The team
Meet the team building the Large Brain Model
Neuroscientists, AI researchers, and engineers committed to building the future of non-invasive brain decoding.
Tan Le
CEO
WEF Global Future Council on Neurotechnology
20 years pioneering mobile EEG & BCI
TED Speaker
Tan Le is CEO and Founder of Emotiv and Dcode.AI. Over 15 years she has built the largest non-invasive EEG dataset in the world — 14.4 million minutes of training-grade signal across 4,000+ institutions in 140+ countries — and the only corpus on which a true Large Brain Model can be trained. She sits on the World Economic Forum's Global Future Council on Neurotechnology, has given three TED talks viewed by millions, and is the author of The NeuroGeneration. A former Young Australian of the Year and National Geographic Emerging Explorer, she is widely recognised as a leading voice on the responsible development of neurotechnology.
Dr Navid Foumani
Foundation model Architect & Lead
Lead author of EEG2Rep (KDD ’24 Audience Appreciation Award) and EEGX (arXiv:2601.17883) — the published Emotiv transformer architectures that benchmark best-in-class. Australia-based. The architect of the foundation-model approach that the round scales to production. Serves as a NeurIPS reviewer in the EEG and brain foundation model area — the cohort that included over 100 submissions this year, signaling the field’s decisive movement toward foundation models. Continues in this role through the round period, partnering with the new VP of Foundation Model Scaling on the production training.
Dr Nam Nguyen
Director of Corpus Curation
Leads corpus quality control and consistency across the pre-training pipeline. Nam defines and enforces the curation standards that govern artifact handling, device-specific metadata schema, and what enters the LBM training corpus — the quality gate between raw EEG collection and model training. His background spans signal processing across demanding real-world environments: at Halliburton, he built acoustic signal processing systems for downhole leak detection in oil wells, developed ML classification pipelines for leak characterisation, and designed embedded firmware for drive-by-wire control of tractor tools operating thousands of feet underground. Earlier at BlackBerry he developed audio localisation algorithms prototyped on DSP hardware. Prior work included statistical software development for clinical research and pharmaceutical drug trials. Twenty-one granted US patents. The combination of rigorous real-world signal processing experience, embedded systems depth, and familiarity with data quality requirements in regulated environments makes him well-suited to the corpus governance role: EEG curation at scale requires the same discipline as industrial signal processing — systematic artifact classification, metadata integrity, and pipeline consistency across heterogeneous hardware configurations. Australia-based.
Dr Geoff Mackellar
CTO
Currently serving as CTO, Geoff transitions to Chief Scientist once the external CTO hire is in role — a deliberate structure that moves Emotiv’s most technically deep founder into the position where his contribution is highest-leverage. As Chief Scientist he will hold senior scientific authority for the LBM and the underlying architecture work, freed from the operational management scope the CTO role carries.
A PhD physicist, he is Australia-based, anchoring the foundation-model research and engineering team there. The team has produced the peer-reviewed architecture papers (EEG2Rep, EEGX, SAMBA, SpellerSSL, and EEGM2) under his scientific direction, with the KDD ’24 Audience Appreciation Award going to Dr Navid Foumani for EEG2Rep. Geoff has led the development of every hardware model the company has produced since its founding — an unusual depth of cross-disciplinary ownership that has directly shaped the quality and range of the corpus the LBM trains on.
The hardware design decisions, sensor specifications, firmware, signal processing pipelines, software capabilities, data management systems, and integration with common EEG research tools (PsychoPy, EEGLab, MNE) were each developed with direct scientific leadership involvement. Emotiv’s approach of using its own products in pure and applied neuroscience research — building features and detections informed by first-hand use across expert and non-expert contexts — means the technical platform has been developed in service of the whole spectrum of users and applications rather than optimised for any single use-case tier. This level of depth across the entire product lifecycle, concentrated into the company’s founding scientific leadership, is not common. It contributes directly to the foundation model program: understanding the hardware and signal processing at the level of the person who designed it is a structurally different starting point for building the denoising, normalisation, and representation-learning capabilities that make the LBM work.
Dr Nikolas Williams
Director of neuroscience & Research
EEG neuroscience and cognitive research authority. Defines the corpus curation protocols that establish the structural consistency of the Emotiv corpus across institutions and devices — the ongoing scientific work that determines what the LBM trains on and how. The senior scientific voice on neuroscience methodology that complements Dr Geoff Mackellar's signal-processing and architecture authority and Dr Navid Foumani's foundation-model architecture work.
Patrick Chu
VP of Software Engineering
Patrick has been with Emotiv since the inception of the company's cloud service and platform architecture — one of the longest-tenured technical leaders in the organization. He has built and owned the full engineering stack from signal acquisition at the hardware layer through platform infrastructure, cloud services, MLOps, and information security. That end-to-end ownership — spanning the physical signal pipeline into the system through to the machine learning infrastructure that the LBM runs on — is uncommon in a single technical leader and reflects the depth of contribution he has made across the company's fifteen-year build. He leads the engineering organization today, including platform engineering, the Cortex API, and the Brainwear consumer product engineering. The engineering organization scales from 30 to approximately 55–65 under his leadership during the round period.
Tan Le
CEO
WEF Global Future Council on Neurotechnology
20 years pioneering mobile EEG & BCI
TED Speaker
Tan Le is CEO and Founder of Emotiv and Dcode.AI. Over 15 years she has built the largest non-invasive EEG dataset in the world — 14.4 million minutes of training-grade signal across 4,000+ institutions in 140+ countries — and the only corpus on which a true Large Brain Model can be trained. She sits on the World Economic Forum's Global Future Council on Neurotechnology, has given three TED talks viewed by millions, and is the author of The NeuroGeneration. A former Young Australian of the Year and National Geographic Emerging Explorer, she is widely recognised as a leading voice on the responsible development of neurotechnology.
Dr Geoff Mackellar
CTO
Currently serving as CTO, Geoff transitions to Chief Scientist once the external CTO hire is in role — a deliberate structure that moves Emotiv’s most technically deep founder into the position where his contribution is highest-leverage. As Chief Scientist he will hold senior scientific authority for the LBM and the underlying architecture work, freed from the operational management scope the CTO role carries.
A PhD physicist, he is Australia-based, anchoring the foundation-model research and engineering team there. The team has produced the peer-reviewed architecture papers (EEG2Rep, EEGX, SAMBA, SpellerSSL, and EEGM2) under his scientific direction, with the KDD ’24 Audience Appreciation Award going to Dr Navid Foumani for EEG2Rep. Geoff has led the development of every hardware model the company has produced since its founding — an unusual depth of cross-disciplinary ownership that has directly shaped the quality and range of the corpus the LBM trains on.
The hardware design decisions, sensor specifications, firmware, signal processing pipelines, software capabilities, data management systems, and integration with common EEG research tools (PsychoPy, EEGLab, MNE) were each developed with direct scientific leadership involvement. Emotiv’s approach of using its own products in pure and applied neuroscience research — building features and detections informed by first-hand use across expert and non-expert contexts — means the technical platform has been developed in service of the whole spectrum of users and applications rather than optimised for any single use-case tier. This level of depth across the entire product lifecycle, concentrated into the company’s founding scientific leadership, is not common. It contributes directly to the foundation model program: understanding the hardware and signal processing at the level of the person who designed it is a structurally different starting point for building the denoising, normalisation, and representation-learning capabilities that make the LBM work.
Dr Navid Foumani
Foundation model Architect & Lead
Lead author of EEG2Rep (KDD ’24 Audience Appreciation Award) and EEGX (arXiv:2601.17883) — the published Emotiv transformer architectures that benchmark best-in-class. Australia-based. The architect of the foundation-model approach that the round scales to production. Serves as a NeurIPS reviewer in the EEG and brain foundation model area — the cohort that included over 100 submissions this year, signaling the field’s decisive movement toward foundation models. Continues in this role through the round period, partnering with the new VP of Foundation Model Scaling on the production training.
Dr Nikolas Williams
Director of neuroscience & Research
EEG neuroscience and cognitive research authority. Defines the corpus curation protocols that establish the structural consistency of the Emotiv corpus across institutions and devices — the ongoing scientific work that determines what the LBM trains on and how. The senior scientific voice on neuroscience methodology that complements Dr Geoff Mackellar's signal-processing and architecture authority and Dr Navid Foumani's foundation-model architecture work.
Dr Nam Nguyen
Director of Corpus Curation
Leads corpus quality control and consistency across the pre-training pipeline. Nam defines and enforces the curation standards that govern artifact handling, device-specific metadata schema, and what enters the LBM training corpus — the quality gate between raw EEG collection and model training. His background spans signal processing across demanding real-world environments: at Halliburton, he built acoustic signal processing systems for downhole leak detection in oil wells, developed ML classification pipelines for leak characterisation, and designed embedded firmware for drive-by-wire control of tractor tools operating thousands of feet underground. Earlier at BlackBerry he developed audio localisation algorithms prototyped on DSP hardware. Prior work included statistical software development for clinical research and pharmaceutical drug trials. Twenty-one granted US patents. The combination of rigorous real-world signal processing experience, embedded systems depth, and familiarity with data quality requirements in regulated environments makes him well-suited to the corpus governance role: EEG curation at scale requires the same discipline as industrial signal processing — systematic artifact classification, metadata integrity, and pipeline consistency across heterogeneous hardware configurations. Australia-based.
Patrick Chu
VP of Software Engineering
Patrick has been with Emotiv since the inception of the company's cloud service and platform architecture — one of the longest-tenured technical leaders in the organization. He has built and owned the full engineering stack from signal acquisition at the hardware layer through platform infrastructure, cloud services, MLOps, and information security. That end-to-end ownership — spanning the physical signal pipeline into the system through to the machine learning infrastructure that the LBM runs on — is uncommon in a single technical leader and reflects the depth of contribution he has made across the company's fifteen-year build. He leads the engineering organization today, including platform engineering, the Cortex API, and the Brainwear consumer product engineering. The engineering organization scales from 30 to approximately 55–65 under his leadership during the round period.
Tan Le
CEO
WEF Global Future Council on Neurotechnology
20 years pioneering mobile EEG & BCI
TED Speaker
Tan Le is CEO and Founder of Emotiv and Dcode.AI. Over 15 years she has built the largest non-invasive EEG dataset in the world — 14.4 million minutes of training-grade signal across 4,000+ institutions in 140+ countries — and the only corpus on which a true Large Brain Model can be trained. She sits on the World Economic Forum's Global Future Council on Neurotechnology, has given three TED talks viewed by millions, and is the author of The NeuroGeneration. A former Young Australian of the Year and National Geographic Emerging Explorer, she is widely recognised as a leading voice on the responsible development of neurotechnology.
Dr Geoff Mackellar
CTO
Currently serving as CTO, Geoff transitions to Chief Scientist once the external CTO hire is in role — a deliberate structure that moves Emotiv’s most technically deep founder into the position where his contribution is highest-leverage. As Chief Scientist he will hold senior scientific authority for the LBM and the underlying architecture work, freed from the operational management scope the CTO role carries.
A PhD physicist, he is Australia-based, anchoring the foundation-model research and engineering team there. The team has produced the peer-reviewed architecture papers (EEG2Rep, EEGX, SAMBA, SpellerSSL, and EEGM2) under his scientific direction, with the KDD ’24 Audience Appreciation Award going to Dr Navid Foumani for EEG2Rep. Geoff has led the development of every hardware model the company has produced since its founding — an unusual depth of cross-disciplinary ownership that has directly shaped the quality and range of the corpus the LBM trains on.
The hardware design decisions, sensor specifications, firmware, signal processing pipelines, software capabilities, data management systems, and integration with common EEG research tools (PsychoPy, EEGLab, MNE) were each developed with direct scientific leadership involvement. Emotiv’s approach of using its own products in pure and applied neuroscience research — building features and detections informed by first-hand use across expert and non-expert contexts — means the technical platform has been developed in service of the whole spectrum of users and applications rather than optimised for any single use-case tier. This level of depth across the entire product lifecycle, concentrated into the company’s founding scientific leadership, is not common. It contributes directly to the foundation model program: understanding the hardware and signal processing at the level of the person who designed it is a structurally different starting point for building the denoising, normalisation, and representation-learning capabilities that make the LBM work.
Dr Navid Foumani
Foundation model Architect & Lead
Lead author of EEG2Rep (KDD ’24 Audience Appreciation Award) and EEGX (arXiv:2601.17883) — the published Emotiv transformer architectures that benchmark best-in-class. Australia-based. The architect of the foundation-model approach that the round scales to production. Serves as a NeurIPS reviewer in the EEG and brain foundation model area — the cohort that included over 100 submissions this year, signaling the field’s decisive movement toward foundation models. Continues in this role through the round period, partnering with the new VP of Foundation Model Scaling on the production training.
Dr Nikolas Williams
Director of neuroscience & Research
EEG neuroscience and cognitive research authority. Defines the corpus curation protocols that establish the structural consistency of the Emotiv corpus across institutions and devices — the ongoing scientific work that determines what the LBM trains on and how. The senior scientific voice on neuroscience methodology that complements Dr Geoff Mackellar's signal-processing and architecture authority and Dr Navid Foumani's foundation-model architecture work.
Dr Nam Nguyen
Director of Corpus Curation
Leads corpus quality control and consistency across the pre-training pipeline. Nam defines and enforces the curation standards that govern artifact handling, device-specific metadata schema, and what enters the LBM training corpus — the quality gate between raw EEG collection and model training. His background spans signal processing across demanding real-world environments: at Halliburton, he built acoustic signal processing systems for downhole leak detection in oil wells, developed ML classification pipelines for leak characterisation, and designed embedded firmware for drive-by-wire control of tractor tools operating thousands of feet underground. Earlier at BlackBerry he developed audio localisation algorithms prototyped on DSP hardware. Prior work included statistical software development for clinical research and pharmaceutical drug trials. Twenty-one granted US patents. The combination of rigorous real-world signal processing experience, embedded systems depth, and familiarity with data quality requirements in regulated environments makes him well-suited to the corpus governance role: EEG curation at scale requires the same discipline as industrial signal processing — systematic artifact classification, metadata integrity, and pipeline consistency across heterogeneous hardware configurations. Australia-based.
Patrick Chu
VP of Software Engineering
Patrick has been with Emotiv since the inception of the company's cloud service and platform architecture — one of the longest-tenured technical leaders in the organization. He has built and owned the full engineering stack from signal acquisition at the hardware layer through platform infrastructure, cloud services, MLOps, and information security. That end-to-end ownership — spanning the physical signal pipeline into the system through to the machine learning infrastructure that the LBM runs on — is uncommon in a single technical leader and reflects the depth of contribution he has made across the company's fifteen-year build. He leads the engineering organization today, including platform engineering, the Cortex API, and the Brainwear consumer product engineering. The engineering organization scales from 30 to approximately 55–65 under his leadership during the round period.
Market opportunity
The global neurotechnology market is projected to reach over $50 billion by 2034, driven by the increasing demand for solutions to neurological disorders. Despite this growth, the market remains highly fragmented by device type and
data protocol, creating a significant barrier to innovation. Dcode AI's universal foundation model is poised to solve this problem by creating a standardized language for the brain, unlocking new applications and accelerating breakthroughs in research, health, and consumer neurotech.
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Interested in learning more about Dcode AI and our mission to build a universal language for the brain? We are actively engaged in discussions with strategic partners and investors.
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