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Francesca CapozziView profile

Professor

• Relational Wellbeing
• LGBTQ+ Mental Health
• Social Cognitive Neuroscience
• +5 more

Francesca Capozzi, PhD, is a Professor at the Department of Psychology, Université du Québec à Montréal, and an Associate Investigator in the Brain Repair and Integrative Neuroscience (BRaIN) Program at RI-MUHC's Montreal General Hospital site. Her work focuses on socio-cognitive factors supporting relational wellbeing through group therapies for LGBTQ+ individuals and families at the MUSIC (McGill University Sexual Identity Centre) clinic, using quantitative/qualitative language analysis. PhD in Cognitive Neuroscience (University of Turin, 2016) Postdoctoral work at McGill University (2016-2021) Clinical training in Couple and Family Therapy (McGill School of Social Work, 2020-2022) Her research explores social perception mechanisms, affective processes, and nonverbal communication in group dynamics and emotion recognition. Key methodologies include dual mobile eye tracking, multisensory integration analysis, and behavioral immune system evaluation. Current projects examine how societal underrepresentation affects emotional distress and how visual/auditory cues shape social event segmentation. She maintains affiliations with CORE research lab and MUSIC clinic. Licensing includes Quebec psychotherapist registration (OTSTCFQ #62045-22, #CAPF2206200TCF). Contact available via francesca.capozzi@muhc.mcgill.ca (academic) and psycho@francescacapozzi.ca (therapy inquiries).

John A. WhiteView profile

Professor

• Mechanisms of Episodic Memory
• Pathophysiology of Epilepsy
• Computational Neuroscience
• +2 more

John A. White serves as Professor and Deputy Chair in the Department of Biomedical Engineering at Boston University, with joint appointments in Pharmacology and Experimental Therapeutics and Neuroscience. He directs the Neuronal Dynamics Lab and maintains affiliations with the Center for Systems Neuroscience, Neurophotonics Center, and Photonics Center. Education: PhD, Biomedical Engineering, Johns Hopkins University B.S., Biomedical Engineering, Louisiana Tech University His research integrates engineering principles with neuroscience to decode brain information processing mechanisms. Key focus areas include episodic memory formation, epilepsy pathophysiology, computational modeling of neural networks, real-time instrumentation design, and advanced imaging of neuronal and astrocytic activity. Current work targets therapeutic interventions for memory disorders through precisely timed brain stimulation and develops computational frameworks for understanding cortical coherence in cognitive functions. Analysis of his 2022-2025 publications reveals dominant themes in hippocampal memory coding (time/distance representation, engram segregation), theta-gamma oscillation dynamics, and neurotechnology development. Recent work emphasizes ultrasound-based deep brain stimulation, calcium imaging analysis toolboxes, and computational models of inhibitory networks, demonstrating consistent innovation at the engineering-medicine interface. Scientific Awards: 2019: Elected Fellow of the International Academy of Medical and Biological Engineering 2019: Elected President of the Biomedical Engineering Society 2014: Meeting Chair, Biomedical Engineering Society Fall Meeting 2011: Distinguished Alumnus Award, Dept. of BME, La. Tech U. 2006: Elected Fellow, Biomedical Engineering Society 2005: Elected Fellow, American Institute for Medical and Biological Engineering 2003-2008: Co-Director, Methods in Computational Neuroscience, Marine Biological Lab Professor White actively collaborates across Boston University's interdisciplinary centers while advancing his lab's mission to translate neural dynamics research into clinical applications, particularly for memory restoration in neurological disorders. His leadership in the Biomedical Engineering Society and ongoing development of real-time electrophysiology tools underscore his commitment to bridging engineering innovation with neurological therapeutics.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +4 more

Andreas Bjerregaard Jeppesen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning. His work bridges theoretical and applied research across diverse domains including quantum computing, biomedical informatics, environmental monitoring, and AI ethics. The Machine Learning section at DIKU explores foundational algorithms and their applications in Medical data analysis Remote sensing Biological modeling Sustainable AI Information retrieval . Andreas contributes to interdisciplinary projects like the SCIENCE AI Centre, leveraging the department's compute cluster for intensive simulations. His recent publications highlight trends in Quantum-inspired neural architectures Generative models for protein sequences Energy-aware AI development Neurological applications of ML Climate-conscious computing . Collaborations span computational biology, quantum chemistry, and federated learning for precision medicine.

• Machine Learning
• Natural Language Processing
• Health Informatics
• +3 more

Maher Harring Kassem serves as a Guest Researcher within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His position falls under the research staff category, reporting to Head of Section Professor Yevgeny Seldin, and contributes to the department's mission in theoretical and applied machine learning research. His research spans Machine Learning , Natural Language Processing , Health Informatics , Sustainable AI , Quantum Machine Learning , and Cross-Cultural Computing . This interdisciplinary profile integrates computational methods with real-world applications in mental health analysis, culinary adaptation systems, emotion recognition, and environmental sustainability, reflecting the department's focus on domains like medical data analysis and biological modeling. Analysis of his 2024-2025 publications reveals a distinct trend toward high-impact interdisciplinary work. Key themes include sustainable AI development (addressing energy consumption in models), quantum-biomolecular applications (free energy calculations), cross-cultural NLP systems (recipe adaptation), and clinical AI (nursing values evaluation). His output demonstrates technical depth across optical neural hardware, EEG-based semantic relevance, and fairness-aware recommender systems, while consistently tackling societal challenges like climate impact and healthcare equity. No scientific awards or fellowships were documented in the available materials. As a Guest Researcher, Kassem leverages the department's powerful compute cluster and participates in initiatives like the SCIENCE AI Centre and TreeSense project for remote sensing of global tree resources. His collaborative work spans medical imaging analysis, quantum computing applications, and sustainable AI development, utilizing the university's infrastructure for large-scale computational tasks in domains ranging from wetland conservation to quantum photonic computing.

• Machine Learning
• Quantum Machine Learning
• Neural Networks
• +6 more

Sebastian Bugge Loeschcke is a PhD Fellow at the Machine Learning Section of the Department of Computer Science (DIKU), University of Copenhagen . His research spans theoretical and applied machine learning with focus on quantum machine learning, language modeling, and sustainability. Current affiliation: Machine Learning Section, DIKU Key research areas: Quantum-classical hybrid models, neural language processing, geospatial analysis Collaborative initiatives: SCIENCE AI Centre, TreeSense Centre Loeschcke's recent work includes Coarse-To-Fine Tensor Trains for compact representations and LoQT: Low-Rank Adapters for Quantized Pretraining , reflecting his focus on efficient neural architectures and quantum-inspired methods. His publications address cross-disciplinary challenges in climate modeling, healthcare, and quantum computing. Scientific contributions include: 2024: Tensor train compression methods for visual representations 2024: Low-rank adapter techniques for quantized models 2025: Quantum computing applications in molecular binding energy calculation 2025: Ethical frameworks for sustainable AI development 2025: Quantum dot array simulation tools (QDarts) Loeschcke contributes to interdisciplinary projects involving: TreeSense (remote sensing of global tree resources) Quantum computing optimization with Danish research consortia

Christopher ChapmanView profile

Lecturer

• Bioelectronics
• Implanted devices
• Cancer neuroscience
• +7 more

Dr Christopher Chapman is a Lecturer in Bioengineering at Queen Mary University of London's School of Engineering and Materials Science. He serves as the Biomedical Engineering Programme Director (Undergraduate) and Outreach and Recruitment Lead for The Centre for Bioengineering. With expertise in bioelectronics design and fabrication for central and peripheral nervous system targets, Chapman leads research focused on developing soft and flexible bioelectronic implants using both metals and conducting polymers. Lecturer in Bioengineering Biomedical Engineering Programme Director (Undergraduate) Outreach and Recruitment Lead, Centre for Bioengineering Member, Institute of Materials, Minerals, and Mining Member, Institute of Physics and Engineering in Medicine Chapman's research interests center around bioelectronics, implanted devices, cancer neuroscience, conductive polymers, and electrical stimulation and recording. His work combines functional materials with laser-based fabrication methods to develop bioelectronic implants for cancer therapeutics and monitoring. He specializes in creating soft, flexible bioelectronic systems that can provide both therapeutic effects and diagnostic feedback from the tumor microenvironment. Analysis of Chapman's recent publications reveals a strong focus on developing novel bioelectronic materials and devices, particularly using conductive polymers and elastomers. His research spans neural interfaces, cancer monitoring, and drug delivery systems, with increasing emphasis on cancer neuroscience applications in recent years. The publications demonstrate a progression from fundamental materials development to more clinically relevant applications, especially in tumor margin detection and cancer microenvironment monitoring. Chapman currently leads the Continuous Advanced Recording for Cancer Lab (CARC Lab), which focuses on four key research areas: materials development, in vitro models of cancer, clinical measurements, and therapeutic drug delivery. His research group includes PhD students Joshua Daoud and Ester Do Couto Lopes, who are working on multimodal bioelectronic sensor development for real-time monitoring of the brain tumor microenvironment. Chapman has secured significant research funding for his work, including a £403,666 grant from the ARIA Advanced Research and Invention Agency for 'Oligodendronics: Engineering biology for scalable neural interfaces', a £74,990 grant from Barts and the London Charity for 'Development of multimodal tumour margin detection paradigm for use in neurosurgical oncology', and a £20,000 grant from the Royal Society for 'Customizable conducting elastomers for bioelectronics sensors'.

Matthew GolubView profile

Assistant Professor

• Computational Neuroscience
• Neuroengineering
• Machine Learning
• +3 more

Matthew Golub is an Assistant Professor at the University of Washington's Allen School of Computer Science & Engineering , where he leads the Systems Neuroscience & AI Lab (SNAIL) . He also holds affiliations with the UW Computational Neuroscience Center (Theory Faculty) and the Allen Institute for Neural Dynamics (Affiliate Investigator). His research focuses on the intersection of computational neuroscience , neuroengineering , and machine learning , particularly in understanding how neural population dynamics drive motor control , decision-making , and learning processes . Education: PhD in Electrical & Computer Engineering from Carnegie Mellon University (2015), awarded the A.G. Milnes Best Thesis Award Postdoctoral Fellowship at Stanford University (2017-2022), advised by Krishna Shenoy, Bill Newsome, and David Sussillo His work develops computational models for analyzing single-trial neural activity , with applications in brain-computer interfaces (BCIs) as tools for basic neuroscience. Recent projects include active learning techniques using two-photon holography , multi-regional latent variable models , and gradient-ascent explanations for local-circuit plasticity . His lab trains PhD students in neuroscience and machine learning across departments. Scientific awards include the NIH K99/R00 Pathway to Independence Award (2019) and NIH T32 Fellowship for trainees in his lab. He has presented invited talks at institutions including Yale, Berkeley, and the Allen Institute. Current Lab Members: Postdoctoral Fellows: Matthew Bull, Joseph Pemberton, Jacob Sacks Graduate Students: Belle Liu, Christina Wang, Lillian Li Undergraduate Researchers: Tanya Naveen, Nahush Shrivatsa, Zeynep Toprakbasti

Mark M. ChurchlandView profile

Professor

• Neuroscience
• Motor Control
• Neural Prosthetics
• +3 more

Mark M. Churchland is a Professor of Neuroscience at Columbia University's Vagelos College of Physicians and Surgeons. He serves as Codirector of the Grossman Center for the Statistics of Mind and Principal Investigator at the Zuckerman Institute, working at the intersection of neuroscience and computational modeling. Research Focus : Churchland investigates how the brain plans, triggers, and executes voluntary movements. His work combines primate neurophysiology with dynamical systems analysis to uncover unified neural mechanisms across different movement types, with applications for Parkinson's disease and neural prosthetics. Scientific Contributions : 2017 BRAIN Initiative Team-Research Circuit Program (U19) for Motor Control Pioneering tensor analysis methods for neural population structure Discovering brain activity compression scales in movement preparation Developing brain-machine interface algorithms from cortical dynamics Recent Publications (2024-2025) reveal his lab's focus on: Neural null-space dynamics in movement Interdisciplinary neurotechnology (DREDge motion correction) Preparation-execution timing mechanisms Motor unit flexibility and variability Cortical prosthetic control improvements

• Channel Coding
• Machine Learning
• Optical Communications
• +3 more

Laurent Schmalen is a full professor at the Karlsruhe Institute of Technology (KIT), co-directing the Communications Engineering Laboratory (CEL). He holds a Dipl.-Ing. and Dr.-Ing. in Electrical Engineering from RWTH Aachen University (2005 and 2011). Previously, he worked at Alcatel-Lucent Bell Labs (2011–2019) and led the Coding for Optical Communications department at Nokia Bell Labs (2016–2019). His research focuses on channel coding, machine learning, optical communications, and high-speed data transmission. Education and Professional Experience: 1999: Abitur at Lycée Classique Diekirch (Luxembourg) 2005: Diploma in Electrical Engineering and Information Technology, RWTH Aachen University 2011: PhD (summa cum laude) in Electrical Engineering, RWTH Aachen University 2011–2019: Research roles at Bell Labs 2014–2019: Visiting Lecturer at University of Stuttgart 2019–present: Full Professor at KIT Research Interests: His work spans channel coding (e.g., LDPC codes), machine learning for communications, modulation formats, and optical transmission systems. Key areas include non-linear channel analysis, probabilistic constellation shaping, and FEC optimization for fiber optics. Awards and Recognition: IEEE Fellow (2023) ERC Consolidator Grant (2023) Best Paper Awards in IEEE journals (2016, 2018) E-Plus Prize for Doctoral Thesis (2011) Editorial and Leadership Roles: Associate Editor, IEEE Transactions on Communications TPC Co-Chair, International Symposium on Topics in Coding (ISTC 2025) General Co-Chair, ITG Conference on Systems, Communications, and Coding (2025) Labs and Teams: He co-leads the Communications Engineering Lab (CEL) at KIT, focusing on cutting-edge research in optical communications and machine learning applications.

Xin ZhangView profile

Lecturer

• Computer Vision
• Deep Learning
• Machine Learning
• +3 more

Xin Zhang is an Instructor in the Department of Computer Science at the University of Southern Maine (USM), joining in January 2024. He holds a Ph.D. in Electrical Engineering from the University of South Carolina (2023), an M.S. in Software Engineering from Chongqing University (2019), and a B.S. in Software Engineering from the same university (2016). His research focuses on computer vision, particularly the robustness of convolutional neural networks (CNNs), partial-label learning problems, and monocular depth estimation. He also explores applied deep learning and machine learning techniques. Current teaching responsibilities include courses on System Programming and Computer Organizations. Xin has extensive programming experience in languages like C++, C, MATLAB, and has contributed to industrial projects involving web development, WinForm applications, Android apps, and applied deep learning systems. His recent publications address challenges in multi-label classification, attention-based neural networks, and quantum circuit optimization.

Oluwasanmi Oluseye KoyejoView profile

Adjunct Associate Professor

• Machine Learning
• Functional MRI
• Computer Science
• +3 more

Oluwasanmi Oluseye Koyejo is an Adjunct Associate Professor at the Siebel School of Computing and Data Science, University of Illinois. His research spans machine learning, neuroscience, and computer science, with specific interests in algorithmic fairness and brain-computer interfaces. He has received prestigious awards including the NSF CAREER Award and Sloan Research Fellowship. His recent publications demonstrate interdisciplinary work combining machine learning with medical imaging and decision theory. His 111 research outputs frequently appear in top-tier conferences and journals.

Gabrielle PouchelonView profile

Assistant Professor

• Neural circuits
• Neurodevelopmental disorders
• Synaptic refinement
• +6 more

Gabrielle Pouchelon is an Assistant Professor at Cold Spring Harbor Laboratory (CSHL), where her research focuses on the interplay between environmental cues and molecular programs in the assembly of neural circuits during brain development. She investigates how transient connectivity and sensory inputs shape cortical maturation, particularly in autism disorder models. Her work combines physiology, mouse genetics, and genomics to decode genetic and environmental contributions to neurodevelopmental dysfunctions.

• Cognitive Neuroscience
• Memory Research
• Semantic Processing
• +3 more

Dominika Varga is a Research Fellow at the School of Psychology, University of Sussex, focusing on cognitive neuroscience and memory research. Her work explores the interplay between episodic memory, semantic processing, and neural network dynamics. Research Fellow, School of Psychology University of Sussex Email: D.Varga@sussex.ac.uk Her research investigates hippocampal mismatch signals, context-dependent memory integration, and the neural basis of semantic-spatial cognition. She utilizes neuroimaging techniques to study default mode network interactions during mind-wandering and reading. Recent publications reveal trends in memory distortion, conceptual representation, and functional connectivity patterns, with a focus on understanding how the brain processes expectancy violations in naturalistic contexts.

Thane FremouwView profile

Associate Professor

• Chemo-fog/chemo-brain
• Category Learning
• Memory
• +3 more

Thane Fremouw is an Associate Professor of Psychology and Chair of the Psychology Department at the University of Maine. His research focuses on cognitive neuroscience topics including chemo-fog/chemo-brain, category learning, memory, and attention. He has published extensively on neural mechanisms underlying sensory processing and the cognitive impacts of chemotherapy in rodent models. His academic work bridges neurophysiological studies (e.g., auditory midbrain function) with behavioral research (e.g., spatial memory in mice). Notable contributions include decoding neural responses to sound duration and investigating neuroplasticity in chemotherapy-affected organisms. No scientific awards are listed, but his publications appear in journals like Journal of Neurophysiology and Behavioural Brain Research . He currently does not plan to recruit graduate students for Fall 2025.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +2 more

Haw-Shiuan Chang is a Research Fellow in the Department of Computer Science at the University of Massachusetts Amherst, based in Room 366 of the Computer Science Building. His research focuses on advancing natural language processing and artificial intelligence through innovative modeling techniques. His primary research interests include: Natural Language Processing (core focus) Machine Learning optimization Large Language Model efficiency Text generation factuality and diversity Computational creativity measurement Recent publications (2021-2025) reveal a strong emphasis on resolving fundamental limitations in language models, particularly addressing entropy extrapolation, softmax bottlenecks, and multi-mode distribution representation. His work bridges theoretical modeling with practical applications in story generation, recommendation systems, and scientific document processing. No scientific awards or mentoring activities are documented in the provided materials. Research is conducted within the Computer Science department infrastructure, with no specific lab or team affiliations mentioned.