MS&E 802: TGR Dissertation
For doctoral students in Terminal Graduate Residency (TGR) status only.
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors:
Aboumrad, G. (PI)
;
Ashlagi, I. (PI)
;
Bambos, N. (PI)
;
Blanchet, J. (PI)
;
Brandeau, M. (PI)
;
Eesley, C. (PI)
;
Eisenhardt, K. (PI)
;
Giesecke, K. (PI)
;
Glynn, P. (PI)
;
Goel, A. (PI)
;
Hinds, P. (PI)
;
Jia, R. (PI)
;
Johari, R. (PI)
;
Karunakaran, A. (PI)
;
Katila, R. (PI)
;
Lo, I. (PI)
;
Pate-Cornell, E. (PI)
;
Pelger, M. (PI)
;
Saberi, A. (PI)
;
Shachter, R. (PI)
;
Sheares, A. (PI)
;
Sidford, A. (PI)
;
Syrgkanis, V. (PI)
;
Udell, M. (PI)
;
Ugander, J. (PI)
;
Valentine, M. (PI)
;
Van Roy, B. (PI)
;
Vitercik, E. (PI)
;
Weyant, J. (PI)
;
Xu, R. (PI)
;
Zrnic, T. (PI)
OIT 611: The Drift Method: from Stochastic Networks to Machine Learning
This course is an exploration of the drift method: a family of simple, yet surprisingly powerful, meta-algorithms that in each step the greedily and incrementally minimizes a certain potential function. Manifested in different forms, MaxWeight, c-mu rule, EXP3, policy gradient, to name a few, the drift method powers some of the most popular algorithmic paradigms in queueing networks, optimization and machine learning. Using the drift method as a unifying theme, we will explore major developments in these areas to understand what features can explain the method's effectiveness, how we can rigorously evaluate its performance, and what are some of the emerging research topics.We will develop rigorous probabilistic and optimization methodologies for answering these questions, such as Lyapunov stability theory, state-space collapse, and weak convergence. Applications to be covered include dynamic control and scheduling in queueing networks, delay and stability analysis of stochastic network
more »
This course is an exploration of the drift method: a family of simple, yet surprisingly powerful, meta-algorithms that in each step the greedily and incrementally minimizes a certain potential function. Manifested in different forms, MaxWeight, c-mu rule, EXP3, policy gradient, to name a few, the drift method powers some of the most popular algorithmic paradigms in queueing networks, optimization and machine learning. Using the drift method as a unifying theme, we will explore major developments in these areas to understand what features can explain the method's effectiveness, how we can rigorously evaluate its performance, and what are some of the emerging research topics.We will develop rigorous probabilistic and optimization methodologies for answering these questions, such as Lyapunov stability theory, state-space collapse, and weak convergence. Applications to be covered include dynamic control and scheduling in queueing networks, delay and stability analysis of stochastic networks, stochastic approximation, and online/supervised/reinforcement learning. The course will be primarily taught in a lecture format, along with some guest lectures and student project presentations.Objective: For students to acquire fundamental methodologies that can be applied to pursuing research topics in theoretical or applied areas.Target Audience: The course is intended for PhD students in Business, Engineering and Economics. The students should have a good background in probability and stochastic processes (e.g., Stat 310A / MS&E 321). Most topics will be self-contained.
Last offered: Winter 2022
| Units: 3
POLISCI 154: Data Science for Social Impact (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, MS&E 134, PUBLPOL 155, SOC 127)
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem s
more »
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem sets involving data analysis in R or python. You'll learn and apply techniques like fixed effects regression, difference-in-differences, instrumental variables, regularized regression, random forests, causal forests, and optimization. Class sessions will feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. Prerequisites (recommended): Experience programming in R or python, or willingness to learn very quickly on your own. A basic statistics or data science course, such as any of the following:
DATASCI 112,
ECON 102 or 108,
CS 129,
EARTHSYS 140,
HUMBIO 88,
POLISCI 150A,
STATS 60,
SOC 180B, or MS&E 125.
Terms: Spr
| Units: 5
| UG Reqs: WAY-AQR, WAY-SI
Instructors:
Hwang, J. (PI)
;
Hwang, S. (PI)
;
Nobles, M. (PI)
;
Lyu, D. (TA)
;
Scott-Hearn, N. (TA)
PUBLPOL 155: Data Science for Social Impact (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, MS&E 134, POLISCI 154, SOC 127)
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem s
more »
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem sets involving data analysis in R or python. You'll learn and apply techniques like fixed effects regression, difference-in-differences, instrumental variables, regularized regression, random forests, causal forests, and optimization. Class sessions will feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. Prerequisites (recommended): Experience programming in R or python, or willingness to learn very quickly on your own. A basic statistics or data science course, such as any of the following:
DATASCI 112,
ECON 102 or 108,
CS 129,
EARTHSYS 140,
HUMBIO 88,
POLISCI 150A,
STATS 60,
SOC 180B, or MS&E 125.
Terms: Spr
| Units: 5
| UG Reqs: WAY-AQR, WAY-SI
Instructors:
Hwang, J. (PI)
;
Hwang, S. (PI)
;
Nobles, M. (PI)
;
Lyu, D. (TA)
;
Scott-Hearn, N. (TA)
SOC 127: Data Science for Social Impact (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, MS&E 134, POLISCI 154, PUBLPOL 155)
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem s
more »
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem sets involving data analysis in R or python. You'll learn and apply techniques like fixed effects regression, difference-in-differences, instrumental variables, regularized regression, random forests, causal forests, and optimization. Class sessions will feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. Prerequisites (recommended): Experience programming in R or python, or willingness to learn very quickly on your own. A basic statistics or data science course, such as any of the following:
DATASCI 112,
ECON 102 or 108,
CS 129,
EARTHSYS 140,
HUMBIO 88,
POLISCI 150A,
STATS 60,
SOC 180B, or MS&E 125.
Terms: Spr
| Units: 5
| UG Reqs: WAY-AQR, WAY-SI
Instructors:
Hwang, J. (PI)
;
Hwang, S. (PI)
;
Nobles, M. (PI)
;
Lyu, D. (TA)
;
Scott-Hearn, N. (TA)
STRAMGT 532: Intellectual Property: Financial and Strategic Management
In today's competitive marketplace, companies -- from Fortune 500 firms to early stage start-ups -- rely on intellectual property (IP) to keep them one step ahead of the game. Yet, critical IP decisions are usually made by lawyers with very little input from management. The purpose of this class is to provide business leaders with the tools, models and institutional knowledge to actively participate in managing and growing their company's IP assets as strategic business assets (with a focus on patents). This class will explore the value of corporate IP assets by thinking strategically on how to effectively leverage the knowledge, trade secrets, patents, technologies, trademarks, structures and processes that are critical across industries. We will focus on the elements of a successful IP strategy, and how that strategy is shaped by economic, technology, legal, regulatory, and market factors. Through a combination of case studies (including a group strategy project), analysis of current
more »
In today's competitive marketplace, companies -- from Fortune 500 firms to early stage start-ups -- rely on intellectual property (IP) to keep them one step ahead of the game. Yet, critical IP decisions are usually made by lawyers with very little input from management. The purpose of this class is to provide business leaders with the tools, models and institutional knowledge to actively participate in managing and growing their company's IP assets as strategic business assets (with a focus on patents). This class will explore the value of corporate IP assets by thinking strategically on how to effectively leverage the knowledge, trade secrets, patents, technologies, trademarks, structures and processes that are critical across industries. We will focus on the elements of a successful IP strategy, and how that strategy is shaped by economic, technology, legal, regulatory, and market factors. Through a combination of case studies (including a group strategy project), analysis of current events, class discussion and guest speakers, we will cover a variety of issues shaping a successful IP strategy in today's global business environment. Some of the topics covered include: * Building and managing an IP portfolio that is aligned with business objectives;* Understanding the forces shaping the IP marketplace in the US and in foreign markets;* The innovation cycle and technology transfer mechanisms;* Using big data analytics in making IP decisions;* IP portfolio monetization strategies (e.g., licensing, sale, enforcement);* IP considerations in Mergers & Acquisitions;* IP valuation and current trends in patent transactions;* Managing corporate IP litigation risk (patent trolls, incumbent litigation);* IP strategies for start-ups & entrepreneurs.Ms. Efrat Kasznik is an IP valuation and strategy expert with more than twenty years of experience advising companies of all sizes, from startups to Fortune 100s, on extracting value from their IP. She is the founder and President of Foresight Valuation Group, an IP consulting and startup advisory firm providing valuation and strategy services for a range of purposes, including M&A, financial reporting, technology commercialization decisions, tax compliance, transfer pricing, and litigation damages. Ms. Kasznik has been a co-founder, CFO and advisor to several startups and incubators in the US and Europe. She is listed on the IAM 300 list of World Leading IP Strategists, and is on the Board of the Licensing Executives Society, USA-Canada.
Last offered: Winter 2023
| Units: 2