Costis Maglaras
- Dean
- Dean's Office
- David and Lyn Silfen Professor of Business
- Decision, Risk, and Operations Division
- Areas of Expertise
- AI, Financial Engineering, Operations & Supply Chain Management
- Contact
- Office: 352 Kravis
- Phone: (212) 8540443
- E-mail: [email protected]
- Links
- Curriculum Vitae
Costis Maglaras is the 16th Dean of Columbia Business School, and the David and Lyn Silfen Professor of Business at Columbia University. Costis received his BS in Electrical Engineering from Imperial College, London, in 1990, and his MS and PhD in Electrical Engineering from Stanford University in 1991 and 1998, respectively. He joined Columbia Business School in 1998, when he joined the Decision, Risk and Operations Division. Prior to becoming dean he served as chair of the Decision, Risk & Operations division at the Business School, Director of the School's doctoral program, and was a member of the executive committee of the University's Data Science Institute.
His research lies on the interface between applied mathematics, economics and engineering, with emphasis on stochastic networks, financial engineering, and algorithmic pricing and revenue management. Recent work has focused on market microstructure of electronic (financial) limit order book markets; the diffusion of information over social networks; the economics and control of queueing networks with strategic agents, such as the ones encountered in ride-hailing; and the application of algorithmic pricing in the residential real-estate market. His work has been recognized through several research awards. and he has advised 20 doctoral students that have gone to academia and industry.
Costis teaches courses in the MBA and PhD programs, and he has also received the Dean's award at Columbia Business School for teaching excellence for the core course Managerial Statistics, and the Dean's award for Teaching Innovation for his work on the Technology and Analytics curriculum in Columbia's MBA and EMBA programs.
Outside of the Business School, his experience has been focused on financial technology, asset management and markets, and digital technology. From 1991 to 1993 he served as a research scientist at Canon Research Center America, working on image processing and optical character recognition. In 2007, Costis helped found Mismi Inc., a venture-backed financial technology firm that introduced quantitative trading algorithms and transaction analytics tools to the equities market. Mismi was a broker dealer and an Alternative Trading System (ATS; dark pool). As chief scientist he co-developed all the firm’s IP, built and directed the quantitative research and engineering teams, and served as president of the firm until 2014. In the last decade he has worked with major financial institutions and hedge funds, including a long-standing collaboration with Goldman Sachs’ Global Markets Division focusing on quantitative research and equity trading. He is a Fellow of INFORMS, an Honorary Fellow of the Foreign Policy Association, and a Member of the Economic Club of New York. He serves on the Board of Trustees of Athens College.
Costis is married to Niki Kouri and lives in Manhattan with their three daughters.
- Education
-
BSEE, Imperial College, London 1990; MSEE, Stanford, 1991; PhDEE, 1998
- Joined CBS
- 1998
All Activities
Dr. Costis Maglaras Named 16th Dean of Columbia Business School
Columbia Bizcast: Dean Costis Maglaras
Standing in Solidarity
Winning Now, Winning Later with David Cote
Statement by Dean Maglaras on the Derek Chauvin Verdict
Welcome to the 2021-2022 academic year
A Look Back on Leadership and Ethics Week
Welcome to the Future
Understanding the Impact of Breakthrough Technologies
CBS Panel at COP27 Tackles Financing the Transition to Net Zero
Welcome to Manhattanville – A conversation with Dean Costis Maglaras
A Roundup of What’s Happening in the Five Pillars
The Climate Call to Action
Columbia Business School Launches New Digital Future Initiative
Columbia Business School Expands Technology Initiative
Columbia Business School Launches New Technology Initiative
Dean's Remarks and Eight Reasons Why Mechanism Design for Blockchains is Hard (and Fascinating)
Becoming Data Fluent
A New Vision Of The Digital Future
Climate and Clean Energy Policy in the Post-inflation Reduction Act World
Harnessing the Entrepreneurial Spirit
NVIDIA CEO Huang on AI's Future, Leadership
Elevating Stories of Black Excellence
Shareholders vs. Stakeholders: How Business Leaders Should Prepare for the Future of Capitalism
NVIDIA CEO Jensen Huang Shares AI Insights, Leadership & Strategy Lessons
Forging the Future of Finance
Reid Hoffman on AI's 'Cognitive Industrial Revolution'
Meeting the Moment on Climate Education
Alumnae Fundraise over $400,000 in Honor of CBS Women in Asia
The Future of AI at Columbia Business School
India’s Clean Energy Revolution: How ReNew is Leading the Charge
- Case ID
- 112104
Did General Motors Produce to Match Demand?
What does the analysis of the data for the Chevy Cavalier suggest about how well management aligns production and sales?
- Case ID
- 100206
Hannah Montana and the Tour of Doom
With scalpers and online secondary markets like eBay driving up the cost of concert tickets, is there a better way to set prices that would protect revenue without inciting the ire of fans?
- Case ID
- 90206
EveryDay Medical - Keyword Bidding Optimization
How should a medical equipment provider optimally mange its internet advertising campaign?
- Case ID
- 70201
Personal Training at the New York Health Club
When a health club manager grows concerned about undisciplined pricing for personal training, how might he overhaul the club's rates?
- Case ID
- 80208
Analyzing the Analysts
Is there statistical evidence to support claims that analysts include a positive bias in their earnings estimates?
- Case ID
- 70203
Markdown Pricing Optimization at Bloomingdale's (A and B)
Do the benefits of a new markdown pricing optimization system outweigh the costs?
Graduate students that I served as their primary or secondary adviser.
- Nur Ayvaz -- dynamic pricing; sequential negotiations.
- Matulya Bansal -- JP Morgan
- Omar Besbes -- Professor, Columbia Business School (primary adviser A. Zeevi)
- Sabri Celik -- Barclays Capital
- Davide Crapis -- social learning, networks -- Lyft
- Ying-Ju Chen -- Professor HKUST
- Serkan Eren -- RBC Captial Markets
- Itai Gurvich -- Professor, Kellogg School, Northwestern University (primary adviser W. Whitt)
- Bar Ifrach -- Uber Freight
- Arseniy Kukanov -- Tower Research
- Joern Meissner -- Professor, Kühne Logistics University, Hamburg, GE
- Gustavo Vulcano -- Associate Professor, Stern School, NYU (primary adviser G. van Ryzin)
- John Yao -- stochastic networks, revenue management
- Hua Zheng -- Citadel
- Cinar Kilcioglu -- data science; revenue management -- Uber
- Davide Crapis -- Lyft; now stealth startup
- Zhe Liu -- data science; supply chain management
- Stefano Vaccari -- data scientist Enel
- Muye Wang -- Two Sigma
- Seungki Min -- Assistand Professor KAIST, Seoul
In order of recency:
- Quantitative finance: high-frequency market microstructure; trade execution algorithms; quant asset management; dark pools
- Social networks: diffusion of information over social networks; the effect of review on information diffusion
- Economics of congestion: ride-hailing networks and platform controls and incentives; queues in equilibrium; optimal (congestion) pricing; product design and customer choice; characterization of economically optimal operating regimes
- Data science: exploration / exploitation; applications of ML in finance
- Algorithmic pricing and revenue management: multi-product pricing; strategic consumer behavior; joint learning and revenue optimization; data-driven, large-scale segmentation and price optimization
- Real estate: quantitative pricing, risk management, valuation of large-scale real-estate portfolios
- Stochastic networks: fluid models; Brownian models; control; stability
- Service operations: multi-server queues; call centers; design, control and performance analysis of such systems; customer behavior; segmentation and revenue optimization
- Joint learning and control of stochastic systems
- Asymptotic analysis: applications to queues; quantitative pricing and revenue management
2019
- B6014: Managerial Statistics (MBA core course; Fall semester)
- B8816: Quantitative Pricing and Revenue Analytics (MBA elective course; offered annually in the Fall semester)
Pricing and revenue optimization --or revenue management as it is also called-- focuses on how a firm should set and update pricing and product availability decisions across its various selling channels in order to maximize its profitability. A familiar example comes from the airline industry. The adoption of such systems has transformed the transportation and hospitality industries, and is increasingly important in retail, telecommunications, entertainment, financial services, health care and manufacturing. In parallel, pricing and revenue optimization has become a rapidly expanding practice in consulting services, and a growing area of software and IT development. In this course students learn to identify and exploit opportunities for revenue optimization in different business contexts. This includes a review of the main methodologies that are used in each of these areas, and a survey of current practices in different industries. The course is about evenly split between lectures, cases and guest speakers.
Spring-A 2016
- Advanced MBA/PhD course on Electronic trading in limit order book markets (Spring-A 2016)
Older courses
- Stochastic Processing Networks. I taught this course in 1999 and covered topics on product form networks, fluid models and their role in stability analysis and control, heavy traffic approximations of single server queues, and Brownian network models.
- Seminar in Operations Management. I taught this course in 2001 and mostly covered topics in revenue management and the economics of queues, circa 2000.
- Seminar on Revenue Management. I have taught this course several times, often jointly with Garrett van Ryzin. It reviews basic topics in the area as covered in the book by Talluri and van Ryzin, plus a collection of "current" papers on various topics in that field. The attached syllabus is from 2004, and is a bit dated. A version of this course will be offered by Garrett van Ryzin in the Fall 2009.