Martial Mermillod is a Full Professor at Grenoble Alpes University , directing the Laboratoire de Psychologie et NeuroCognition (LPNC, CNRS UMR 5105) since 2021. His academic career spans institutions like Clermont-Ferrand 2 and Liège University, with a focus on Neural Computation, Psychology, and Cognitive Neuroscience . As a holder of MIAI 3IA Chairs (2019-2023, 2023-2025) and CNRS delegations, he bridges AI and human cognition. PhD in Cognitive Sciences (University of Liège, 2004) Post-doctoral research at LPNC (2004-2005, Fyssen Foundation) Master's DEA (University of Savoie, 2000) His research explores bio-inspired AI , particularly lifelong learning in neural networks, the Predictive Brain Hypothesis (top-down cognitive modulation), and the role of spatial frequencies in threat detection . He has developed models for robust AI against adversarial attacks, inspired by human brain mechanisms. Key contributions include: 15+ peer-reviewed articles (2015-2023) on AI, emotion recognition, and cognitive neuroscience Grants from MIAI 3IA , CEA , ANR , and NVIDIA Leadership in national and international projects (MIT MISTI, Franco-German AI4HP) He leads the Vision & Emotion team at LPNC and has held administrative roles including UGA Council member and co-founder of the Institut Carnot Cognition steering committee.
Manfred Herrmann is a Full Professor (C4) and Chair of the Department of Neuropsychology and Behavioral Neurobiology at the University of Bremen, affiliated with the Center for Cognitive Sciences (ZKW). He holds a dual background in medicine (M.D. 1992) and psychology (Ph.D. 1988), with habilitation in 1994. His academic career includes roles as Associate Professor at Magdeburg University and Visiting Professor at Berlin Free University. He leads the high-profile 'Minds.Media.Machines' initiative at Bremen. Education : - Diplom Psychology (1985, Freiburg University) - Ph.D. Psychology (1988, Freiburg University) - M.D. (1992, Freiburg University) - Ph.D. Philosophy (1993, Freiburg University) - Habilitation (1994). Research Interests : Focuses on neuropsychological mechanisms of decision-making, stress impact on cognition, neuroimaging (fMRI/ERP), and neurobiological underpinnings of addiction/obesity. Active in clinical neuropsychology, particularly stroke recovery and neuroprotection strategies. Articles Trends : Recent work examines brain activation patterns in food choice (dorsolateral prefrontal cortex), stress effects on decision-making, and neurobiological correlates of robotic agent design. His studies often bridge clinical and computational neuroscience. Awards : - Honorary Membership, Society for Neuropsychology (2023) - Honorary Research Associate, University of Sydney (1998). Labs & Teams : Directs the Department’s research group, collaborating with international institutions like the DFG Cluster of Excellence 'Languages of Emotion'. Engages in interdisciplinary projects merging cognitive science with robotics (e.g., 'Minds.Media.Machines').
Paulo M. M. Rodrigues is a Full Professor at the Nova School of Business and Economics, Universidade Nova de Lisboa , and a senior research economist at the Department of Economic Studies, Bank of Portugal , where he has been employed since 2008. He previously served as Associate Professor and held leadership roles including vice-dean and dean at the Faculty of Economics, University of Algarve. Education Agregação, University of Algarve (2015) PhD in Econometrics, University of Manchester (1998) Masters in Economics and Econometrics, University of Manchester (1995) Degree in Business Management, University of Algarve (1993) His primary research interests lie in time-series econometrics, financial econometrics, and empirical macroeconomics and finance . His work emphasizes methodological innovation in nonstationary and seasonal time series, long memory processes, predictive regression, and quantile-based modeling. He has made significant contributions to the development and application of unit root tests, cointegration analysis, and forecasting techniques under structural breaks and heteroskedasticity. The recent trends in his publications show a strong focus on advanced econometric methods, including IVX and residual-augmented approaches for predictive regression, fractional integration in multivariate settings, tail risk modeling in financial markets, and applications in tourism and labor economics. His work frequently appears in top journals such as the Journal of Econometrics , Econometric Theory , and Review of Economics and Statistics . Editorial and Professional Service Serves on the editorial board of several scientific journals. Advising and Grants While specific details on PhD students or grant funding are not provided in the text, his extensive collaborative research—particularly with scholars like Matei Demetrescu, João Nicolau, and A.M. Robert Taylor—indicates a strong record of academic mentorship and collaborative research leadership. His position at the Bank of Portugal also suggests involvement in policy-relevant research projects and potential funding from central bank or national research agencies. Laboratories and Research Teams He is affiliated with the Economics and Research Department at the Bank of Portugal, a leading institution for economic research in Portugal. This affiliation provides a collaborative environment for empirical macroeconomic and financial research with policy implications.
Thomas LaToza is an Associate Professor in the Department of Computer Science at the George Mason University, School of Computing . His research focuses on the intersection of software engineering and human-computer interaction, exploring how developers interact with code and designing novel software development methodologies. He has pioneered crowdsourced programming environments and microtask programming frameworks, with notable projects including RulePad for checkable design rules and Hypothesizer for hypothesis-based debugging. His work emphasizes empirical studies of developer behavior, tool impact analysis, and integration of AI assistants in software development. His recent publications highlight trends in AI-driven development , debugging mental models , and developer information seeking . He has received recognition including the George Mason University Teacher of Distinction award and has served on the editorial board of Empirical Software Engineering . His advising includes PhD students Ruochen Wang, Mainul Hossain, and alumni Abdulaziz Alaboudi, Emad Aghayi, David Samudio, Sahar Mehrpour, and Maryam Arab. He co-founded the OurCode developer tools startup, translating academic research into industry applications.
Dr. Yulia Sandamirskaya is the Head of Research Center "Cognitive Computing in Life Sciences" at Zurich University of Applied Sciences (ZHAW), focusing on neuromorphic computing applications for embodied artificial intelligence. Her work bridges computational neuroscience and robotics, emphasizing neural-dynamic architectures for real-time decision-making, learning, and sensorimotor integration in autonomous agents. Key Research Areas: Neuromorphic hardware, dynamic neural fields, spiking neural networks, spatial language modeling, and autonomous sequence generation. Projects: Developed controllers for UAVs and robotic arms using event-based vision sensors, explored on-chip unsupervised learning, and designed models for spatial language interpretation in robots. Scientific Contributions: Her publications span robotics conferences and journals like Science Robotics and Frontiers in Neurorobotics , addressing topics such as path integration, obstacle avoidance, and cognitive architectures. Recent work (2024) includes visual odometry with resonator networks and hyperdimensional scene factorization on neuromorphic chips. Advising: Supervised multiple MSc theses at ETH Zurich and NSC/INI programs, mentoring students on neuromorphic navigation, spiking networks, and tactile learning. Collaborated with institutions like ETH Zurich, University of Queensland, and INI Bochum. Labs & Collaborations: Leads the "Neuromorphic Computing Applications: Embodied AI" group at ZHAW, partnering with INIvation (Zurich) and Jörg Conradt (KTH) on neuromorphic hardware implementations. Projects integrate cognitive models with robotic platforms, emphasizing energy efficiency and low-latency interaction.
Professor Holger G Krapp is a Professor of Systems Neuroscience in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He holds affiliations with the Centre for Neurotechnology, Neuromuscular Rehabilitation Technology Network, and Robotics Forum. His research focuses on sensorimotor control mechanisms in insects, particularly blowflies, integrating neurobiology with engineering principles. Key areas include visual processing, flight dynamics, and biohybrid robotics. Education: Earned a Diploma in Biology (Neurobiology) from the University of Tübingen (1992) and a Dr. rer. nat. (PhD) from the Max-Planck Institute for Biological Cybernetics (1995). Postdoctoral research included work at Caltech and Bielefeld University before joining Imperial College as a Senior Lecturer in 2005. Research interests emphasize how insects process visual and sensory information to control movement, with applications in robotics and neurotechnology. Recent work explores optic flow processing, closed-loop control systems, and biohybrid interfaces for real-time behavioral analysis. His publications highlight innovative methodologies like high-speed X-ray imaging and neural recording platforms to study flight motor mechanics and neurophysiological responses. Publications span 25+ years, demonstrating sustained contribution to understanding multisensory integration, neuronal adaptation, and energy-efficient neural coding. Active in interdisciplinary collaboration, his work bridges neuroscience, engineering, and robotics to advance biomimetic technologies and biological system analysis.
Professor Yannick Blandin is affiliated with the University of Poitiers (Faculty of Sports Sciences) and the Center for Research on Cognition and Learning (CeRCA) . His work focuses on motor learning mechanisms, observational learning, mental imagery, and feedback systems in sensorimotor integration. Ph.D. in Physical Activity Sciences (University of Montreal, 1994, directed by Dr. Luc Proteau) Current research themes: specificity of practice, eye movement analysis, point-light display applications His publications reveal trends in sensorimotor coding , motor sequence acquisition , and visual feedback optimization , often intersecting neuroscience , psychology , and sports rehabilitation . He has developed the PLAViMoP platform for visualizing and modifying motor sequences. International collaborations include Dr. Stefan Panzer (Saarland University) and Dr. Charles Shea (Texas A&M). His research spans applications in stroke rehabilitation , athletic training , and cognitive-motor interfaces .
Prof. Dr. Alexander Asteroth is a Professor of Computer Science at the Department of Computer Science , Hochschule Bonn-Rhein-Sieg (H-BRS). His work bridges Machine Learning , Surrogate Modeling , and Aerodynamic Analysis through interdisciplinary collaborations with the Institute of Technology, Resource Conservation, and Energy Efficiency (TREE) . Project leadership roles in GARRULUS (drone-based reforestation), eTa (sustainable mobility), and ELaBoR (EV charging infrastructure). Research themes: Quality Diversity Algorithms for design exploration, Bayesian Optimization , and AI in Sports Science . His publications (2017–2025) demonstrate expertise in evolutionary computation , generative models , and human-AI co-creativity . Collaborations with industry partners like GKN Driveline and academic peers (Houben, Sebastian; Hagg, Alexander) underscore his applied research focus on energy-efficient systems and sports performance modeling.
Aurélien Saussay is an Assistant Professorial Research Fellow at the London School of Economics' Grantham Research Institute, specializing in environmental economics. He currently holds a Leverhulme Early Career Fellowship (2022-2025) and will be visiting Harvard Kennedy School in Fall 2024. His primary research focuses on the interaction between economic inequality and climate change mitigation policies, aiming to address social and political acceptance challenges that hinder effective decarbonization. His research interests span Environmental Economics, Empirical Econometrics, Machine Learning, Natural Language Processing, and Macroeconomics. Saussay employs empirical methods to estimate the impacts of climate change mitigation on economic agents, with the goal of improving decarbonization policy design. His work often combines economic modeling with data science techniques to analyze climate policy impacts. His recent publications reveal a strong focus on understanding the distributional impacts of climate policies, green job transitions, and international dimensions of environmental policy. He has developed sophisticated methodologies using firm-level data, job advertisements, and macroeconomic modeling to assess how climate policies affect different economic sectors and social groups. His research demonstrates particular expertise in analyzing the ThreeME model (Multi-sector Macroeconomic Model for the Evaluation of Environmental and Energy policies), which is used across multiple countries. Scientific Awards: Leverhulme Early Career Fellowship (2022-2025) Saussay previously worked as an economist at OFCE, Sciences Po, where he led the environmental economics team. He remains an associate researcher there and is one of the main co-authors of the ThreeME model, which is used extensively in France, the Netherlands, Mexico, and Indonesia. His work on climate policy visualization, including the Climate Nightmares project that used AI to interpret IPCC reports, demonstrates his commitment to making climate research accessible to broader audiences. His upcoming visiting position at Harvard Kennedy School further highlights his standing in the environmental economics research community.
Linxi Liu is an Assistant Professor of Statistics at the University of Pittsburgh’s Dietrich School of Arts and Sciences, Department of Statistics, and serves as Seminar Coordinator. She previously held a Term-Assistant Professorship at Columbia University (2016–2020) and earned her Ph.D. in Statistics from Stanford University in 2016. Her research focuses on Nonparametric Statistics, Bayesian Methods, Multiple Hypothesis Testing, Statistical Machine Learning, and Statistical Genetics. These areas intersect computational methods with applications in genomics and causal inference, particularly in identifying causal variants in genome-wide association studies. Recent publications highlight contributions to Bayesian unsupervised tree models, knockoff-based methods for causal variant identification, and high-dimensional statistical testing. Her work bridges theoretical advancements with practical applications in biomedical research. Dr. Liu teaches a range of courses including Intermediate Probability, Statistical Computing, and Advanced Statistical Methods. Her CV and webpage provide additional details on her academic contributions and current projects.
Adway Girish is a third-year Ph.D. candidate and Doctoral Assistant at the School of Computer and Communication Sciences (IC), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is affiliated with the Information Processing Group (IPG) and the Laboratory for the Theory of Information (LTHI), working under the supervision of Prof. Emre Telatar. He has also collaborated with leading researchers including Michael Gastpar, Hyeji Kim, and Shlomo Shamai. His educational background includes a B.Tech. in Electrical Engineering with honors and a minor in Mathematics from the Indian Institute of Technology Bombay (IITB), completed in 2022. Adway's research centers on information theory and its applications in communication, security, and machine learning. He is particularly interested in foundational aspects of information measures, entropy optimization, and the theoretical underpinnings of modern deep learning architectures such as transformers and large language models. His recent work explores rate-distortion frameworks for prompt compression, learning dynamics in transformers, and entropy-constrained communication channels. His research bridges theoretical rigor with practical implications in AI and communication systems. The trend in his recent publications shows a shift from classical signal processing and micro-Doppler analysis during his undergraduate years to advanced topics in information theory and machine learning during his Ph.D. His contributions are published in top-tier venues such as ISIT, NeurIPS, ICLR, and ICML workshops, indicating a strong trajectory in theoretical computer science and applied mathematics. Scientific Awards and Recognitions: ICLR 2025 Spotlight Paper (awarded to top 5% of accepted papers) Oral presentation at ICML 2024 Workshop on Theoretical Foundations of Foundation Models (selected as one of top 4 out of 58 submissions) Adway advises no students currently, as he is himself a doctoral candidate. However, he plays an active role in collaborative research projects involving multiple co-authors across institutions. He has not received specific mention of external grants in the provided text, but his position as a Doctoral Assistant at EPFL suggests institutional funding. His collaborations with renowned researchers suggest involvement in larger research initiatives and potential access to grant-supported projects. He is a core member of the Information Processing Group (IPG) at EPFL, a research lab focused on theoretical and applied aspects of information science, including coding, communication, learning, and data analysis. The group fosters interdisciplinary research and hosts regular seminars, candidacy reviews, and internal presentations, all of which Adway actively participates in.
Frederick Kingdom is a Senior Scientist at the McGill Vision Research unit within the Research Institute of the McGill University Health Centre (RI-MUHC), Montreal General Hospital site. He holds the academic rank of Professor in the Department of Ophthalmology and Visual Sciences at McGill University 's Faculty of Medicine and Health Sciences. His research focuses on bridging local feature detection (edges, bars, surface markings) with intermediate visual processing that forms contours, textures, and surfaces. Key domains include spatial vision , color vision , stereopsis , binocular vision , and transparency perception , combining psychophysical methods with natural scene image analysis and mathematical modeling . Recent publications explore stereoscopic depth adaptation , color-enhanced edge classification , co-circularity in texture , and binocular contrast adaptation . His work appears in journals like Vision Research and PLoS Computational Biology . Kingdom's lab maintains the Palamedes Toolbox for psychophysical data analysis. He investigates perceptual mechanisms in contexts ranging from 2D visual illusions to 3D environmental interpretation , with ongoing projects examining visual biases (e.g., falling objects) and contextual modulation in neural coding.
Ralph Miller is a Distinguished Professor of Psychology at Binghamton University, specializing in elementary information processing in humans and animals. His research focuses on learning, memory, and decision-making, particularly through Pavlovian conditioning frameworks. He holds a PhD and MS in Psychology from Rutgers University, an MS in Physics from Rutgers, and a BS in Physics from MIT. His work emphasizes contextual modulation of learning, temporal coding hypotheses, and the role of retrieval processes in explaining behavioral phenomena. Education: Post-Doctoral Fellowship, University of Cambridge PhD, MS (Psychology), Rutgers University MS (Physics), Rutgers University BS (Physics), Massachusetts Institute of Technology Miller’s research investigates how retrieval rules (e.g., extended comparator hypothesis) simplify conditioning theories, as well as temporal relationships in learning and relapse prevention in exposure therapy. His lab collaborates internationally with teams in England and France, and he mentors postdoctoral fellows and undergraduates. He has authored over 280 papers, with >20,500 citations and an h-index of 71. His awards include NIH Fellowships, SUNY Distinguished Professorship, and international honors like Fulbright and Erskine Fellowships. He has served as editor-in-chief of leading journals and lectured globally. Research trends in his articles emphasize renewal effects, contingency learning, and the integration of temporal coding with clinical applications. Current work explores retrieval mechanisms and extinction protocols to improve therapies for anxiety disorders.
Dr. Venkata S.S. Gandikota is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. He is an Affiliate Faculty at the EnCORE Institute for Emerging CORE Methods in Data Science and an IEEE Senior Member. His research focuses on algorithmic principles for data recovery under noise, leveraging coding theory and structured redundancy to design efficient machine learning and distributed computing algorithms. Key areas include sparse recovery, error-correcting codes, and lattice-based methods. Education Ph.D. Computer Science, Purdue University MS Computer Science, Purdue University MSc Mathematics & B.E. Computer Science, Birla Institute of Technology and Science, Goa, India Research interests span Foundations of Machine Learning, Algorithms for Big Data, Coding Theory, Information Theory, and Lattice Algorithms. His work integrates combinatorial coding principles with modern machine learning challenges, emphasizing robustness against noise and computational efficiency. Recent trends in his publications include advancements in compressed sensing, distributed clustering, and quantum hypothesis testing. Awards: CUSE Seed Grant, SOURCE RA Grant, IEEE Senior Member designation Grants: Supported by CUSE and SOURCE RA grants for research in algorithmic data recovery and distributed systems Labs/Teams: Active contributor to the EnCORE Institute, focusing on emerging data science methodologies
Overview Dr. Jean-François DOLLINGER is a Researcher-Lecturer at CESI LINEACT (Strasbourg campus), affiliated with the Engineering and Numerical Tools research team. His academic roles include teaching Computer Science courses (undergraduate/graduate) and supervising student projects in algorithmics, programming, databases, and networks. Education: PhD in Computer Science (2011-2015), University of Strasbourg - ICube Lab MSc in Computer Science (2009-2011), University of Strasbourg (Highest Honors) BSc in Computer Science (2008-2009), University of Strasbourg (Honors) Research Interests: Focuses on edge-cloud computing, high-performance distributed systems, combinatorial optimization in IoT networks, and smart city infrastructure. Specializes in optimizing federated learning, WSN deployment strategies, and hybrid CPU/GPU execution frameworks. Advising & Collaboration: Supervises PhD/Master’s students (e.g., A. BAAHMED on federated learning, K. BOUHOUCH on OpenStack edge deployment) Collaborated with Indonesian universities (Mercu Buana) on RPL protocol extensions Research Team: Leads the Engineering and Numerical Tools group, developing frameworks for edge-cloud infrastructures and BIM-based WSN deployments in smart buildings.
