LDV Capital invests in early-stage companies in North America and Europe with a focus on visual technologies. We are the only venture fund with a thesis of investing in businesses powered by visual technologies such as computer vision, machine learning, and AI to analyze visual data. Some examples of visual technology verticals: photonics, autonomous vehicles, mapping, robotics, food/agriculture, augmented reality, logistics, manufacturing, search, security, entertainment, healthcare, and much more. The unique LDV Capital platform includes an annual LDV Vision Summit, LDV Community, annual LDV Insights reports, and an extensive expert network.

Our annual LDV Vision Summit has two competitions. Finalists receive a chance to present their wisdom in front of 600 top industry executives, venture capitalists, and companies recruiting. The winning competitor is also awarded $5,000 Amazon AWS credits. The competitions:

1. Startup competition for promising visual technology companies with less than $2M in funding

2. Entrepreneurial Computer Vision Challenge (ECVC) for computer vision and machine learning students, professors, experts, or enthusiasts working on a unique solution to empower businesses and humanity.

Competitions are open to anyone working in our visual technology sectors such as empowering photography, videography, medical imaging, analytics, robotics, satellite imaging, computer vision, machine learning, artificial intelligence, augmented reality, virtual reality, autonomous cars, media and entertainment, gesture recognition, search, advertising, cameras, e-commerce, visual sensors, sentiment analysis, and much more.

The ECVC provides contestants the opportunity to showcase the technology piece of a potential startup company without requiring a full business plan. It provides a unique opportunity for students, engineers, researchers, professors, and/or hackers to test the waters of entrepreneurism in front of a panel of judges including top industry venture capitalists, entrepreneurs, journalists, media executives, and companies recruiting.

For the 2017 ECVC, we had an outstanding lineup of finalists, including:

• Timnit Gebru, Ph.D. from Stanford University on “Predicting Demographics Using 50 Million Images”

• Anurag Sahoo, CTO, and Mick Das, CPO of Aitoe Labs

• Akshay Bhat, Ph.D. Candidate and Charles Herrmann, Ph.D. Candidate from Cornell University on “Deep Video Analytics”

• Elena Bernardis, Ph.D. of the University of Pennsylvania Children’s Hospital with “Spot It - Quantifying Dermatological Conditions Pixel-by-Pixel”

• Bo Zhu, Ph.D. of Harvard Medical School’s Martinos Center for Biomedical Imaging presenting “Blink” about synthetical human vision

• Gabriel Brostow from University College London with “MonoVolumes” a combination of MonoDepth and Volume Completion to understand 3D scene layout

Congratulations to our 2017 LDV Vision Summit Entrepreneurial Computer Vision Challenge Winner: Timnit Gebru.

What was the focus of your winning research project?
We used computer vision algorithms to detect and classify cars in 50 million Google Street View images. We then used the characteristics of these detected cars to predict race, education, income levels, voting patterns and income segregation levels. We were even able to see which city has the highest/lowest per capita CO2 footprint.
 
As a PhD candidate - what were your goals for attending our LDV Vision Summit? Did you attain them?
I mostly wanted to meet other people in the field who might have ideas for future work or collaborations. After the competition, I was contacted by venture capitalists and people whose startups are working on related things. In addition to that, I received some interesting ideas from  conference attendees (e.g. analyzing the frequency of trash collection in neighborhoods to get some signal regarding neighborhood wealth).
 
Why did you apply to our LDV Vision Summit ECVC? Did it meet or beat your expectations and why?
I applied because Serge Belongie (Professor at Cornell Tech and Expert in Residence at LDV Capital) thought it was a good idea. One of his many research interests is similar to my line of work. Since our work has real world applications, I think he felt that presenting it to the LDV community would help us think of ways to make it more accessible. I didn’t know what to expect but it definitely beat my expectations. I have never been at a conference that brings together entrepreneurs who are specifically interested in computer vision. I didn’t know there that the vision community was so large, and that many VCs were thinking of companies with a computer vision focus (this is different from thinking of AI in general).
 
Why should other computer vision, machine learning and AI researchers attend next year?
This is unlike any other conference out there because it is the only conference I know of that is only focused on computer vision but also brings together researchers, investors and entrepreneurs. 
 

What was the most valuable part of your LDV Vision Summit experience aside from winning the ECVC?
Meeting others whose work is in a similar space: for example, people who founded companies that are based on analyzing publicly available visual data. One of the judges founded such a company. It helped me think of ways in which my research could be commercialized (if I decided to go that route).
 
Do you have any advice for researchers & PhD candidates that are thinking about evolving their research into a startup business and/or considering submitting their work to the ECVC?
I advise them to think of who exactly their product would benefit and what their API would be like. Even though I was an entrepreneur for about a year, I am still coming from a research background. So I wasn’t thinking about who exactly the customers of my work would be (except for other researchers) until my mentoring sessions with Evan [Nisselson, GP of LDV Capital].
 
What are you looking to do with your research & skills now that you have completed your PhD?
I will be a postdoctoral researcher continuing the same line of work but also studying the societal effects of machine learning and trying to understand how to create fair algorithms. We know that machine learning is being used to make many decisions. For example, who will get high interest rates in a loan, who is more likely to have high crime recidivism rates, etc...The way our current algorithms work, if they are fed with biased datasets, they will output biased conclusions. A recent ProPublica investigation started a debate on the use of machine learning to predict crime recidivism rates. I am very worried about the use of supervised machine learning algorithms in high stakes scenarios.
 

In 2020, she became our first guest in LDV Capital’s Women Leading Visual Tech Series. Check out this interview on algorithmic bias and data mining ethics.