Instructors & Mentors

Arham holds a Masters degree in Technology Management from Texas A&M University and has a background of managing information systems.

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.

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`.

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.

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.

Elissa’s interests include using smart grid big data, predictive analytics, and machine learning to create data-driven solutions to energy efficiency and electrical grid management. She currently works as the senior software engineer with OhmConnect. Elissa received her M.S. in Energy Tech. Engineering from Stanford University and her B.S. in Civil and Environmental Engineering from Carnegie Mellon University.

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.

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.

Svetlana is a member of the technical staff at eBay, working as an Machine Learning Engineer in the Structured Data research team. She has also worked on the Search Frontend Applied Research Team at eBay. Prior to working at eBay, Svetlana was a Software Development Engineer at Microsoft. Svetlana holds a Ph.D. in computer science from the University of Cincinnati, and she is skilled in data mining and machine learning.

I am a Data Science leader, with 10+ years of extensive experience across different facets of businesses; managing data scientists, leading data science initiatives across Product, Growth, Sales, and Marketing. My experience includes defining performance metrics, designing and tracking experiments to drive product success; building machine learning models to detect anomalies, drive account prioritization for effective targeting. Led multiple successful growths and product initiatives by collaborating with Product, Engg, and Marketing teams. Experienced across e-Commerce, Technology, Logistics, and Social media domain, focusing on desktop and mobile platforms. Currently, I am leading Consumer Analytics, Customer Support, and Fraud Analytics teams at DoorDash, Inc based out of San Francisco.