ODSC WEST 2022 CONFERENCE & EXPO
AI STARTUP
SHOWCASE & LAB
Meet founders, investors, speakers, and teams powering some of the worlds leading AI startups
San Francisco | November 1-3
How to Qualify?
Meet with Investors
Startups Participating
Meet Investors 1x1
Format
10 Investor Meetings
20 Minutes Each
You get 20 minutes to make an impression, but that doesn’t mean 20 minutes to pitch. Leave plenty of time for questions. At least half of the meeting or more should be focused on questions, both from you and the investor.
Past Investors
ODSC West VCs & Investors Coming Soon
If you are an active investor or VC and would like to participate please email us at info@odsc.com
Showcase Your Startup at the Leading Data Science and AI Conference
No industry is immune from disruption and AI has become a core strategic advantage for many businesses. As part of our efforts to grow the data science community, we are offering our support to startups in these fields with free or deeply discounted exhibitor spaces to help you spread the word to both our conference and online communities.
Speak and Connect
ODSC offers a unique opportunity to speak at our conference and pitch to investors. Selected startups get a talk session in front of our audience and a chance to pitch to some of the top companies investing in data science and AI.
We welcome startups from across every industry to Apply and Attend
Data science is impacting not only industries from retail to energy but also across the enterprise. Sales, marketing, 50E3C2 development, human resources, and operations are just of few of the many enterprise departments employing AI and data science solutions.
Technology
Healthcare
Biotech
Pharma
Life Sciences
Online Services
Financial Services
Quant Finance
Insurance
Banking
Cross Industry
Auto and Manufacturing
Retail and Online
Media and Marketing
Energy and Logistics
How to Qualify?
Depending on your startup size and funding, ODSC offers free or steeply discounted exhibitor packages to showcase at our conference. We provide a limited number of spaces at each event and they are generally allocated on a first-come basis to qualified startups
Your startup must have a demonstrable product or service to exhibit
Your startup can be B2B, B2C or any combination. However, the core technology must be data science or artificial intelligence
Founders should explain the unique aspect of their technology and how it employees AI or data science.
Ideal startup showcase candidates range from pre-funding or seed round to Series A round
We accept startups from the global community that are a registered legal entity
Your startup should demonstrate that it can benefit from connecting with our community of investors, data scientists, and attending companies
If you are a later stage startup or company please visit our partner page. Generous discounts will still apply to qualified later stage startups.
PAST SPEAKERS AND FOUNDERS FROM TOP AI STARTUPS

Matthew Rocklin, PhD
Matthew Rocklin, CEO and founder of Coiled, and the initial author of Coiled’s underlying technology, Dask. He developed Dask to help people solve challenging distributed computing problems while working at Anaconda. While he is primarily known for his work on Dask, he also coordinates and maintains several dozen libraries within Python’s numeric computing ecosystem, with a substantial focus on efficient and scalable computing. Matthew is a frequent speaker at several technical, academic, and industry events, such as PyData, SciPy, Google Next, O’Reilly’s Strata, AGU, AMS, and ICML. He has a Doctorate of Philosophy in Computer Science from the University of Chicago, and a Bachelors in Physics, Mathematics, and Astronomy from the University of California.

Dr. Jacqueline Nolis
Dr. Jacqueline Nolis is a data science leader with 15 years of experience in running data science teams and projects at companies ranging from Airbnb to Boeing. She is the Chief Product Officer at Saturn Cloud where she helps design products for data scientists. Jacqueline has a PhD in Industrial Engineering and her academic research focused on optimization under uncertainty. Data science is also her hobby—like making an R package that mails physical postcards of your plots.
Make Your Data Science Environment Just Right With Saturn Cloud(Demo Talk)

Josh Tobin, PhD
Josh Tobin is the founder and CEO of Gantry. Previously, Josh worked as a deep learning & robotics researcher at OpenAI and as a management consultant at McKinsey. He is also the creator of Full Stack Deep Learning (fullstackdeeplearning.com), the first course focused on the emerging engineering discipline of production machine learning. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel.

Adrien Treuille, Phd
Adrien is co-founder and CEO of Streamlit. Previously, Dr. Treuille has been VP of Simulation Zoox, lead a Google X project, and was a Professor of Computer Science and Robotics at Carnegie Mellon. He gives talks around the world, including to the President’s Council of Advisors on Science and Technology, and has won numerous scientific awards, including the MIT TR35. Adrien and his work have been featured in the documentaries “What Will the Future Be Like” by PBS/NOVA, and “Lo and Behold” by Werner Herzog.
Streamlit: Next-generation Communication of Data Insights(Workshop)

Alexander Dean
Alexander Dean is Co-founder and Chief Executive Officer at Snowplow Analytics. Alexander is a keen technologist with a passion for functional programming, cloud-based architectures and big data technologies. He also has a passion for innovation and organizational change. Before co-founding Snowplow, Alexander worked in technology roles at OpenX and in the Business Intelligence department at Deloitte Consulting, as well as strategy roles at Fathom Partners and Keplar LLP. Alexander holds a BA in History from the University of Cambridge.
Snowplow: Creating AI-ready Data for Better Models and Predictions(Demo Talk)

Milecia McGregor
Milecia is a senior software engineer, international tech speaker, and mad scientist that works with hardware and software. She will try to make anything with JavaScript first. In her free time, she enjoys learning random things, like how to ride a unicycle, and playing with her dog.
Preventing Stale Models in Production(Talk)

Ed Shee
Ed Shee, Head of Developer Relations at Seldon. Having previously led a tech team at IBM, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.
An Introduction to Drift Detection(Workshop)

Stephanie Wang
Stephanie is a final-year PhD student at UC Berkeley and a software engineer at Anyscale. She is interested in abstractions for distributed computing and problems in fault tolerance. Towards this end, she is also a maintainer for the open-source project Ray, which provides a simple, universal API for building distributed applications in Python.
Distributed Python with Ray: Hands-on with the Ray Core APIs (Tutorial)

Andrew Engel, PhD
Andrew Engel is the Chief Data Scientist at Rasgo. He has been working as a data scientist and leading teams of data scientists for over ten years in a wide variety of domains from fraud prediction to marketing analytics. Andrew received his Ph.D. in Systems and Industrial Engineering with a focus on optimization and stochastic modeling. He has worked for Towson University, SAS Institute, the US Navy, Websense (now ForcePoint), Stics, HP and led DataRobot’s efforts in Entertainment, Sports and Gaming before joining Rasgo in August of 2020.
Feature Engineering on the Modern Data Stack(Demo Talk)

Jimmy Whitaker
Jimmy Whitaker is the Data Science Evangelist at Pachyderm. He focuses on creating a great data science experience and sharing best practices for how to use Pachyderm. When he isn’t at work, he’s either playing music or trying to learn something new, because “You suddenly understand something you’ve understood all your life, but in a new way.
MLOps: From 0-60 with Pachyderm(Demo Talk)

Anais Dotis-Georgiou
Anais Dotis-Georgiou is a Developer Advocate for InfluxData with a passion for making data beautiful with the use of Data Analytics, AI, and Machine Learning. She takes the data that she collects, does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, boarding, or chasing after a soccer ball.
InfluxDB: The Database for Your Time Series Data Science Problems(Demo Talk)

Danny D. Leybzon
Danny D. Leybzon has worn many hats, all of them related to data. He studied computational statistics at UCLA, before becoming first an analyst and then a product manager at a big data platform named Qubole. He went on to be the primary field engineer for data science and machine learning at Imply, before taking on his current role as MLOps Architect at WhyLabs. He has worked to evangelize machine learning best practices, talking on subjects such as distributed deep learning, productionizing machine learning models, automated machine learning, and lately has been talking about AI observability and data logging. When Danny’s not researching, practicing, or talking about data science, he’s usually doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, etc.
AI Observability: How To Fix Issues With Your ML Model(Talk)

Jirka Borovec, PhD
Jirika is working in Machine learning and Data science for several years. He has done Ph.D. in Medical Imaging. In parallel, he gains practical experience while he has been working for a few IT companies as a consultant or data scientist. Actually,he is focusing on exploring interesting world problems and solving them with state-of-the-art techniques. He has developed several open-source python packages, He is the core contributor of `PyTorch-Lightning` and `TorchMetrics` and actively participating in other well-known projects.

Keegan Hines, PhD
Keegan is VP of Machine Learning at ArthurAI and is also an Adjunct Assistant Professor at Georgetown University. Previously, he was the Director of Machine Learning Research at Capital One and has also held roles at cyberdefense firms. He is a Co-Founder of the Conference on Applied Learning for Information Security (CAMLIS) and holds a PhD in Neuroscience from the University of Texas.

Trevor Grant
Trevor is the Director of Developer Relations at Arrikto and an international speaker excited to be back on the road after a 2 year COVID hiatus. He is also a member and involved with leadership of several projects at the Apache Software Foundation, PMC Chair of Apache Mahout, and Author of Kubeflow For Machine Learning: From Lab to Production.
Tower of Babel: Making Apache Spark, Apache Mahout, Kubeflow, and Kubernetes Play Nice(Talk)

Ryan Wright
Ryan Wright is the creator of Quine, and has been leading software teams focused on data infrastructure and data science for two decades. He has served as principal engineer, director of engineering, principal investigator on DARPA-funded research programs, and is currently the founder and CEO of thatDot—the company supporting Quine. Ryan particularly enjoys taking the philosophical ends of computer science—usually problems related to language, meaning, and data—and making them more practical.
Noiseless Anomaly Detection with Streaming Graph A.I.(Demo Talk)
Quine: A Streaming Graph for Event-Driven Data Pipelines(Talk)

Devavrat Shah, PhD
Devavrat Shah is Andrew (1956) and Erna Viterbi Professor with the department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology. He is the founding director of Statistics and Data Science at MIT. He is also a member of IDSS, LIDS, CSAIL and ORC at MIT. He co-founded Celect, Inc. (now part of Nike) in 2013 to help retailers decide what to put where by accurately predicting demand using omni-channel data. He is a co-founder and CTO of IkigaiLabs with the mission to build self-driving organizations by enabling data-driven operations with human-in-the-loop. His research focuses on statistical inference and stochastic networks. His contributions span a variety of areas including resource allocation in communications networks, inference and learning on graphical models, algorithms for social data processing including ranking, recommendations and crowdsourcing and more recently causal inference. He has made foundational contributions to the development of “gossip” protocols and “message-passing” algorithms for statistical inference which have been the building blocks of modern distributed data processing systems.His work spans a range of areas across electrical engineering, computer science and operations research. His work has received broad recognition, including prize paper awards in Machine Learning, Operations Research and Computer Science, and career prizes including 2010 Erlang prize from the INFORMS Applied Probability Society, awarded bi-annually to a young researcher who has made outstanding contributions to applied probability. He is a distinguished alumni of his alma mater IIT Bombay from where he graduated with the honor of President of India Gold Medal. His work has been covered in popular press including NY Times, Forbes, Wired and Reditt.
Automation for Data Professionals(Training)

Ryan Blue
Ryan is the co-creator of Apache Iceberg and spent the last decade working on big data infrastructure at Netflix, Cloudera, and now Tabular. He is an ASF member and a committer in the Apache Parquet, Avro, and Spark communities.

Yuan Tang
Yuan is currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platform. He’s a PMC member of XGBoost and Apache MXNet, co-chair of Kubeflow, maintainer of TensorFlow, Argo, Couler, and ElasticDL, as well as author of numerous open source projects. He’s the author of Distributed Machine Learning Patterns (https://github.com/terrytangyuan/distributed-ml-patterns) as well as the co-author of TensorFlow in Practice and Dive into Deep Learning (with TensorFlow).

Hadrien Jean, PhD
Hadrien Jean is a machine learning scientist working at My Medical Assistent where he is developing deep learning models in the medical domain. He wrote the book Essential Math for Data Science (https://www.essentialmathfordatascience.com/) aimed at helping people to get the math needed in data science from a coding perspective. He previously worked at Ava on speech diarization. He also worked on a bird detection project using deep learning. He completed his Ph.D. in cognitive science at the École Normale Supérieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https://hadrienj.github.io/posts/).
Introduction to Linear Algebra for Data Science and Machine Learning With Python(Bootcamp)
Startups Sessions Include:
Human-Friendly, Production-Ready Data Science with Metaflow
What I love and hate about Dask
🤗 Transformers & 🤗 Datasets for Research and Production
AI for Healthcare: A Practical Application of AI/ML in Pediatric Behavioral Healthcare
AI Observability: How To Fix Issues With Your ML Model
An Introduction to Drift Detection
Automation for Data Professionals
Building and Deploying the World’s Largest Rock/Paper/Scissors Competitive Ladder App in X Minutes with Roboflow and Streamlit
Creating a Benchmark for a Large-Scale Image Captioning Pipeline
Data Boards: A Collaborative and Interactive Space for Data Science
Data Science in the Cloud-Native Era
Deep Dive Workshop for Apache Superset
Deep Learning Enables a New View in the Agriculture Industry
Intro to Deep Learning in R
Drift Detection in Structured and Unstructured Data
Feature Engineering on the Modern Data Stack
Full-stack Machine Learning for Data Scientists
Introduction to the PyTorch Lightning Ecosystem
MLOps: From 0-60 with Pachyderm
Noiseless Anomaly Detection with Streaming Graph A.I.
The Future of Software Development Using Machine Programming
The Origins, Purpose, and Practice of Data Observability
What I love and hate about Dask
Tower of Babel: Making Apache Spark, Apache Mahout, Kubeflow, and Kubernetes Play Nice
Unlocking the Value of Siloed Data with Multi-party ML
Vector Databases
MORE WAYS TO PARTICIPATE AT ODSC WEST
AI Investors Reverse Pitch
Join us at the AI Investors Reverse Pitch to hear top investment firms & VCs explain why YOUR AI and Data Science Startup should choose THEM. Discover what top firms look for in startups when they are looking to invest, learn what types of businesses they currently have in their portfolios, and then introduce yourself when you find the right potential investor for your startup.
AI Founders
Build awareness and recognition and share your story of success during our AI Founders event. This event will feature short presentations from founders like you, who have done the hard work of coming up with an innovative idea, finding investors, and starting a company from scratch.
AI Expo & Demo Hall Registration
Interested in Attending And Meeting Top Startups?
Free and Paid Passes are available now.
Limited time offer
Limited time offer
Founder Office Hours
Meet over 10+ iwith some of the top founders in the field


Who Attends?
As an applied data science event, each ODSC conference attracts professionals including data scientists, managers, founders, innovators and CxOs from many companies across the industry. Request Who Attends brief to see some of the attending companies from 2022.