UMass Boston招收Data Science博士生

马萨诸塞大学波士顿分校（University of Massachusetts-Boston，UMB）管理科学与信息系统学院今年秋季招收Data Science博士生。提供Ph.D. Assistantships，包括全额学费、学杂费全免，且前三年每年给予25,000美元津贴，除此之外，还有更多补贴。

Data science is the study of extracting knowledge from data, encompassing statistics, data analytics, and information systems. It is closely related to data mining, business intelligence, and informatics.

中国科大学生的数学和计算机背景对于申请该项目的博士生更加有利，有望在4年内毕业。感兴趣的同学可以和张炜（8811，wei.zhang@umb.edu)联络，了解更多信息。也欢迎转载给其他的非科大校友。

PDF文档阅读请点击此处下载，为方便部分校友和学生阅读，将文档中的文字也张贴如下。

The Need for Advanced Study in the Field of Business

An understanding of business and organizations is central for knowledge creation about many phenomena in the social sciences. Studies of business shed light on vital processes such as: how labor and capital flow around the globe; how vast information systems are parsed to allocate resources, from R&D investment to health care; how the environment can be stewarded sustainably through multi-sector partnerships involving business, government, and NGOs; how the livelihoods and well-being of diverse populations will be secured in the future; and more. UMass Boston has expanded its role in knowledge creation with the advanced study of business to fully grasp many complex and vexing contemporary issues. Our PhD in Business Administration has three tracks: Organizations and Social Change, Finance, and Information Systems for Data Science (launching Fall 2016).

Information Systems for Data Science

Data science, the study of extracting knowledge from data, encompasses statistics, data analytics, and information systems. The field is growing rapidly, creating a need for professionals who can use insights derived from big data to make effective business decisions. Data analytics has become a critical need in industries ranging from health care and financial services to marketing and government. Leveraging the strengths of the College of Management’s faculty, the University of Massachusetts Boston is offering a cutting-edge and flexible doctoral program in this field.

As a newly instituted program, the PhD program at the MSIS department of UMass Boston started with a focus on information systems for data science. Positioned at the intersection of technology, business, and strategy, our program allows students to have a holistic view of data science and the role it plays in competition. Students will get exposed to a variety of state-of-art research streams in information systems and data science, with a relative focus on both data analytical techniques from a design science perspective and the application and management of data analytics in business settings from an organizational perspective. The program will offer students flexibility to investigate other topics they find interesting in data science and technology fields.

Who should apply?

All students with master degrees who are interested in information technology and data analytics are welcome to apply. Students with degrees in quantitative fields such as statistics, economics, math, computer science, management sciences, information systems, and other related disciplines are particularly encouraged to apply. A master degree in these related fields is a plus, although not required. Previous full-time working experiences in related positions are also a plus.

How will my learning experience impact organizations, society, and future global challenges?

Rapid increase in the amount of published data results in a data deluge that imposes significant challenges in data analytics. By offering a carefully tailored combination of courses in Information Technology, Applied Statistics, and Business Analytics, our Ph.D. program provides rigorous and in-depth courses of study with emphasis on various research methodologies, tools for data analytics, and relevant academic skill sets involving research design, literature review, theoretical development, empirical validation, and academic writing. Our program also provides students with extensive knowledge in the various emerging research areas in information systems (IS) field through IS research seminars and research collaboration opportunities with faculty members.

Academic advisers will help students configure a program of study which includes a rigorous sequence of courses in a variety of research methodologies, theories, and topics. Students will develop theoretical and methodological competencies in a variety of topics in the field of information systems and data science. Students will develop teaching competences through the teaching seminar, GA assignments to support a professor, and independently deliver courses. In addition to course work, students will actively engage in research with faculty members.

What kinds of research are faculty engaged in now?

The PhD program involves close, apprentice-like working relationships with faculty members, and students are introduced early to the world of conferences and publishing. A sampling of faculty projects includes:

 CyberSecurity Analytics for Massive Communication Graphs

 Home Healthcare Management for Dually Diagnosed Individuals with Mental and Physical Health Problems

 Characterizing managers' decision making patterns under uncertain and competitive environment

 Business intelligence as an IT-enabled agile and competitive business platform

 Social Media, Big Data, and Innovation: An Investigation of the Software Industry in India

 strategic use of cloud computing and data assets for sustainable competitive advantage

 decision modeling applications to areas such as technology development, policy, resource management.

 Abysmal behavior in Online Social Networks

 The role of health IT in hospital acquisitions

 Social influence on Bayesian learning process in post-adoption stage

What are the career opportunities for me when I graduate?

There are two main career opportunities for the individuals graduated from this program.

They can pursue a career in academia as a faculty member or join an organization as a data scientist. In the first case, they can educate other data scientists and conduct state of the art research to be published in peer-reviewed journals.

For the second case, they can become data scientists who use the acquired knowledge to excel the effectiveness of data collection and analytics in their organization and improve its competitiveness in today’s economy.

The Curriculum

PhD Courses–All Tracks 

BUS ADM 700: Business in Context: Markets, Technologies, SocietiesIn this course, students from across our tracks encounter the complex dilemmas in businesstoday, which span business disciplines. They learn about the range of theoretical approaches andmethods that can be mobilized to understand and address these dilemmas.

BUS ADM 775: Teaching and Professional Development SeminarStudents will work on having presence and engaging an audience, with specific applications toteaching, giving professional presentations, and being persuasive on policy matters informed byresearch. Students will develop a philosophy of teaching and prepare materials to enable them tostart teaching undergraduates the next year.

BUS ADM 896: Independent StudyThis course involves the comprehensive study of a particular topic in business administrationunder the direction of a faculty member. An independent study course can fulfill one electiverequirement. A detailed proposal must be submitted to the faculty member prior to registration.

BUS ADM 897: Special Topics in Business AdministrationThis course provides an opportunity for presentation of current topics in business administrationthat do not fall under the purview of any other course.

BUS ADM 899: Dissertation Research (1 to 9 CR)Research is conducted under supervision of the doctoral committee, leading to the presentationof a doctoral dissertation.

PhD Courses – Track Specific

BUSADM 740 - Information Systems Theory I

This course is the first part of two-course series of Ph.D. seminars on classic literature ofinformation systems. It is designed to provide doctoral students a broad introduction to variousresearch issues and challenges in topics of information systems (IS) and information technology(IT) management. As the first one of this series, this course is focused on theories at thebehavioral and group levels. Typical topics covered in the course include, but are not limited to,technology adoption and diffusion, IT-enabled communication, decision support, virtual teams,online community, cultural and power issues in IT activities, and other emerging topics in theresearch field.

 BUSADM 741 - Information Systems Theory II

This course is the second part of two-course series of Ph.D. seminars on classic literature ofinformation systems. It is designed to provide doctoral students specialized in informationsystems and business analytics a broad introduction to various research issues and challenges intopics of information systems (IS) and information technology (IT) management. As the secondone of this series, this course is focused on theories at the organizational and economic levels.Topics covered include strategic IT planning, business value of IT, IT strategies, IT governanceand controls, IT sourcing models, electronic marketplaces, economics of digital products, datascience and business analytics, and other emerging topics in the research field.

BUSADM 742 - Regression

This course will introduce the fundamental concepts and applications of linear regressions, suchas simple linear regression, multiple regression, model fit, transformations, variable selection andlogistic regression etc., and also various issues that we might face during those applications.This course will be the foundation for applied quantitative research.

BUSADM 743 - Decision Analysis

Decision and risk analysis combine elements of probability, economics, logic, psychology anddomain knowledge to characterize and analyze complex decision problems. Researchers in thisscholarly discipline develop theoretical mathematical results, develop computational decisionsupport tools grounded in formal theory, methods for populating models, as well as a largenumber of applied models for different real world problems or problem classes. Students willgain familiarity with the basic theory and methods from classic and recent texts, and willexamine some real world applications from recent journal publications. There will be particularfocus on connections between the approaches covered and developments in information systemsand in analytics. The course will involve portions of problem sets, student led discussions.Students emerging from the class will be prepared to incorporate decision analysis into researchinvolving applications or IS/Analytics, or to further investigate decision analysis in order toresearch in the methods of the field itself. Students will also keep a journal of ideas one of whichwill be the basis for a project or research paper that has the potential for expansion intopublishable results.

 BUSADM 744 - Quantitative Research

This course focuses on understanding, evaluating, and designing quantitative methods andmethodologies for information systems research. Through this course, students will review andexercise the basic skills required for quantitative research at the post-graduate level, includingliterature review, research design, data collection and analysis, and report writing.To gainhands-on experience, students will work on an original research project during the semester andwill be expected to submit a research outcome to an IS journal or conference. This course will be especially helpful to students who wish to use the quantitative research methods (e.g., survey,experimental and/or quasi-experimental methods) in their dissertations and subsequent researchendeavors.

 BUS ADM 775: Teaching and Professional Development

As an advanced student of business, skills are needed to effectively and persuasively disseminateknowledge. This course will provide knowledge needed to engage an audience (with specificapplications on teaching), giving professional presentations, and being persuasive on policymatters informed by research.

BUSADM 780 - Advanced Data Mining and Predictive Modeling

One of premiere challenges businesses face today is how to take advantage of the vast amountsof data they can easily collect. Data mining is used to find patterns and relationships in data, andis integral to business analytics and fact-based decision-making. This course covers current datamining techniques including algorithms for classification, association, and clustering; the coursealso covers text mining techniques such as Latent Semantic Analysis and Latent Dirichletallocation. Current software tools will be introduced to apply data mining techniques withapproaches used for building effective models, such as sampling strategies, data transformation,feature selection and ensemble methods, will be incorporated. The techniques and approachescovered in this course will be examined in the context of current research and methodologicaluse in the field of Information Systems.

 BUSADM 782 – Optimization

This course teaches optimization theory and techniques that are powerful and important tools forconducting research in Data Science area. Optimization techniques can be used for mining andanalytics of complex systems in Data Science field, which can greatly impact the decisionmaking process in this area. This course covers mathematical programming techniques includinglinear programming, integer programming, and network optimization; and emphasizes on howthey can be applied to research problems. It focuses on effective formulation techniques, basicmathematical and algorithmic concepts, and software solution of large-scale problems arising inData Science applications.

 BUSADM 785 - Big Data

This course covers a new and increasingly popular method of conducting research using largescale data analysis. The advent of the Internet, Social Media and subsequently machine generateddata has enabled social scientists to have access to extremely large datasets about the behavior ofmillions (or billions) of people or objects. However, collecting, storing, and analyzing this dataisn’t straightforward and requires specific skills.The goal of this course is to help students gain the skills required for this type of research whileexposing them to tools and big data research streams. The course will help students understandboth the challenges and the opportunities and assist them to appreciate research related to BigData.

 Why Choose UMass Boston?

UMass Boston is recognized as a world-class research university with a reputation for linkingresearch to economic development and community well-being. The College of Management hasbeen recognized by the Aspen Institute’s Business & Society program as among the top 75 businessschools in the world for leadership in researching and teaching about the social impacts of business.The location, breadth, and depth of University offerings, and faculty expertise, all offer doctoralstudents the chance to make a difference through their education.

 Our Location

The College of Management at UMass Boston is centrally located in one of the world’s premiercities for finance, technology, health care, social services, not-for-profits, consulting, and the arts.The opportunities for study, research, and outreach by doctoral students are in abundance in theBoston area. Students will be able to leverage relationships among faculty and industry leaders,while conducting their own research.

 Our Faculty

Over a dozen dedicated faculty members are devoted to student learning in this track alone, withadditional faculty serving in supporting roles. Faculty are leaders in their fields who regularlypublish scholarly articles in top academic journals. Doctoral students will be paired with facultyadvisors based on their area of interest. This intense mentorship program allows students to learn thecrafts of research and teaching in a highly collaborative environment.

Current faculty include:

Noushin Ashrafi

Professor of Management Information Systems

Professor Ashrafi’s areas of expertise include object oriented system analysis and design,business analytics, health informatics, privacy and security in digital age.Her currentresearch looks at the application of operations research and management science tools tomeasuring, controlling, and predicting reliability of conventional software; expert systemsextension of software reliability studies to the problem of quality in software development;software process improvement; business agility; security and privacy issues in e-business;business intelligence; and healthcare informatics.

Ramakrishna Ayyagari

Graduate Program Director, Information Systems for Data Science PhD track

Associate Professor of Management Information Systems

Professor Ayyagari’s research investigates the impact of IT on individuals and organizations.

 Pratyush Bharati

Associate Professor of Management Information Systems

Professor Bharati’s research interests are in: social media and big data, social media inorganizations and society, green information systems, and international software servicesindustry. He currently serves as the Senior Editor of The Data Base for Advances inInformation Systems Journal, an Association for Computing Machinery (ACM) SIGMISjournal.

Roger Blake

Associate Professor of Management Information Systems

Professor Blake’s areas of expertise include object-oriented software development,databases, systems architecture, quantitative analysis, systems analysis and design.Hisresearch interests include data quality, data and text mining

Romilla Chowdhuri

Assistant Professor of Management Information Systems

Professor Chowdhuri’s current research stream is influenced by the ubiquity of web 2.0platforms that generate “social data” at an unprecedented level and the availability of noveldata analytical tools and techniques. In particular, her research focuses on two interestingaspects of web 2.0 platforms. One, threats to the Identity and Privacy of web 2.0 users is realand unsettling. The prevention requires revealing the threats in the content and the structureof the social platforms and then systematically planning for the prevention of those threats.Two, there is a greater need to define the approaches and frameworks for the Big DataIntegration for Decision-Making. Big Data is characterized by structured and unstructureddata originating from the heterogeneous sources. Consequently, there arises a need fordesigning novel artifacts that integrate the heterogeneous data and thus provide insights forbusiness or individual decision-making.

 Kui Du

Assistant Professor of Management Science and Information Systems

Professor Du primarily works at the intersection of information technology and strategicmanagement. His overarching research interest is to understand how increasinglycommoditized IT can still contribute to differential firm performance. Under this umbrellaresearch question, he specialized in the role of IT during corporate transactions such asacquisitions, divestitures, alliances, and spin-offs. Some of his most recent work also focuseson the transforming role of IT in healthcare organizations. Most of his research projectsleverage quantitative methods to analyze organization-level data.

Ehsan Elahi

Associate Professor of Management Science

Professor Elahi’s current research focuses on the behavioral aspects of managerial decisionmaking in supply chain management. He intends to characterize decision making patternswhen the decision makers face uncertainty and/or competitive environments. By usinglaboratory experiments to collect relatively large sets of data regarding how decision makers(subjects) act in practice, he tries to identify the reasons behind the gap between what theorypredicts and how decision makers behave in practice.

Davood Golmohammadi

Associate Professor of Management Science

Professor Golmohammadi’s areas of expertise include lean manufacturing and Six Sigma,supply chain modeling and analysis, simulation modeling, and scheduling.His researchinterests include operations management, supply chain management, health care operationssystems, and decision analysis.

Haijing Hao

Assistant Professor of Management Information Systems

Professor Hao’s research interests are to investigate how information systems impacthealthcare, technology adoption in healthcare, Bayesian learning, online health community by using quantitative methods, econometrical modeling/statistical modeling.

Jeffery Keisler

Professor of Management Information Systems

Professor Keisler’s research focuses on decision analysis. Particular interests are: methods for estimating value of information and making uses of these estimates; combining different modeling tools; understanding and improving decision processes; decision making in resource allocation and portfolios.

Jonathan Kim

Assistant Professor of Management Information Systems

Professor Kim’s has three research streams: (1) Information Security, (2) Software Development, and (3) IT in Inter-organizational Networks. A research stream on information security focuses on how organizations protect their information systems. A research stream on software development focuses on how organizations can develop information systems more efficiently and effectively. A research stream on IT in inter-organizational networks focuses on how IT in inter-organizational networks can help organizations innovate and enhance productivity in a turbulent business environment

Jean-Pierre Kuilboer

Associate Professor of Management Science and Information Systems

Professor Kuilboer’s areas of expertise include database design, object-oriented systems, e- commerce, data communications, and information systems security. His research interests include modern systems development methodologies, security, and performance.

Daniel Lee

Associate Professor of Management Information Systems

Professor Lee’s research and scholarly activities can be summarized as research on the roleof information technology (IT) in agile organizations. Under this umbrella, I have conductedmultiple research projects to answer the following few questions: (1) Using IT, how canfirms create their dynamic capabilities, such as organizational agility, business intelligence,and knowledge management capability? and (2) How can firms or project teams managedynamic risks in their global IT project and what is the role of IT in achieving their agile riskmitigations? In particular, I focus on some specific aspects of organizational IT for theseresearch agenda, which include ambidextrous strategy of IT management (i.e., IT explorationand exploitation), business analytics as an agile IT infrastructure, and IT as a boundary objectof organizational engagement.

Josephine Namayanja

Assistant Professor of Management Information Systems

Professor Namayanja’s primary area of research is in the area of Data Mining withapplications in Cyber Security and Health Care Informatics.

Foad Mahdavi Pajouh

Assistant Professor of Management Information Systems

Professor Pajouh’s research interests are theoretical, computational and algorithmicoptimization with applications in Big Data analytics of complex networks. Specifically, I aminterested in applications of optimization in Business Analytics, Social Network Analysis,Financial Network Analysis and Energy Management.

Peng Xu

Associate Professor, Chair of Management Science and Information Systems

Professor Xu’s has two main research streams. In the first research, she investigates how ITcapabilities and big data capability can transform business processes and improve businessperformance. In the second research stream, Dr. Xu investigates agility in business processessuch as software development.

Wei Zhang

Associate Professor of Management Information Systems

Professor Zhang’s research interests include knowledge management, strategic use of IS/IT,and IS/IT education.

 

 

2015-10-14 上一篇: 李文秀 下一篇: 行晋，项目专员