Manuel Amunategui is vice president of data science at SpringML, a startup offering Google Cloud TensorFlow and Salesforce enterprise solutions. Prior to that, he worked as a quantitative developer on Wall Street for a large equity-options market-making firm and as a software developer at Microsoft. He holds master degrees in predictive analytics and international administration.

He is a data science advocate, blogger/vlogger (amunategui.github.io) and a trainer on Udemy and O'Reilly Media, and technical reviewer at Packt Publishing.

Nikhil Borkar holds a CQF designation and a postgraduate degree in quantitative finance. He also holds certified financial crime examiner and certified anti-money laundering professional qualifications. He is a registered research analyst with the Securities and Exchange Board of India (SEBI) and has a keen grasp of laws and regulations pertaining to securities and investment. He is currently working as an independent FinTech and legal consultant. Prior to this, he worked with Morgan Stanley Capital International as a Global RFP project manager. He is self-motivated, intellectually curious, and hardworking. He loves to approach problems using a multi-disciplinary, holistic approach. Currently, he is actively working on machine learning, artificial intelligence, and deep learning projects. He has expertise in the following areas:

• Quantitative investing: equities, futures and options, and derivatives engineering
• Econometrics: time series analysis, statistical modeling
• Algorithms: parametric, non-parametric, and ensemble machine learning algorithms
• Code: R programming, Python, Scala, Excel VBA, SQL, and big data ecosystems.
• Data analysis: Quandl and Quantopian
• Strategies: trend following, mean reversion, cointegration, Monte-Carlo srimulations, Value at Risk, Credit Risk Modeling and Credit Rating
• Data visualization: Tableau and Matplotlib

Sonali Dayal is a masters candidate in biostatistics at the University of California, Berkeley. Previously, she has worked as a freelance software and data science engineer for early stage start-ups, where she built supervised and unsupervised machine learning models as well as data pipelines and interactive data analytics dashboards. She received her bachelor of science (B.S.) in biochemistry from Virginia Tech in 2011.

Kuntal Ganguly is a big data machine learning engineer focused on building large-scale data-driven systems using big data frameworks and machine learning. He has around 7 years of experience building several big data and machine learning applications.

Kuntal provides solutions to AWS customers in building real-time analytics systems using managed cloud services and open source Hadoop ecosystem technologies such as Spark, Kafka, Storm, Solr, and so on, along with machine learning and deep learning frameworks such as scikit-learn, TensorFlow, Keras, and BigDL. He enjoys hands-on software development, and has single-handedly conceived, architectured, developed, and deployed several large scale distributed applications. He is a machine learning and deep learning practitioner and very passionate about building intelligent applications.

Kuntal is the author of the books: Learning Generative Adversarial Network and R Data Analysis Cookbook - Second Edition, Packt Publishing.

Shilpi Saxena is a seasoned professional who leads in management with an edge of being a technology evangelist--she is an engineer who has exposure to a variety of domains (machine-to-machine space, healthcare, telecom, hiring, and manufacturing). She has experience in all aspects of the conception and execution of enterprise solutions. She has been architecting, managing, and delivering solutions in the big data space for the last 3 years, handling high performance geographically distributed teams of elite engineers. Shilpi has around 12+ years (3 years in the big data space) experience in the development and execution of various facets of enterprise solutions, both in the product/services dimensions of the software industry. An engineer by degree and profession who has worn various hats--developer, technical leader, product owner, tech manager--and has seen all the flavors that the industry has to offer. She has architectured and worked through some of the pioneer production implementation in big data on Storm and Impala with auto scaling in AWS. LinkedIn: http://in.linkedin.com/pub/shilpi-saxena/4/552/a30