ERM CAPSTONE

Teams will work through a case assignment, demonstrating mastery of key learnings gained throughout the program on an integrated basis. A simulated case study is used: this is a combination of publicly-available information of an actual company and simulated ERM program details, based on a blend of current ERM programs and practices in the marketplace. Each team will assess the case study and recommend enhancements.

• Department: Enterprise Risk Management
• M 6:10 p.m.-8 p.m.
• R 6:10 p.m.-8 p.m.
• T 6:10 p.m.-8 p.m.
• T 8:10 p.m.-10 p.m.
• W 8:10 p.m.-10 p.m.
• W 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• M 8:10 p.m.-10 p.m.
• R 6:10 p.m.-8 p.m.
• Instructor: Xue Bai
  Info
• Instructor: Aristargos Christodoulou
  Info
• Instructor: Bruce Fox
  Info
• Instructor: Benjamin M Perlman
  Info
• Instructor: Jiajing Wang
  Info
• Instructor: Mobarka F Ansari-Sheikh
  Info
• Instructor: Narae Lee
  Info
• Instructor: Richard R. Ranade
  Info
• Instructor: Rich Lauria
  Info
• 3 points

INSURANCE RISK MANAGEMENT

• Department: Insurance Management
• Instructor: Kenneth Radigan
  Info
• 3 points

Capstone in Political Analytics

The capstone course is the culminating experience for students in the Political Analytics program. Students will have the opportunity to tackle a complex, real-world political analytics challenge for a sponsoring organization. The capstone provides students with analytics experience in a “live” setting and is intended to expose students to the problems, timelines, and communications needs of actual political decision-makers. Working in small teams while being mentored by a program faculty member, students will apply core knowledge, concepts, and frameworks acquired during the program and practice the hands-on skills they have developed in their classes. Throughout the semester, student project teams will interact with the sponsoring organizations as virtual consultants, scoping the problem, acquiring the data, conducting analyses, and ultimately presenting their findings and recommendations to the project sponsor.

• Department: Political Analytics
• T 4:10 p.m.-6 p.m.
• 3 points

CAPSTONE

In the fourth semester, students will develop a comprehensive financial plan based
upon a specific client case, taking all elements of a client’s financial profile into
consideration. Students will be expected to demonstrate foundational knowledge
of all aspects of the wealth management curriculum in this course in order to
develop this financial plan. The capstone experience for this program is a dualfocus
on both the written as well as the oral components of the financial plan.

• Department: Wealth Management
• Instructor: Jason R Papier
  Info
• 3 points

DATA SCIENCE CONSULTING

• Department: Applied Analytics
• F 2:10 p.m.-4 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: David Shilane
  Info
• 3 points

DATA SCIENCE CONSULTING

DATA SCIENCE CONSULTING ONLINE

• Department: Applied Analytics
• T 8:10 p.m.-10 p.m.
• Instructor: Day Yi
  Info
• 3 points

APPLIED DEEP LEARNING AND AI

Deep Learning has become a cornerstone of Artificial Intelligence (AI), with applications in finance, healthcare, sports, autonomous vehicles, chatbots, national security, and artistic creations using elements of Natural Language Processing, Computer Vision, and Speech Recognition. Students will gain a solid foundation in Deep learning and its applications, starting with a compressed review of some Statistical Learning models followed by much deeper dive into Deep Neural Networks. Topics covered include Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures and Generative Adversarial Networks (GAN). Students will also learn training of agents to make optimal decisions in complex environments using Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate these models.

• Department: Applied Analytics
• T 6:10 p.m.-8 p.m.
• Instructor: Siddhartha Dalal
  Info
• 3 points

INDEPENDENT STUDY

• Department: Visual Arts
• Instructor: Seth Cluett
  h-index: 4 Info
• Instructor: Miya Masaoka
  Wikipedia Info
• 1-6 points

INDEPENDENT STUDY

• Department: Visual Arts
• Instructor: Matthew E Buckingham
  Wikipedia Info
• Instructor: David A Cruz
  Info
• Instructor: Delphine A Fawundu
  Wikipedia Info
• Instructor: Jon Kessler
  CULPA: 5 Wikipedia Info
• Instructor: Naeem Mohaiemen
  Wikipedia h-index: 8 Info
• Instructor: Sable Smith
  Wikipedia Info
• Instructor: Rirkrit Tiravanija
  Wikipedia Info
• Instructor: Tomas Vu Daniel
  CULPA: 4 Info
• Instructor: Daphne Arthur
  Info
• Instructor: Kenny Rivero
  Info
• Instructor: Miya Masaoka
  Wikipedia Info
• 1-3 points

SUPERVISED INTERSHIP IN SPORTS MANAGEMEN

INTERNSHIP IN SPORTS MGMT

• Department: Sports Management
• Instructor: Leonard Elmore
  Info
• 3 points

AI-ENHANCED FINDABILITY & SEMANTIC SYSTE

AI-EnhancedFindabil&Seman

The fusion of traditional information science principles with advanced AI technologies is revolutionizing knowledge management. This course explores how artificial intelligence, particularly Large Language Models (LLMs), is transforming information organization, retrieval, and utilization in digital environments.

Students explore the world of AI-enhanced findability, learning to architect intelligent knowledge platforms that maximize the value of structured and unstructured data. The course covers:



Large Language Models (LLMs) and their game-changing applications in information retrieval



Vector databases and cognitive search capabilities



Advanced Natural Language Processing (NLP) and semantic technologies



Knowledge graphs and how they are being used to add meaning to LLMs



Machine learning for sophisticated classification and categorization



Ethical considerations in AI-powered information systems



Through hands-on projects culminating in the design of real-world applications, students will gain practical experience in architecting AI-enhanced information systems. You'll learn to make informed decisions about integrating AI technologies into information architecture, balancing traditional methods with cutting-edge solutions.

This course is ideal for future knowledge strategists, information architects, and AI enthusiasts who want to lead the next generation of intelligent information systems. Join us to explore the intersection of AI and information science and prepare to shape the future of knowledge work.

No programming experience is required—just your curiosity and readiness to engage with the forefront of information technology.

• Department: Information & Knowledge Strat
• W 8:10 p.m.-10 p.m.
• Instructor: Ralph Poole
  h-index: 0 Info
• 3 points

APPLIED WRITING

• Department: Narrative Medicine
• W 3:10 p.m.-6 p.m.
• W 6:10 p.m.-9 p.m.
• Instructor: Jenessa R Abrams
  Info
• 4 points

SUPERVISED PROJECT IN SPORTS MANAGEMENT

FC BAYERN MUNCH

Projects are research intensive and vary according to partners and specialty.

 

Advanced standing in the Sports Management program, with at least 12 points/credits (4 courses) completed is required. A student may not exceed 6 points/credits (2 courses) of Supervised Projects, or take more than 3 points/credits (1 course) per semester.

• Department: Sports Management
• M 4:10 p.m.-6 p.m.
• Instructor: Grant J Son
  Info
• 3 points

MASTER THESIS WORKSHOP

TAKEN WITH BIET 5992 Master Thesis (2-credit). 

The Workshop meets six times over four months. These sessions will assist students in starting to focus more fully on a topic and approach. During the Thesis Workshop, students will first speak informally for five minutes about a possible topic, followed by a more formal five-minute presentation and a draft of a one-page outline or abstract, proceeding to a more finalized outline or abstract. At each of these stages, students will receive feedback from the course director as well as fellow students.

• Department: Bioethics
• F 11:30 a.m.-1 p.m.
• Instructor: Arthur Kuflik
  Info
• 1 points

LEADING LARGE COMPLEX PROJECTS

Project management has been important to many types of missions, projects, and activities for many years; however, it has been especially critical to the success of large complex projects across decades and centuries.  Large complex projects span the globe across all industries and sectors. They also span concepts, product design, development, manufacturing, operations, and logistics, etc. Products may include hardware, software, services, product support, systems, and systems of systems, etc.

The primary focus of this course will be around project leadership as projects are planned and executed (project management).  The course will start by recognizing the need and benefits of project management for large complex global projects, explore characteristics of project managers, and study the commonality and differences in types of projects.  The course will continue with understanding the essential capabilities of project management, and analyze the variations in project lifecycles. The course will address managing risk throughout the project lifecycle, controls, and performance measurement, and maximizing the use of knowledge.  Lastly, the course will visualize the future of projects and project management structure and core capabilities.

Our fundamental goal is to better prepare leaders for large complex global projects.  This will be gained via readings; real-world case studies; and study, research, analysis, and exploration by the students.  Therefore, the course will require students to engage in reflection, discussion, activities, and assignments aimed at personal unlearning and learning.  The assignment and class discussions will be quite provocative to drive maximum learning.

• Department: Information & Knowledge Strat
• M 6:10 p.m.-8 p.m.
• Instructor: Gregory Robinson
  Info
• 3 points

MASTER THESIS (2 CREDIT)

Thesis requirement for Bioethics program. Taken with the Thesis Workshop (BIET K5991).

• Department: Bioethics
• Instructor: Arthur Kuflik
  Info
• 2 points

AI AND THE KNOWLEDGE-DRIVEN ORGANIZAT

AI & THE KNOWLEDGE-DRIVEN

OVERVIEW: Artificial Intelligence is one of the most important technological developments in decades and has already begun to demonstrate significant improvements in healthcare, military, finance, retail, and the arts. In this class we will cover an intro to artificial intelligence with a specific lens on how knowledge driven organizations can benefit from AI. This course is not a coding or a computer science course, but does touch on high level concepts in statistics, data science, and software engineering, though no experience is necessary in these fields.

CONTENT & OBJECTIVES: You will learn how AI works, what are the best and worst use cases for AI, and the implications of implementing AI. As exciting as this space can be, there are real risks, ethical considerations, and new challenges that we will cover and discuss. By the end of the course, you will have a clear understanding of the possibilities with AI, how to implement AI in a knowledge driven organization, and the global nature of this technology. You will build on previous coursework of knowledge strategy and learn how AI accelerates knowledge management including search ranking, content recommendations, and people analytics.

LOGISTICS
:
Class meets once a week.

• Department: Information & Knowledge Strat
• M 8:10 p.m.-10 p.m.
• 3 points

Perspectives on International and World

Persptvs on Int'l & World

This course gives students the opportunity to design their own curriculum: To attend lectures, conferences and workshops on historical topics related to their individual interests throughout Columbia University. Students may attend events of their choice, and are especially encouraged to attend those sponsored by the History Department (www.history.columbia.edu). (The Center for International History - cih.columbia.edu - and the Heyman Center for the Humanities - heymancenter.org/events/ - also have impressive calendars of events, often featuring historians.) The goal of this mini-course is to encourage students to take advantage of the many intellectual opportunities throughout the University, to gain exposure to a variety of approaches to history, and at the same time assist them in focusing on a particular area for their thesis topic.

• Department: History
• Instructor: Line Lillevik
  Info
• 1-3 points

Internship In IKNS

This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.

 

To receive instructor approval, the internship:

● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level

● Must fit into the planned future program-related career path of the student

You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.

 

At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor

• Department: Information & Knowledge Strat
• Instructor: Gonzalo Miranda Madrigal
  Info
• 3 points

INDEPENDENT STUDY

This course offers students an opportunity to expand their curriculum beyond the established course offerings. Interested parties must consult with the QMSS Program Director before adding the class. This course may be taken for 2-4 points.

• Department: Quantitative Methods/Social Sciences
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 1-4 points

INDEPENDENT STUDY

Independent Study is a one- or three-credit course that can count toward the curriculum area requirement in Integrative Sustainability Management, Economics and Quantitative Analysis, Physical Dimensions, Public Policy, General and Financial Management, or Elective, with the approval of the faculty advisor. A final deliverable relating to the Sustainability Management curriculum is required at the end of the semester, and will be evaluated for a letter grade by the faculty advisor and reported to the SUMA program office.

• Department: Sustainability Management
• Instructor: Maria L Gray
  Info
• 1-3 points

INDEPENDENT STUDY

• Department: Sustainability Science
• Instructor: Maria L Gray
  Info
• 3 points

INDEPENDENT STUDY

• Department: Sustainability Management
• Instructor: Maria L Gray
  Info
• 3 points

IKNS INDEPENDENT STUDY

Overview
: This 1 semester course (elective, IKNS students only, hybrid) provides an opportunity for a student to extend or supplement their educational experience via a deep-dive into an established or novel area of research of their choice (the topic), under the guidance & supervision of a faculty member (the supervisor). An independent study course allows a student to work one-on-one with a faculty member to gain & contribute new insight into the discipline of Knowledge Management.

Topic/objective
: The topic is chosen by the student as long as it falls within the general realm of Knowledge Management or its specific content areas in the IKNS curriculum, such as IT systems, knowledge organizing systems, data repositories, business data analytics including machine learning & AI, learning processes, collaboration, dialogue, team & project management, transformational leadership, change management, digital transformation, or digital product innovation. The course will therefore serve the dual purpose of allowing a student to pursue their own intellectual curiosity & to make a contribution to the wider discipline of Knowledge Management. In addition, students will deepen their understanding of the content they acquired in other courses, by applying this content to the specific topic chosen for the Independent Study.

Logistics
: Ahead of registration, the student meets with the supervisor to discuss & agree on (i) the topic & the relevant IKNS curriculum area(s); (ii) the timeline of deliverables, milestones, & contact hours for the semester; & (iii) the number of credits. The student summarizes these points in a ~1 pg
Independent Study Proposal
. The student can register for the course only once the supervisor & the Academic Director agree to & sign the
Independent Study Proposal
(which includes the topic, the IKNS curriculum area, the number of credits, & the assigned supervisor). The number of credits (1-3) will be commensurate with the scope of the Independent Study. The scope can range from a summary of existing sources (typically 1 credit. 5-10 pg report), to a synthesis or meta-analysis of existing & new sources, e.g., interviews withSMEs (typically 2 credits, 10-15 pg report), to a comprehensive study which adds the student’s own critical discussion & suggestions to the topic (typically 3 credits; 15-20 pg report).

• Department: Information & Knowledge Strat
• 1-3 points

IKNS INDEPENDENT STUDY

Overview
: This 1 semester course (elective, IKNS students only, hybrid) provides an opportunity for a student to extend or supplement their educational experience via a deep-dive into an established or novel area of research of their choice (the topic), under the guidance & supervision of a faculty member (the supervisor). An independent study course allows a student to work one-on-one with a faculty member to gain & contribute new insight into the discipline of Knowledge Management.

Topic/objective
: The topic is chosen by the student as long as it falls within the general realm of Knowledge Management or its specific content areas in the IKNS curriculum, such as IT systems, knowledge organizing systems, data repositories, business data analytics including machine learning & AI, learning processes, collaboration, dialogue, team & project management, transformational leadership, change management, digital transformation, or digital product innovation. The course will therefore serve the dual purpose of allowing a student to pursue their own intellectual curiosity & to make a contribution to the wider discipline of Knowledge Management. In addition, students will deepen their understanding of the content they acquired in other courses, by applying this content to the specific topic chosen for the Independent Study.

Logistics
: Ahead of registration, the student meets with the supervisor to discuss & agree on (i) the topic & the relevant IKNS curriculum area(s); (ii) the timeline of deliverables, milestones, & contact hours for the semester; & (iii) the number of credits. The student summarizes these points in a ~1 pg
Independent Study Proposal
. The student can register for the course only once the supervisor & the Academic Director agree to & sign the
Independent Study Proposal
(which includes the topic, the IKNS curriculum area, the number of credits, & the assigned supervisor). The number of credits (1-3) will be commensurate with the scope of the Independent Study. The scope can range from a summary of existing sources (typically 1 credit. 5-10 pg report), to a synthesis or meta-analysis of existing & new sources, e.g., interviews withSMEs (typically 2 credits, 10-15 pg report), to a comprehensive study which adds the student’s own critical discussion & suggestions to the topic (typically 3 credits; 15-20 pg report).

• Department: Information & Knowledge Strat
• 1-3 points

IKNS INDEPENDENT STUDY

Overview
: This 1 semester course (elective, IKNS students only, hybrid) provides an opportunity for a student to extend or supplement their educational experience via a deep-dive into an established or novel area of research of their choice (the topic), under the guidance & supervision of a faculty member (the supervisor). An independent study course allows a student to work one-on-one with a faculty member to gain & contribute new insight into the discipline of Knowledge Management.

Topic/objective
: The topic is chosen by the student as long as it falls within the general realm of Knowledge Management or its specific content areas in the IKNS curriculum, such as IT systems, knowledge organizing systems, data repositories, business data analytics including machine learning & AI, learning processes, collaboration, dialogue, team & project management, transformational leadership, change management, digital transformation, or digital product innovation. The course will therefore serve the dual purpose of allowing a student to pursue their own intellectual curiosity & to make a contribution to the wider discipline of Knowledge Management. In addition, students will deepen their understanding of the content they acquired in other courses, by applying this content to the specific topic chosen for the Independent Study.

Logistics
: Ahead of registration, the student meets with the supervisor to discuss & agree on (i) the topic & the relevant IKNS curriculum area(s); (ii) the timeline of deliverables, milestones, & contact hours for the semester; & (iii) the number of credits. The student summarizes these points in a ~1 pg
Independent Study Proposal
. The student can register for the course only once the supervisor & the Academic Director agree to & sign the
Independent Study Proposal
(which includes the topic, the IKNS curriculum area, the number of credits, & the assigned supervisor). The number of credits (1-3) will be commensurate with the scope of the Independent Study. The scope can range from a summary of existing sources (typically 1 credit. 5-10 pg report), to a synthesis or meta-analysis of existing & new sources, e.g., interviews withSMEs (typically 2 credits, 10-15 pg report), to a comprehensive study which adds the student’s own critical discussion & suggestions to the topic (typically 3 credits; 15-20 pg report).

• Department: Information & Knowledge Strat
• 1-3 points

MASTER'S THESIS

This course fulfills the Masters Thesis requirement of the QMSS MA Program. It is designed to help you make consistent progress on your master’s thesis throughout the semester, as well as to provide structure during the writing process. The master’s thesis, upon completion, should answer a fundamental research question in the subject matter of your choice. It should be an academic paper based on data that you can acquire, clean, and analyze within a single semester, with an emphasis on clarity and policy relevance.

• Department: Quantitative Methods/Social Sciences
• R 4:10 p.m.-6 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Elena B Krumova
  Info
• Instructor: Hadi Katesari
  Info
• 3-4 points

SUPERVISED INDIVIDUAL RESEARCH

• Department: Graduate School of Arts and Sciences
• Instructor: Syantani Chatterjee
  Info
• 1-4 points

CAPSTONE WORKSHOP IN SUSTAINABLE SCIENCE

Students study the sustainability science behind a particular sustainability problem, collect and analyze data using scientific tools, and make recommendations for solving the problem. The capstone course is a client-based workshop that will integrate each element of the curriculum into an applied project, giving students hands-on experience.

• Department: Sustainability Science
• T 6:10 p.m.-8 p.m.
• 3 points

GRADUATE SPECIAL TOPIC

Health as a System

Current topics in biomedical engineering. Subject matter will vary by year.

• Department: Biomedical Engineering
• W 10:10 a.m.-12:40 p.m.
• Instructor: Henry Hess
  CULPA: 1 h-index: 45 Info
• 3 points

GRADUATE SPECIAL TOPIC

SciStory:Grants,Papers,En

Current topics in biomedical engineering. Subject matter will vary by year.

• Department: Biomedical Engineering
• M 2:30 p.m.-5:15 p.m.
• Instructor: Santiago Correa
  Info
• 3 points

GRADUATE SPECIAL TOPIC

Bioreactor Design & Proto

Current topics in biomedical engineering. Subject matter will vary by year.

• Department: Biomedical Engineering
• T 4 p.m.-7 p.m.
• Instructor: Joseph Viola
  Info
• 3 points

GRADUATE SPECIAL TOPIC

Optical Microscopy

Current topics in biomedical engineering. Subject matter will vary by year.

• Department: Biomedical Engineering
• M 10:10 a.m.-12:40 p.m.
• Instructor: Henry Hess
  CULPA: 1 h-index: 45 Info
• 3 points

GRADUATE SPECIAL TOPIC

Signal Proc Med Devices

Current topics in biomedical engineering. Subject matter will vary by year.

• Department: Biomedical Engineering
• Instructor: Megan Heenan
  Info
• 3 points

APPLIED RESEARCH

• Department: Insurance Management
• Instructor: Teresa W Chan
  Info
• 3 points

WORKSHOP (CERT)

This workshop provides an intense immersion in the methods and skills of narrative medicine. Lectures will open up themes of how stories work, creativity, ethics, bearing witness, and empathy, while the small groups practice rigorous skills in close reading, creative writing, and responding to the writings of others. The learning objectives of the workshops are to 1) provide personal contact to introduce and solidify intersubjective relationships among participants; 2) to ignite use of methods that have been and will be utilized in the on-line component, e.g., writing to prompts from literary texts and responding to both form and content of colleagues’ writing; 3) plenary lectures from the architects of the discipline of Narrative Medicine in the foundational theories to be studied; 4) scheduled cultural learning opportunities of New York City (music, museums, literary readings) for shared creative experiences; 5) contact with Master of Science in Narrative Medicine graduate program for certification participants toward their understanding of the breadth of the field and the potential for their continuing to study NM after the CPA; 6) introduction to the national and international reach of Columbia Narrative Medicine so that participants grasp the value and magnitude of the community they have entered as certification program students. Participants will be given the chance to present their own works-in-progress to assembled participants and faculty as a jump-start to collaborative projects during and after the participation in the certification program.

• Department: Narrative Medicine
• 0 points

Intro Social Data Analysis I

Basic techniques for analyzing quantitative social science data. Emphasis on conceptual understanding as well as practical mastery of probability and probability distributions, inference, hypotheses testing, analysis of variance, simple regression, and multiple regression.

• Department: Sociology
• R 10:10 a.m.-noon
• Instructor: Yao Lu
  h-index: 25 Info
• 4 points

PROJECTS-BIOMEDICAL INFORMATCS

Research in medical informatics under the direction of a faculty adviser.

• Department: Biomedical Informatics
• Instructor: Noemie Elhadad
  Wikipedia h-index: 46 Info
• Instructor: Chunhua Weng
  h-index: 37 Info
• Instructor: Mohammed Alquraishi
  Info
• Instructor: Andrea Califano
  h-index: 77 Info
• Instructor: Dennis Vitkup
  Info
• Instructor: Raul Rabadan
  Wikipedia h-index: 72 Info
• Instructor: Rita Kukafka
  h-index: 28 Info
• Instructor: George M Hripcsak
  Info
• Instructor: Herbert S Jr Chase
  Info
• Instructor: Olena Mamykina
  Info
• Instructor: Yufeng Shen
  h-index: 47 Info
• Instructor: Karthik Natarajan
  h-index: 17 Info
• Instructor: Gamze Gursoy
  Info
• Instructor: Sarah A Rossetti
  Info
• Instructor: Richard A Friedman
  Info
• 1-12 points

PROJECTS-BIOMEDICAL INFORMATCS

Research in medical informatics under the direction of a faculty adviser.

• Department: Biomedical Informatics
• Instructor: Harris Wang
  h-index: 32 Info
• Instructor: Soojin Park
  Info
• Instructor: Tal Korem
  h-index: 18 Info
• Instructor: Shalmali Joshi
  Info
• Instructor: Pierre Elias
  Info
• 1-12 points

EARTH SCIENCE COLLOQUIUM

Current topics in the Earth sciences.

• Department: Earth and Environmental Sciences
• F 3:30 p.m.-4:30 p.m.
• Instructor: Kaleigh B Matthews
  Info
• 1 points

ADVANCED PROJECTS

May be repeated for up to 6 points of credit. Graduate-level projects in various areas of electrical engineering and computer science. In consultation with an instructor, each student designs his or her project depending on the students previous training and experience. Students should consult with a professor in their area for detailed arrangements no later than the last day of registration.

• Department: Electrical Engineering
• Instructor: Dimitris Anastassiou
  Wikipedia Info
• Instructor: Keren Bergman
  CULPA: 1 Wikipedia h-index: 69 Info
• Instructor: Shih-Fu Chang
  CULPA: 3 Wikipedia h-index: 122 Info
• Instructor: Javad Ghaderi
  h-index: 17 Info
• Instructor: Christine P Hendon
  Wikipedia h-index: 21 Info
• Instructor: Predrag R Jelenkovic
  CULPA: 1 h-index: 35 Info
• Instructor: Xiaofan Jiang
  h-index: 24 Info
• Instructor: Ethan Katz-Bassett
  h-index: 31 Info
• Instructor: Dion Khodagholy Araghy
  Info
• Instructor: Peter Kinget
  CULPA: 1 h-index: 47 Info
• Instructor: Zoran Kostic
  CULPA: 1 h-index: 24 Info
• Instructor: Harish Krishnaswamy
  Info
• Instructor: Ioannis Kymissis
  CULPA: 4 h-index: 48 Info
• Instructor: Aurel A Lazar
  h-index: 53 Info
• Instructor: Michal Lipson
  CULPA: 1 Wikipedia h-index: 114 Info
• Instructor: Nima Mesgarani
  h-index: 35 Info
• Instructor: Debasis Mitra
  Wikipedia h-index: 61 Info
• Instructor: John W Paisley
  CULPA: 1 Info
• Instructor: Matthias Preindl
  h-index: 26 Info
• Instructor: Mingoo Seok
  h-index: 31 Info
• 1-4 points