XMOHANOracle · Cloud management and enterprise software
Systems management, application virtualization, enterprise integration, open-source and software licensing, and technology diligence associated with acquisitions and partnerships.
Personal scientific research · 2005–present
Scientific Research
2005–Present
From enterprise software and cloud computing to affective AI, neural networks, specialized AI hardware, embodied intelligence, aviation and law, my work since 2005 has joined scientific questions with the constraints of deployed systems.
Research and engineering across two decades
The projects below distinguish original research and experimental systems from applied engineering, product exploration, legal-access research, and technical advisory work. Their scientific questions overlap, but they are not interchangeable forms of evidence.
Skive it, Inc. · AI systems designed to recognize experience
Extracting contextual signals such as style and mood from video, searching real-world experiences using natural language, and exploring specialized AI chips for future robots.
Scientific and technical advisory research
Assessment of AI model architectures, on-device computing, semiconductor and data-center requirements, cybersecurity, enterprise infrastructure and the evidence behind technical claims.
Robometrics® Machines · Aviation and physical-world AI
Applied AI and human–machine interfaces for aircraft, constrained computing platforms and robotics-related settings.
Robometrics® Law Foundation · Equitable access to justice
Research questions concerning legal information retrieval, understandable guidance, AI reliability, legal access and governance.
Robometrics® AGI · Embodied intelligence for the physical world
Original AI model architectures, learning through experience, perception–memory–action integration, continuous on-device learning and recursive self-improvement.
Connecting questions
Across these efforts, the recurring questions concern how machines perceive context, retain useful information, make decisions under constraints, and interact meaningfully with people. Early work on decentralized and embedded systems provides historical background; later work brought those themes into deep learning, model architecture, human-centered automation and physical AI.
Research programs, planned technology, deployed products and professional technical evaluations are identified separately. Neither a proposed chip nor an affective-AI interface demonstrates subjective machine feeling or consciousness.
Earlier foundations
Research at Brown University, HP Labs, MIT Media Lab, IBM Research Zurich and Intel Research Berkeley preceded this period, including multicast protocols, distributed discovery, software-defined radio, pervasive monitoring and sensor networks. Explore the 1999–2005 historical research chapter →