Business Analytics and Artificial Intell
Business Analytics and AI
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Instructor:
Thomas Vladeck
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Instructor:
Benjamin J Tubiana
Info - 1.5 points
Managerial Statistics
- T 11 a.m.-12:30 p.m.
- TWR 2 p.m.-3:30 p.m.
- TWR 4 p.m.-5:30 p.m.
- T 9 a.m.-10:30 a.m.
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Instructor:
Paul Glasserman
Info - 1.5 points
Business Analytics and Artificial Intell
Business Analytics and AI
- MW 2:20 p.m.-3:50 p.m.
- MW 4:10 p.m.-5:40 p.m.
- MW 9 a.m.-10:30 a.m.
- MW 10:50 a.m.-12:20 p.m.
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Instructor:
David Holtz
Info - 1.5 points
OPERATIONS MANAGEMENT
- MW 4:10 p.m.-5:40 p.m.
- MW 10:50 a.m.-12:20 p.m.
- MW 2:20 p.m.-3:50 p.m.
- MW 9 a.m.-10:30 a.m.
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Instructor:
Nicola Rosaia
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Instructor:
Hannah Q Li
Info -
Instructor:
Mark Rodgers
Info -
Instructor:
Nicola Rosaia
Info -
Instructor:
Hannah Q Li
Info -
Instructor:
Mark Rodgers
Info -
Instructor:
Nicola Rosaia
Info -
Instructor:
Hannah Q Li
Info -
Instructor:
Mark Rodgers
Info -
Instructor:
Nicola Rosaia
Info - 1.5 points
Operations Strategy
A firm's operations encompass all the activities that are performed in order to produce and deliver a product or a service. An operations strategy refers to a set of operational decisions that a firm makes to achieve a long-term competitive advantage. These decisions may be about the firms facilities, its technology/process choices, its relationships with both upstream and downstream business partners etc. The goal of this course is to provide students with an understanding of how and why operational decisions are integral to a firms success. The course builds on concepts from the core Operations Management course and the core Strategy Formulation course. It is highly relevant to anyone whose work requires the strategic analysis of a firms operations, including those interested in consulting, entrepreneurship, mergers and acquisitions, private equity, investment analysis, and general management. The course consists of four modules. The first module, Strategic Alignment," explores the question of how a firms operations should be structured so as to be consistent with the firms chosen way to compete. The second module, "Firm Boundaries," considers the question of what operational activities should remain in house and what should be done by a business partner and the long-term implications of these decisions on competitive advantage. This module also addresses the issue of managing the business relationships with supply chain partners. The third module, "Internal Operations," considers key decision categories in operations, e.g., capacity decisions, process choices, IT implementation, and managing networks, and shows how these decisions can lead to distinctive capabilities. The final module, "New Challenges," is set aside to address new topics that reflect the current trends in the business environment."
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Instructor:
Medini Singh
Info - 3 points
Process Improvement and Growth
This class brings business operations and management science classes to the field with real-world experience. Through experiential learning, we will bridge the gap between theory and practice with international case discussions, conversations with guest speakers and hands-on company sponsored projects. Different to most classes in the school, in this class students will be exposed to a series of international cases and examples based on medium-sized, fast-growing entrepreneurial ventures. Each session will also include a guest speaker, often times the protagonist of the case studied, giving the students the opportunity to learn directly from successful entrepreneurs and senior executives.
Additionally, students will put into practice the concept of process improvement by working on a company-sponsored applied project. Teams of 4-5 people, 3-4 MBA/EMBA students and 1-2 engineering (SEAS) students, will work hand in hand with the instructors and company representatives to achieve company goals. For example, teams may be tasked with re-designing the logistical strategy of distribution of the company to get rid of inefficiencies, or identify and find strategies to eliminate areas of waste within the companies’ processes, or analyze customer feedback and design operational solutions to increase customer satisfaction, etc.
Enrollment in this course is by application only. To apply, please follow this link: https://forms.gle/EG6buNZqQYEgN2EH9
- MW 6 p.m.-7:30 p.m.
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Instructor:
Angela Quintero
Info - 3 points
Business Analytics II: Foundations of AI
Business Analytics II: AI
This course extends and deepens the material you learned in business analytics. We will apply these methods in more unstructured and diverse situations, introduce new analytics tools and methods (including Tableau Visualization, text mining, and random forests), and study a modern framework for overfitting reduction called regularization that underlies much of modern machine learning. This course does not require coding or knowledge beyond Business Analytics, but the mathematical sophistication level will be somewhat more advanced.
- TR 9 a.m.-10:30 a.m.
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Instructor:
Vineel Chakradhar
Info - 1.5 points
Operations Strategy
- M 9 a.m.-12:15 p.m.
- M 2:20 p.m.-5:35 p.m.
- T 9 a.m.-12:15 p.m.
- W 9 a.m.-12:15 p.m.
- R 9 a.m.-12:15 p.m.
- T 2:20 p.m.-5:35 p.m.
- R 2:20 p.m.-5:35 p.m.
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Instructor:
Medini Singh
Info - 3 points
Leading Sustainable Operations
- W 6 p.m.-9:15 p.m.
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Instructor:
Nicole Dehoratius
Info - 1.5 points
DIGITAL LITERACY-DECISION MKRS
- T 9 a.m.-12:15 p.m.
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Instructor:
Marco A Morales Barba
Info - 1.5 points
Healthcare Venture Capital And Private E
Healthcare Venture Cap &
This course analyzes the unique characteristics and strategies of investing in the healthcare sector from the perspectives of venture capital firms investing in early-stage healthcare enterprises, entrepreneurs creating and managing such business entities, and private equity firms seeking to build value-creating health care platforms. The course is focused on innovative business models of early to mid-stage healthcare services companies (payers, providers, HCIT firms) that improve quality of patient care, lower costs, and facilitate access to such services, as well as the opportunities and challenges of early-stage biotechnology companies discovering and developing novel compounds. It considers how investors and entrepreneurs can assess, value and manage the inherent risks to succeed in this large, complex, and dynamic sector. This course will address these issues through a mixture of lectures, case studies, and guest speakers (investors and entrepreneurs) from the healthcare sector. Note: Some understanding and prior experience in the healthcare/pharma industry will be highly useful. Students need to attend the first class session to understand material covered later in the course. Evaluation is 25% class participation, 25% mid-term assignment (short paper on questions or case study), and 50% final (individual) paper. "
- M 2:20 p.m.-5:35 p.m.
- 1.5 points
Intro to Databases for Busines
- W 2:20 p.m.-3:50 p.m.
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Instructor:
Mattan Griffel
Info - 1.5 points
Strategic Negotiations
- R 2:20 p.m.-5:35 p.m.
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Instructor:
Harborne Stuart
h-index: 10 Info - 3 points
Foundations of AI for Business
- TR 10:50 a.m.-12:20 p.m.
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Instructor:
Vineel Chakradhar
Info - 1.5 points
Applying Digital Health
- TR 2:20 p.m.-3:50 p.m.
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Instructor:
Raymond G Falci
Info - 1.5 points
The Analytics Advantage
This class will focus on how analytics have generated value in a broad range of industries. Each class will be taught by a different faculty member with specific subject matter expertise and will focus on one specific industry and on how it has been transformed through the use of analytics.
DROMB8152
- R 9 a.m.-12:15 p.m.
- R 2:20 p.m.-5:35 p.m.
- 1.5 points
Python for MBAs
- T 6 p.m.-9:15 p.m.
- R 6 p.m.-9:15 p.m.
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Instructor:
David L Bendes
Info -
Instructor:
Sunny Israni
Info - 1.5 points
Digital Marketplaces
This course will cover some of the fundamental product decisions together with the basic analytic and data science tools to support them that are currently being used to run the most exciting online marketplaces in the world. More specifically, among others, we will address the following questions: How does Uber or Lyft match drivers to passengers? How does Airbnb select the set of listings to show to a guest in a search? How can we build an algorithmic, scalable reputation and trust system in an e-commerce platform such as Amazon? How should advertisers optimize their decisions in today’s online advertising marketplaces run by Google and others?
Verticals of interest include the following:
• Matching platforms like those for ride-hailing, lodging, dating, labor, and food delivery.
• Internet advertising platforms including search engine advertising, display advertising, and sponsored products.
• Retail platforms including those for physical goods like Amazon, Etsy, and possibly also those for virtual goods like the App Store/Play Store and gaming platforms.
- M 2:20 p.m.-5:35 p.m.
- M 6 p.m.-9:15 p.m.
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Instructor:
Yashodhan Kanoria
h-index: 18 Info - 1.5 points
AI Agents
AI Agents and the Future of Business explores one of the most transformative shifts in modern enterprise: the rise of autonomous AI systems that think, act, and collaborate to achieve business goals. As artificial intelligence evolves from predictive models to agentic systems, organizations are redesigning how decisions are made, how workflows operate, and how value is created.
Unlike traditional large language models that respond to prompts, AI agents pursue goals. They perceive their environment, reason about tasks, use tools and APIs, maintain memory, and adapt dynamically to feedback. These capabilities are reshaping enterprise productivity, decision-making authority, and organizational structure.
This course blends strategy and studio. Students will examine agentic architectures (tool use, memory, orchestration, multi-agent coordination), analyze real enterprise deployments, and prototype or evaluate AI-agentic workflows using low-code tools. Through simulations, interactive discussions, and a final team project, students will develop the managerial and strategic literacy required to lead in the emerging agentic economy.
- TR 2:20 p.m.-3:50 p.m.
- TR 4:10 p.m.-5:40 p.m.
- TR 10:50 a.m.-12:20 p.m.
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Instructor:
Santiago R Balseiro
Info - 1.5 points
(PhD) Foundations of Stochasti
This course covers basic concepts and methods in applied probability and stochastic modeling. The intended audience is master's and doctoral students in programs such as EE, CS, IEOR, Statistics, Mathematics, and those in the DRO division in the Business School. In terms of prerequisites, basic familiarity with probability theory and stochastic processes will be assumed (an ideal preliminary course is IEOR 6711: Stochastic Modeling I, but a more basic substitute will do as well). The topics and material covered in this course complement those covered in IEOR 6712: Stochastic Modeling II, hence the two courses can be taken simultaneously. The exposition will be (mostly) rigorous, yet intentionally skirting some measure-theoretic details; for those interested in such details they can be found in measure theoretic textbooks and other courses (e.g., Probability Theory I/II given in the statistics/math department).
- W 9 a.m.-12:15 p.m.
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Instructor:
Assaf Zeevi
h-index: 40 Info - 5 points
(PhD) Dynamic Programming
This course offers an advanced introduction Markov Decision Processes (MDPs)-a formalization of the problem of optimal sequential decision making under uncertainty-and Reinforcement Learning (RL)-a paradigm for learning from data to make near optimal sequential decisions. The first part of the course will cover foundational material on MDPs. We'll then look at the problem of estimating long run value from data, including popular RL algorithms like temporal difference learning and Q-learning. The final part of the course looks at the design and analysis of efficient exploration algorithms, i.e. those that intelligently probe the environment to collect data that improves decision quality. This a doctoral level course. Students should have experience with mathematical proofs, coding for numerical computation, and the basics of statistics, optimization, and stochastic processes.
- W 2:20 p.m.-5:35 p.m.
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Instructor:
Daniel J Russo
Info - 3 points
(DRO) Topics Seminar
The purpose of the course is to broadly cover topics in Operations Management and Operations Research, as well as areas of interest to the Decision, Risk, and Operations division at CBS. It will consist of 3-hour sessions, each on a different topic, with the intent to introduce you to the topic, pique your interest, expose you to the methodologies and research areas people are working on, and help you think about what types of courses to take in the future in order to prepare yourself for this course. We will ask students to submit a one-page summary of each session or a particular paper discussed in the session. PhD students are invited to take the course and/or select certain sessions to attend. Spring 2022 Faculty instructors (each teaching one session) will be Mark Broadie, Paul Glasserman, Fanyin Zheng, Jing Dong, Will Ma, Hongyao Ma.
- R 2:20 p.m.-5:35 p.m.
- 1.5 points
Statistical Physics, Markets and Algorit
Stat Physics, Markets & A
Statistical physics studies the macroscopic phenomena that emerge when a large number of simple units (like atoms or molecules) come together. Deep understanding developed in such study, especially the study of so-called
“spin glasses”
by Parisi, Mezard and others, has been leveraged to great effect in diverse areas including image processing, machine learning, computer science, and theoretical biology. This research-oriented PhD class will begin with the fundamentals of spin glasses. We will then proceed to the cavity method and message passing algorithms, including exciting recent developments in obtaining (1-\epsilon)-approximation algorithms for a range of problems satisfying “full replica symmetry breaking”. Lastly, we will cover some recent work by Peski on matching markets which has a distinct statistical physics flavor, with agents replacing atoms/molecules. More broadly, we will ask if statistical physics can tell us something about market equilibria, and perhaps about how to design better markets.
The detailed syllabus is available at:
https://docs.google.com/document/d/1zuxJglbabDMOTTVoF3gfo1GgwaU9zaLAuT6s_plv_w4/edit?usp=sharing
- T 2:20 p.m.-5:35 p.m.
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Instructor:
Yashodhan Kanoria
h-index: 18 Info - 1.5 points
DROM Preseminar Discussion
- T 11:45 a.m.-12:45 p.m.
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Instructor:
Nicola Rosaia
Info - 0.5 points