![](https://freight.cargo.site/t/original/i/2dcae5cd2168f0fa6b5adab900556a7705a1bdbe312665557c01930e6c214fcf/Property-1Light.png)
Ian J. Latchmansingh
Human-Centric Design Leader & Technologist in NYC![](https://freight.cargo.site/t/original/i/2521faae03787e848a93233463217751db949d2c0ac5c3ad4c06d91d6e9d3952/AMR-Showcase.jpg)
![](https://freight.cargo.site/t/original/i/74345d35010d831cc2505a82610a7999de96398b3e89a5425c1fe886c885aea8/Screenshot-2023-10-26-at-8.55.01-AM.png)
![](https://freight.cargo.site/t/original/i/e085f6a1e8e9a18202801fdb477dde426126ac8cb1eba7e57c332e509bedda92/IOT-Repair-Mock-Up.jpg)
![](https://freight.cargo.site/t/original/i/6ba11a8e63b3b7dddc1f8bc555c83f82009b083caf9c11bb94a0aea0bb51c518/DynamicLandscape.gif)
![](https://freight.cargo.site/t/original/i/a89d038d5728220173adedf90f2d4b0a7d57059ba823659a86a60634c04ec48d/Screenshot-2023-10-26-at-8.54.29-AM.png)
My résumé is best seen on LinkedIn. Otherwise, you can reach me via this contact form. I am currently the Head of Design for PanasonicWELL, a US-based R&D group working at the intersection of connected devices, families, and wellness.
Organizations I’ve worked with include:
- NASA/JPL
- Wendy's
- Lloyds of London
- Better.com
- Bridgestone
- The New York Times R&D
- Veeva
- Disney
- John Deere
- PODS
Most recently served as Principal Design Technologist for AWS Prototyping & Cloud Engineering in NYC from 2019 - 2023. I designed and built human-centric prototypes that employ Artificial Intelligence, Machine Learning, Robotics, Internet of Things, and Spatial Computing. Previously a UX Director for start-ups and digital creative advertising.
PROTOTYPES & PRODUCTS
Industrial
- Virtual Remote Auditing for GxP Compliance
- CV/ML Detection of Product Defects in Aluminum Manufacturing with Fully-Synthetic Data
- Agricultural Cost Center Business Intelligence for IoT-equipped Farms
- Realtime Inventory Forecasting for Just-in-Time Manufacturing
- Supply Chain Weakness Dashboards for Agricultural Logistics
- Municipal Infrastructure Prediction and Planning Tools
- Disaster Risk Monitoring Search Interfaces for Restoration Contractors
Consumer
- VUIs for Drive Thru Restaurants
- Consumer Care Care Subscription Services
- Live Interactive Journalism
- Virtual Doping Detection for Physical E-Sports
- Online Learning for First-Time Homebuyers
Business
- VUI Design for Clinical Trials and Medication Adherence
- Distributed Consumables Procurement for Container Shipping
- Connected Devices for C-Store Cleanliness Monitoring and Task Management
- Predictive Fleet Vehicle Maintenance for SMBs
- Intelligent Damage Detection for Rental Vehicles
- Hybrid UIs for Realtime Customer Service Analytics
IoT Provisioning, Monitoring, and Maintenance for Light & Wonder
Functional Prototype
Functional Prototype
Network monitoring and repair application for an electronic gaming manufacturer. Enables technicians to identify, queue, procure parts for, and provision devices on-site through sensors for predictive maintenance. Reduces wasteful service visits by 90%.
AWS Blog Post
AWS Innovation Ambassadors Podcast
AWS Blog Post
AWS Innovation Ambassadors Podcast
Filed under:
IoT
IoT
![](https://freight.cargo.site/t/original/i/e085f6a1e8e9a18202801fdb477dde426126ac8cb1eba7e57c332e509bedda92/IOT-Repair-Mock-Up.jpg)
![](https://freight.cargo.site/t/original/i/2120ce1578a40b280f9afeee6f4b8fc9818748109230568df18173fa1943d026/Networks.png)
![](https://freight.cargo.site/t/original/i/5a4fc518176449d2dcc4070fd7cb67117e923703d7d9b9bdc26eff32efee8f77/Devices-_-Issues.png)
![](https://freight.cargo.site/t/original/i/f95cfc3c72af31445ea51ffafc05be7c403d2178507c2db53a1bc4af693412d2/Queue_-Devices.png)
![](https://freight.cargo.site/t/original/i/779c38dfa20f646bce9f49b0e43f3d98473eb5da06a9b19c8aac626bde67084f/Device-_-Details.png)
![](https://freight.cargo.site/t/original/i/1a33aa77563d2036a441a27d9b4985132742563eafa929a30cb7fbbe12fe0db0/Device-_-Servicing.png)
![](https://freight.cargo.site/t/original/i/e123f6c72d0ce0a992a4f2cfbd35c6419b8893e1e42ce6f0bb9aff4697c40a96/Device-_-Service-Notes.png)
![](https://freight.cargo.site/t/original/i/93302cdbef7cfb7dbf88f860555753fa7de88a47beb7e5c32fbe4ac1bbdb9f3f/Networks-_-Queued.png)
![](https://freight.cargo.site/t/original/i/ccf95a822794f52efe26ca8095bea55ef8f016cce459525d2fded4ae4d0c8074/Parts.png)
![](https://freight.cargo.site/t/original/i/6d7c423ec79abf0480e55c41be330f1ca159729abe33bb4a9926fbc972754d98/Devices-_-All.png)
![](https://freight.cargo.site/t/original/i/906c05c9fcefc036e30ea8021653abcb89ba1fd63ddd359c5b6d15526a4f9e17/Devices-_-All-_-Add-New.png)
![](https://freight.cargo.site/t/original/i/aac46ff079ebddefaf7152d1aa79b2207a491724f52355165cb067ac22284c55/Devices-_-All-_-Add-New-_-Configure.png)
Autonomous Material Delivery for Just-In-Time Manufacturing
Functional Prototype
Functional Prototype
Facing a labor shortage due to a lack of licensed forklift drivers, I worked with an industrial agricultural manufacturer to support cobot orchestration for material fulfillment from a centralized warehouse (“Marketplace”) to transport parts via Autonomous Mobile Robots (AMRs) to workstations in an assembly line.
![](https://freight.cargo.site/t/original/i/2521faae03787e848a93233463217751db949d2c0ac5c3ad4c06d91d6e9d3952/AMR-Showcase.jpg)
![](https://freight.cargo.site/t/original/i/c4b63b267f2da0e52b246d8dbe2f2dd99aa341e9cf72f185c4bb6206f0384a4d/Log-In.jpg)
![](https://freight.cargo.site/t/original/i/3637420417aa765bf8ff0befaaa0397a72058b2d4845100125256dffb107749c/Fulfillment.jpg)
![](https://freight.cargo.site/t/original/i/a3a697d22f7499d85b5b1a072e3b7ea0cd232d928acb04bc20a079016e3de10f/Empty-Cart.jpg)
![](https://freight.cargo.site/t/original/i/f4e93b7e0f227ea75bce5acff8d91b1d93a9f242174c6d4f27bd81060bd42beb/Confirm.jpg)
![](https://freight.cargo.site/t/original/i/49338a6db9ef5b7bd43a9f7b07e43f8f71c69083c11d1445704f7845a96b6f02/Scan.jpg)
![](https://freight.cargo.site/t/original/i/1bfe323e0802984d78a7487c693a0293b456945386c49850b471cbd4ee1566cb/Arrival.jpg)
![](https://freight.cargo.site/t/original/i/6da85d327fb3257c14a6c1502833a39949ef4194cd5fe3410c7ed685622ff0ba/Orders.jpg)
Broadcast Monitoring using Machine Learning and Computer Vision
Functional Prototype
Functional Prototype
Functional Prototype
Functional Prototype
This solution allows for the automation of lower-level broadcast monitoring quality checks like audio and video anomalies that were previously manual chores. This enables human workers to focus on higher-level tasks, take action sooner, and handle a higher volume of channels without sacrificing efficacy.
AWS for M&E: Broadcast Monitoring Architecture and Design
AWS for M&E: Broadcast Monitoring Architecture and Design
![](https://freight.cargo.site/t/original/i/d123cac00e9ce6fb3a51760a0f22bc056468fa46bc8e3da1cf442eaac15bdc91/Picture1-4.png)
![](https://freight.cargo.site/t/original/i/1210d48103d5bc3fc0c03609964bbc588cb165f327df818d47d4f8a94847fc89/Picture14.png)
![](https://freight.cargo.site/t/original/i/74e7029490663c8b3060feb390156f4374e07c97be4a7062488bda42ba7f08fd/Picture13.png)
Contextual Augmented Reality Interfaces for the Internet of Things
Proof of Concept
Proof of Concept
Visualizing and manipulating IoT devices in a singular, contextual, spatial interface. I worked with Ramin Firoozye︎︎︎ to craft a UI prototype for what his ARIoT concept would look like in practice. The results of which were revealed at AWS Reinvent 2019.
![](https://freight.cargo.site/t/original/i/2155ec50c2633910db74d7b5dad39989aa8baf892edf42f93bc591e414a3b66d/Livestock.png)
![](https://freight.cargo.site/t/original/i/19bc7dc8d989a3e038f5fabbb64c64fa75d6d38e62bda50c12257b8f4339b44c/Home-Shot-iPad-10.5-.jpg)
![](https://freight.cargo.site/t/original/i/1289dc3270476a4820bec7672c2dccfc10377c415916f390f591d0598d5210bd/Lamp.png)
![](https://freight.cargo.site/t/original/i/467e31fa10b6dd5ec183b88093d1923f79c4df333702d8d42041de9ba6b31c54/Air-Conditioner.png)
![](https://freight.cargo.site/t/original/i/123f1a7e7fdfe495d4d2fa8526c654fb123b42065984d85e50665a3a25893041/Refrigerator.png)
![](https://freight.cargo.site/t/original/i/5de7db3c7ba3e026a597eed839d251f346096a95f66126d95f040deae8ba2631/Tractor.png)
![](https://freight.cargo.site/t/original/i/d4baefffa7386ab84045834c8a74fd3d91678781a5bf2d4b496d783c07a8c31d/Speaker.png)
![](https://freight.cargo.site/t/original/i/27b43b6a3477070225159562d8a76591ebbaa66de1a0819ec4a48b655a5c3e5e/AgTech_Tablet.jpg)
Procedural Terrain Generation for Robotics Simulation and Machine Learning
Synthetic Spatial Data
Synthetic Spatial Data
Terrain generators were initially developed for the AWS + JPL (Jet Propulsion Laboratory) Open Source Rover Challenge. This was a virtual hackathon that challenged contestants to improve how rovers on Mars may operate on the unpredictable terrain. This design is a fully-procedural particle system that can: distort the surface, scatter obstacles of varying complexity, size, and frequency, and dynamically set the rover origin point to a flat, transitional surface.
![](https://freight.cargo.site/t/original/i/6ba11a8e63b3b7dddc1f8bc555c83f82009b083caf9c11bb94a0aea0bb51c518/DynamicLandscape.gif)
![](https://freight.cargo.site/t/original/i/2791a2a67e4f1d3f454791f09999c33b07d3e7986efd683b32abbc4141622742/Indoor_Environment.gif)
![](https://freight.cargo.site/t/original/i/629356001c6fe4171e77881651eb63c31383d789ded50a00aa8cddceb9a9f5cd/rover-banner.png)
©MMXXI
Perfection is achieved, not when there is nothing more to add,
but when there is nothing left to take away.