The department offers two degree programs: the Doctor of Philosophy Ph. These programs provide a great deal of flexibility for students in designing individual plans of study and research according to their needs and interests. The department is a major participant in the Master of Finance M. The Ph. The aim of the program is to provide a strong disciplinary background in at least one of the core areas of research in the department. The emphasis is on the theoretical foundations, mathematical models, and computational issues in practical problem-solving.
Current teaching and research activities include probability and stochastic processes, stochastic analysis, mathematical statistics, machine learning, analysis of big data, linear and nonlinear optimization, stochastic optimization, convex analysis, stochastic networks, queueing theory, mathematical and computational finance, and financial econometrics.
The departmental faculty is affiliated with a number of interdisciplinary programs and centers, including the Program in Applied and Computational Mathematics, the Bendheim Center for Finance, the Andlinger Center for Energy and the Environment, the Princeton Environmental Institute, and the Center for Statistics and Machine Learning. Students may combine their departmental work with courses and research opportunities offered by such programs and centers and also by other departments including Computer Science, Economics, and Mathematics.
In the first year of graduate study, students must take all six core courses. By the end of the first year, students are expected to narrow the area of doctoral research and choose an appropriate adviser. Second-year students are required to complete a qualifying examination, undertake advanced coursework, research projects, and prepare for the general examination.
The general examination is normally taken at the end of the second year. Beyond the general examination, the completion of a dissertation usually takes two to three years. Upon acceptance of the dissertation by the department and the Graduate School, candidates for the Ph. Every admitted Ph. In addition, all admitted Ph.
D students are normally supported through a research or teaching assistantship throughout their studies while making progress toward their Ph. Students, in consultation with the director of graduate studies DGS , develop a course plan. The required plan consists of six courses, emphasizing the foundations of the program, probability, statistics, and optimization. Each student must satisfy qualifying requirements.
If this is not the case, the student will meet with the DGS to decide which exams need to be taken in September to satisfy the requirements. The results of the qualifying exam are determined by a vote of the faculty. Students who fail must transfer out of the PhD program. There is no option to retake the exam. The student must also show adequate progress on research and an acceptable level of understanding of his or her area of specialization. The general examination consists of two parts, a written and oral component, both covering the student's area of specialty.
The written component is completed by submitting a written report on the research conducted in ORF It is due one week before the exam takes place. The oral component is completed by giving a presentation on the research presented in the comprehensive written report. The oral exam may be up to 3 hours in length. For each student, an examining committee is selected by the student and advisor. It has to be approved by the Director of Graduate Studies. A departmental faculty vote determines the final outcome. The Master of Arts M.
It may also be awarded to students who, for various reasons, leave the Ph. Upon completion and acceptance of the dissertation by the department, the candidate will be admitted to the final public oral FPO examination. As such the M. Students enrolled in this program are eligible for financial support in the form of research or teaching assistantships if such funds are available.
However, conditions need to be just right and these can be tricky to replicate in a pond. In addition, the fish will need to be about a foot long, as this size generally indicates sexual maturity for orfes. In the wild, orfes tend to reproduce in the spring in areas with thick vegetation and shallower water, so you may need to recreate these conditions for your orfes and incorporate a shallow area with plenty of plants for them to use in the spring. They seem to also prefer water with some movement and, again, plenty of oxygen!
Water temperatures should be between 46 and 74 degrees Fahrenheit to ensure optimum fertility and successful breeding. My orfe is 30 years old it swims around the pond ok but when it settles it turns up side down is there any thing we can do to help him. It sounds like it could be a problem with the swim bladder, which may be age-related, but more likely an infection of some kind.
I have had Orfe in my pond for about 12 years. For the past 7 days my Orfe are not coming up to eat pellets. All of my other species of fish including carp, goldfish, tench, koi and Shubunkin are continuing to eat. I have tested my pond water to find ammonia and nitrite at 0 ppm, nitrate at 10 ppm and pH at 8. I have had the endoscope in the pond and the Orfe are active at the mid levels but there is no food for them down there.
Any ideas why they would refuse to eat like this? What type of behaviour are they showing when you see them? Are they just swimming around aimlessly, or are they also browsing foraging on the bottom? Have you used any new treatments or chemicals in the last few months? This site uses Akismet to reduce spam. Learn how your comment data is processed. Help Spread Pond Keeping Knowledge! ORF Electronic Commerce. Electronic commerce, traditionally the buying and selling of goods using electronic technologies, extends to essentially all facets of human interaction when extended to services, particularly information.
The course focuses on both the software and the hardware aspects of traditional aspects as well as the broader aspects of the creation, dissemination and human consumption electronic services. Covered will be the physical, financial and social aspects of these technologies. Regression: linear, nonlinear, and nonparametric kernel and projection pursuit. Neural networks, convolution networks, deep learning: Tensor Flow and Keras.
This is an introduction to the stochastic models inspired by the dynamics of resource sharing. Topics discussed include: early motivating communication systems telephone and computer networks ; modern applications call centers, healthcare operations, and urban planning for smart cities ; and key formulas from Erlang blocking and delay to Little's law. We also review supporting stochastic theories like equilibrium Markov chains along with Markov, Poisson and renewal processes.
An introduction to the uses of simulation and computation for analyzing stochastic models and interpreting real phenomena. Topics covered include generating discrete and continuous random variables, stochastic ordering, the statistical analysis of simulated data, variance reduction techniques, statistical validation techniques, nonstationary Markov chains, and Markov chain Monte Carlo methods.
Applications are drawn from problems in finance, manufacturing, and communication networks. Students will be encouraged to program in Python. Office hours will be offered for students unfamiliar with the language. The management of complex systems through the control of physical, financial and informational resources. The course focuses on developing mathematical models for resource allocation, with an emphasis on capturing the role of information in decisions. The course seeks to integrate skills in statistics, stochastics and optimization using applications drawn from problems in dynamic resource management which tests modeling skills and teamwork.
Students will develop mathematical modeling skills in the context of sequential decisions under uncertainty. Students will learn the five elements of a sequential decision problem: state variables, identifying and modeling decisions, uncertainty quantification, creating transition functions, and designing objective junctions.
They will learn how to design policies, and the principles of policy search and evaluation in both offline and online settings. All concepts will be taught through a series of carefully chosen problems designed to bring out specific modeling features. ORF Optimal Learning. Optimal learning addresses the challenge of collecting information efficiently when information is expensive. Applications include topics such as finding the best price for a product, identifying the best treatment for a disease, tuning the parameters in a bidding policy, or choosing the best player for a sports team.
Students learn how to formulate a learning problem, identify a belief model, and quantify the value of information. The course covers online and offline learning problems, and introduces students to a range of policies for collecting information. This course is an introduction to commodities markets energy, metals, agricultural products and issues related to renewable energy sources such as solar and wind power, and carbon emissions. Energy and other commodities represent an increasingly important asset class, in addition to significantly impacting the economy and policy decisions.
Emphasis will be on the application of Financial Mathematics to a variety of different products and markets. Topics include: energy prices including oil and electricity ; cap and trade markets; storable vs non-storable commodities; financialization of commodities markets; applications of game theory. Studied is the transportation sector of the economy from a technology and policy planning perspective. The focus is on the methodologies and analytical tools that underpin policy formulation, capital and operations planning, and real-time operational decision making within the transportation industry.
Case studies of innovative concepts such as "value" pricing, real-time fleet management and control, GPS-based route guidance systems, automated transit systems and autonomous vehicles will provide a practical focus for the methodologies. Class project in lieu of final exam focused on major issue in Transportation Systems Analysis.
Undergraduate Program | Operations Research and Financial Engineering
In recent years, financial services have significantly evolved through data-driven innovation. This course will review the landscape broadly and then focus on case studies in automated lending enabled by machine learning. Students will study specific machine learning methods in current use and will be introduced to issues of fairness and explainability, which are increasingly important in this sector and beyond.
Overall, the class will integrate mathematical finance models, machine learning methods and practical industry perspectives.
An introduction to the theory and practice of high frequency trading in modern electronic financial markets. We give an overview of the institutional landscape and basic empirical features of modern equity, futures, and fixed income markets. We discuss theoretical models for market making and price formation.
Then we dig into detailed empirical aspects of market microstructure and how these can be used to constructe effective trading strategies. Course work will be a mixture of theoretical and data-driven problems. Programming environment will be a mixture of the R statistical environment, with the Kdb database language. ORF Senior Thesis.
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A formal report on research involving analysis, synthesis, and design, directed toward improved understanding and resolution of a significant problem. The research is conducted under the supervision of a faculty member, and the thesis is defended by the student at a public examination before a faculty committee.
The senior thesis is equivalent to a year-long study and is recorded as a double course in the Spring. ORF Senior Project. Students conduct a one-semester project. Topics chosen by students with approval of the faculty. A written report is required at the end of the term.
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