MS Elective Specializations
The MS EE program allows students to tailor their course selection within the MS program to their interests. There are no required classes.
The following is a list of recommended specializations and associated courses that MS EE students can pursue to gain expertise in focused areas. The specializations are informal, do not have required courses, and the MS EE diploma does not state the specialization. Moreover, the graduate Flowcharts show sequencing constraints for most EE graduate courses grouped roughly into seven areas. Students can also fully custom-build a sequence of courses outside of the described specializations or flowcharts by selecting courses from a large pool of SEAS and broader Columbia University courses - please see the course requirements at the page describing the MS EE program. Note that not all of the elective courses are offered every year.
Faculty
Dimitris Anastassiou, Homayoon Beigi, Shih-Fu Chang, Micah Goldblum, Predrag Jelenkovic, Zoran Kostic, Aurel A. Lazar, Nima Mesgarani, John Paisley, John Wright, Xiaofan (Fred) Jiang. AI and its applications are the subject of study by faculty across all research areas in the EE department.
Suggested course selection
Choose 6 courses from the list of 4000- and 6000-level courses below, and add 4 other courses that satisfy the MS EE credit requirements:
- ECBM E4040: Neural Networks and Deep Learning
- ECBM E4060: Introduction to Genomic Information Science and Technology
- ELEN E4720: Machine Learning For Signals, Information and Data
- EECS E4764: AIoT - Artificial Intelligence of Things
- ELEN E4903: Topic - Machine Learning (or equivalent)
- ELEN E4904: Topic - Statistical Learning with Applications in Quantitative Trading
- ECBM E6070: Topic - Computing with Brain Circuits of Model Organisms
- MEEC E6600: Mathematics of Machine Learning, Signals, and Control
- MEEE E6620 Applied Acoustics and Signal Recognition
- EECS E6690: Topic - Machine Learning in Biological & Information Systems
- EECS E6791 Advanced Deep Learning
- EECS E6792: Deep Learning on the Edge
- EECS E6694: Topic - GenAI and Modern Deep Learning
- EECS E6699: Topic - Mathematics of Deep Learning
- EECS E6720: Bayesian models for machine learning
- ELEN E6772: Topic -Machine Learning for Computer and Communications Networks
- ELEN E6873: Statistical Signal Processing and Learning
- ELEN E6908: Topic - Embedded AI
- EECS E6892: Topic- Reinforcement Learning
Faculty
Dimitris Anastassiou, Homayoon Beigi, Shih-Fu Chang, Christine Hendon, Javad Ghaderi, Predrag Jelenkovic, Xiaofan (Fred) Jiang, Zoran Kostic, Aurel A. Lazar, Nima Mesgarani, John Paisley, Xiaodong Wang, John Wright
Suggested course selection
Choose 6 courses from the list of 4000- and 6000-level courses below, and add 4 other courses that satisfy the MS EE credit requirements:
Courses
- MEEE E4600 Continuous Control Systems
- EEME E4601 Discrete Control Systems
- ELEN E4720 Machine Learning for Signals, Information, and Data
- EECS E4750 Heterogeneous Computing for Signal and Data
- ELEN E4810 Digital Signal Processing
- ELEN E4815 Random Signals and Noise
- ELEN E4830 Digital Image Processing
- MEEC E6600 Mathematics of Machine Learning, Signals, and Control
- MEEE E6610 Nonlinear and Adaptive Control
- EEOR E6616 Convex Optimization
- MEEE E6620 Applied Acoustics and Signal Recognition
- ELEN E6717 Classical & Quantum Information Theory
- ELEN E6820 Speech/Audio Processing and Recognition
- ELEN E6876 Sparse and Low-Dimensional Models for High-Dimensional Data
- EEME E6910-6919 Topics in Control
- ELEN E6873 Statistical Signal Processing and Learning
- ELEN E6880-E6889 Topics in Signal Processing
Faculty
Dimitris Anastassiou, Shih-Fu Chang, Christine Hendon, Predrag Jelenkovic, Zoran Kostic, Aurel A. Lazar, Nima Mesgarani, John Paisley, John Wright, Xiaofan (Fred) Jiang
Suggested course selection
Satisfy M.S. degree requirements.
Take at least two courses from:
- BMEB W4020: Computational Neuroscience: Circuits in the Brain
- ELEN EE4620: Numerical Methods for Data Analysis
- ELEN E4810: Digital Signal Processing
- ELEN E4903: Topic: Machine learning (or equivalent)
- EEOR E6616: Convex optimization
- EECS E6893: Topic: Big data analytics
Take at least one course from:
- EECS E6720: Bayesian models for machine learning
- EECS E6895: Topic: Advanced big data analytics
Take a course from:
- ELEN E6690: Topics in data-driven analysis and computation
- ELEN E6886: Sparse representation and high-dimensional geometry
- ELEN E9601: Seminar in data-driven analysis and computation
Faculty
Predrag Jelenkovic, Javad Ghaderi, Ethan Katz-Bassett, Debasis Mitra, Gil Zussman, Xiaofan (Fred) Jiang
Suggested course selection
Satisfy M.S. degree requirements.
One basic networking course from the following:
- ELEN E6761: Computer Communication Networks I
- CSEE W4119: Computer Networks
One basic systems or analytical course from the following:
Systems courses:
- CSEE E4140: Networking laboratory
- COMS 4113: Distributed Systems
- COMS W4118: Operating Systems I
Analytical courses:
- ELEN E6772: Topic: Network Algorithms
- ELEN E6950: Wireless and mobile networking
- CSEE 6180: Modeling and Performance Evaluation
Three courses from the following list (a course cannot be used to fulfill both this requirement and any of the above requirements).
- ELEN E6488 Optical interconnects and interconnection networks
- ELEN E6761: Computer Communication Networks I
- ELEN E6767: Internet Econ, Eng & Society
- ELEN E6770: Topic: Next Gen networks
- ELEN E6772: Topic: Network Algorithms
- ELEN E6775: Topic: Computer Networks: A Systems Approach
- ELEN E6776: Topic: Content Distribution Networks
- ELEN E6950: Wireless and mobile networking I
- EEOR E4650: Convex optimization for EE
- EEOR E6616: Convex optimization
- CSEE E4140: Networking laboratory
- CSEE E4951: Wireless and mobile networks and systems.
- CSEE 6180: Modeling and Performance Evaluation
- COMS W4180: Network security
- COMS 4995: Internet Technology, Economics and Policy
- COMS 6181: Advanced Internet Services
- COMS E6998: Cloud Computing and Big Data
- IEOR E6704: Queueing theory
- IEOR E4106: Stochastic models
Other relevant advanced topic courses on networking, such as ELEN E677*, COMS 4995, or COMS E6998, or other course numbers, may be used to fulfill this requirement.
Faculty
Gil Zussman, Predrag Jelenkovic, Xiaodong Wang
Suggested course selection
Satisfy M.S. degree requirements.
One basic circuits course, such as:
- ELEN E4312: Analog electric circuits
- ELEN E4314: Communication circuits
- ELEN E6314: Advanced communication circuits
- ELEN E6312: Advanced analog ICs
Two communications or networking courses, such as:
- CSEE W4119: Computer networks
- ELEN E4702: Digital communications
- ELEN E4703: Wireless communications
- ELEN E6711: Stochastic signals and noise
- ELEN E4810: Digital signal processing
- ELEN E6950: Wireless and mobile networking, I
- ELEN E6951: Wireless and mobile networking, II
- ELEN E6761: Computer communication networks, I
- ELEN E6712: Communication theory
- ELEN E6713: Topics in communications
- ELEN E6717: Information theory
- ELEN E677x: Topics in telecommunication networks
At least two additional approved courses in wireless communications or a related area.
Faculty
Peter Kinget, Harish Krishnaswamy, Mingoo Seok, Kenneth Shepard, Yannis Tsividis, Charles Zukowski
Suggested course selection
Satisfy M.S. degree requirements.
One digital course from:
- EECS E4321: Digital VLSI circuits
- EECS E6321: Advanced digital electronic circuits
One analog course from:
- ELEN E4312: Analog electronic circuits
- ELEN E6312: Advanced analog integrated circuits
- ELEN E6316: Analog circuits and systems in VLSI
- ELEN E4314: Communication circuits
- ELEN E6314: Advanced communication circuits
- ELEN E6320: Millimeter-wave IC design
Two courses from:
- ELEN E6350: VLSI design laboratory
- ELEN E6304: Topics in electronic circuits
- ELEN E6318: Microwave circuit design
- ELEN E9303: Seminar in electronic circuits
At least one additional course in integrated circuits and systems or a related area.
Faculty
M. Preindl, X. Jiang, G. Zussman, K. Shepard, Xiaodong Wang, Debasis Mitra
Suggested course selection
Satisfy M.S. degree requirements.
Take at least two power conversion or power systems courses from:
- ELEN E4361 Power Electronics
- ELEN E4511 Power systems analysis
- ELEN E4510 Solar energy and smart grid power systems
- ELEN E4901 Photovoltaic Systems Eng. and Sustainability
- EAEE E4220 Energy system economics and optimization
- ELEN E6901 Energy Storage for the Electric Grid
- ELEN E6901 Smart Grid Technologies
- ELEN E6902 Renewable power systems
- ELEN E6904 Motor drive systems
- ELEN E6570 Future Energy: Economics, Systems, Policies
Take at least one control or optimization course from:
- EEME E4601 Digital control systems
- EEME E6601 Introduction to control theory
- EEME E6602 Modern control theory
- ELEN E6873 Detection and estimation theory
- EEOR E4650 Convex optimization for electrical engineering
- EEOR E6616 Convex optimization
- EECS E4764 AIoT - Artificial Intelligence of Things
- CSEE W4840 Embedded Systems
Take at least one non-electric energy course from:
- EAEE 4002 Alternative energy resources
- CHEN E4201 Engineering applications of electrochemistry
- EAEE E4180 Electrochemical energy storage systems
- MECE E4430 Automotive dynamics
- MECE E4210 Energy infrastructure planning
- MECE E4211 Energy: sources and conversion
- MECH E4320 Intro to combustion
- MECE E4302 Advanced thermodynamics
- EAEE E4190 Photovoltaic systems engineering and sustainability
- EAEE E4257 Environmental data analysis and modeling
- EAEE E4302 Carbon capture
Recommended to take one of the following energy policy or market non-technical elective courses (this course will fill the quota of non-technical courses of the MS Checklist):
- EAEE E4001 Industrial ecology of earth resources
- EAIA W4200 Alternative energy resources
- INAF U6057 Electricity markets
- INAF U6072 Energy systems fundamentals
- SUMA K4135 Energy analysis for energy efficiency
- INAF U6065 The economics of energy
- INAF U6061 Global energy policy
- INAF U6242 Energy policy
- INAF U6135 Renewable energy markets and policy
Faculty
Dimitris Anastassiou, Christine Hendon, Pedrag Jelenkovic, Aurel A. Lazar, Nima Mesgarani, Kenneth Shepard, Xiaodong Wang
Suggested course selection
Satisfy M.S. degree requirements.
Take both:
- BMEB W4020: Computational neuroscience: circuits in the brain
- ECBM E4060: Introduction to genomic information science and technology
Take at least one course from:
- BMEE E4030: Neural control engineering
- ECBM E4040: Neural Networks and deep learning
- ECBM E4090: Brain computer interfaces (BCI) laboratory
- CBMF W4761: Computational genomics
- ELEN E6010: Systems biology: Design Principles for Biological Circuits
- EEBM E6020: Methods in computational neuroscience
- BMEE E6030: Neural modeling and neuroengineering
Take at least one course from:
- ECBM E6040: Neural networks and deep learning research
- ECBM E607x: Topics in neuroscience and deep learning
- ELEN E608x: Topics in systems biology
- EEBM E609x: Topics in computational neuroscience and neuroengineering
- ELEN E6261: Computational methods of circuit analysis
- ELEN E6717: Information theory
- ELEN E6860: Advanced digital signal processing
Faculty
Keren Bergman, Ioannis (John) Kymissis
Suggested course selection
Satisfy M.S. degree requirements.
Take both
- ELEN E4411: Fundamentals of photonics
- ELEN E6412: Lightware devices (or an E&M course, such as APPH E4300: Applied electrodynamics or PHYS GR6092: Electromagnetic theory)
One more device/circuits/photonics course, such as:
- ELEN E6413: Lightwave systems
- ELEN E6414: Photonic integrated circuits
- ELEN E4314: Communication circuits
- ELEN E4488: Optical systems
- ELEN E6488: Optical interconnects and interconnection networks
- ELEN E4193: Modern display science and technology
At least two additional courses in photonics or a related area. Options include courses outside EE, such as:
- APPH E4090: Nanotechnology
- APPH E4100: Quantum physics of matter
- APPH E4110: Modern optics
- CHAP E4120: Statistical mechanics
- APPH E4112: Laser physics
- APPH E4130: Physics of solar energy
- APPH E6081: Solid state physics, I
- APPH E6082: Solid state physics, II
- APPH E6091: Magnetism and magnetic materials
- APPH E6110: Laser interactions with matter
- MSAE E4202: Thermodynamics and reactions in solids
- MSAE E4206: Electronic and magnetic properties of solids
- MSAE E4207: Lattice vibrations and crystal defects
- MSAE E6120: Grain boundaries and interfaces
- MSAE E6220: Crystal physics
- MSAE E6229: Energy and particle beam processing of materials
- MSAE E6225: Techniques in X-ray and neutron diffraction
Faculty
Professors Wen Wang, Ioannis (John) Kymissis
Suggested course selection
Satisfy M.S. degree requirements.
One basic course, such as:
- ELEN E4301: Introduction to semiconductor devices
- ELEN E4411: Fundamentals of photonics
One advanced course, such as:
- ELEN E4193: Modern display science and technology
- ELEN E4944: Principles of device microfabrication
- ELEN E4503: Sensors, actuators, and electromechanical systems
- ELEN E6151: Surface physics and analysis of electronic materials
- ELEN E6331: Principles of semiconductor physics, I
- ELEN E6332: Principles of semiconductor physics, II
- ELEN E6333: Semiconductor device physics
- ELEN E6945: Nanoscale fabrication and devices
At least two other courses in devices or a related area. Options also include courses outside EE, such as:
- APPH E4090: Nanotechnology
- APPH E4100: Quantum physics of matter
- APPH E4110: Modern optics
- CHAP E4120: Statistical mechanics
- APPH E4112: Laser physics
- APPH E4130: Physics of solar energy
- APPH E6081: Solid state physics, I
- APPH E6082: Solid state physics, II
- APPH E6091: Magnetism and magnetic materials
- APPH E6110: Laser interactions with matter
- MSAE E4202: Thermodynamics and reactions in solids
- MSAE E4206: Electronic and magnetic properties of solids
- MSAE E4207: Lattice vibrations and crystal defects
- MSAE E6120: Grain boundaries and interfaces
- MSAE E6220: Crystal physics
- MSAE E6229: Energy and particle beam processing of materials
- MSAE E6225: Techniques in X-ray and neutron diffraction