Matilda Makkonen is a Doctoral Researcher at Aalto University's Department of Neuroscience and Biomedical Engineering, specializing in transcranial magnetic stimulation (TMS) and electroencephalography (EEG) integration with machine learning techniques. She holds a Master's degree in Engineering and Technology from Aalto University (2023) and a Bachelor's degree in the same field (2021). Key Research Areas: Machine learning for real-time signal processing, closed-loop EEG-TMS systems, and artifact rejection techniques. Research Trends: Focuses on improving brain stimulation methods through advanced signal decoding and noise reduction algorithms. Makkonen has been actively involved in organizing international workshops like the TMS–EEG Summer School and the Brain Stimulation and Imaging Meeting (2022-2024).
Marijn van Vliet is a Research Fellow at Aalto University's Department of Neuroscience and Biomedical Engineering, specializing in computational neuroscience and brain imaging. He focuses on decoding cognitive processes through advanced analysis of MEG/EEG data. Academy Research Fellow (2021-present) Principal investigator in projects like 'Unraveling language in the brain through biologically plausible modeling' His research combines machine learning with neuroimaging to explore language comprehension, semantic processing, and functional connectivity. Key methodologies include convolutional networks, representational similarity analysis, and beamforming techniques. Active in open science initiatives Developed tools like mne-rsa and mne-faster Recent work investigates cortical dynamics during semantic processing, feedforward/backward visual word recognition, and test-retest reliability of MEG connectivity metrics. His projects are supported by the Academy of Finland and RCF Academy funding.
Jihye Bae is an Assistant Professor at the University of Kentucky within the Stanley and Karen Pigman College of Engineering , leading the Neural Interfaces and Signal Processing (NISP) Laboratory . Her research focuses on developing systems and methods to assist patients with neuromuscular disabilities and neurological disorders using signal processing , machine learning , and neural decoder techniques. Education: Ph.D. in Electrical and Computer Engineering, University of Florida (2013) M.S. in Electrical and Computer Engineering, University of Florida (2009) B.Eng. in Electrical Engineering and Computer Science, Kyungpook National University, South Korea (2007) Her research spans brain-machine interfaces , EEG signal processing , and source imaging for epilepsy and prosthetic development . Recent publications explore fNIRS semantic reconstruction , EEG-fMRI artifact removal , and reinforcement learning in neural decoders. She has also contributed to metric learning and correntropy kernel methods for brain-computer interfaces. Jihye Bae supervises active researchers including PhD students Bhoj Raj Thapa and Santiago Posso Murillo , as well as Master's and undergraduate students. Her lab has received recognition through Lighthouse Beacon Foundation Graduate Fellowships for trainees. She teaches courses like EE 421G Signals and Systems and EE 599/699 Neural Signal Processing and Interfaces .
Mingzhou Ding is Distinguished Professor and J. Crayton Pruitt Family Professor in the J. Crayton Pruitt Family Department of Biomedical Engineering at the University of Florida, within the College of Engineering. His research bridges physics, engineering, and neuroscience to investigate brain function through advanced neuroimaging and signal processing techniques. His research interests include cognitive neuroscience , multimodal neuroimaging , signal processing , affective neuroscience , and computational modeling of brain networks . He employs methods such as EEG-fMRI integration, multivariate pattern analysis, and effective connectivity modeling to study how emotional and cognitive processes are represented in the brain. Recent publications highlight a strong trend in decoding emotional content in visual cortex, attentional control mechanisms, and neuromodulation of brain networks. His work frequently involves analyzing neural representations of affective scenes, conflict processing in psychiatric conditions, and the role of reentrant feedback from limbic structures in shaping visual perception. Specialty Chief Editor, Frontiers in Human Neuroscience Guest Associate Editor, Frontiers in Neural Circuits Guest Associate Editor, Cognitive Neuroscience , Frontiers in Human Neuroscience Prof. Ding has contributed to numerous editorial roles and has collaborated widely across neuroscience and biomedical engineering. He advises several researchers and has been involved in studies supported by significant NIH and NSF funding, though specific grants are not detailed here. His lab focuses on brain imaging and stimulation, with a strong emphasis on experimental design and computational analysis of neural data. He leads a research team specializing in brain network dynamics, neuroimaging methodology, and cognitive neuroscience experiments, particularly in the domain of emotion and attention.
Arthur M. Jacobs is a Full Professor of General Psychology at the Free University of Berlin, where he serves as the Founding Director of the Dahlem Institute for Neuroimaging of Emotion (D.I.N.E.). His academic career spans several prestigious institutions including the Catholic University of Eichstätt-Ingolstadt and Philipps University of Marburg. He leads research in the Department of General and Neurocognitive Psychology within the Department of Education and Psychology at the Free University of Berlin. Professor Jacobs earned his doctorate from Université René Descartes (Sorbonne, Paris V) with a dissertation titled "Le controle oculomoteur dans l'exploration visuelle: mecanismes sensori-moteurs et processus cognitifs" in 1986. His academic journey includes positions at the CNRS in France, RWTH Aachen, and several German universities before settling at the Free University of Berlin in 2003. His research focuses on the intersection of cognitive psychology, neuroscience, and literary studies. Jacobs has pioneered the field of Neurocognitive Poetics, investigating how readers process emotional and aesthetic aspects of literature. His work combines advanced neuroimaging techniques with computational analysis of texts, particularly examining Shakespeare's works, poetry, and emotional language processing. He has developed influential tools like the Berlin Affective Word List (BAWL) for measuring emotional responses to words. His approach integrates eye-tracking, fMRI, EEG, and computational modeling to understand the cognitive and neural mechanisms underlying reading and emotional responses to literature. Analysis of his recent publications reveals a strong trend toward computational approaches to literary analysis, with increasing use of machine learning and deep learning techniques to analyze text features, emotional responses, and neural correlates of reading. His work bridges humanities and neuroscience, creating a unique interdisciplinary field that examines how literary texts affect the brain and cognition. Professor Jacobs leads the D.I.N.E. Labs, which include specialized facilities for fMRI, EEG, eye-tracking, transcranial magnetic stimulation, simulation, and near-infrared spectroscopy research. These labs support his research on emotional and aesthetic processing in reading, bilingualism, and neurocognitive aspects of literature reception.
Mohamed F. Tolba is a Professor at Ain Shams University in Cairo, Egypt, affiliated with the Department of Scientific Computing within the Faculty of Science. He also maintains a research affiliation with Nile University's NISC Research Center in Giza, Egypt. His career spans over two decades of active research and publication in computational intelligence and its applications. Dr. Tolba's research interests focus on the intersection of artificial intelligence and practical applications, particularly in medical imaging, bioinformatics, and computer vision. His work demonstrates expertise in deep learning architectures, neural networks, and image processing techniques applied to medical diagnostics and biological data analysis. He has made significant contributions to cancer detection systems, EEG signal processing, and hyperspectral image classification. His recent publications (2023-2025) reveal a strong trend toward developing specialized deep learning models for medical applications, including cancer classification, Parkinson's disease diagnosis, and medical image analysis. He frequently collaborates with researchers in Egypt, particularly with Howida A. Shedeed, Hala Mousher Ebeid, and Aboul Ella Hassanien, producing numerous high-impact publications in international conferences and journals. Dr. Tolba has been instrumental in organizing and contributing to major conferences in his field, including the International Conference on Advanced Intelligent Systems and Informatics (AISI), where he has served as editor and contributor to conference proceedings.
Dr. Garima Bajwa is an Assistant Professor in the Department of Computer Science at Lakehead University, where she joined in January 2021. Her research focuses on authentication systems, brain-computer/machine interfaces, cybersecurity, and machine learning applications. She holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2016), an MEng in Electrical and Computer Engineering from the University of Waterloo (2011), and a B.Tech in Electronics and Communication Engineering from Mody Institute of Technology & Science, India (2009). Education: Ph.D., Computer Science & Engineering, University of North Texas (2016) MEng, Electrical & Computer Engineering, University of Waterloo (2011) B.Tech, Electronics & Communication Engineering, Mody Institute (2009) Her research interests span cybersecurity frameworks for neurotechnology, explainable AI in healthcare diagnostics, and multimodal fusion of EEG and image data. Recent work includes EEG signal anonymization, federated learning for medical diagnosis, and reproducibility in BCI research. Her publications explore cutting-edge topics like BCI standardization, driver distraction detection, and quantum computing integration. Grants and lab affiliations are not explicitly mentioned in the provided text.
Wim Strijbosch is a Researcher at the Breda University of Applied Sciences within the Academy for Leisure & Events , focusing on the Leisure and Tourism Experiences department. His work integrates neuroscience methods like EEG and skin conductance to study emotional dynamics in tourism and leisure experiences. He leads projects on dark ride experiences, visitor inclusivity, and storytelling in museums. Research Interests: Emotions in tourism, temporal dynamics of experiences, themed entertainment design, and neuroscientific approaches to measuring lived experiences. His Experience Lab employs cutting-edge tools to decode how emotions shape memorable experiences. Notable Projects: Includes 'Measuring Emotions and Experiences' (2016-2021), 'Dark Ride Cube Conceptualization' (2024), and studies on inclusivity for hearing-impaired visitors. Collaborations span institutions like Apenheul, Van Gogh Brabant, and IAAPA. Awards: Literati Award 2020 (shared) Grants: Multiple projects funded by NWO, CELTH, and industry partners His Experience Design research emphasizes practical applications for attractions, museums, and theme parks, with media presence in podcasts like Theme Park Science and Dutch news outlets.
Tushar Chauhan is a Postdoctoral Fellow at the Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT). His research focuses on computational neuroscience, visual processing, and neural decoding, with a particular emphasis on understanding color perception, neural representations in the cortex, and biologically plausible learning mechanisms. He employs advanced techniques such as EEG signal analysis, spiking neural networks, and neuroimaging (fMRI/PET) to investigate how sensory information is encoded and processed in the brain. Key research areas include optic flow processing, developmental models of visual systems, and the neural correlates of spatial and color perception. His work bridges theoretical neuroscience with experimental validation, often using unsupervised learning approaches to model biological systems. Chauhan has contributed to understanding sub-optimality in early visual systems and the role of orientation information in face recognition. His publications span topics from neural geometry analysis to event-based robotics, demonstrating interdisciplinary applications of neuroscience principles. Despite no explicitly listed awards or grants, his prolific output (14+ articles from 2017–2024) indicates sustained academic engagement. No advising relationships or lab affiliations beyond the Picower Institute are documented in the provided materials.
Danilo P. Mandic is a Professor of Machine Intelligence at Imperial College London, Department of Electrical and Electronic Engineering. His research focuses on machine learning, signal processing, and biomedical engineering, with notable contributions to graph data analytics, tensor networks, and wearable sensors. Mandic has supervised numerous students, including those who have won prestigious awards such as the Ivor Tupper Prize and Sir Bruce White Prize. Education: PhD in Nonlinear Adaptive Signal Processing (Imperial College London, 1999). Previously taught at the Universities of East Anglia and Banja Luka. Research Interests: Machine Intelligence, Signal Processing (including EEG/ECG analysis), Wearable Health Technologies, Financial Signal Processing, and Tensor Networks. He has pioneered work on graph signal processing and in-ear biosensors. Key Awards: 2019 Dennis Gabor Award (INNS), 2023 IEEE Engineering in Medicine Prize Paper Award, 2018 IEEE Signal Processing Magazine Best Paper Award. Grants & Labs: Leads projects in neurotechnology, sensor signal processing, and smart grid analysis. Collaborates with industry and international institutions like RIKEN Brain Science Institute. Edited books on complex-valued adaptive filters and recurrent neural networks. Teaching: Courses on signal processing and machine learning. Notable for integrating wearable sensors into curricula. Awarded Imperial College's Excellence in Research Supervision (2014).
Bo Bernhardsson is a full Professor at Lund University's Department of Automatic Control within the Lund Institute of Technology (LTH). He also holds roles in ELLIIT (Linköping-Lund initiative on IT and mobile communication) and is a profile area member in Engineering Health, AI and Digitalization, and Natural and Artificial Cognition. After earning his Ph.D. in Automatic Control from Lund University in 1992 and a postdoctoral fellowship at the University of Minnesota, he returned to Lund, becoming a docent (1998) and full professor (1999). From 2001–2010, he worked at Ericsson as a mobile system design and optimization expert before resuming his academic role at Lund University. Education: Ph.D. in Automatic Control (Lund University, 1992), Docent (1998) Affiliations: ELLIIT, LTH Profile Areas (Engineering Health, AI & Digitalization), LU Profile Area (Natural and Artificial Cognition) His research focuses on EEG signal analysis , control of uncertain systems , radio-based sensing and communication , and medical data modeling , blending theoretical analysis with practical applications. His work contributes to UN Sustainable Development Goals through advancements in healthcare and communication systems. Recent research highlights include advancements in 5G NR localization, EEG-based auditory attention decoding, and transfer learning for medical signal processing. He has supervised over 37 students, including doctoral candidates like F. Heskebeck and D. Pjanić. Active in interdisciplinary collaborations, he organized workshops such as the 2021 AI Lund seminar on uncertain system modeling and contributed to projects like the 2020–2022 pandemic dynamics initiative. Bernhardsson has authored 146 publications, 19 research projects, and engaged in 41 academic activities, reflecting his leadership in both technical and educational spheres within control engineering and applied systems.
Dr. Yuxing Fang is a Researcher at the Centre for Speech, Language, and the Brain (CSLB) within the Department of Psychology at the University of Cambridge. His work explores the neural mechanisms underlying language processing, semantic representation, and brain connectivity. Affiliation: University of Cambridge, Department of Psychology Contact: yf292@cam.ac.uk Research Interests: Dr. Fang’s studies focus on neurocognitive dynamics during speech comprehension, brain hubs in lesion models, and semantic networks in healthy and pathological conditions. His expertise spans neuroimaging, cognitive science, and clinical psychology. Publications: He has contributed to understanding Alzheimer’s disease, semantic dementia, and tool use through multimodal neuroimaging and lesion-behavior mapping. His work integrates neuroscience with computational approaches to language and cognition.
Dr. Andrew Thwaites is a Research Fellow at the Centre for Speech, Language, and the Brain (CSLB) within the Department of Psychology, University of Cambridge. His work focuses on the neurocognitive mechanisms underlying speech and language processing, integrating cortical activity analysis with machine learning approaches. Research Interests: Neurocognitive linguistics EEG/MEG signal analysis Cortical entrainment to speech Human-machine speech recognition comparisons Auditory perception Computational modeling of neural systems Publication Trends: Recent work (2025) includes frameworks for cortical processing mapping and unified EEG/MEG models. Earlier studies explore tonotopic representations (2017-2018), cross-domain comparisons between AI and neural networks (2016-2017), and sensory entrainment mechanisms (2015-2023). Keywords span Neuroscience , Machine Learning , and Speech Processing . Labs & Teams: Affiliated with the CSLB, a multidisciplinary research hub focusing on speech, language, and neuroimaging technologies.
Abigail Noyce is an assistant research professor at the Carnegie Mellon Neuroscience Institute , affiliated with the Department of Psychology . She investigates cognitive mechanisms underlying perception, attention, and memory using EEG and fMRI neuroimaging techniques, focusing on how the brain exploits predictability and sensory specialization. Her research spans auditory and visual cognition , examining dual-task interference, working memory organization, and multisensory integration. Current work explores how task demands interact with sensory processing limitations and individual neural architectures. Recent publications emphasize auditory attention (2024), functional cortical mapping (2023), and predictive coding (2022). Her 2025 study on spatial selection dynamics reveals novel mechanisms in attentional control systems. Dr. Noyce's methodological expertise includes EEG signal decoding, fMRI connectivity analysis, and connectome fingerprinting techniques for individual prediction models. Contact: abigail.noyce@cmu.edu
Xuan Wang is an Assistant Professor in the Department of Computer Science at Virginia Tech, affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with additional M.S. degrees in Statistics and Biochemistry from UIUC, and a B.S. in Biological Science from Tsinghua University. Her research focuses on Natural Language Processing (NLP), Data Mining, AI for Sciences, and AI for Healthcare, emphasizing applications in complex reasoning with LLMs, multi-modal science foundation models, and healthcare informatics. Her work has been recognized through awards including the Nvidia Academic Grant (2025), Cisco Research Award (2025), and NSF NAIRR Pilot Award (2024-2025). She has organized workshops at ACL, VL/HCC, and ICDM, and serves on program committees for top conferences like NeurIPS, EMNLP, and KDD. Xuan's research spans scientific text mining (e.g., knowledge extraction from biomedical literature), multi-agent LLM systems for clinical triage, and foundational models for multi-omics data analysis. Her lab actively collaborates with institutions like Children’s National Hospital and the Fralin Biomedical Research Institute, with funding from NSF, CCI, and industry partners. Education: Ph.D. in Computer Science, UIUC (2022) M.S. in Statistics, UIUC (2017) M.S. in Biochemistry, UIUC (2015) B.S. in Biological Science, Tsinghua University (2013) Grants & Awards: NVIDIA Academic Grant (2025) – Small LLM Agent Systems Cisco Research Award (2025) – Complex Reasoning with LLMs NSF NAIRR Pilot (2024-2025) – Multi-omics Analysis Lab & Teams: Wang Lab focuses on AI-driven biomedical research, including single-cell omics analysis, brain signal interpretation, and LLM-based scientific discovery. Collaborations include the Virginia Tech Presidential Postdoctoral Fellowship program and industry initiatives like the Amazon + VT Center for Efficient ML.
