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Esther MondragónView profile

Senior Lecturer

• Computational Cognitive Neuroscience
• Bio-inspired Artificial Intelligence
• Reinforcement Learning
• +15 more

Dr. Esther Mondragón is a Senior Lecturer in the Department of Computer Science at City St George's, University of London, within the School of Science and Technology. She is a member of CitAI (the Artificial Intelligence Research Centre at City) and directs the virtual Centre for Computational and Animal Learning Research. Her academic leadership includes serving as Chair of the School of Science and Technology Research Degrees Committee and former Director of the MSc in Artificial Intelligence. PhD Psychology, University of the Basque Country, Spain MSc Psychology, University of the Basque Country, Spain BSc Psychology, University of the Basque Country, Spain Dr. Mondragón is a computational cognitive neuroscientist whose research lies at the intersection of artificial intelligence and cognitive neuroscience. Her primary focus is on bio-inspired AI, particularly reinforcement learning models of cognition grounded in associative learning principles. She develops computational frameworks that bridge natural and artificial cognition, including influential models like the Double Error Dynamic Asymptote (DDA) and the Rescorla-Wagner Drift-Diffusion Model (RWDDM). Her work extends to deep learning integration, Hebbian learning, representational learning, and episodic memory in AI. She has also contributed to understanding perceptual learning, rule acquisition in animals, and timing mechanisms in conditioning. The 15 most recent publications reflect a strong trend in computational modeling of learning, with increasing integration of deep learning and neuro-inspired architectures. Her work spans theoretical cognitive science, applied AI, and software development for simulation. Key themes include associative mechanisms in representation, temporal modeling, and biologically plausible neural networks. She frequently collaborates with researchers like Eduardo Alonso and others across disciplines. Member, Spanish Society for Comparative Psychology (SEPC) Member, Cognitive Science Society International Affiliate, Pavlovian Society Member, Division 6: Society for Behavioral Neuroscience and Comparative Psychology, American Psychological Association (APA) Senior member, The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB) Dr. Mondragón actively supervises PhD students including Corina Cătărău-Cotuțau, Alexander Dean, and Mpagi Kironde. She has supervised several to completion, such as Esther Mulwa, André Luzardo, and Niklas Kokkola. She has secured significant research funding as PI/Co-PI on projects like 'INDUSTRIAL PhD SCHOLARSHIP, BOSCH AASS' (£140,000, Innovate UK), 'DEEPSYNC: AUTOMATED VFX FOR VIDEO DUBBING' (£143,000, Innovate UK), and 'FREE ENERGY PRINCIPLE FOR ADAPTIVE COGNITIVE ARCHITECTURES' (£98,000, DSTL). She also serves as a reviewer for the US National Science Foundation, BBSRC, NCN Poland, and numerous high-impact journals. She directs the virtual Centre for Computational and Animal Learning Research, which fosters interdisciplinary collaboration among cognitive scientists, neuroscientists, biologists, mathematicians, and computer scientists. She also leads the Knowledge Graphs Interest Group at The Alan Turing Institute.

Patrick ForréView profile

Assistant Professor

• Causal Inference
• Machine Learning Theory
• Information Theory
• +4 more

Patrick Forré is an Assistant Professor and Lab Manager of the AI4Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. His work bridges theoretical machine learning and scientific applications, fostering interdisciplinary collaboration across informatics, mathematics, ecology, chemistry, physics, biology, and astrophysics. His research centers on mathematical foundations of machine learning including causal inference, graphical models, information theory, conditional independence structures, and geometric deep learning. He specializes in applying these techniques to scientific data problems, particularly in electro-catalysis and nitrogen fixation, where machine learning enhances molecular simulations and quantum chemical modeling. His theoretical work addresses non-linear structural causal models with cycles and latent confounders. The AI4Science Lab under his management focuses on detecting hidden patterns in scientific data through projects like electrode-electrolyte interface modeling, nitrogen-fixing coordination complexes analysis, and classical DFT neural approximations. Located in LAB42 Building at Amsterdam Science Park, the lab connects diverse scientific disciplines through machine learning innovation while organizing colloquia, workshops, and PhD defenses.

Nicoletta NocetiView profile

Associate Professor

• Computer Science
• Robotics
• Machine Learning
• +7 more

Nicoletta Noceti is an Associate Professor at the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) of the University of Genoa. Her academic roles include teaching courses such as Computer Vision, Deep Learning, and Machine Learning across undergraduate and master's programs in Robotics Engineering and Computer Science. She also contributes to innovative education initiatives like the 'Smart rogaining' model for computer science orientation. Her research focuses on interdisciplinary areas spanning computer vision, human-centered artificial intelligence, and robotics. Key themes include human motion analysis, causal discovery, and the application of AI in healthcare and social interaction. Recent work emphasizes disentangled representations for microscopy images, scene-unbiased action recognition, and understanding engagement dynamics in virtual teams of older adults. She also explores uncertainty-aware systems for head pose estimation and kinematic primitives in action similarity judgments. Her research frequently intersects with robotics, cognitive science, and biomedical engineering, addressing challenges such as causal inference in nonlinear models and embodied interaction design for robots. These efforts aim to bridge computational methods with human perceptual and motor systems, with applications in assistive technologies and social robotics.

• Machine Learning
• Statistical Inference
• Natural Language Processing
• +3 more

Tommi S. Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, with appointments in the School of Engineering and the Institute for Data, Systems, and Society. He leads a research group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research advances machine learning for efficient, principled, and interpretable learning, prediction, and control. Key interests include: Statistical inference and estimation Generative modeling for molecular design and natural language Game-theoretic interactions and strategic modeling Applications in biomedical domains and drug discovery Recent publications demonstrate strong focus on diffusion models and flow-based methods for protein structure generation and molecular optimization, with increasing integration of physical principles and symmetries. His group actively publishes at top AI conferences (ICML, NeurIPS, ICLR) with significant biomedical applications. Jaakkola advises numerous PhD students and has mentored graduates now at companies like Boltz PBC, DE Shaw, and Xaira. His CSAIL laboratory fosters interdisciplinary collaboration between computer science, biology, and chemistry to address complex biomedical challenges.

Richard YuView profile

Professor

• Wireless Networks
• Edge Computing
• Federated Learning
• +7 more

Richard Yu is a Professor at the Carleton School of Information Technology, Carleton University. His research focuses on cross-layer design in wireless systems, security in wireless networks, green information technologies, and multimedia over wireless networks. He holds a Ph.D. from the University of British Columbia and is a Professional Engineer (P.Eng). His work integrates machine learning, edge computing, and reinforcement learning to address challenges in vehicular networks, autonomous systems, and IoT. Education Ph.D., University of British Columbia Research Interests Professor Yu’s research spans wireless communication systems , edge computing , and AI-driven optimization . He explores novel paradigms such as federated learning for industrial IoT, secure UAV-assisted satellite networks, and generative AI for video understanding. His work emphasizes practical applications in autonomous vehicles, smart infrastructure, and industry 4.0 systems. Recent Trends in Publications His recent articles highlight advancements in reinforcement learning frameworks for resource allocation, safety-critical AI in video generation, and cross-modal fusion for embodied agents. He also investigates hybrid NOMA/OMA systems and quantum-inspired algorithms for next-generation networks. Advising & Grants No specific advisees are listed, but his research is supported by grants focusing on edge intelligence, vehicular networks, and secure computing. Labs & Teams Associated with Carleton’s School of Information Technology research groups, focusing on wireless systems and AI for industrial applications.

Brittany I. JohnsonView profile

Assistant Professor

• Sociotechnical Systems
• Software Engineering
• Human-Computer Interaction
• +4 more

Brittany I. Johnson is an Assistant Professor in the Department of Computer Science at George Mason University. She leads the INSPIRED Lab, focusing on interdisciplinary research that bridges software engineering, human-computer interaction, and machine learning with a strong emphasis on ethics and social good. Ph.D. in Computer Science (2017), North Carolina State University B.A. in Computer Science (2011), College of Charleston Research Interests center around sociotechnical challenges in developer productivity, ethical software practices, and technology for marginalized communities. Her work includes: Fairkit-learn - A fairness evaluation toolkit for ML models Quintessence - Intersectional data analysis framework Themis - Automated discrimination testing tool Holmes - Causal testing prototype Current collaborations include researchers from University of Massachusetts Amherst and University of Texas at Austin. She advocates for Black lives in computing through public scholarship and mentoring activities.

Juan-Pablo OrtegaView profile

Professor

• Machine Learning
• Dynamical Systems
• Mathematical Physics
• +3 more

Juan-Pablo Ortega is a Professor of Mathematics and Applied Geometry & Dynamics at Nanyang Technological University (NTU), Singapore, holding the Lee Soo Ying Professorship. He serves as Head of the Division of Mathematical Sciences and Associate Chair (Faculty) in the School of Physical and Mathematical Sciences. His academic affiliations include part-time roles as an Extraordinary Professor at the University of Pretoria and membership in the Real Academia de las Ciencias de España. He is on leave (détachement) from CNRS (France). Ortega's research focuses on machine learning, dynamical systems, and their applications in areas such as reservoir computing, mathematical physics, and econometrics. He co-leads the Machine Learning and Dynamical Systems Special Interest Group at The Alan Turing Institute and has organized workshops at institutions like ETH Zürich and Scuola Normale Superiore, Pisa. His work bridges theoretical mathematics with practical applications in forecasting, signal processing, and quantum computing. Ortega has delivered keynote speeches at prestigious events like the Geometric Science of Information conference (France, 2023) and the Non-autonomous Dynamics in Complex Systems workshop (Germany, 2023). He contributed to the Nowcasting Lab at the KOF Swiss Economic Institute and has authored influential books, including the award-winning Momentum Maps and Hamiltonian Reduction (2004), which earned the Ferran Sunyer i Balaguer Prize. His research also extends to financial mathematics, stochastic dynamics, and geometric mechanics. Ortega’s academic contributions include leadership in international research networks, such as co-organizing semester programs at the Bernoulli Institute (EPFL, 2017) and workshops on dynamical systems and brain-inspired information processing. His current projects emphasize geometric learning, structure-preserving algorithms, and the mathematical foundations of reservoir computing.

Lianhua ChiView profile

Lecturer

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +7 more

Lianhua Chi is a Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia, since February 2018. He holds a dual PhD in Machine Learning and Data Mining from the University of Technology Sydney (Australia) and Huazhong University of Science and Technology (China). Before joining academia, he worked as a Postdoctoral Researcher at IBM Research Australia on Watson Education and Health, and as a Data Specialist at the University of New South Wales (UNSW). His research focuses on big data analytics, particularly in healthcare applications, AI-driven Autism Screening, and hashing techniques for real-time data processing. Chi has received notable awards including the 'Young Global Changer' title from G20/T20 Germany, 'Top 200 Young Researchers Globally' by the Heidelberg Laureate Forum, and the IBM External Honors award. Education: PhD in Machine Learning and Data Mining (2015), University of Technology Sydney & Huazhong University of Science and Technology Previous roles: Postdoc at IBM Research Australia, Visiting Researcher at UTS Research Interests: Chi’s work emphasizes adaptive hashing algorithms, graph stream classification, geolocation prediction in social media, and time series analysis. He applies these techniques to healthcare challenges such as AI-based Autism Screening and medical imaging diagnostics. His methodologies prioritize scalability and real-time adaptability. Awards & Grants: Best Paper Award (PAKDD13) Romberg Grant (Heidelberg Laureate Forum, 2016) Innovation Connection Grant (NostraData, 2022–2023) Advising & Collaborations: Chi has collaborated with Harvard Medical School’s Gehlenborg Lab and led projects on healthcare AI and cybersecurity. His work integrates advanced hashing techniques with clinical data to improve diagnostic accuracy and patient care efficiency. Labs/Teams: Active in interdisciplinary teams focusing on AI ethics, healthcare analytics, and big data security at La Trobe University.

• Artificial Neural Networks
• Anomaly Detection
• Machine Learning
• +4 more

Mehran Hossein Zadeh Bazargani is a Marie Curie Post Doctoral Fellow at the School of Mathematics and Statistics , University College Dublin. His research focuses on brain-inspired artificial neural networks, anomaly detection in medical data, and interpretable machine learning. He has contributed to the MED-I consortium and founded the educational platform MLDawn . Education: PhD in Machine Learning (University College Dublin) Research Interests: Developing artificial neural networks for perceptual learning and decision-making, anomaly detection in time-series (ECG, EEG) and medical images (fMRI, X-ray), and computational neuroscience applications under the Free Energy Principle. Teaching Activities: Founder of MLDawn (2018–Present), lecturer at Queens University Belfast (2020), teaching assistant at UCD (2017–2020), and instructor in Iran (2013–2015). Scientific Contributions: 8 publications across anomaly detection, medical image analysis, and molecular communication. Key works include the D-RBFDD network and de-identification frameworks for medical data. Emails: mehran.hosseinzadehbazargani@ucd.ie, mldawn2018@gmail.com

• Theology and Science
• Philosophical Theology
• Analytic Theology
• +3 more

George M. Coghill is an Emeritus Professor at the University of Aberdeen’s School of Natural and Computing Sciences, holding degrees in Engineering Science (B.Sc.), Computing (M.Sc.), Theology (M.Th.), and a Ph.D. in Intelligent Systems. His interdisciplinary research bridges theology-science dialogue, philosophical informatics, and computational models. Current work explores Donald MacKay’s contributions to information theory and free will, AI ethics in Christian theology, and immune-inspired machine learning. Publications reflect dual expertise in computational intelligence (e.g., swarm optimization, clustering algorithms) and analytic theology. Honors include Fellowship in the Royal Society of Arts. Previous roles include Head of Computing Science and leadership in university research committees. Vocal performance (LTCL diploma) complements academic pursuits.

Debasree DasView profile

Researcher

• Data Anonymization
• Data Privacy vs Utility
• Ubiquitous Computing
• +3 more

Debasree Das is a Research Assistant at the University of Bamberg , working under the Chair of Mobile Systems within the Faculty of Business, Information Systems and Applied Computer Science. She holds a Ph.D. in Computer Science and Engineering from Indian Institute of Technology, Kharagpur (2019-2024) and has held prior roles as a Teaching Assistant at IIEST Shibpur (2017-2018) and Associate at Cognizant Technology Solutions (2018-2019). Ph.D. in Computer Science and Engineering, IIT Kharagpur (2019-2024) M.Tech in Computer Science and Engineering, IIEST Shibpur (2016-2018) B.Tech in Computer Science and Engineering, West Bengal University of Technology (2012-2016) Her research focuses on data anonymization for intermodal trajectories, privacy-utility trade-offs , and ubiquitous computing in transportation systems. She explores spatiotemporal data modeling , contextual mobility analysis , and safety mechanisms for micromobility (e-scooters, bicycles) using causal inferencing and pervasive sensing techniques. Her work bridges human-computer interaction with urban mobility challenges , emphasizing differential privacy and dynamic road safety mapping. Recent publications highlight her contributions to privacy-preserving mobility analytics , causal modeling for driving behavior , and context-aware transportation systems . She has served as a co-organizer for IEEE TrustSense Workshop, Publicity Co-Chair for ICDCN 2026, and TPC member for multiple conferences including COMSNETS and IEEE AIoT. Best Paper Runner Up Award at IEEE MDM 2022 Second Winner Prize at IBM Maitreyee 2022 Institute Silver Medal at IIEST Shibpur 2018 Shri Dewang Mehta IT Award 2016 Inspire Award Warrant 2010 She supervises masters theses on mobility data anonymization and micromobility service optimization , contributes to projects like the Urban Data Platform for Bamberg , and is part of research groups including UbiNET and CNeRG . Her work integrates machine learning , sensor telemetry , and urban dynamics to address modern transportation challenges.

Rosina WeberView profile

Professor

• Explainable Artificial Intelligence (XAI)
• Case-Based Reasoning
• Machine Learning
• +6 more

Rosina Weber is a Professor of Information Science and Computer Science at Drexel University's College of Computing & Informatics. She specializes in explainable artificial intelligence (XAI), case-based reasoning (CBR), and combining neural/symbolic methods for use-inspired AI applications. Her research addresses interpretability challenges, medical decision support, and ethical AI frameworks. Education: PhD in Production Engineering (Federal University of Santa Catarina, Brazil, 1998) MS in Production Engineering (Federal University of Santa Catarina, Brazil) BA in Business Administration (Federal University of Rio Grande do Sul, Brazil) Research Interests: Focus areas include XAI, causal discovery, medical AI applications, and AI ethics. Recent work emphasizes interpretable models for clinical diagnostics and military medical triage scenarios. Key Projects: Co-PI for DARPA ITM project with Parallax Advanced Research Former co-lead in DHS-EPA Center for Microbial Risk Assessment Swedish Vinnova-funded international XAI collaboration Awards: 2023 Research Excellence Award (College-level) Best Paper Awards (various conferences) Grants & Collaboration: Holds active grants from DARPA, NCATS-NIH, and Swedish Vinnova. Her work has been featured in Good Day Philadelphia and NBC Nightly News . Labs/Teams: Leads AI research teams focused on biomedical data translation, CBR integration, and XAI implementation in healthcare systems.

• Systems Dynamics Modeling
• Systems Safety
• Mineral Supply Chains Resiliency
• +11 more

Dr. H. Sebnem Düzgün is a Full Professor and Fred Banfield Distinguished Chair in Mining Engineering at the Colorado School of Mines. She holds a joint appointment in the Department of Computer Science and serves as Associate Department Head of Mining Engineering. Her roles further include Associate Director of the GIS and Geoinformatics program and coordinator of the geoscience section in the Data Science program. Additionally, she leads the mineral supply chain pillar of the Mines Global Future Initiative (GEFI). Dr. Düzgün's educational background includes a B.Sc., M.Sc., and Ph.D. in Mining Engineering from Middle East Technical University (METU), Turkey, and a Diploma in Analysis and Management of Geological Risks from the University of Geneva. She has also conducted postdoctoral research at institutions such as the Norwegian Geotechnical Institute and the Karlsruhe Institute of Technology. Her research focuses on systems dynamics modeling , mineral supply chain resiliency , decarbonization strategies , sustainability , big data analytics , and GIS/remote sensing applications . She also explores AI and machine learning in geosciences, mine safety , and autonomous operations , with interdisciplinary contributions to resilience engineering and environmental monitoring. Dr. Düzgün's recent publications emphasize system safety in mining , AI-driven geothermal exploration , mineral supply chain transparency , and post-disaster urban resilience . Her work bridges geospatial technologies with environmental sustainability and risk mitigation strategies . 100 Globally Inspiring Women in Mining (2020) Elates Fellow, Drexel University (ELATES Program) Newmont STEM Equity Instructional Fellow Mines’ University Public Fellow 100 Resilience Fellows in Engineering Program (TU Delft) Dr. Düzgün has received significant research support, including the Alexander von Humboldt Experienced Researcher Grant (2014) , enabling her work at Karlsruhe Institute of Technology. Her leadership in interdisciplinary initiatives reflects her commitment to advancing STEM equity and academic technology. She leads the mineral supply chain pillar within the Mines Global Future Initiative (GEFI) and founded a startup focusing on AI-driven solutions for resource exploration. Her collaborations span academic, corporate, and governmental sectors to address global challenges in mining and sustainability.

Aniket DidolkarView profile

Researcher

• Deep Learning
• Cognitive Science
• Large Language Models
• +5 more

Aniket Didolkar is a Ph.D. student at Mila and The University of Montreal, advised by Prof. Yoshua Bengio, Dr. Anirudh Goyal, and Prof. Michael Mozer. He is also a visiting researcher at Meta, working with Dr. Nicolas Ballas. Education: Pursuing Ph.D. in Deep Learning and Cognitive Science at The University of Montreal. Research Focus: Developing cognitive science-inspired deep learning techniques, including metacognitive LLMs for mathematical problem solving, hybrid recurrent-transformer architectures for long-context modeling, and object-centric visual representation learning. Article Trends: His work spans object-centric learning (CVPR 2025, ICLR 2025, ICLR 2024), LLM reasoning (NeurIPS 2024), hybrid neural models (NeurIPS 2022), and NLP/speech augmentation (COLING 2020, Interspeech 2020). Scientific Awards: Unique Excellence Scholarship ($15,000) at Mila (December 2023). Scholarship by University of Alberta and Amii ($1,500) for Amii AI Week (April 2022). Scholarship by University of Montreal and Quebec Ministry of Higher Education ($4,000) (April 2022). Collaborations: Worked with institutions including Valence Labs, Microsoft Research NYC, IISc Bangalore, IIIT Delhi, Preferred Networks, and Ubisoft India Studios.

Ava KhamsehView profile

Lecturer

• Biomedical Genomics
• Cancer Evolution
• Causal Machine Learning
• +3 more

Dr. Ava Khamseh is a Lecturer in Biomedical AI at the Institute of Genetics and Cancer , University of Edinburgh. She leads a research group at the intersection of cancer biology, population biomedicine, and causal machine learning, focusing on quantifying molecular interactions in biological systems. Research spans cancer initiation , evolutionary dynamics , and causal machine learning for biomedical data analysis. Specializes in single-cell biology , transcriptional trajectories , and higher-order genetic interactions . Key research themes include: Developing experimental-computational models to trace early cancer evolution via mutational and transcriptional dynamics. Designing non-parametric probabilistic ML methods for causal inference in UK Biobank and scRNA-seq data. Collaborations with experts in experimental biology , mathematics , and machine learning . Recent publications highlight her work on: Causal modeling of single-cell gene expression to resolve cryptic states in cancer progression. Investigating MITF-low melanoma subtypes and residual disease mechanisms. Machine learning applications in quantitative biomedicine , including Myalgic Encephalomyelitis biomarker discovery. Physics-inspired computational approaches, such as Ising model parameter estimation for biomedical applications. Projects funded by Cancer Research UK , MRC , and GlaxoSmithKline support her work on cancer inception, research culture, and biomarker prediction.