Jul 12, 2024  
Rensselaer Catalog 2022-2023 
Rensselaer Catalog 2022-2023 [Archived Catalog]

Electrical, Computer, and Systems Engineering

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Department Head: Dr. John T. Wen

Curriculum Chair: Dr. James J.-Q. Lu

Undergraduate Program Director: Dr. A. Agung Julius

Graduate Program Director: Dr. Richard J. Radke 

Department Home Page: https://www.ecse.rpi.edu/

Electrical Engineering at Rensselaer started in 1907, right at the dawn of the age of electricity. A century later, the curriculum has evolved with time, but the same philosophy of combining fundamental engineering principles with relevant contemporary applications remains. Electrical, computer, and systems engineers have always been at the forefront of new discoveries. The impact of electrical, computer, and systems engineers on society can be seen in areas as diverse as robots and artificial intelligence, communications and networking, medicine and medical technology, microelectronics and photonics, power systems, entertainment and gaming, and more. Inventions in these areas, such as integrated electronics and optical devices, stimulate innovations in computers, control, and communications. New systems theory and mathematical techniques are then developed for analysis and design. 

As perhaps the broadest of scientific disciplines, Electrical, Computer, and Systems Engineering (ECSE) rests on a wide range of scientific fundamentals and therefore offers numerous advantages for undergraduate and graduate study. Among them is the ability to attack many facets of modern problems of social relevance that cut across disciplinary lines. The flexibility for students to embark on individually tailored programs and for the department to launch new areas of research is a hallmark of ECSE.

The department offers Bachelors, Masters, and Doctoral degrees in Electrical Engineering (EE), and Computer, and Systems Engineering (CSE). The ECSE curriculum builds upon a broad foundation of math, science, humanities and social science, and multiple disciplines in engineering, while allowing flexibility in electives to pursue specialized focus areas. ECSE students are trained in engineering fundamentals and application-specific domains with emphasis on hands-on education, design experience, and teamwork. ECSE faculty conduct cutting edge research across multiple disciplines and physical scales, including semiconductor design and processing, smart sensors and devices, 5G communication, and cyber-physical systems such as smart grid and collaborative robots. ECSE research is powered by federal funding agencies including NSF, NIH, DOE, DARPA, ONR, ARO and AFOSR, state funding agencies including NYSTAR and NYSERDA, private foundations, and industry partners.

Research Innovations and Initiatives

The following area descriptions capture activities in research and education across a set of fairly broad technical areas. Individual research groups reside within these broad curricular areas, overlap to sometimes significant degrees, and are highly dynamic and fluid in size and composition as new research initiatives and opportunities arise. These descriptions are therefore best taken as a snapshot of the Department’s research and curricular profile, and the lists of topics and areas are not necessarily exhaustive. Prospective graduate students are encouraged to visit the Department Home Page for the most up to date information, including faculty listings and web pages, and to contact individual faculty with whom they may share technical interests to learn more.

Information Science and Systems 
The Fourth Industrial Revolution is driven by understanding how information flows between computers, systems, and devices in diverse applications including the internet of things, cloud and cognitive computing, artificial intelligence (AI), manufacturing and automation, and cyber-physical systems. 

ECSE research in this area includes fundamental advances in AI and machine learning, theories of learning, optimization, novel AI algorithms and architectures, and software engineering principles for AI systems.  The department has particular strength in the areas of computer vision and image/video processing (e.g., deep learning, object and activity recognition, and camera network analysis) as well as in communications, information, and signal processing (e.g., statistical signal processing, information theory, and inverse problems).

The Information Science and Systems group investigates wide-ranging and ever-changing applications in areas including industrial robotics, power systems, wireless systems, the internet of things, augmented and virtual reality, medical image analysis, biomedical instrumentation, synthetic aperture imaging, remote sensing, geographic information science, and occupant-aware environments.

In addition to ECSE researchers’ own labs, collaborative and multidisciplinary work takes place in School and Institute-level centers including the Rensselaer Augmented and Virtual Environment, the Center for Lighting Enabled Systems and Applications, and the Cognitive and Immersive Systems Laboratory. 

Computer Systems Design 
The development of advanced computer systems and their interconnection to facilitate ubiquitous and pervasive computing capabilities is the primary focus of this group. Specific research areas include data management in computer and distributed systems, software and hardware co-design for AI/ML applications, heterogeneous and near-data computing systems, three-dimensional (3D) and chiplet integration/packaging for computer design, elastic processing to enhance robustness, privacy, and environmental impact of ML. Research activities further expand into elastic architectures in new computing paradigms, such as data-driven domain-specific architectures, and network-centric computing, and a wider range of applications include autonomous systems, graph processing, and bioinformatics.

Research is further directed towards advanced new computational paradigms, including those that are a hybrid of conventional, neuromorphic, and quantum computing—combining bits, neurons, and qubits—allowing humanity to address challenges at a new level of complexity. It aims to advance and use quantum computing, edge computing for networks and cyber-physical systems, and hacker-proof quantum communications.

Other ongoing research activities include silicon-based radio-frequency power amplifiers; multi-Gb/s broadband communications circuits; wafer-level 3D integration for millimeter-wave smart antenna transceivers; RF-powered wireless communication circuits for bio-implantable microsystems; devices, circuits, systems, algorithms, and methodologies to enable intelligent yet inexpensive portable platforms for environmental and biomedical diagnostics; detection and quantification of low levels of biological signals reliably, conveniently, safely, and quickly. 

Communications and Networking
Study and research in Communications and Computer Networking deals with the encoding, transmission, retrieval, and interpretation of information in many forms. Students may pursue programs of study focusing on mathematical modeling and analysis, algorithm design, and hardware/software implementation of solutions for efficient information transmission and retrieval.

Communications research focuses on efficient transmission of information over wireless and wired channels. Research in statistical communications aims at reducing adverse effects on signal transmission over error-prone channels through probabilistic channel modeling and appropriate design of signal encoding, decoding, estimation, and detection mechanisms. The channels considered range from subminiature communication channels inside a computer chip through cable, satellite, and cellular communication channels. 

Research in Computer Networking focuses on the design and analysis of algorithms and protocols used for transferring information between end-systems like those through a network of forwarding devices such as routers and switches. This involves comprehensive study of channel access control, routing, and transport mechanisms for wireless and wired networks using mathematical modeling and simulation. The types of networks studied include: cellular, wireless ad-hoc and sensor networks, the Internet, and the emerging Internet-of-Things (IoT).

Control and Autonomy
With focuses on the methodologies and applications in control, robotics, and automation the ECSE faculty in this area conduct cutting-edge research with emphasis on high impact applications. The methodological tools used in this research area include robotics, control theory, formal methods, machine learning, vision and perception, and cognitive engineering. The domain of applications includes human-robot systems, advanced manufacturing, cyber-physical systems, autonomous systems, electric power systems, thermal management, biological systems, personalized devices for human health, microrobotics, and materials processing. Current research projects in this area address both fundamental theories and a variety of applications. Faculty interests include advanced control algorithms development in the areas of nonlinear control, formal methods in control, reinforcement learning, multivariable control, robust control, distributed control, and optimal control. 

ECSE faculty in this area coordinates closely with the Mechanical, Aerospace, and Nuclear Engineering; Computer Science; and Cognitive Science departments for joint research and curriculum development. Current collaborative research projects include planning and control for advanced manufacturing systems, multi-robot actuator and sensor networks for coordinated monitoring and manipulation, and precision motion and force control with vision guidance in micro and nano assembly manufacturing and control systems in next-generation aircraft. Extensive experimental and computational facilities, as well as undergraduate and graduate research opportunities, are available in the institute-wide and school-wide research centers.

Energy and Power Systems 
Research in power electronics and power system engineering is becoming critically important to meet the world’s increasing energy needs and demands within the environmental, economic, and national security constraints. Department faculty are actively conducting research programs in power systems and power electronics.

The power systems area includes research on renewable resource integration into the power grid, focusing on using inverters to enhance stability and power transfer limits. Another research area is the application of high-sampling rate synchronized phasor data to improve the operation of large power grids. The research covers phasor data disturbance event analysis, state estimation, and data recovery and cybersecurity. 

Optimization theory and advanced computational tools are used to obtain high efficiency and reliability at minimum cost, particularly for distributed generation and multi-energy integration, such as electric and thermal networks. This research includes developing control and energy management systems (EMS) for renewable energy, energy storage and microgrids. Intelligent protective relaying to deal with the problem of islanding can utilize the Department’s real-time and hardware-in-the-loop (HIL) simulation tools.

Power electronics provide the foundation for the utilization of renewable energy, power management for IT devices, and electrification of transportation systems. Current research activities include the development of new converter control system designs for large-scale PV and wind energy, modeling of wind turbines and PV inverters for power system stability studies, high-voltage dc transmission technologies and applications, and stability theory for future converter-based power systems.

Power semiconductor devices research includes both discrete transistors and power integrated circuits on silicon and compound semiconductors. Compound semiconductor devices include conventional (GaAs), wide bandgap (SiC and GaN) and ultra-wide or extreme bandgap (BN and AlN) semiconductors. Novel device structures are bi-directional Si and SiC IGBTs, GaN and diamond MOS Channel-Heterojunction Field-Effect Transistors. Device models are developed for performance evaluation and impact on energy efficiency. Recent experimental demonstrations of monolithic integration of power devices with photonic devices in GaN are particularly noteworthy.

Electronic and Photonic Devices, Circuits and Systems
ECSE faculty are developing cutting-edge devices, systems, and process technologies for future sensing, computation, communications, and energy applications. Our state-of-the-art facilities include the Microscale and Nanoscale Fabrication Cleanroom (MNCR) and devices and circuits characterization tools that span frequencies from DC to terahertz-wave (THz), visible and ultraviolet ranges and nanoscale characterization facilities. Our facilities also include the electronic materials laboratory for the growth of III-V, and II-VI semiconductors, the high-voltage power device laboratory, integrated digitized, color-tunable lighting, advanced sensing, and control platform managed by the Center for Lighting Enabled Systems and Applications (LESA), and machine learning tools enabling “Systems that Think™” platform. 

The ECSE faculty, students, and research staff use these facilities and commercial and custom modern software tools for the discovery and invention of new electrical, optical, and thermal interconnect solutions, 3D heterogeneous integration (3D HI) and advanced packaging, for future zetta-scale systems in AI, Internet of Things, quantum computing, photonic ICs, beyond 5G systems, power system-on-chip and biomedical devices and systems. 

Our current projects include heterogeneous integration and advanced packaging of chiplets and components for future chips, chip-scale photonic computing, devices and circuits for mmWave/THz ICs for 6G communications and THz gas phase spectroscopy, terahertz AI-driven testing of VLSI for hardware cyber security and defect identification, beyond CMOS devices (graphene, diamond and thin-film transistors), and digitized light field sensing for more energy efficient buildings. 



Abouzeid, A.A.—Ph.D. (University of Washington); computer and sensor networks, Internet of Things, stochastic modeling, sequential decision problems, network algorithms and optimization, next generation wireless networks.

Bhat, I.—Ph.D. (Rensselaer Polytechnic Institute); sold state, electronic materials.

Chow, J.H.—P.E., Ph.D. (University of Illinois); large-scale system modeling, multivariable control systems.

Chow, T.P.—Ph.D. (Rensselaer Polytechnic Institute); semiconductor power devices and integrated circuits, device physics and processing technologies, monolithic integration.

Franklin, W.R.—Ph.D. (Harvard University); computational geometry, graphics and CAD applications, large geometric databases, geographic information systems, terrain visibility and compression, parallel computing on GPUs.

Hella, M.—Ph.D. (Ohio State University); RF and mixed signal VLSI circuits for wireless/optical transceivers; analog/RFIC design for biomedical applications.

Ji, Q.—Ph.D. (University of Washington); computer vision, image processing, pattern recognition, robotics.

Julius, A.A.—Ph.D. (University of Twente); mathematical systems theory and control, systems biology, control of biological systems, hybrid systems.

Kar, K.—Ph.D. (University of Maryland); computer networks, wireless and sensor networks, Internet-of-Things, smart grid.

Karlicek, R.F.—Ph.D. (University of Pittsburgh); compound semiconductor materials and devices, device packaging, lasers and light emitting diodes, solid state lighting.

Lu, J.-Q.—Ph.D. (Technical University of Munich); electronic materials, devices, frabrication, integration, and advanced packaging; 3D heterogeneous integration (HI) technology; 3D-IC; hybrid bonding; chiplet packaging; LED display technology.

Radke, R.J.—Ph.D. (Princeton University); computer vision, image processing, social signal processing, occupant-aware environments, visual effects.

Sawyer, S.M.—Ph.D. (Rensselaer Polytechnic Institute); optoelectronics, characterization, design, sensor development.

Schubert, E.F.—Ph.D. (University of Stuttgart); compound semiconductor devices and materials, light emitting diodes, heterobipolar transistors, semiconductor device physics, solid state lighting.

Shur, M.S.—D.Sc. (Ioffe Institute); semiconductor materials and devices, integrated circuit simulation, characterization, and design.

Schmidt, M.—Ph.D. (Massachusetts Institute of Technology); micro and nanofabrication of sensors, actuators and electronic devices, microelectromechanical systems (MEMS), design of micromechanical sensors and actuators, and micro/nanofabrication technology. 

Sun, J.—Ph.D. (University of Paderborn); power electronics and its application in renewable energy, IT, transportation, and power systems.

Vanfretti L. —Ph.D. (Rensselaer Polytechnic Institute); synchrophasor and electrical energy technology and applications; cyber-physical power system modeling, simulation, stability and control.

Wen, J.T.—Ph.D. (Rensselaer Polytechnic Institute); modeling and control of dynamical systems with applications to robot manipulation, materials processing, thermal management, and circadian rhythm and sleep regulation.

Yazici, B.—Ph.D. (Purdue University); inverse problems in biomedical imaging, tomography, diffuse optical tomography, biomedical optics, free space optical communications, ultrasonics, statistical pattern recognition theory and application.

Zhang, T.—Ph.D. (University of Minnesota); VLSI signal processing, error-correcting coding.

Associate Professors 

Huang, Z.R.—Ph.D. (Georgia Institute of Technology); optoelectronic devices, integration and packaging, 3D integrated microsystems, lightwave circuits, integrated slow wave structures, photodetectors, electro-optic modulators, and laser diodes.

Schoch, P.M.—Ph.D. (Rensselaer Polytechnic Institute); plasma diagnostics, instrumentation, engineering education.

Tajer, A.—Ph.D. (Columbia University); information theory, wireless communication, statistical signal processing, smart grids.

Wang, M.—Ph.D. (Cornell University); machine learning, high-dimensional data analytics, power systems, signal processing.

Assistant Professors 

Chen, T.—Ph.D. (University of Minnesota); machine learning and resource allocation for Internet-of-Things and cyber-physical systems, network communication and machine learning co-design.

Paternain, S.—Ph.D. (University of Pennsylvania); theory of constrained learning, counterfactual theory of constraint programming, navigation functions in structured problems, safety and correctness of autonomy.

Liu, L.—Ph.D. (University of California Santa Barbara); the intersection of computer architecture and AI/ML, including heterogeneous computer architecture, emerging non-volatile memory system design, hardware acceleration, and hardware security.

Professors of Practice 

Kanai, J.—Ph.D. (Rensselaer Polytechnic Institute); engineering education, software engineering, systems engineering.

Neti, P.—Ph.D. (University of Victoria); electrical power engineering, power equipment, technology development and project management in the areas of asset management of various power applications.

Senior Lecturers 

Kraft, R.P.—Ph.D. (Rensselaer Polytechnic Institute); embedded systems and control education, electronic manufacturing inspection, high-speed digital circuits.


Oakes, K.—Ph.D. (Rensselaer Polytechnic Institute); control of robotic manipulators with focus on flexible joint manipulators utilized for satellite servicing and collaborative robotics. 

Patterson, A.—Ph.D. (Massachusetts Institute of Technology); theory, experiment and modelling of nanoelectronic and photonic devices.

Peterson, A.—Ph.D. (Rensselaer Polytechnic Institute); semiconductor device modeling and simulation focused on metal oxide devices and ultraviolet sensors.

Rees, J.D.—Ph.D. (Rensselaer Polytechnic Institute); microbiology and bio-sensors with semiconductor, device, sensor, and materials engineering. 

Wilt, K. R.—Ph.D. (Rensselaer Polytechnic Institute); acoustic and ultrasonic wave theory, piezoelectric transducer theory and design, embedded systems.

Senior Research Scientist 

Shah, M.—Ph.D. (Virginia Polytechnic Institute and State University); electric machines design and analysis, machine system interaction, low frequency electromagnetics.

Emeritus Faculty

Connor, K.A.—Ph.D. (Polytechnic Institute of New York); plasma diagnostics, instrumentation, engineering education.

Das, P.K.—Ph.D. (University of Calcutta); microwave acoustics, solid-state devices, integrated circuits.

Desrochers, A.A.—Ph.D. (Purdue University); discrete event dynamic systems, robotics, automated manufacturing systems control.

Dutta, P.S.—Ph.D. (University of Calcutta); (Indian Institute of Science); compound semiconductor materials and devices, crystal growth and substrate engineering, semiconductor quantum dots and nano-particles, photovoltaics, optoelectronics and microelectronics technologies..

Frederick, D.K.—Ph.D. (Stanford University); automatic control, process modeling and control, computer simulation.

Gutmann, R.J.—Ph.D. (Rensselaer Polytechnic Institute); solid-state devices, microwave techniques, and interconnection technology.

Jennings, W.C.—Ph.D. (Rensselaer Polytechnic Institute); plasma diagnostics, electronics manufacturing, multimedia educational materials.

Kelley, R.B.—Ph.D. (University of California, Los Angeles); methods to give machines smart behaviors, sensor-based automation/robotic systems, teaching methods.

Nagy, G.—Ph.D. (Cornell University); pattern recognition, document-image analysis, optical character recognition, geometric computation, computer-mediated learning, computer vision.

Nelson, J.K.—C.Eng., Ph.D. (University of London); dielectrics and insulation systems, computer-based diagnostics, electrostatic phenomena.

Pearlman, W.A.—Ph.D. (Stanford University); information theory and source coding; image, video, and audio compression; digital image and signal processing.

Rose, K.—Ph.D. (University of Illinois); semiconductor and superconductor materials and processing, VLSI design and testing.

Salon, S.J.—P.E., Ph.D. (University of Pittsburgh); machine design, system component modeling and simulation.

Sanderson, A.C.—Ph.D. (Carnegie Mellon University); robotics, knowledge-based systems, computer vision.

Saxena, A.N.—Ph.D. (Stanford University); solid-state materials, devices, integrated circuits, and advanced technologies.

Woods, J.W.—Ph.D. (Massachusetts Institute of Technology); digital signal processing, image processing, digital image and video compression.

Wozny, M.J.—Ph.D. (University of Arizona); computer graphics, computer-aided design, digital simulation, rapid prototyping systems.

* Departmental faculty listings are accurate as of the date generated for inclusion in this catalog. For the most up-to-date listing of faculty positions, including end-of-year promotions, please refer to the Faculty Roster section of this catalog, which is current as of the May 2022 Board of Trustees meeting.

Undergraduate Programs

Outcomes of the Undergraduate Curriculum

Students who successfully complete this program will be able to demonstrate:

  • an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
  • an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
  • an ability to communicate effectively with a range of audiences.
  • an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts. 
  • an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.
  • an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
  • an ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Objectives of the Undergraduate Curricula

The Electrical and Computer and Systems Engineering programs are each designed to prepare students for continued learning and successful careers in industry, government, academia, and consulting. Within a few years of graduation graduates of the Bachelor of Science programs are expected to:

  • pursue professional positions and/or graduate study in their areas of interest.
  • contribute to the body of knowledge in their professional disciplines through problem-solving, discovery, leadership, and responsible application of technology.
  • continue to develop both professionally and personally through activities such as participation in professional societies, continuing education, and community service.

The Electrical Engineering and Computer and Systems Engineering degree programs are each accredited by the Engineering Accreditation Commission of ABET, http://www.abet.org.

Baccalaureate Programs

Within the department, students may obtain a Bachelor of Science degree in either of two disciplines: Electrical Engineering (EE) or Computer and Systems Engineering (CSYS). The department also encourages students to consider graduate study in either of these curricula.

ECSE Majors Description
Electrical Engineering (EE) is a dynamic and broad field that applies physics and mathematics to the creative design, analysis, research, development, testing, and monitoring of diverse systems and products in society today. From cell phones to smart cars, light-emitting diodes to autonomous robots, power electronics to power systems, electrical engineering plays an essential role in advancing our multidisciplinary, technological society. 

EE Key & Related Courses: Electric Circuits, Introduction to Electronics, Embedded Control, Computer Components and Operations, Signals and Systems, Engineering Probability, Microelectronics Technology, Fields & Waves I, and Electrical Energy Systems Areas of Concentration: Microelectronics and Photonics; Electric Power and Energy; Communications and Network Science; Information and Decision Science; Computer Vision and Imaging; Control, Robotics, and Automation.

Computer and Systems Engineering (CSE) is a dynamic field that creatively applies computers and mathematics to the design, analysis, research, development, testing, and implementation of a wide range of systems and products. From secure wireless networks to medical imaging systems, autonomous mobile robots to face recognition security systems, aircraft control systems to mapping the world, distributed underwater pollution sensors to the next generation Internet, handheld games to drones, these systems are built by RPI computer engineers.

CSE Key & Related Courses: Electric Circuits, Introduction to Electronics, Embedded Control, Computer Components and Operations, Signals and Systems, Engineering Probability, Computer Architecture, Networks, and Operating Systems, and Introduction to Algorithms.

Both majors enable graduates to apply to be a Professional Engineer (PE), after four years of experience and passing licensing exams.

Engineering design is introduced and developed throughout the program, setting the stage for a capstone design experience. The capstone design experience is a communications-intensive course and satisfies the Institute writing requirements as it prepares the student for a professional career, providing valuable engineering experience in a team-based environment.

Degree Requirements
The electrical engineering curriculum requires completion of a minimum of 128 credit hours; the computer and systems engineering curriculum requires 129 credit hours. In either case, the Pass/No Credit option may be used only for humanities and social sciences electives (up to a maximum of 6 credits) or free electives. All other courses used to satisfy the degree requirements must be taken on a graded basis.

Starting templates are available for students who select either of the ECSE disciplines. However, various arrangements can be made with the help of an adviser. In all cases, a department approval of individual plans of study is necessary to ensure the satisfaction of departmental and accreditation requirements. 

Focus Areas
Focus areas provide students with a critical level of expertise in a particular domain within the Electrical Engineering major or Computer and Systems Engineering major, preparing the student for employment or graduate school in that domain. 

EE Focus Areas: Microelectronics and Photonics; Electric Power and Energy; Communications and Network Science; Information and Decision Science; Computer Vision and Imaging; Control, and Robotics and Automation.

CSE Focus Areas: Machine Learning and Artificial Intelligence; Internet of Things; Automatic Control and Robotics; Communications and Information Processing; Computer Graphics & Applications; Computer Vision; Computer Hardware and VLSI Design; Computer Networks.

The focus areas may be updated or changed. See more details about the focus areas on the ECSE homepage. 

Dual Major Programs

Dual majors lead to a single baccalaureate degree embracing two fields. Special programs which can be completed in eight semesters have been developed. Common dual majors include Electrical Engineering and Computer and Systems Engineering, and Computer and Systems Engineering and Computer Science. Many other dual majors are possible but may require more than eight semesters to complete. These should be discussed with an adviser. 

See the ECSE homepage for detailed/updated information about these programs. Further information is also available in the ECSE Student Services office.

Minor Programs

The ECSE department offers two minor programs: Electrical Engineering and Computer and Systems Engineering. Students seeking to obtain one of these minors need to complete at least 15 credits of courses relevant to the degree. No more than 50% of the course credits applied to the minor requirement may be drawn from credits applied to the major. More details about the requirements for these minor programs can be found in this catalog. 

Special Undergraduate Opportunities

The Grainger Scholar Program
This program is for well-qualified U.S. students whose individual studies emphasize energy sources and systems. The Grainger Scholars Award is given annually in the amount of $5,000 for junior undergraduates who are entering their senior year and $10,000 for current co-terminal students or students entering the department’s Master’s or Ph.D. programs. Eligible students must pursue the focus area in Electric Power and Energy. See more information for the Grainger Scholar Program on the ECSE homepage. 

Graduate Programs

The department offers graduate programs leading to the Master of Engineering, Master of Science, and Doctor of Philosophy degrees in both of the department curricula. In all cases, particular emphasis is placed on developing a coherent individualized Plan of Study with the help of a faculty adviser.  See more details at the ECSE department homepage.

Master’s Programs

Both the M.S. and the M.Eng. degrees require 30 credits beyond the bachelor’s degree. See more details about the Master’s Program at ECSE homepage. 

Co-terminal Baccalaureate/Master Programs

The five-year co-terminal degree timeline is achievable by many students in good academic standing. See more details the Co-terminal Master’s Program on the ECSE department homepage. 

Doctoral Programs

Advanced study and research for a Ph.D. degree is conducted under the guidance of a thesis adviser representing the department. The student formulates an individual Plan of Study in consultation with the adviser. Major milestones for the Ph.D. program in ECSE include passing a doctoral qualifying exam (DQE), a doctoral candidacy exam (DCE), and successfully defending the dissertation in an open presentation to his or her committee. The doctoral qualifying examination should be taken during the first year of the doctoral program. The doctoral degree requirements include 72 credits for students entering the graduate program with a bachelor’s degree or 48 credits for students entering with a master’s degree. The ratio of 6000-level to 4000-level credits on 72-credit Plan of Study must be 2 or greater with maximum of 15 credits at 4000-level. The doctoral dissertation credits accounted for time spent on research are 12 credits minimum and 36 credits maximum. The Ph.D. dissertation must be scholarly, creative, and original. The department expects the Institute requirements for candidacy and residency to be satisfied.

B.S. – Ph.D. Program

The ECSE department offers a B.S.-Ph.D. program for ECSE undergraduate students with a passion for research.  In this unique program, students are able to conduct research during their undergraduate studies and begin their Ph.D. immediately after receiving their B.S. degree.  As admitted B.S.-Ph.D. students transition to graduate status, they will participate in graduate program seminars and activities. For more information, visit the ECSE department homepage: https://ecse.rpi.edu/.

Outcomes of the ECSE Graduate Curriculum 

Students who successfully complete this program will be able to:

  • develop and demonstrate substantial breadth and depth of knowledge beyond the bachelor’s degree with a focus on one area of ECSE.  [Coursework and DQE.]
  • demonstrate an expertise in the student’s thesis area that covers both the background and current research in that area. [DCE and defense.]
  • create substantial original knowledge in an advanced area of ECSE. [DCE, defense, and thesis.]
  • clearly and thoroughly document both the state of the art and the student’s own research in the form of a dissertation. [Candidacy proposal and thesis.]

Special Graduate Opportunities

In collaboration with the various campus centers and other departments, ECSE sponsors master’s and doctoral program options in manufacturing systems and semiconductor technology. Descriptions of these programs are available upon request.

Course Descriptions

Courses directly related to all Electrical, Computer, and Systems Engineering curricula are described in the Course Description section of this catalog under the department codes CSCI, ECSE, ENVE, ISYE, ITEC, MATH, MATP, MTLE, and PHYS.


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