“Neil was a pleasure to work with. I did some freelance industrial design work with him for Perceptiv Labs. The entire process was a delight. He understands the power of fast, iterative prototyping, and was flexible and willing to hear new ideas throughout the design process. Furthermore, Neil was excellent at staying connected, especially in light of us working remotely. Communication was open, fast and easy as we worked together to come to a final product we were both happy with. It was a pleasure!”
About
I'm passionate about turning advanced research in computer vision and AI into delightful products.
Experienced with design and engineering for products leveraging computer vision, AI/ML, Augmented Reality and 3D SLAM in both consumer and enterprise applications.
YCombinator founder with 10 years experience building products that reached 200,000 users and $2M in revenue. Currently, I'm building dabble.so, a synthetic photo studio that uses AI to generate high converting marketing content for e-commerce brands.
Specialities:
- AI/ML Product Management for products in Computer Vision, AI, Robotics and Spatial Computing
- Product discovery and strategy for hard tech products
- Balancing audacious long term goals with realistic short term goals
- Business development, building enterprise partnerships
- Leading product and engineering teams
Previously a robotics researcher at the University of Waterloo, building novel multi-robot path planning algorithms. Published in top robotics journals - ICRA, TRO, IJRR, IROS with over 500 citations.
Activity
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Exciting news! I'm taking another step on my entrepreneurial journey. Ruby Care joined Vision Health Pioneers Incubator to continue our mission to…
Exciting news! I'm taking another step on my entrepreneurial journey. Ruby Care joined Vision Health Pioneers Incubator to continue our mission to…
Liked by Neil Mathew
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Anybody ever play BC: Quest For Tires? #commodore64 #aivideo #aifilmmaking #midjourney #runway #gen3 Http://lamotta.studio…
Anybody ever play BC: Quest For Tires? #commodore64 #aivideo #aifilmmaking #midjourney #runway #gen3 Http://lamotta.studio…
Liked by Neil Mathew
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Thanks for ALWAYS catching my good side Gary Vaynerchuk
Thanks for ALWAYS catching my good side Gary Vaynerchuk
Liked by Neil Mathew
Experience
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Education
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Activities and Societies: NASA Sample Return Robotics Team, UW Robotics Team, NSERC Canadian Field Robotics Network, Graduate Student Association, Graduate House Pub and Lecture Series Staff
Graduated with the thesis-based M.A.Sc in Mechanical and Mechatronics Engineering.
Thesis title: "Discrete Path Planing Strategies for Coverage and Multi-Robot Rendezvous"
Honours:
Teaching Assistant Excellence Award (2012)
University of Waterloo Graduate Research Scholarship (2011-2013)
University of Waterloo Special Graduate Scholarship (2011 - 2012) -
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Activities and Societies: President of UW-Micro Aerial Vehicle Team, Member- Midnigt Sun Solar Car Team, UW Robotics Team, Sanford Fleming Technical Speaking Competition, English Tutor for international students, Engineering Orientation Volunteer
Recipient of President's Scholarships and Engineering International Student Scholarships
Volunteer Experience
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Career mentor
New City Public Schools
- Present 13 years
Education
Tutoring and mentorship of students from underprivileged communities to reduce drop-out rates in high school.
Publications
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Multi-robot Rendezvous Planning for Recharging in Persistent Tasks
IEEE Transactions on Robotics
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Optimal Path Planning in Cooperative Heterogeneous Multi-robot Delivery Systems
Workshop on the Algorithmic Foundations of Robotics
This paper addresses a team of cooperating vehicles performing autonomous deliveries in urban environments. The cooperating team comprises two vehicles with complementary capabilities, a truck restricted to travel along a street network, and a quadrotor micro-aerial vehicle of capacity one that can be deployed from the truck to perform deliveries. The problem is formulated as an optimal path planning problem on a graph and the goal is to find the shortest cooperative route enabling the…
This paper addresses a team of cooperating vehicles performing autonomous deliveries in urban environments. The cooperating team comprises two vehicles with complementary capabilities, a truck restricted to travel along a street network, and a quadrotor micro-aerial vehicle of capacity one that can be deployed from the truck to perform deliveries. The problem is formulated as an optimal path planning problem on a graph and the goal is to find the shortest cooperative route enabling the quadrotor to deliver items at all requested locations.
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Mapping, Planning and Sample Detection Strategies for Autonomous Exploration
Journal of Field Robotics
This paper presents algorithmic advances and field trial results for autonomous exploration and proposes a solution to perform simultaneous localization and mapping (SLAM), complete coverage, and object detection without relying on GPS or magnetometer data. We demonstrate an integrated approach to the exploration problem, and we make specific contributions in terms of mapping, planning, and sample detection strategies that run in real-time on our custom platform. Field tests demonstrate…
This paper presents algorithmic advances and field trial results for autonomous exploration and proposes a solution to perform simultaneous localization and mapping (SLAM), complete coverage, and object detection without relying on GPS or magnetometer data. We demonstrate an integrated approach to the exploration problem, and we make specific contributions in terms of mapping, planning, and sample detection strategies that run in real-time on our custom platform. Field tests demonstrate reliable performance for each of these three main components of the system individually, and high-fidelity simulation based on recorded data playback demonstrates the viability of the complete solution as applied to the 2013 NASA Sample Return Robot Challenge.
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A Graph-Based Approach to Multi-Robot Rendezvous for Recharging in Persistent Tasks
IEEE Conference on Robotics and Automation
This paper addresses the problem of maintaining persistence in coordinated tasks performed by a team of autonomous robots. We introduce a dedicated team of charging robots to service a team of primary working robots. Given that the trajectories of the working robots are known within a planning interval, the objective is to plan routes for the charging robots such that they rendezvous with and recharge all working robots to guarantee their continuous operation.
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Projects
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NASA Centennial Sample Return Challenge
- Present
As a member of the navigation team, my responsibilities involve developing robust coverage planning and motion control algorithms in a 3D SLAM based map for the UW rover. The challenge involves exploring an unknown forested area of 80,000 square meters within 2 hours in order to find samples to be excavated by the rover in a GPS denied environment.
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Vision-based Alignment for Hip Replacement Surgeries
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See projectThis project involved developing a monocular camera based alignment system for surgeons performing hip replacement surgeries. A system of infra-red LED's was used to estimate and track the 3D position and orientation of a hip-implant tool with respect to the initial femoral allignment in the pelvis. The accuracy of the system was tested using the Optitrack Motion Capture System, in lighting conditions typical of operation theatres. The project was undertaken in a collaboration between the…
This project involved developing a monocular camera based alignment system for surgeons performing hip replacement surgeries. A system of infra-red LED's was used to estimate and track the 3D position and orientation of a hip-implant tool with respect to the initial femoral allignment in the pelvis. The accuracy of the system was tested using the Optitrack Motion Capture System, in lighting conditions typical of operation theatres. The project was undertaken in a collaboration between the University of Waterloo and Intellijoint, a Waterloo company that develops intelligent surgical equipment..
Honors & Awards
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Teaching Assistant Excellence Award
University of Waterloo
Course taught: ME360 - Introduction to Control Systems Design
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University of Waterloo Special Graduate Scholarship
University of Waterloo
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University of Waterloo Graduate Research Scholarship
University of Waterloo
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ASME - Northern Alberta Design Challenge Award
University of Waterloo
Best technical design project for Mechatronics Engineering in 2010
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Sandford Fleming Foundation Best Technical Speaker Award
University of Waterloo
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University of Waterloo President’s Scholarship
University of Waterloo
Languages
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English
Native or bilingual proficiency
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Hindi
Full professional proficiency
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Marathi
Full professional proficiency
Recommendations received
1 person has recommended Neil
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Tom Celinski, PhD
Control Theory… that classical subject, arguably under-appreciated, and yet applicable to such a diverse range of systems: continuous (linear / non linear), digital, discrete event, etc. And since literally EVERYTHING in the world (physical stuff and digital stuff) is one of these systems, maybe it’s not surprising that Control Theory can be applied to rigorously study LLMs? #genai #llm #controltheory
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Bojan Tunguz, Ph.D.
I just got my own copy of “Building LLMs for Production” by Louis-François Bouchard and Louie Peters. As I am getting more into applications of GenAI I expect this book to become one of my go-to resources in the upcoming months. https://amzn.to/3Yv5FmL #AI #GenAI #LLMs
51 Comments -
Joshua Patterson
You're so right Vikram Sharma Mailthody... The most overlooked fact of this chart is 2 GPU Nodes out perform 200 CPU nodes at 10TB and 30TB. Not only are the GPU clusters cheaper per hour AND faster... it's significantly less data center space and energy consumption. When you have a tight SLA for workloads, and you have to scale out to meet those time constraints... CPUs get extremely expensive.
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Warren Powell
Mohammad Hossein Amini did a nice study of LLMs trying to play chess (it went badly). This exercise helps to highlight different methods for making decisions, and since LLMs (or any form of machine learning) only uses one of these methods, they would never be able to play a game of chess (or any strategy game). There are two primary methods used to make decisions in chess: pattern based logic (a form of PFA), and direct lookaheads (DLAs) (DLAs can also be combined with VFAs, but PFAs and DLAs are the two primary ones). PFAs are used near the beginning of a game when a player simply looks at the board and decides on a move, without thinking about future moves. Beginners only do this for a few moves, while experienced players can use this longer - the benefit is that the moves are very fast. Once a board becomes too complicated (this depends on the player), it is necessary to start planning into the future, anticipating what the opponent might do. This kind of planning (a form of tree search) requires a model of the underlying problem. This means understanding the rules of the game, which for chess is relatively simple compared to real-world problems (such as managing a supply chain). LLMs can only do pattern matching, albeit a very sophisticated form. This is why they will never be able to solve a shortest path problem through a transportation network, which requires knowing the structure of the network (one-way streets, left-hand turns, ...) as well as travel times (which can change dynamically). Chess (and other strategy games) require the ability to plan into the future, which means having an internal model of the problem. It needs to be able to simulate the different moves that each can be made with each piece, by both a player and their opponent (which requires the ability to simulate the decisions of the opponent).
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