing Dr. Kamath actively mentors students, with notable successes including Valentio Iverson winning the Germain-Erdős Undergraduate Award and Chris Trevisan receiving the CRA Outstanding Undergraduate Researcher Award. His service to the research community is extensive, serving as Editor-in-Chief of TMLR, on the Executive Committee of the Learning Theory Alliance, and on steering committees for major conferences including ICML, COLT, and ALT. He has organized numerous workshops focused on privacy-preserving machine learning and differential privacy. Through The Salon research group, Dr. Kamath fosters a collaborative environment where postdoctoral fellows, graduate students, and undergraduates work together on cutting-edge problems at the intersection of statistics, algorithms, machine learning, and optimization. The group maintains strong connections with industry partners and participates in major research initiatives, including the Vector Institute's privacy and security research efforts. Looking forward, Dr. Kamath will be moving to the Computer Science department at NYU's Courant Institute of Mathematical Sciences in September 2026, where he plans to expand his research program.
Camillo J. Taylor is the Raymond S. Markowitz President’s Distinguished Professor in the Department of Computer and Information Science at the University of Pennsylvania , where he has been a faculty member since 1997. He also serves as Associate Dean for Diversity, Equity, and Inclusion at the School of Engineering and Applied Science. His research focuses on Computer Vision and Robotics , particularly in 3D reconstruction, semantic mapping, and autonomous navigation. Education: A.B. in Electrical Computer and Systems Engineering, Harvard College (1988) M.S. and Ph.D. in Computer Science, Yale University (1990, 1994) Research Interests: Dr. Taylor’s work bridges Computer Vision and Robotics to enable autonomous systems to perceive and navigate complex environments. Key themes include semantic SLAM, event camera applications, and meta-learning for adaptive controllers. His projects often integrate vision, physics, and multi-agent collaboration, as seen in systems like EvMAPPER and OCCAM . Recent Article Trends: His 2024–2025 publications focus on semantic mapping , event-based vision , and multi-agent LLM systems , reflecting his lab’s emphasis on real-time perception, physics-informed reconstruction, and rational decision-making in robotics. These works span applications from solar eclipse imaging to wildfire analysis and natural hazard resilience. Awards: NSF CAREER Award (1998) Lindback Minority Junior Faculty Award (2001) IEEE WACV Best Paper Award (2012) Lindback Distinguished Teaching Award (2012) Advising and Service: Dr. Taylor has advised numerous PhD students, including Jason Hughes and Bowen Jiang. He has served as a Program Chair for CVPR (2006, 2017) and General Chair for ICCV (2021). His contributions to the GRASP Laboratory have advanced autonomous micro-UAVs and semantic SLAM.
Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.
