Jacqueline B. Hynes, Phd Email and Phone Number

VERSES is a cognitive computing company building next-generation intelligent software systems. Our flagship product, Genius™ is modeled after the Wisdom and Genius of Nature. (www.verses.ai/genius )R&D: The hypothesis guiding research and development at VERSES AI is that artificial general intelligence (AGI) can be attained by discovering the deeper principles underlying biological intelligence and deploying them as design principles to construct the kind of distributed, collective intelligence that emerges from collaborative, harmonious, and mutually beneficial interactions among a network of intelligent agent ( www.verses.ai/rd )Safety & Governance: We believe that any safe AI requires built-in systems of governance. It cannot be an afterthought.Our R&D approach allows us explicit access to the beliefs of agents. That is, it gives us tools for discovering what an agent believes about the world and how it would change its beliefs in light of new information. Our models are fundamentally Bayesian and thus tell us what an optimal agent should believe and how it should act to maintain a safe and stable environment. Due to their explainability and intrinsic safety, Active Inference-based agents lend themselves to proper frameworks for governance. In particular, in partnership with its nonprofit arm, the Spatial Web Foundation (SWF), and with the Institute of Electrical and Electronics Engineers (IEEE), VERSES has developed and shared a standardized world modeling language that can expedite the creation of socio-techncial standards to address current concerns surrounding AI safety, governance, and alignment. ( https://www.verses.ai/ai-governance )(https://standards.ieee.org/ieee/2874/10375/)( https://spatialwebfoundation.org/swf/our-mission/ )

Collaborative Affiliation, developing neuroinformatics tools and AI-BCI neural decoding models for the translation and interpretation of high-dimensional neural/behavioral datasets.

Donoghue Lab - Computational Principles of Motor Control & Intelligent Brain-computer-interface systems. The goal of our current research is to develop a next-generation, ‘hybrid’ iBCI system that can rely on both the user’s movement-related neural signals (decoded from an implanted neural sensor chip) and a computer vision system (an artificial intelligence (AI) system trained to “see” the world). This new AI-iBCI technology is expected to provide paralyzed individuals with significant improvements in their ability to control assistive communication and robotic devices in everyday clinical settings, taking iBCI systems one step closer to becoming a useful, real world medical device. Through this work, I get to develop new analytical and conceptual frameworks to help us better understand how networks of dynamically interacting neurons perform computations and process complex information.

Donoghue Lab - Computational Principles of Motor Control & Brain-computer-Interface neural decoding algorithms. I worked as a postdoctoral research associate in the Lab of Dr. John Donoghue and as a collaborator with the BrainGate team at Brown University. Our lab investigates how the brain turns perceptual information and thoughts into goal-directed action. At the core of this problem is understanding how large populations of neurons represent complex information. More specifically, we use novel multi-electrode recording arrays and neural ensemble analysis tools to examine ways that populations of cerebral cortical neurons acquire and code information related to planning and enacting voluntary arm movements. My specific work explores the relationship between sensory signals and context-dependent encoding in motor networks. I also enjoy developing new analytic tools and frameworks for the analysis of large-scale neural data. We are also applying our knowledge of neural codes for movement to build prosthetic devices that provide an interface between the brain and the external world for individuals with paralysis. (http://www.braingate.org/about-braingate/)

Thesis Title: “Network Interactions during Contextual Information Processing in Macaque Primary Visual Cortex.”Thesis Advisor: Michael Paradiso, Ph.D.Thesis Committee: Thomas Seere, PhD, Barry Connors, PhD, Richard Born.I conducted my doctoral research at the Paradiso Lab at Brown University. This lab focuses on understanding the computations performed by interacting neurons, and the adaptive use of neural circuitry, with the goal of understanding the neurocomputational mechanisms and principles underlying visual perception dynamics.Historically, our comprehension of vision has primarily been influenced by research treating neurons as independent functional units, each responsible for processing information about a specific visual feature within a distinct region of the visual field. However, this general approach disregards the inherently contextual nature of visual perception and the collaborative neuronal processes believed to facilitate it. In response, my research project aimed to highlight the circuitry involved in contextual information processing. Using Blackrock's 96-channel micro-electrode array recording technology, I characterized the spatiotemporal dynamics of inhibitory and excitatory network activity within a 4x4 mm region of the cortex during a visual stimulation paradigm that elicits the contextual perceptual phenomenon referred to as "surround suppression". This research sought to clarify the intricate neural interactions that shape the contextual aspects of our visual perception.

A collaborative project under the supervision of Dr. Siobhan McMahon, between the Department of Anatomy at National University of Ireland, Galway, REMEDI (Regenerative Medicine Institute), and the Mayo clinic. Our goal was to mitigate the molecular and cellular repercussions of contusions spinal cord injuries, and to encourage regain of function with the use of novel cellular therapies and stem cell transplantation.