Nima Shahbazi, Ph.D.
Award winning AI scientist, entrepreneur, speaker.
Bio
Nima Shahbazi is the CEO of Mindle.ai where top data scientists advance AI by tackling the most challenging prediction problems:

  • AI-enabled capital market signals for hedge funds
  • High-value, business-critical predictions and recommendations for enterprise

Nima recently won the biggest computer science or AI competition on record, the $1M Zillow Prize, for creating the most accurate home valuation algorithm. On Kaggle, a Google-owned data science competition platform with 1M members, Nima's models consistently rank in the Global Top 10. He has won several competitions and achieved Grandmaster status.

His speaking experience includes the ReWork Deep Learning Summit, Re-Work Machine Learning Summit, Re-Work Deep Learning in Finance Summit, and RBC Disruptors, with 500 attendees and an online audience of over 174,000.

Nima co-founded Deepnify, an ML saas company that worked to reduce food waste. Deepnify raised seed funding and was accepted to the NextAI and Creative Destruction Lab accelerators. He previously worked in big data analytics, specifically on Forex and Stock Market predictions.

AI Awards
Winner of the $1M 2019 Zestimates prize
Nima was part of the team that won the $1M Zillow Prize for creating the most accurate home valuation algorithm.

The algorithm won by a comfortable margin, beating more than 3800 teams from 91 countries.

The teams's improvements will help push the Zestimate's current nationwide error rate of 4.5 percent to below 4 percent. The tool automates valuations on 110 million homes across the U.S.
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Prize winner in the 2018 ACM WSDM recommendation challenge
Nima was part of the prize winning team that ranked 2nd in the 2018 ACM Recommendation Challenge against over 1000 data science teams.

The algorithm helped Asia's leading music streaming service recommend more relevant music to its listeners.
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Prize-winner in the Mercari Price Suggestion Challenge
Nima was part of the 2nd place, prize winning team of the Mercari Kagglle competition with 2000 competitors. Mercari, Japan's biggest community-powered shopping app, challenged data scientists to build an algorithm that automatically suggests the right product prices to sellers.
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Prize winner in the Two Sigma Financial Modeling Challenge
Nima was part of the 2nd place, prize winning team of the Two Sigma Financial Modeling Challenge against 2000 teams. The model searched for signal in financial markets data with limited hardware and computational time accurately predicting financial movements.
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Prize Winner in the Rossmann Sales Forecasting Competition
Nima ranked 2nd and was a prize winner in the Rossmann Store Sales Kaggle competition against 3,738 data scientists. Rossmann operates over 3,000 drug stores in 7 European countries. Our model most accurately forecasted daily sales using historical sales data, product data, store data, and third party data such as weather and distance to competitors.
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Prize winner in the Home Depot Search Relevance Competition
Nima was part of the 2nd place, prize wining team in the Home Depot Product Search Relevance Kaggle competition against over 2000 data science teams. The algorithm helped them improve their customers' shopping experience by accurately predicting the relevance of search results.
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7th place in the ICDM 2015 Drawbridge Cross-Device Connections
Nima placed 7th against 340 teams in the ICDM 2015 Drawbridge Cross-Device Connections Competition. Given usage data and a set of fabricated non-personally-identifiable IDs, this competition tasked Kagglers with making individual user connections across a variety of digital devices, thereby improving marketers' ability to identify individual users as they switch between devices.
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Finalist in the Grupo Bimbo Inventory Demand Forecasting Competition
Nima ranked 7th in the Grupo Bimbo Inventory Demand Forecasting Kaggle competition against 1,969 teams. Before the model, Grupo Bimbo direct delivery sales employees made daily inventory calculations based on their personal experiences with each store. Our model accurately forecasted daily sales using historical sales data, product data, store data, and third party data such as weather to help Grupo Bimbo maximize sales and minimize returns in over 1M stores.

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5th place in the IEEE BIG DATA Bosch Production Line Performance Competition
Nima was part of the team that placed 5th in the Bosch Production Line Performance Competition. Bosch challenged Kagglers to predict internal failures using thousands of measurements and tests made for each component along the assembly line. This enabled Bosch to bring quality products at lower costs to the end user.
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Speaking
It takes sizzling passion to win the most competitive data science competitions in the world. Students, event organizers, and event attendees can attest to the fact that Nima's passion comes across on the stage.

Whether speaking to experienced data scientists or to non-technical executives, Nima's obvious data-love-and-obsession captivates his audience, leaving them excited and curious about the future of Machine Learning.





Nima was selected as a speaker for ReWork 2018 Machine Learning Summit in Montreal where he presented on Demand Forecasting.

Nima also spoke at RBC Disruptors with a live audience of 500 and 174,000+ online views.

News
"Shahbazi has competed in 20 Kaggle events, coming second five times and finishing in the top 15 another five times. He finished in the top five per cent of challengers on contests for everything from a Grupo Bimbo (the Mexican bakery multinational that owns Dempsters, Hostess, Vachon brands in Canada) inventory algorithm, a Home Depot online search relevancy challenge, a drug store sales volume prediction engine, a music recommendation system and even the 2017 Data Bowl that looked at lung-cancer detection".
Toronto man shares $1-million prize for real estate price predictions
"Zillow has slowly improved its Zestimate from a median error rate of 14 per cent when it started in 2006 to 5.7 per cent when the contest began in mid-2017. It's now down to 4.5 per cent nationally (it's higher in some cities and lower in others), and once the winners' tweaks to the algorithm are incorporated, the company expects the error rate to dip to about 4 per cent".
Online real-estate firm Zillow pays three guys, one from Toronto, $1M to improve home-value estimates

"Jordan Meyer of the United States, Chahhou Mohamed of Morocco, and Nima Shahbazi of Canada bested more than 3,800 teams representing 91 countries with an algorithm that beat Zillow's benchmark model (evaluated against real-time home sales between August and October 2018) by approximately 13 percent."
Meet the 'Zillow Prize' winners who get $1M and bragging rights for beating the Zestimate

"It's amazing to know that millions of people will benefit from our ideas," said Shahbazi, who competed as a team with Mohamed against Meyer in the contest's initial qualifier, but who decided to join forces with Meyer for the final round. "We brought every novel idea we could to our code and kept experimenting. For every idea that worked, there were a hundred that didn't work. But we kept going."
Zillow awards $1 million to team that reduced home valuation algorithm error to below 4%
On average, Zillow said, the Zestimate is $10,000 off the actual sale price for a median-priced home of about $223,900, and the information gleaned from the Zillow prize winnings could shave $1,300 off that discrepancy. It also moves the Zestimate's national median error rate below 4%.
Zillow's Zestimate got an upgrade — and this trio got $1 million for the new algorithm
"[the co-founders started Deepnify] to help food companies predict daily demand for their products, and reduce foot waste, by analyzing a mix of historic sales data and consumer trends.

"It's an important niche," Shahbazi says. "If you lower the waste, you can lower the price, which is good for the customers. More customers is good for the stores, and it's also environment friendly."
NextAI People's Choice winner is using machine learning to reduce food waste at grocery stores
"Drug store giant challenged Kagglers to forecast 6 weeks of daily sales for 1,115 stores located across Germany. The competition attracted 3,738 data scientists, making it our second most popular competition by participants ever.
Nima Shahbazi took second place in the competition, using his background in data mining to gain an edge. By fully exploring and understanding the dataset, Nima was able to engineer features that many participants overlooked."
Rossmann Store Sales, Winner's Interview: 2nd place, Nima Shahbazi
"Asked to search for signal in financial markets data with limited hardware and computational time, this competition attracted over 2000 competitors. In this winners' interview, 2nd place winners' Nima and Chahhou describe how paying close attention to unreliable engineered features was important to building a successful model."
Two Sigma Financial Modeling Challenge, Winner's Interview: 2nd Place, Nima Shahbazi, Chahhou Mohamed
"The winning team leveraged the power of NVIDIA TITAN Xp GPUs for both training and inference. For software, the team used the cuDNN-accelerated Keras, and TensorFlow deep learning frameworks".
NVIDIA GPUs Help Developers' Score $1 Million Prize For Improving Zillow's Zestimate
"Shahbazi worked across continents and multiple time zones with a team of two others – one from Morocco and another from the United States, to beat the Zestimate Algorithm. The team's winning solution beat the Zillow Benchmark Model by over 13 percent. They also held a comfortable lead against the second and third place teams."
Lassonde PhD Graduate wins $1 Million Zillow Award, improves the Zestimate

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