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Newton Howard

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This sandbox is in the article namespace. Either move this page into your userspace, or remove the {{User sandbox}} template. Prof. Newton Howard (DM, PhD, DPhil, HDR, MA, CO) is an American cognitive scientist and former Director of the Mind Machine Project at the Massachusetts Institute of Technology (MIT). He is currently the Professor of Computational Neuroscience and Functional Neurosurgery at the University of Oxford, where he directs the Oxford Computational Neuroscience Laboratory. He is also the Director of the Synthetic Intelligence Lab at MIT, the founder of the Center for Advanced Defense Studies and the Chairman of the Brain Sciences Foundation.

His research areas include Cognition, Memory, Trauma, Machine Learning, Comprehensive Brain Modeling, Natural Language Processing, Nanotech, Medical Devices and Artificial Intelligence.

Education

Dr. Howard earned his B.A. in Behavioral and Military Science from Concordia University, then obtained an M.A. in Technology & Information Security from Eastern Michigan University. He went on to continue his studies at the University of Paris-Sorbonne, where he received a Doctorate in Cognitive Informatics and Mathematics and was awarded the prestigious Habilitation a Diriger des Recherches for his work on the Physics of Cognition (PoC). He then studied at the Massachusetts Institute of Technology and at Oxford University, earning a Doctorate in Philosophy. While a graduate member of the Faculty of Mathematical Sciences at the University of Oxford, he proposed the Theory of Intention Awareness (IA), which made a significant impact on the design of command & control systems and information exchange systems at tactical, operational and strategic levels.

Career

Professor Howard is an international executive, author and national security advisor to several U.S. Government organizations. He has directed leading international cooperation programs on emerging trends in global security and information assurance and has been instrumental in the creation of National Centers of Excellence in medicine, psychiatry, and computation at multiple research institutions in the US. His work has contributed to more than 30 U.S. patents and over 90 publications, fully half of which are in the areas of cognitive and computational theory. His work has garnered him several awards and honors, including a nomination to the White House fellowship.

In 2009, Prof. Howard founded the Brain Sciences Foundation (BSF), a nonprofit 501(c)3 organization to help fund research and promote awareness for combating neurological and neurodegenerative diseases. The foundation provides grants and scholarships to support the development and promotion of several highly promising new diagnostic and therapeutic technologies. The organization includes a multitude of fellows from various leading research institutions, including Oxford University, Massachusetts Institute of Technology, La Sorbonne University of Paris and INSERM. The goal of the foundation is to improve the quality of life for those suffering from neurological disorders.

Research

Prof. Howard's initial work focused on intentionality within the individual and various formal and informal command structures. His Theory of Intention Awareness (IA) is grounded in the systematic design and construction of naturalistic systems and serves as a possible model for explaining volition in human intelligence, recursively at all layers of biological organization. IA informs intelligent action planning; being both the process and product of planning and becomes the intelligent architecture to which we can credit purposeful action. His work on IA led him to expand his research into signal processing, linguistic analysis and Artificial Intelligence (AI), eventually bringing him to MIT.

While at MIT, he developed the Mood State Indicator Algorithm (MSI) a system that can model and explain the mental processes involved in human speech and writing to predict emotional states. In 2011, he collaborated on the development of the Language Axiological Input/Output system (LXIO)Cite error: The <ref> tag has too many names (see the help page).. The LXIO was developed as a practical application of the Mood Sate Indicator (MSI) technology and is a system capable of detecting both sentiment and mood states based on the analysis of written or verbal discourse. The LXIO represents a patient’s mood state by the sum of values generated by a given sentence or word string. Each word's context and grammatical function is computed with the use of a mind default axiology (MDA) while a learning algorithm tracks patients’ word analysis histories to enhance accuracy. LXIO parses sentences into words that are processed through modules to evaluate cognitive states. Words are processed through time orientation, contextual-prediction and consequent modules. The significance of LXIO is its ability to incorporate conscious thought and bodily expression (linguistic or otherwise) into a uniform code schema.

While at the University of Oxford, Professor Howard turned his focus to neurology, neurosurgery and nanoscale medical devices and developed the Fundamental Code Unit (FCU) theory, which serves as a blueprint for conscious thought and may be used to map higher order function to neurophysiological process. His work on the FCU provides the foundation for his subsequent theory, the Brain Code (BC). The Fundamental Code Unit cannot be simplified to a neuron, yet the difference between our intellectual abilities lies in the combination of quantitative differences of these neurons and neural networks.

Selected Works

  • Howard, N., Argamon, S. (Eds.) (2009). Computational Methods For Counterterrorism. Berlin: Springer-Verlag.
  • Howard, N. (2002) Theory of Intention Awareness in Tactical Military Intelligence: Reducing Un- certainty by Understanding the Cognitive Architecture of Intentions. Bloomington, Indiana: Author House First Books Library.
  • Howard, N. (2001) Seeking Peace in our Time: Global Defense System of Law. Bloomington, Indiana: Author House First Books Library.
  • Howard, N. (2001) The Challenges in Weapon Systems Design. Bloomington, Indiana: Author House First Books Library.
  • Howard, N. (1999) The Logic of Uncertainty and Situational Understanding. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
  • Howard, N. (1999) Multidimensional Time Understanding Combinatorial Manifold. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
  • Neuman, Y., Howard, N., (2015). The Embodied Nature of Abstract Words: A Proposed Neuro-Mechanism. Neuroscience and Biobehavioral Reviews, in review.
  • Nave, O., Lehavi, Y., Ajadi, S., Howard, N. & Goldshtein, V. (2015). Analysis of Polydisperse Fuel Spray Flame. Springer Plus, in press.
  • Poria, S., Cambria, E., Hussain, A. & Howard, N. (2015). Extending Text based Sentiment Analysis to Multimodal Sentiment Analysis. Cognitive Computation, in press.
  • Poria, S., Cambria, E., Howard, N. (2015). Fusing Audio, Visual and Textual Clues for Big Social Data Analysis. Neurocomputing, in press.
  • Howard, N. (2015). The Case for Intention Awareness in Security Systems., Journal of Cyber-Security and Digital Forensics, 1(1), in press.
  • Cambria, E., White, B., Durrani, T., Howard, N. (2014) Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1)
  • Poria, S., Agarwal, Basant., Gelbukh, A., Hussain, A., Howard, N. (2014) Dependency-Based Semantic Parsing for Concept-Level Text Analysis. Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, 8403, 113-127
  • Hussain, A., Cambria, E., Schuller, B., Howard, N. (2014). Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis, Neural Networks, Special Issue, 58, 1-3.
  • Cambria, E., Howard, N., Song, Y. & Wang, H. (2014). Semantic Multidimeensional Scaling for Open Domain Sentiment Analysis. IEEE Intelligent Systems, 29 March/April.
  • Bermann, J., Langdon, P., Mayagoita, R. & Howard, N. (2014). Exploring the use of sensors to measure behavioral interactions: An experimental evaluation of using hand trajectories. PLoS ONE, 9, e88080.
  • Nave, O., Neuman, Y., Perlovsky, L. & Howard, N. (2014) How Much Information Should We Drop to Become Intelligent? Applied Mathematics and Computation, 245: 261-264.
  • Howard, N., Leisman, G. (2013). DIME (Diplomatic Information Military and Economic) Power) Effects Modeling System: Applications for the Modeling of the Brain. Journal of Functional Neurology, Rehabilitation and Ergonomics. 3:2-3.
  • Howard, N. (2013). Identifying and modeling language, brain and perception: A narrative approach. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2.
  • Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S. & Howard, N. (2013). Music Genre Classification: A Semi-supervised Approach. Pattern Recognition. Springer Berlin Heidelberg,
  • Poria, S., Gelbukh, A., Agarwal, B., Cambria, E. & Howard, N. (2013). Common Sense Knowledge Based Personality Recognition from Text. Advances in Soft Computing and Its Applications,. Lecture Notes in Computer Science, Springer, 8266:2013, 484-496.
  • Howard, N., Cambria, E. (2013). Development of a Diplomatic, Information, Military, Health, and Economic Effects Modeling System. International Journal of Privacy and Health Information 1:1, pp. 1-1.
  • Cambria, E., Mazzocco, T., Hussain, A., Howard, N. (2013). Sentic Neurons: A Biologically inspired cognitive architecture for affective common sense reasoning. In Procedia Computer Science, 00, 1-6.
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  • Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N. & Frieder, O. (2013). Metaphor identification in large texts corpora. PLoS One, 8.
  • Howard, N., Bergmann, J. & Stein, J. (2013). Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes. Frontiers Special Issue INCF Course Imaging the Brain at Different Scales.
  • Bergmann, J., Graham, S., Howard, N. & Mcgregor, A. (2013). Comparison of Median Frequency Between Traditional and Functional Sensor Placements During Activity Monitoring. Measurement, 46, 2193-2200.
  • Howard, N., Stein, J. & Aziz, T. (2013). Early Detection of Parkinson's Disease from Speech and Movement Recordings. Oxford Parkinson's Disease Center Research Day 2013.
  • Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer’s Disease. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2(1), 29-38
  • Bergmann, J. & Howard, N. (2012). Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease. BMC Neuroscience, 13(supp), 178.
  • Cambria, E., White, B., Durrani, T., & Howard, N. (2012). Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1), 19-63.
  • Howard, N. (2012). Brain Language: The Fundamental Code Unit. The Brain Sciences Journal, 1(1), 4-45.
  • Howard, N. (2012). Energy Paradox of the Brain. The Brain Sciences Journal, 1(1), 46-61.
  • Howard, N., Lieberman, H. (2012). Brain Space: Automated Brain Understanding and Machine Constructed Analytics in Neuroscience. Brain Sciences Journal, 1(1), 85-97.
  • Howard, N., Guidere, M. (2012). LXIO The Mood Detection Robopsych. The Brain Sciences Journal, 1(1), 98-109.
  • Howard, N., Kanareykin, S. (2012) Transcranial Ultrasound Application Methods: Low-frequency ultrasound as a treatment for brain dysfunction. The Brain Sciences Journal, 1(1), 110-124.
  • Dunn, J., Heredia, J. B. D., Burke, M., Gandy, L., Kanareykin, S., Kapah, O., Taylor, M., Hines, D., Freieder, O., Grossman, D., Howard, N., Koppel, M., Morris, S., Ortony, A. & Argamon, S. *Howard, N., Jehel, L. & Arnal, R. (2014). Towards a Differntial Diagnostic of PTSD Using Cognitive Computing Methods. International Conference on Cognitive Informatics and Cognitive Computing ICCI CC. London, UK: IEEE.
  • Howard, N., Bergmann, J. & Fahlstrom, R. (2013). Exploring the Relationship Between Everyday Speech and Motor Symptoms of Parkinson's Disease as Prerequisite Analysis for Tool Development. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
  • Howard, N. (2013). Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
  • Howard, N. & Cambria, E. (2013). Intention awareness: improving upon situation awareness in human-centric environments. Human-centric Computing and Information Sciences, 3;9, 1-17.
  • Howard, N. (2013). The Twin Hypotheses: Brain Code and the Fundamental Code Unit: Towards Understanding the Computational Primitive Elements of Cortical Computing. Lecture Notes in Artificial Intelligence, MICAI, November 24-30, 2013, Mexico City, Mexico.
  • Howard, N., Pollock, R., Prinold, J., Sinha, J., Newham, D., Bergmann, J. (2013). Effect of Impairment on upper limb performance in an Ageing Population. The Human Computer Interaction International 2013 Conference (HCI) July 21-26, 2013, Las Vegas, Nevada.
  • Howard, N. (2013). Toward Understanding Analogical Mapping and Ideological Cataloguing in the Brain. Research Challenges in Information Science Series Conference (RCIS) May 29-31, 2013, Paris, France.
  • Last, M., Assaf, D., Neuman, Y., Cohen, Y., Argamon, S., Howard, N., Frieder, O., Koppel, M. (2013) Towards Metaphor Analysis for Natural Language Understanding. The 35th Annual Conference on Information Retrieval, March 24-27, 2013, Moscow, Russia.
  • Assaf, D., Howard, N., Neuman, Y., Last, M., Cohen, Y., Frieder, O., Argamon, S., Koppel (2013). Identifying -Noun Metaphors. 2013 IEEE Symposium Series on Computational Intelligence April 15-19, 2013, Singapore.
  • Bergmann, J., Howard, N. (2013). Design Considerations for a Wearable Sensor Network that Measures Accelerations during Water-Ski Jumping. IEEE Body Sensor Network Conference, May 6-9, 2013, Cambridge, Massachusetts.
  • Howard, N., Rao, D., Fahlstrom, R. & Stein, J. (2013). The Fundamental Code Unit: A Framework for Biomarker Analysis. . The 2nd Neurological Biomarkers Conference at the 2013 Biomarker Summit. San Francisco, California. .
  • Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer's Disease. Organization for Computational Neurosciences, July 21-26, 2012, Atlanta/Decatur.
  • Howard, N. (2012). Intention Awareness in Human-Machine Interaction Sensemaking in Joint Cognitive Systems. International Conference on Information Sciences and Interaction Science, June 26-28, 2012, Jeju Island, Korea, 293-299.
  • Howard, N. Global Defense Policy System of Laws: Graph Theory Approach to Balance of Power Theory. European Intelligence and Security Informatics Conference (EISIC), 2011a. IEEE, 248-258.

Patents (US)

Patents (International)

References

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