“Working with Moe is pleasure and a privilege. Moe is a visionary and result driven leader. He has innate ability to build and lead multi disciplinary innovation teams that execute at peak performance. Moe launched several key product initiatives at Samsung and was instrumental in creating new business opportunities in areas of artificial intelligence, digital health and IoT. He is customer focused and exceptionally talented in executing the ideas that matter most. Moe is adept at fusing technology and design that fostered innovation and creativity among teams. He has been a great guide and mentor to me and many other leaders he managed. Moe is an asset to any organization and I whole-heartedly recommend him for any endeavors he choses to pursue. If our career paths cross in the future I wouldn’t pass on the opportunity to work with Moe.”
Publications
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Accent Adaptation in Speech User Interface
IEEE INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERFACES (VECIMS 2005)
Enhancing speech recognition accuracy for speakers with non-native accent, leveraging Neural Networks accent classifiers.
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Automatic Speaker Recognition with Formant Trajectory Tracking Using CART and Neural Networks
IEEE 2005 Canadian Conference on Electrical and Computer Engineering, Saskatoon, SK
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Speech Accent Identification with Vocal Tract Variation Trajectory Tracking Using Neural Networks
IEEE international conference on Computational Intelligence for Homeland Security and Personal Safely
Identifying native accent of the speaker speaking in non-native language by tracking Vocal Tract Temporal Variation Trajectory using Neural Networks
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Projects
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Hobby project: Arduino based Twitter Client
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See projectThis was a fun hobby project. I used publicly available Twitter API's on an Arduino Mega to read the latest tweet that I had and print it on a 2x16 LCD display and scrolling the text.
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Mobi (weekend hobby project): A Robotic mobile App test Platform
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See projectMobi is a Robotic Test Automation Platform for Mobile Apps on Android and iOS. It includes a robot arm that can learn use cases when the test engineer performs them, and can run them automatically for regression testing every time a new build is released. Mobi uses computer vision, fuzzy logic and image signal processing to determine if a test case has succeeded or failed.
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Zachary Debelak
THE SILICON ERA IS HITTING A PHYSICAL WALL. TO SCALE AI, WE HAVE TO SCALE THE GRID, THE FACILITY, AND THE HARDWARE-SOFTWARE STACK IN UNISON. The 2026 mHUB HardTech Summit was a fantastic gathering of minds and a definitive validation of the massive “scale wall” we’ve been tracking at z2sixty. The summit confirmed what we’ve long believed: We’ve hit a scaling impasse with technologies like AI. It’s no longer just a silicon problem; it’s a challenge that exists at the “nexus” of the physical and the digital—the intersection of usable power, efficient hardware-software stacks, and pragmatic applications. We’ve moved past the phase of "just making more stuff" in the U.S. and into an era where manufacturing converges with the energy and compute stacks that power it. It’s not just about having existing facilities or owning the models—it’s about having the ideas, people, space, policies, and regional will to BUILD future-ready facilities. A FEW OBSERVATIONS THAT CONFIRM OUR CONVICTION: THE INTEGRATED STACK IS THE NEW MOAT: We’re observing an unprecedented convergence of hardware, software, and the power grid. As Jeff Pontius (Generac), Jacob Balma (Maxwell Labs), and Rashid Bashir (UIUC) highlighted, you can't scale any of them without the others. The bottleneck for AI and advanced computing isn't just code; it's the physical infrastructure—the exact friction point our thesis addresses. POLICY AS A REAL-WORLD TAILWIND: It was encouraging to hear Spencer Gore (Roland Berger) and Sibendu Som (Argonne) discuss the nexus of policy and funding. Illinois’ CRGA legislation—tackling nuclear, quantum, and battery storage—is a prime example of the state-level support we anticipated would be necessary to turn these "nexus" challenges into industrial advantages. THE HEARTLAND AS AN EXECUTION HUB: The Midwest doesn’t just have the "ideas"—it has the unique combination of technical talent, labor, policies, and the underutilized industrial space to actually build and deploy these solutions at scale. The optimism from leaders like Alaina Harkness (Current) and Patricia Grospiron (The Engine Accelerator) confirms that this region is the sandbox for the industrial evolution we’ve been preparing for. Innovation doesn't happen in a vacuum; it happens at the intersection of the grid, the facility, and the tech stack. Seeing Larry Gies (Madison Industries) discuss leadership as a stewardship for both people and the products that build our future was the perfect capstone. Huge thanks to Haven Allen and the entire mHUB team for being a cornerstone of this movement. Meeting several fantastic founders and seeing the products on the floor only strengthened our belief that the "nexus" isn't just coming—it’s here. 🛠️⚡ #mHUBSummit #HardTech #EnergyNexus #Manufacturing #MidwestInnovation #Infrastructure #z2sixty #VentureCapital
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Tal Cohen
Thank Dani Cherkassky Great piece on the architecture shift voice AI needs. The core thesis: cloud-centric voice systems fail in real-world conditions because they are too slow, too expensive to keep always-on, and deaf to spatial context. The solution is a hybrid architecture—fast, always-on edge processing (Spatial Hearing AI and Cognition AI) handling 80% of interactions locally, with cloud LLMs reserved for complex reasoning. But the Kahneman thesis runs deeper than the article suggests. System 1 and System 2 are not just useful metaphors—they reflect an actual biological sequence. Fast, intuitive processing (System 1) evolved first; slow, deliberate reasoning (System 2) was layered on top. The brain's architecture reflects this: spatial-acoustic processing happens in milliseconds at the brainstem and primary auditory cortex, long before language centers engage. Kardome is not just borrowing Kahneman as marketing. Their architecture is neurologically correct—it replicates the hierarchical, parallel, spatially-aware processing that makes human auditory cognition so robust. Here is the deeper point: hearing preceded language in human evolution, and spatial hearing preceded both. The mammalian auditory system evolved to detect predators and locate prey—survival functions that required sub-second latency and continuous environmental awareness. Language processing was layered on top of this infrastructure. Current voice AI inverts this evolutionary logic: it starts with language models and treats spatial audio as an afterthought. Kardome restores the natural hierarchy. 🙌 Danny Shapiro, Rory Sutherland
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