Partnering and Collaboration
I have served as assistant professor at the University of Guadalajara and currently provide consultancy services, I have proposed and led several lines of research, and I also lecture on pure and applied mathematics and computer science. My work focuses on foundational mathematics, where I developed a canonical set theory that addresses key problems such as Hilbert’s 24th Problem and Benacerraf’s Identification Problem. This theory offers novel insights in set theory, category theory, and abstract algebra, yielding practical applications in computer science. Notable innovations include a fast and scalable in-memory adder for matrix multiplication and a homomorphic encryption scheme that reduces noise, time, and energy consumption. These developments have transformative potential for current fields of research like next-generation Compute-In-Memory processor architectures, privacy-preserving AI and secure cloud computing. I am now seeking investment and collaboration to further advance and commercialize these innovations.
What is our company going to make? The product and what it does.
We develop a modern mathematical foundation to solve core computer science challenges, backed by global patents that enhance computing throughput, efficiency, privacy, and performance. I currently manage three key projects, with the first two aimed at solving fundamental problems in computer science and improving AI implementation: data privacy/security and computational efficiency/throughput.
I) HOMOMORPHIC ENCRYPTION (HE) BY RANDOM CHANGE OF VARIABLES: Today’s biggest challenge in cryptography is the problem of processing encrypted data—without decrypting. Methods for achieving this exist. They are called Homomorphic Encryption schemes, but they have not been fully implemented in the most important applications because of significant setbacks such as computing power requirements, noise (loss of precision), time latency, and energy consumption. That is why HE is a topic of current R+D with industry giants such as Microsoft and IBM investing heavily in this technology. I have developed a novel “Random Change of Variables” method to overcome HE’s traditional limitations. This innovation enables secure, privacy-preserving AI, federated learning, and cloud computing without sacrificing efficiency and performance. A PoC is available, and I’ve presented the theoretical framework at international conferences. Currently I am working with my team to deploy a Minimum Viable Product, to outperform IBM’s and Microsoft’s encrypted arithmetic library.
II) SIMPLE AND LINEAR FAST ADDER (SLFA) FOR IN-SITU MATRIX MULTIPLICATION: I have also redesigned the key subunit in a processor that is responsible for executing the basic arithmetic operations. Modern processor architecture, or “Von Neumann Architecture”, separates the memory and the arithmetic unit into two distinct subunits connected by a bus that moves information back and forth between them. The Von Neumann Bottleneck refers to the bottleneck (bandwidth) in this bus, which is responsible for most of the processor’s energy consumption and time delay. We improve efficiency and performance by addressing the Von Neumann Bottleneck through a Compute-In-Memory architecture. The SLFA design enables faster, low-energy matrix multiplication for AI, cryptography, and digital processing. A patent application that has been filed in multiple countries is supported by USPTO analysis, peer-reviewed research and an external valuation.
III) SEMI-SUPERVISED ALGORITHMIC TRADING FIX-API: I lead the development of a trading system based on real-time technical analysis. My roles include project management, architecture, testing, and debugging, collaborating with a developer and analyst to optimize performance and strategy execution.
How far along are we?
I) HOMOMORPHIC ENCRYPTION (HE) BY RANDOM CHANGE OF VARIABLES: A Proof-of-Concept (PoC) with a white paper and source code is available for audit under NDA. While key technical details are confidential, I have been invited to present the work at international conferences. The software is evolving into a full library, targeted for late-2025 deployment, aiming to surpass IBM’s HELib and Microsoft’s SEAL by eliminating noise, latency, high energy consumption, and development time. Patents for the Minimum Viable Product (MVP) are being filed in strategic countries.
II) SIMPLE AND LINEAR FAST ADDER (SLFA) FOR IN-SITU MATRIX MULTIPLICATION: I have presented the mathematical foundation of this design at various conferences and filed for global protection under the Patent Cooperation Treaty (PCT) in eight key countries, along with a patent valuation. Next steps include simulations and benchmarking in collaboration with the University of Guadalajara. We are also exploring a prospect partnership for building an Application-Specific Integrated Circuit (ASIC) for the crypto-mining industry.
III) SEMI-SUPERVISED ALGORITHMIC TRADING FIX-API: The system is being tested, troubleshooted and debugged. We are now building a transaction history in order to offer a robust model for various markets.
How long has our team been working on this?
For the past 10 years, I have focused full-time on my own independent research while remaining self-employed. The other team members each hold at least six years of experience in software development, modeling and/or market analysis.
When will we have a version people can use?
I) HOMOMORPHIC ENCRYPTION (HE) BY RANDOM CHANGE OF VARIABLES: The Proof of Concept (PoC) was developed in three (3) months by a two-person team. A fully functional version with competitive features is expected to take six (6) months with a three-person team. Development of the MVP has already started, and the PoC demonstrates that our method successfully addresses latency, energy consumption, and noise issues in traditional homomorphic encryption techniques.
II) SIMPLE AND LINEAR FAST ADDER (SLFA) FOR IN-SITU MATRIX MULTIPLICATION: We estimate eight to ten (8 to 10) months for simulating, testing, troubleshooting, and benchmarking our fast adder architecture once the necessary funding is secured. Expenses include proprietary software tools, lab hardware, a senior electrical engineer, and technical assistants, travel and lodging for key participations at industry events, patent expenses such as translations, attorneys, application fees, maintenance fees, etc. We have two patents on standby that extend the functionality of the original SLFA adder to enable parallel matrix multiplication. Expenses for one year total around $150,000 USD.
III) SEMI-SUPERVISED ALGORITHMIC TRADING FIX-API: In a few months, once we have built a track record and a solid statistical foundation for accurate ROI projections, we will release a proprietary API for licensing. This API will help investment funds make real-time decisions based on our decision model.
Why did we pick these ideas to work on? What is our domain expertise in this area? How do we know people need what we're making?
I have redefined mathematical foundations with new theorems that connect various areas of mathematics and provide clearer proofs, applying these methods to computer science. This led to the development of two key innovations:
I) HOMOMORPHIC ENCRYPTION (HE) BY RANDOM CHANGE OF VARIABLES: Our novel approach to HE addresses critical challenges such as noise, latency, energy demands, and computing requirements that have hindered large-scale implementation. Unlike traditional methods, our approach doesn't rely on homomorphisms but uses alternative mathematical concepts to preserve data privacy and security during processing.
II) SIMPLE AND LINEAR FAST ADDER (SLFA) FOR IN-SITU MATRIX MULTIPLICATION: Today’s processors are facing the challenge of overcoming limitations for applications demanding massive processing power. Although processors may seem fast and efficient to us, the truth is that at the frontier of modern applications, the processing power of the most advanced technology is still insufficient, and our capability to improve them simply by making smaller transistors (Moore’s Law) is reaching its theoretical limits. Our design addresses the Von Neumann Bottleneck with a Compute-In-Memory (CIM) architecture that features a scalable circuit for efficient matrix multiplication, crucial for high-performance computing. The innovative addition algorithm has been presented in conferences and published in peer-reviewed journals.
III) SEMI-SUPERVISED ALGORITHMIC TRADING FIX-API: Our easy-to-use FIX-APIs support semi-supervised algorithmic trading with robust risk management, which is highly valued in the financial sector for maximizing profitability and optimizing positions.
Who are our competitors? What do we understand they don't?
We focus on pressing computer science problems and develop solutions based on a new theoretical foundation. My work as a mathematician revolves around finding optimal mathematical representation of numbers, and defining operations at the most fundamental level. Through numerous internationally recognized, peer-reviewed publications and conferences, I have presented insights that provide simple solutions to previously complex problems. Key businesses like Microsoft, IBM, Google, AWS, Zama, Inpher, Nvidia, Micron, Intel, Qualcomm, AMD, and academia are working on the two main problems we are solving (Homomorphic Encryption and Compute-In-Memory). However, we see these organizations more as potential clients and partners rather than competitors.
How valuable are our products to the market?
By solving core inefficiencies in encryption and processor design, we drive innovation while creating substantial commercial value. We deliver advanced, deployable solutions to enable seamless horizontal and vertical integration in computing and IT, potentially setting new industry standards. Our enterprise-level B2B products and services enhance the technological ecosystem by addressing critical challenges.
I) HOMOMORPHIC ENCRYPTION (HE) BY RANDOM CHANGE OF VARIABLES: We have developed a software solution enabling computation on encrypted data, protected by trade secrets, patents, and legal measures. Currently, the HE market is valued at ~$200M (2024), hindered by latency, power consumption, and noise issues that do not allow for a full implementation in the most critical industries. However, our breakthrough HE scheme aims to overcome these limitations, unlocking massive market growth while positioning us as a key player.
II) SIMPLE AND LINEAR FAST ADDER (SLFA) FOR IN-SITU MATRIX MULTIPLICATION: Our hardware-level optimization for high-performance ASICs, GPUs, and TPUs using Compute-In-Memory processor architecture has been filed for protection in strategic tech hubs (Canada, China, India, Japan, S. Korea, Singapore, UK, USA) to maximize licensing opportunities with leading chip designers and manufacturers. The proposed design has the potential to set new industry standards in efficiency and performance, largely surpassing the competitor’s processor architectures, with little R+D resources invested.
III) SEMI-SUPERVISED ALGORITHMIC TRADING FIX-API: A non-patentable software solution protected through a combination of technical and legal IP safeguards and is offered for licensing.
