Montevideo

A 4 weeks hands-on workshop, led by grad students mentors, focused on developing technical and business knowledge/skills in the field of data science and machine learning. 

OBJECTIVES

• Recognize, manipulate and apply to a basic level a variety of machine learning techniques.
• Understand the current and future business opportunities in the fields of machine learning and data science.
• Develop entrepreneurship and intrapreneurship skills.
• Develop effective communication skills for Data Science-related pitch.
• Promote a space to build working teams in the community of data scientists. 

CORE ELEMENTS

• First module/week: Assessing the learners’ Python skills and machine learning knowledge, introducing the fundamental concepts of machine learning and its applications in industry.  Students will learn strategies for ideation, particularly for data science and machine learning applications, and receive an initial look at three specific industries that we will cover in the remainder of the course – e-commerce, healthcare, and finance.
• Second module/week: Understanding machine learning techniques for e-commerce and its current and future applications in industry. After learning the business aspects of machine learning applied to e-commerce, students will gain the tools to effectively communicate as a team and persuade an audience both with and without data.
• Third module/week: Understanding machine learning techniques in Healthcare and its applications in the field. students will learn and apply considerations in determining decision-making units within an organization and how to navigate implementation. 
• Fourth module/week: Machine Learning techniques in the Financial industry, and its current and future applications in finances. Students will have two days to work on their own projects, finalizing with a pitch competition on Friday.

INSTRUCTORS

Amauche Emenari is a PhD candidate, studying the root of intelligence at the MIT Media Lab. He currently works as a member of the Synthetic Neurobiology Group specializing in using biological and machine learning techniques to build structural models of the central nervous system. Prior to arriving at MIT, he designed and developed a neuroscience-themed crowdsourcing platform at the National Institutes of Health. He has worked in industry as a software developer on Apple’s iPhone software team, on Microsoft’s Cloud Infrastructure team, and on IBM’s high-performance microprocessors team. 

Outside of his research, he works on projects related to the application of new technologies (Machine Learning, Blockchain, Virtual Reality, and Robotics) to address challenges in cybersecurity, finance, education, and governance. He travels around the world teaching courses on machine learning and robotics. 

He was born and raised in Washington DC. He received three degrees in Biomedical Engineering, Electrical Engineering and Computer Science from Duke University and a master’s degree in Neuroengineering from Boston University. 

Devin Zhang is a dual degree candidate in the Leaders for Global Operations (LGO) program, pursuing an MBA from the MIT Sloan School of Management and an SM from the MIT Department of Electrical Engineering and Computer Science. Prior to LGO, he was a data science and advanced analytics consultant at Oliver Wyman, helping implement machine learning and data-driven processes across retail, manufacturing, and financial services industries. He has experience leading both technical solution development and organizational change in prior initiatives. Devin completed his undergraduate degree in Chemical Engineering and Applied Mathematics at MIT.

Evan Pu holds a PhD from MIT co-advised by Armando Solar-Lezama and Leslie P. Kaelbling. His current research interest is improving the communication between the user and the computer by borrowing frameworks in cognition and linguistics. In this view, the computer can better infer the underlying intention of the user, rather than taking it literally at face value and causing frustration to the user. He is also the machine learning advisor at learnable.ai, a start-up focusing on automatic grading of scanned exams, primarily from Chinese middle-school math classes.

Geeticka Chauhan is a PhD candidate in the MIT Computer Science and Artificial Intelligence Lab (CSAIL), working on Machine Learning for Healthcare. She works using clinical text to improve disease prediction in medical imaging. she completed an undergraduate degree in Computer Science from Florida International University in Miami, and grew up in New Delhi, India.

Hans Nowak is a dual MS/MBA candidate at MIT specializing in data analytics and machine learning applied manufacturing and supply chain operations. 

At MIT, Hans is a tutor and mentor to undergraduate students, VP of the Coders Club, a research fellow, and a teaching assistant for multiple courses ranging from Leadership & Organizational Change to Optimization Methods in Business Analytics. This summer, he worked as an Operations Research Fellow at Raytheon, applying machine learning techniques to manufacturing capacity planning.

Prior to graduate studies at MIT, Hans served five years as a nuclear submarine officer in the US Navy and two years teaching Naval Engineering in MIT’s Naval Science Department. He is a certified nuclear engineer and holds a BS in both Applied Mathematics and Quantitative Economics from the United States Naval Academy.

Julian Alverio is a Master’s of Engineering candidate at the MIT Computer Science & Artificial Intelligence Lab (CSAIL), researching how to integrate natural language understanding with reinforcement learning in robotics. He holds a Bachelor’s of Science in Electrical Engineering and Computer Science also at MIT.

Kenny Li is currently pursuing his MBA at MIT Sloan. Prior to business school, Kenny was an entrepreneur who started a B2B company in the cloud computing space servicing enterprises with cloud strategy and migration. Over the summer, Kenny worked in Mergers and Acquisitions at SenseTime, the world’s largest pure-play AI company. In his free time, Kenny enjoys archery and meditation.

Madhav Kumar is a PhD candidate at the Sloan School of Management at MIT, he is currently working on blending causal inference and machine learning methods to inform business decisions. He has worked for over five years in multiple sectors such as consulting, analytics, research, and government. During this period, he worked on solving challenging business and policy problems using machine learning and data analysis. In addition, he has worked with two non-profits in the education space. He likes corgis.  

Maya Murad is a Master in Integrated Design and Management candidate at MIT.  She has worked extensively in cross-functional teams to deliver projects in multiple industries in the context of developing markets, including Bain & Company and at the United Nations WPF. She also has direct experience developing and deploying digital products for clients. In her spare time, she has mentored aspiring social entrepreneurs in the Middle East and worked with them to improve their business models and fundraising pitches.

Ryan Sander is a 2020 BS / 2021 MEng candidate at MIT, studying Electrical Engineering and Computer Science and mathematical economics with a concentration in artificial intelligence.  Professionally, he is interested in applying his background in computer vision and machine learning to the fields of autonomous vehicles, remote sensing, and robotics. He is currently conducting research in MIT CSAIL’s Distributed Robotics Laboratory on autonomous vehicles and deep reinforcement learning, and he has also worked as a Lidar imagery scientist in the U.S. Department of Defense, a data science intern at Spacemaker AI, and an electrical engineering intern at Raytheon. In his free time, Ryan enjoys running, hiking, lifting weights, and listening to music.

Taylor Baum is a computational neuroscientist and controls engineer, broadly interested in fundamentally understanding the brain and developing novel technologies. At MIT, she is an Electrical Engineering and Computer Science PhD candidate, currently advised by Dr. Emery Brown and Dr. Munther Dahleh. In her current work, she is developing brain state estimation algorithms for use in Closed-Loop Anesthetic Delivery (CLAD) systems and exploring mechanisms of control with the human brain through computational models. In addition to her research, she is a highly experienced educator and pursuing ventures in the medical and med-tech industries. 

Tiffany Fung is an MBA candidate at MIT Sloan with a focus in technology and analytics. Her background is in consulting at Ernst & Young and analytics and product management at Expedia. Most recently, she interned at Google over the summer as a Technical Solution Consultant. Prior to Sloan, she lived and worked in Hong Kong, London, and Los Angeles. Outside of work, she is a huge fan of traveling, windsurfs occasionally, and enjoys topics around language, culture and psychology. 

Victoria Pisini is an MBA candidate at MIT Sloan, Victoria is involved in the entrepreneurship community, teaching assisting the course New Enterprises. Prior to MIT Sloan, Victoria worked at A.T. Kearney, a management consulting firm, in both their Global Business Policy Council think tank in Washington, DC and as a consultant out of their New York City office. This past summer, she worked at Lightmatter, a startup building computer chips to accelerate Artificial Intelligence workloads in Boston founded by MIT graduates. Victoria graduated from the University of Pennsylvania with a major in International Relations. Outside of school, she enjoys exercising, being outdoors, reading, and cooking.