Meet the Team

Hi, I’m Ramona, an Applied Machine Learning Engineer at Kumo. My work involves not just delving into the technicalities of GNNs, but also actively engaging with our clients to
tailor our product to their needs. I got my Master’s degree in Data Science from KTH and the Technical University of Berlin, which helped me prepare for my varied role at
Kumo.

I discovered Kumo through my work with PyG. The startup’s fast-paced environment and broad scope of responsibilities immediately appealed to me. Since joining in May 2022, I’ve
been thrilled by the team’s expertise and the diverse range of tasks I get to tackle.

When not immersed in technology, I’m an avid traveler and cooking enthusiast. Whether it’s skiing in the mountains or kite surfing on the beach, I love exploring new places. Back
home, I enjoy making pizzas in our pizza oven.

https://www.linkedin.com/in/ramona-bendias/

I am Vid, a machine learning engineer at Kumo.ai, working on predictive query and text processing. I did my undergrad at the University of Ljubljana, studying math and computer
science, continuing with an MSc and DPhil of computer science at the University of Oxford focusing on natural language processing and its relation to knowledge bases.

I wrote my thesis under the supervision of Thomas Lukasiewicz on the topic of large-scale pretraining, its impact on model performance, commonsense reasoning, and societal biases.

I was introduced to Kumo through Jure Leskovec, who was my supervisor on my undergrad thesis. I was attracted to the kind of problems that Kumo was solving and the type of solutions
they were developing. Reasoning on graphs that requires high-quality text processing was exactly what my research focus was in. I knew that I had a chance to contribute to
an impactful project that my knowledge and skills could significantly contribute to.

The experience at Kumo has been great. I joined for two reasons – great mission and a great team. And none of the two have let me down since. Rarely have I had a chance to work
with such a talented and skilled team, while continuously working on exciting but hard technical problems.

Outside of work, you can find me on some mountain, trying to find my way to the top. Sometimes just by hiking, sometimes by climbing rocks, and in winter, climbing ice and snow.
When I am not outside, you can find me looking for the thrill and adventure in books or role-playing games. You can find me on Twitter or LinkedIn, but you might be left disappointed
to find that I am not very active on either.

As a founding engineer at Kumo AI, I spend my time building, optimizing, and scaling our graph neural network platform to support predictive queries on large scale data warehouses.
I received both my BS and MS in computer science from Stanford, with concentrations in theory, machine learning, and systems. Prior to my current role, I worked on machine
intelligence problems at Google AI, Snorkel AI, and Deep Valley Labs. Additionally, I actively contribute to the PyTorch Geometric (PyG) library.

I started working on the foundations of Kumo while completing my master’s degree, advised by our very own Dr. Jure Leskovec. Throughout the iterative process of designing
and building the first version of the product, I grew captivated by our revolutionary approach to solving predictive problems on relational data as well as the unique intersection
of machine learning (ML), infrastructure, and system optimization problems involved. From distributed systems design to low-level kernel implementation details, the process
of building a scalable and production-ready GNN platform has proven to be a fascinating and thrilling adventure at a pivotal moment in the widespread industry adoption of
ML.

Along with Kumo’s remarkable mission and set of novel technical problems to explore, I never fail to be amazed by the sheer talent, curiosity, and passion of our team. Blending
the best parts of industry and academia, the engineering culture at Kumo emphasizes innovation and discovery while paying close attention to platform reliability, stability,
and robustness. Despite being distributed across the globe, this mindset begets a constant buzz of new ideas and discussions permeating the company that continuously pushes
us to new heights. The uniqueness and intricacy of the engineering problems we face on a daily basis is always treated as an exciting opportunity to build something truly
special — and we might just make it happen!

Outside of Kumo, I enjoy playing guitar, reading far too many horror novels, and running. I’m on a mission to hike as many trails in the Bay as possible, so please send
your favorites my way 🙂

Conclusion: How can folks stay connected with you? Include any social links (i.e. LinkedIn, Github, Twitter, personal website, etc) for people to further connect.

https://twitter.com/manans99

https://github.com/mananshah99

https://www.linkedin.com/in/mananshah99/

My name is Alexandra Porter and I’m a Founding Engineer at Kumo. I am primarily on the infra team at Kumo, working on data analysis and MLOps features. I also work with
the customer success team to help deploy new features. I finished my PhD at Stanford University in 2022, during which I studied graph algorithms for infrastructure applications,
advised by Prof. Mary Wootters and Prof. Jure Leskovec.

I’ve been interested in graphs and graph algorithms since I first learned about them in high school. During my PhD I did a little bit of graph neural network-related research
and also became interested in designing scalable systems. Spending time in Prof. Leskovec’s research group, I got to learn about the cutting edge GNN work being done
for a wide range of applications. Working at Kumo was the perfect opportunity to combine these interests, getting experience building infrastructure while applying graphs
to many different applications.

Working at Kumo has been a great way to start my career working with and learning from experts in a wide range of fields, from infrastructure design to customer engagement.
The mission to create a product that is so broadly useful is also an exciting challenge, requiring us to think ahead about the future of machine learning while also meeting
requirements quickly. It’s inspiring working with such a strong team up for the ambitious challenge.

Outside of work I like to swim, bike, and run, enjoying the California weather and the great trails we have in the Bay. Connect with me on LinkedIn!

I am a Software Engineer at Kumo.ai. Working on predictive query, making it more expressive and scalable. I graduated in 2018 with a Masters in Math and Bachelors in CS from BITS
Pilani, India.

I started using GNNs while working at Qualcomm R&D from 2018-2022. We were building GNNs to design optimal semiconductors. This is when I used PyG and contributed to it in
my free time. This lead me to joining Kumo in 2022.

Kumo is an exciting place to work for multiple reasons. Firstly, the product seems magical at times, simply connect your data and define your prediction problem and it does the
rest. Secondly the people at kumo are amongst the most talented people I’ve worked with, plus they are really nice and supportive as a team too.

When I am not in from of my workstation, I enjoy playing and watching football. I also love going on treks.

https://github.com/wsad1

https://www.linkedin.com/in/jinu-sunil-90093094/

As a founding engineer at Kumo AI, my focus lies in developing the machine learning infrastructure and platform. I graduated from the University of Edinburgh with a Master’s
degree.

At Amazon, I encountered PyG while deeply involved in crafting a training framework for knowledge graphs. This led me to a research project centered on the distributed training
of graph neural networks, which proved to be scalable and efficient, seamlessly integrating with PyG. Kumo found interest in this project and reached out to me. I was captivated
by Kumo’s mission and promptly decided to join. Nowhere else offers such a significant impact on enterprise machine learning.

Working at Kumo, I’ve experienced the genuine passion of the team for the product and our customers. The engineering talent and clear direction from the leadership team create
an environment where everyone is eager to help each other. In the infrastructure team, I’ve played a key role in contributing to customer features, system design, machine
learning algorithms, and DevOps. Our collective effort is dedicated to achieving our goals with a customer-focused mindset.

Outside of work, I find relaxation in hiking, video games, and playing the piano. Feel free to connect with me!

https://zenotan.github.io

https://github.com/ZenoTan

https://www.linkedin.com/in/zeyuan-tan-474a99184

I am a founding engineer at Kumo.ai, working on bridging the gap between our predictive query language and graph learning architectures. I obtained my PhD in Computer Science
from TU Dortmund University in 2022, doing research on 3D computer vision using graph neural networks. I also hold a research group leader position at the Max-Planck Institute
for Informatics, heading the geometric representation learning group.

My first contact with graph neural networks was in 2018 when I worked on efficient GPU algorithms for message passing, which laid the foundation for Pytorch Geometric (PyG), mapping
large-scale graph neural networks to parallel computing hardware. I worked on applying GNNs in several different areas of computer vision, graphics, and molecule analysis,
making use of the versatility of the message-passing formulation. I joined Kumo in 2021 as a founding engineer to bring graph neural networks and PyG into real-world products.

It is an amazing experience to work in Kumo’s international team of highly skilled and experienced people, getting to know the industry and learning from the best. In contrast
to academia, creating a product for the real world holds a whole list of different challenges. It is exciting to think about and solve these problems, especially if the venture
takes the team to entirely novel grounds, which regularly happens at Kumo.

Outside of working I really enjoy snowboarding and skiing if the season allows it! If it doesn’t, I like watching shows, playing games or working out. You can reach out to me
on LinkedIn.

I’m a founding engineer at Kumo.ai, and I’m working on the machine learning platform that makes machine learning models faster and more scalable. I got my M.S degree
from UCLA and B.S from Tsinghua university. Before Kumo.ai, I was working on machine learning for self driving cars, search, and recommendation systems.

I was involved at the beginning of Kumo.ai and mainly focused on bridging the gap between the machine learning models and the infrastructure. There are unique platform challenges
when we adopt GNN to real world applications coming from both algorithms and data scalability point of view. I was excited to see how I can optimize the end to end pipeline
from CUDA kernels to distributed system design.

What excites me the most about working at Kumo.ai  is the mission of the company. I have a deep interest in applying machine learning to daily applications and I’m
lucky to be working with a group of talented people in academia and industry to make this dream come true. Beyond daily platform development, I’ve learned a lot about
the latest trends in machine learning and challenging problems in different industries. The variety of problems we are facing at Kumo.ai is unique compared to other companies.

In my free time, I love building Lego and watching movies. Connect with me on Twitter, or LinkedIn.

I am a founding engineer at Kumo.AI and work on graph machine learning design, its efficiency and scalability, as well as the integration into the Kumo platform as part of our
predictive query language. I obtained my PhD at the TU Dortmund University in Machine Learning on graphs. During this time, I created the PyG (PyTorch Geometric) library to
easily write and train Graph Neural Networks for a wide range of applications. I am very excited to see PyG being used as a central part of Kumo to drive its machine learning
needs, also benefitting PyG and the open-source community in return. I’ve always found the idea of making recent research advancements quickly and widely accessible
fascinating. With Kumo, we are finally able to make Graph ML methodologies applicable to non-domain experts as well.

I really enjoy being part of Kumo. I am very grateful for the trust to contribute ideation, prototypes and platform design right from the beginning, and it is amazing to innovate
every day with some of the most talented people I’ve ever met. At Kumo, research, collaboration and industrial requirements blend seamlessly.

While away from the computer, I love playing soccer (or as we like to call it: football) and love being outside with our dog. I also play bass in a fun ska band and we tour all
over Europe. You can reach me on Twitter and GitHub.

https://www.linkedin.com/in/matthias-fey-b4a947221/?locale=en_US