Tarun holds a master’s degree in business analytics from Seattle University (Seattle, WA) and a bachelor’s degree in electrical engineering from the Jamia Millia Islamia (New Delhi). He has a diverse experience spread across brand and customer research, academics, content development, and engineering.
Margaux is a data scientist at McKinsey & Company, Sydney. Margaux holds a Bachelor’s in Computer Science and Mathematics from Télécom Paristech (“Grande Ecole”), and a Master’s in Statistics from Imperial College, London.
A generalist with a passion for people, data, and technology currently working as an enthusiast software developer with a data scientist twist in the Software Innovation team at Equinor. He is focused on exploring new data-driven ideas and challenges by using predictive analytics, machine learning, and software engineering practices.
Jiri has worked in machine learning and data science for several years. He holds a PhD in Medical Imaging. Nowadays, he is focusing on Computer vision and Deep learning. He has also developed several open-source Python packages, moreover, he is a core-contributor of `pytorch-ligthning` and actively participating in other well-known projects such as `scikit-image` and `auto-sklearn`.
Ben Bell is a Data Science Mentor at Sciencing Data. He has a degree in Economics and Philosophy with Honors Distinction. He has worked on projects such as WeTunes group playlist recommendation engine, graph visualization of Rdio Network, and visualizing rates of toxic subreddit comments.
Parvez has an extensive background in computer vision, machine learning, image processing and signal processing. He possesses several years of experience in working with multi-disciplinary cross-functional teams and translating real-world problems into a mathematical setting. He is experienced in diverse application domains such as medical imaging, bioinformatics, neuroscience, multimedia delivery, remote sensing and camera networks.
Parvez obtained his Ph.D. in Electrical Engineering and Computer Science from University of California at Berkeley.
Melissa is a Data Scientist at Uber. Before Uber, Melissa worked as data scientist at Square supporting the product team with her own machine learning models. Melissa got a B.S in Computer Science and Mathematics with a minor in Economics from Duke University. Her thesis research was in computational game theory, applying game theoretic models to prevent cheating in casinos.
Raja Iqbal is a data scientist, a passionate educator, and an internationally recognized speaker on data science. Prior to Data Science Dojo, Raja worked at Microsoft in a variety of research and development roles involving machine learning and data mining at very large scale. Raja has a Ph.D in Computer Science from Tulane University with a focus on machine learning and data mining.
Pavel has extensive skills in machine learning, data mining, and distributed systems. Formerly a principal data scientist at Microsoft, Pavel is familiar with web content management algorithms and real-time content analysis. Pavel obtained his Ph.D. in Computer Science from Cornell University.
Roman obtained his Ph.D. in Computer Science from University of British Columbia. He worked on Monte Carlo framework method based on Markov chain and sequential Monte Carlo for efficient sampling from high-dimensional distributions. Roman specializes in Monte Carlo methods (MCMC, SMC), Bayesian statistics, machine learning, natural language understanding. He previously worked as a Senior Sceintist with Bing query understanding team at Microsoft.
Julie is a senior data scientist at Textio, with expertise in machine learning and statistical experimentation. She has applied and honed her skills on projects ranging from sensor-based activity recognition, to marketing response prediction models, to deduplication and ranking algorithms for business listings. Prior to joining Microsoft in 2010, she earned her undergraduate degree at Stanford University and her PhD at the University of Washington, both in computer science. Julie is passionate about teaching and about outreach and mentorship to groups that are underrepresented in computing careers.
Yuan has extensive statistical modeling experience in linear models, logistic models, time series models, decision trees, model regularization, and cross validation. Yuan has a PhD in Economics from the University of California at Davis.
David is an experienced statistician. He specializes in using statistical analysis and mathematical modeling to aid in data-driven decision making and forecasting.
Ike has spent time consulting with fortune 500 companies and startups and has helped them discover and extract actionable insights from their data to drive business decisions. He has worked on a variety of problems from algorithmic trading to auction price prediction and using machine learning for intent resolution. Ike holds a Masters in Industrial & Systems Engineering and Bachelors degree in Physics and Electrical Engineering. He is excited about the possibilities that abound with the proliferation of data.
John is constantly amazed by the amounts of data available around us and thinks about how to squeeze the maximum amount of meaning from those data. He also thinks about how to offload as much work as possible to machines without losing accuracy. John holds a BS in Electrical Engineering and Computer Science and a BA in Cognitive Science from UC Berkeley. John worked on automatic computer virus classification using machine learning and collaborative filtering using social networks.