Dr. Howard's passion for science and technology began during his childhood. He pursued his interests in his studies and in 2000 while a graduate member of the Department of Mathematical Sciences at the University of Oxford, he proposed the Theory of Intention Awareness (IA). In 2002, he received a second doctoral degree in cognitive informatics and mathematics from the prestigious La Sorbonne in France. In 2007 he was awarded the habilitation a diriger des recherches (HDR) for his leading work on the Physics of Cognition (PoC) and its applications to complex medical, economical, and security equilibriums. His work has made a significant impact on the design of command and control systems as well as information exchange systems used at tactical, operational and strategic levels. As the creator of IA, Dr. Howard was able to develop operational systems for military and law enforcement projects. These utilize an intent-centric approach to inform decision-making and ensure secure information sharing.

His work has brought him into various academic and government projects of significant magnitude, which focus on science and the technological transfer to industry. While Dr. Howard's career formed in military scientific research, in 2002 he founded the Center for Advanced Defense Studies (CADS) a leading Washington, D.C, national security group. Currently, Dr. Howard serves as the Director of the Board. He also is a national security advisor to several U.S. Government organizations.

Dr. Howard's several years of working on systems design and dynamic systems analysis in military applications, as well as his personal research experiences, led him to studying the human brain.

In 2008, Dr. Howard founded the Mind Machine Project at MIT; an interdisciplinary initiative to reconcile natural intelligence with machine intelligence, which led to the establishment of the Brain Sciences Foundation (BSF) in 2011. That same year, he published the Mood State Indicators (MSI) algorithm which models and explains the mental processes involved in human speech and writing to predict emotional states.

His cognitive linguistic natural-language approach to systems understanding and design has led to great advancement in building more accurate engines for modeling behavioral and cognitive feedback. Due to this work, in 2012, Dr. Howard became the Director of the Synthetic Intelligence Lab at the Massachusetts Institute of Technology (MIT) where he focuses on the molecular basis for human intelligence. This could yield significant benefits and enable the progress in artificial intelligence and neuroscience as a whole.

As Dr. Howard has been directed towards the development of functional brain and neuron interfacing abilities, he concentrated on theoretical mathematical models to represent the exchange of information inside the human brain. This work, published in 2012, called the Fundamental Code Unit (FCU), has proven applicable in the diagnosis and study of brain disorders and has aided in developing and implementing necessary pharmacological and therapeutic tools for physicians. He has also developed individualized strategies to incorporate solutions for psychiatric and brain prosthetics. Through collaborative research efforts with MIT and Boston University, Dr. Howard has been working on interventions for early detection and novel treatment strategies for neurodegenerative diseases and affective disorders.


Massachusetts Institute of Technology
20 Ames Street, E15-411
Cambridge, MA 02139

Phone: (202) 256-2363


Dr. Newton Howard is the director of the Synthetic Intelligence Lab and a resident scientist at the Massachusetts Institute of Technology (MIT). He is also a Professor in the Department of Anatomy and Neurobiology at Boston University School of Medicine. From 2008 to 2012 Dr. Howard was the Director of the Mind Machine Project at MIT. From 2002 to 2006 Dr. Howard was a professor of Psychiatry and Computer Science at The George Washington University. During his time at GW, Dr. Howard held multiple teaching and research positions, including Senior Research Professor at the Cyber Security Policy Research Institute and thesis advisor. He taught multiple courses and created a novel Washington D.C.-based graduate program in security and computing for the Rochester Institute of Technology. He directed graduate theses at the Center of Informatics Research at the University of Paris. He has held positions of Visiting Professor, Associate and Defense Diplomat in Europe; he is still an active member of several research laboratories worldwide, including Descartes Institute, and the Brain Physics Group.

Large-scale academic projects include an IARPA funded automated metaphor detection project called ADAMA (Autonomous Dynamic Analysis of Metaphor and Analogy). Dr. Howard is part of a collaborative team, including Illinois Institute of Technology, Massachusetts Institute of Technology and Georgetown University, working to better understand, define and automate detection of metaphors using a novel software program that works in 4 languages: English, Russian, Farsi, and Spanish

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During his early career he was a member of the U.S. National Security Officers Community, serving in several operational posts. He is a National Security Advisor to several U.S. Government organizations. Newton has served on multiple U.S. Government Science Advisory Boards and was the Defense Diplomat Attache at Defense Offices. His work has garnered him several awards and honors, including a nomination to the White House Fellowship during the Clinton administration. In 2002 Dr. Howard founded the Center for Advanced Defense Studies (CADS) in Washington D.C., a non-profit national security organization dedicated to researching innovations for peace and conflict resolution. He is the former chairman and current Vice-Chairman and Chief Science Officer at CADS.

Additional government positions include:

  • Department of Homeland Security (DHS)
  • Department of Defense (DoD) (ORAU) (Expert)
  • Advisor to the National Security Expert Team (ORAU)
  • Scientific Advisor to Army Research Laborator (ARL-SLAD): Information Operations Survivability Scientist
  • Special Operations (SOCOM-SOTCE): Information Operations Scientist
  • Special Advisor to the Department of Defense (DoD): Strategic Intelligence Officer / US Defense Attache,
  • US Defense Attache Office, Paris, France


Dr. Howard is an expert in identifying and securing investments for emerging technologies, his wealth of experiences in structuring and directing multinational large-scale projects, particularly those that require participation and buy-in at the level of the heads of state and local governments, is acknowledged in several international landmark projects. For example, Dr. Howard directed special multinational inter-governmental programs for Intel Corporation that spanned several continents. He was an early participant and architect in a unique blend of strategic investments and equity funds, including a multi-million European equity fund. He directed special projects for a strategic investment vehicle of the U.S. government focusing on technology needs of specialized communities. He is also a science advisor to several private investment vehicles totaling 20 billion dollars in assets and spanning 4 continents.

Oxantium Ventures (Managing Director)
  • $100m fund, early-stage start-up focus
  • Early stage investments in solar energy and infrastructure start-ups
  • Active management of portfolio of 8 early stage tech companies
Science advisory activities:
  • Intel Capital joint fund with SAGIA (100m fund Intel Capital, with focus on investments in MENA-based innovation)
  • Science advisor to large Middle-Eastern private equity holdings and sovereign funds (approx. $10b total funds size)
  • Intel Corporation, Government Programs Advisor, Co-founder of the Advanced XML Security Laboratory (AXSL)
  • Harris Corporation, Special Programs Advisor
  • Boeing Aircraft Inc., Co-founder the Intent-Centric Warfare Lab
Ford Motor Company (FMC) / SciLabs
  • Internet/Intranet Research and Development at FMC with Microsoft IE/NetMeeting research and development, White Pine Software Reflector Research Group
  • MIT Media Laboratory with the Ford Motor Company Virtual Co-Location research team
  • UK Product Development Group, Advanced Engineering Center (AEC), Project Leader for the Development of Web Applications, systems design and software engineering for Web-enabled applications
  • SHAPE / System Health and Preparation Environment (Health Charts for Quality, Ford Motors)
  • Virtual Education System (Web-enabled remote learning system developed in concert with other companies such as Intel and Executrain Corp.)


Intent: Development of functional brain and neuron interfacing abilities, using intent-based models to facilitate representation and exchange of information.

Open Brain project initiative: Implementing necessary tools for physicians to assist in the process of pharmacotherapy to produce individualized treatment strategies to incorporate solutions for psychiatric treatments and brain prosthetics.

Memory: Advanced theories about how memories can be latent in the synaptic connections of a recurrent neural network.

Trauma: In collaboration with the MIT's Picower Institute for Learning and Memory, research has been studying the molecular mechanism that governs the formation of fears stemming from traumatic events such as in PTSD.


Textbooks and Monographs

Jehel L, Arnal R, Carmelo D, Howard N. (2016). Understanding Suicide: From Diagnosis to Personalized Treatment, Courtet P (ed). Springer International Publishing Switzerland. DOI 10.1007/978-3-319-26282-6, Chapter 6: Suicidal Crisis in the Digital Age.

Howard, N. (2015). Approach to Study the Brain: Towards the Early Detection of Neurodegenerative Disease, London, UK, Cambridge Scientific Publishing. ISBN 978-1-908106-49-0.

Howard, N. (2015). The Brain Language, London, UK, Cambridge Scientific Publishing. ISBN 978-1-908106-50-6.

Howard, N. (2014). Approach to Study the Brain: Towards the Early Detection of Neurodegenerative Disease, Oxford University, Bodleian Library.

Hussain, A., Cambrai, E., Schuller, B., & Howard, N. (2014). Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis. Neural Network, 58, 1-3.

Howard, N., Argamon, S. (Eds.) (2009). Computational Methods For Counterterrorism. Berlin: Springer-Verlag.

Journal Articles

Bergmann, J. H. M., et al. (2017). "A Bayesian Assessment of Real-World Behavior During Multitasking." Cognitive Computation.

Cambria E, Howard N, Xia Y, Chua T-S. Computational Intelligence for Big Social Data Analysis [Guest Editorial]. IEEE Computational Intelligence Magazine. 2016;11:8-9.

Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., et al. (2016). Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study. IEEE Access, 4, 7940-7957.

Malik, Z. K., Hussain, Z. U., Kobti, Z., Lees, C. W., Howard, N., & Hussain, A. (2016). A New Recurrent Neural Network Based Predictive Model for Faecal Calprotectin Analysis: A Retrospective Study. arXiv preprint arXiv:1612.05794.

Elgendi M, Howard N, Lovell N, Cichocki A, Brearley M, Abbott D, Adatia I. A Six-Step Framework on Biomedical Signal Analysis for Tackling Noncommunicable Diseases: Current and Future Perspectives JMIR Biomed Eng 2016;1(1):e1

Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016). Fusing Audio, Visual and Textual Clues for Sentiment Analysis from Multimodal Content. Neurocomputing, 174, Part A, 50-59.

Jehel, L., Howard N., Pradem M., Simchowitz Y., Robert Y., Messiah A. (2015). Prendre en compte la dimension transculturelle dans l’évaluation du risque suicidaire et du psychotraumatisme. European Psychiatry, vol 30, issue 8, page S79.

Wang, Y., Rolls, E. T., Howard, N., Raskin, V., Kinsner, W., Murtagh, F., et al. (2015). Cognitive Informatics and Computational Intelligence: From Information Revolution to Intelligence Revolution. International Journal of Software Science and Computational Intelligence (IJSSCI), 7(2), 50-69.

Cambria E, White B, Durrani TS, Howard N. Computational Intelligence for Natural Language Processing [Guest Editorial]. IEEE Computational Intelligence Magazine. 2014;9:19-63.

Howard N, Jehel L, Arnal R: Towards a Differntial Diagnostic of PTSD Using Cognitive Computing Methods. in 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing (ICCI CC). London, UK, IEEE; 2014, p 9-20.

Dunn, J., Beltran de Heredia, J., Burke, M., Gandy, L., Kanareykin, S., Kapah, O., ... & Argamon, S. (2014, June). Language-Independent Ensemble Approaches to Metaphor Identification. In Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence.

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 Multidimensional 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.

Poria S, Gelbukh A, Hussain A, Howard N, Das D, Bandyopadhyay S. Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining. IEEE Intelligent Systems. 2013;28:31-38.

Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S. & Howard, N. (2013). Music Genre Classification: A Semi-supervised Approach. Pattern Recognition. Springer Berlin Heidelberg, 7914: 254-263.

Howard N, Leisman G. (2013) DIME (Diplomatic Information Military and Economic) Power) Effects Modeling System: Applications for the Modeling of the Brain. 3:2-3. Journal of Functional Neurology, Rehabilitation and Ergonomics. 2013;3:257-273.

Howard, N., & Cambria, E. (2013). Development of a Diplomatic, Information, Military, Health, and Economic Effects Modeling System. International Journal of Privacy and Health Information Management (IJPHIM), 1(1), 1-11. doi:10.4018/ijphim.2013010101

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.

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.

Howard N, Cambria E. Intention awareness: improving upon situation awareness in human-centric environments. Human-centric Computing and Information Sciences. 2013;3:9.

Howard, N. Intention Awareness Theory in Information Risk Engineering: Contrived Balance in Integrating Information Assurance and Situation Awareness Journal of Information Assurance and Security. Volume 8 (2013) pp. 009-016

Howard, N. Intention Awareness Theory; Risk Engineering Architecture Integrating Situation Awareness and Intention Awareness in Network-Centric Information Policy Journal of Information Assurance and Security. Volume 8 (2013) pp. 001-008

Howard N, Lieberman H. BrainSpace: Relating Neuroscience to Knowledge About Everyday Life. Cognitive Computation. 2014;6:35-44.

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),

Howard, N., Lieberman, H. (2012). Brain Space: Relating Neuroscience to Knowledge About Everyday Life. Cognitive Computation, published online August 2012.

Howard N, Kanareykin S. Transcranial Ultrasound Application Methods: Low-frequency ultrasound as a treatment for brain dysfunction The Brain Sciences Journal. 2012;1:110-124

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. Cognitive architecture: Integrating situational awareness and intention awareness. Brain Sciences Journal. 2012;1:62-84.

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.

Howard N, Guidere M. Computational Methods for Clinical Applications: An Introduction. Functional Neurology, Rehabilitation, and Ergonomics. 2011;1:237-250.