Master's in Neuroscience

Looking for a master's in neuroscience that fits your career aspirations? WPI’s MS in neuroscience advances understanding of the human brain—one of the most significant and urgent scientific challenges of our time. This complex biological system holds the key to who we are and how we perceive and interact with the world. A growing number of individuals are affected by neurological and psychiatric illnesses that are poorly understood. The field of neuroscience is at a point where deep learning, AI, neuroengineering, and related advances will stimulate major breakthroughs.

The MS in neuroscience provides students with a strong foundation in computational, molecular, psychological, quantitative, and interdisciplinary approaches to neuroscience. Students gain expertise in basic and translational neuroscience coupled with a strong computational base, links to industry partners, and supported study-abroad opportunities. In addition to a program partnership with WPI's Department of Biomedical Engineering (BME), our expertise in cutting-edge data science methods such as deep learning and AI will stimulate novel and productive purpose-driven research projects.

Our team of interdisciplinary faculty and students thrive from the synergy of our diverse approaches to understanding the brain and nervous system. The faculty involved in the neuroscience master's program have a strong record of funding and provide an excellent research-oriented environment that provides collaboration and one-on-one mentorship.

Admissions Requirements for Master's in Neuroscience

Students applying to the master's in neuroscience are expected to have a bachelor's degree in biology, biochemistry, computer science, mathematics, psychology, neuroscience, or a related field, and to have taken introductory courses in a neuroscience-related field such as biology, biochemistry, computer science, mathematics, and/or psychology.

Students who are not WPI undergraduates or alumni must submit GRE/TOEFL scores.

Graduate Admissions

Curriculum

Looking for a neuroscience master's program that fits your career aspirations? The master's in neuroscience will train students in the complexity of the nervous system and position them to work on the many unanswered questions about the brain and how it functions. WPI’s core strengths in the areas of computational and data sciences, as well as in artificial intelligence and the life sciences areas, give students a comprehensive and cutting-edge approach to the field.

The four main participating departments—Computer Science, Biology & Biotechnology, Chemistry & Biochemistry, and Social Science & Policy Studies—define four broad areas of the neuroscience MS program:

Computational Neuroscience: Training in the use of experimental and theoretical methods for the analysis of brain function

Cellular and Molecular Neuroscience: Training in neurophysiological methods such as electrophysiology, optogenetics, molecular biology, genetics, biochemistry and biophysics, appropriate to topics in neurobiology

Systems Neuroscience: Training in structure-function relationship of neural networks, neural substrates of learning and memory, psychopharmacology of nervous system disorders including Alzheimer’s disease

Psychological Science: Training in how the brain and nervous system interact with development, mental health, cognition, and social processes to mediate behavior

The rapid advances in neuroscience require a workforce ready to tackle the scientific, engineering, and ethical challenges of this fascinating field.

WPI’s neuroscience program is rooted firmly in a comprehensive and multidisciplinary approach to neuroscience with a computational methodology.

WPI’s neuroscience program challenges students to think about how to better understand the brain while considering things like genetics, neurological diseases, and human behavior.

We're not your typical tech school. Your experiences outside the classroom play an equally important role to academics in helping you learn, grow, and succeed. At WPI, you're a member of a close-knit, inclusive, and safe community.

Sheryl and John Powers ’91 started as MQP partners, now they are partners for life celebrating 25 years of marriage this year!

Relevant Neuroscience courses
NEU 501 Neuroscience
NEU 502 Neural Plasticity
NEU 503 Computational Neuroscience
NEU 504 Advanced Psychophysiology
NEU 505 Brain-Computer Interaction

Relevant Bioinformatics and Computational Biology courses
BCB 501/BBT 581 Bioinformatics
BCB 502/CS 582 Biovisualization
BCB 503/CS 583 Biological and Biomedical Database Mining
BCB 504/MA 584 Statistical Methods in Genetics and Bioinformatics
BCB 510 Bioinformatics and Computational Biology Seminar

Relevant Biology and Biotechnology courses
BBT 561 Model Systems: Experimental Approaches and Applications
BBT 581/BCB 501 Bioinformatics
BB 570/CH 555 Cell Signaling

Relevant Biomedical Engineering courses
BME 550 Tissue Engineering
BME 555 BioMEMS and Tissue Micro engineering
BME 560 Physiology for Engineers
BME 583 Biomedical Microscopy and Quantitative Imaging

Relevant Chemistry and Biochemistry courses
CH 538 Medicinal Chemistry
CH 541 Membrane Biophysics
CH 555D Drug and Regulations
CH 555R Drug Safety and Regulatory Compliance
CH 555/PH597 Cell Mechanics
CH 555/BB570 Cell Signaling

Relevant Computer Science courses
CS 5007 Introduction to Applications of Computer Science with Data Structures and Algorithms
CS 5084 Introduction to Algorithms: Design and Analysis
CS 528 Mobile and Ubiquitous Computing
CS 534 Artificial Intelligence
CS 539 Machine Learning
CS 541/DS 541 Deep Learning
CS 542 Database Management Systems
CS 546 Human-Computer Interaction
CS 548 Knowledge Discovery and Data Mining
CS/RBE 549 Computer Vision
CS/SEME 565 User Modeling
CS/SEME 566 Graphical Models for Reasoning under Uncertainty
CS/SEME 567 Empirical Methods for Human-Centered Computing
CS 573 Data Visualization
CS 584 Algorithms: Design and Analysis
CS 585/DS 503 Big Data Management
CS 586/DS 504 Big data Analytics

Relevant Data Science courses:
DS 501 Introduction to Data Science
DS 502/MA 543 Statistical Methods for Data Science

Relevant Mathematical Sciences courses:
MA 508 Mathematical Modeling
MA 543/DS 502 Statistical Methods for Data Science
MA 510/CS 522 Numerical Methods
MA 511 Applied Statistics for Engineering and Scientists
MA 542 Regression Analysis
MA 546 Design and Analysis of Experiments
MA 550 Time Series Analysis
MA 556 Applied Bayesian Statistics

In addition to the 19 credits in the core neuroscience coursework requirement, MS students must complete either the thesis option or the non-thesis option described below. Students supported with a teaching assistantship, research assistantship or fellowship for more than one academic year are required to do the thesis option.

Sarah St. Pierre is junior at Worcester Polytechnic Institute majoring in biomedical engineering with interests in biomechanics and computational modeling. She first did computational modeling last summer at the University of Minnesota as part of the Life Sciences Summer Undergraduate Research Program, and it was then that she realized she wanted to pursue biomechanics research further. Sarah really enjoys the research process, from putting together experiments and testing models, to working with other graduate and undergraduate students. On campus, she is involved with the Varsity Women’s crew team. Upon receiving the Prof. Robert A. Peura Biomedical Engineering Founder Scholarship she was very surprised. She would like to thank the BME professors at WPI, as they have inspired her with their passion and delight in teaching biomedical engineering. In the future, Sarah plans on pursuing a Ph.D. in biomechanics.

Every year, thousands of people are killed or maimed because of land mines. Researchers at Worcester Polytechnic Institute (WPI) are working on a robotic solution that would be inexpensive enough for small towns and organizations to use.

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Graduate Studies Series

Learn from our enrollment team members and other guests by attending quick and convenient 30-minute webinars we designed to highlight popular topics when starting grad school. Take a deep dive into specific areas of interest such as how to secure funding, how to ace your application, an overview of student services, and more!

Jean Adelina King

Dean of Arts & Sciences,

School of Arts & Sciences

Dr. Jean King is the WPI Peterson family Dean in the School of Arts and Sciences. She also serves as a Professor of Biology and Biotechnology, affiliate Professor in Biomedical Engineering Department, Professor in the Neuroscience Program and Director, NeuroTech Suite at WPI. Prior to joining WPI, she was vice provost for biomedical research at the University of Massachusetts Medical School; a tenured professor of psychiatry, radiology, and neurology; and director of the university’s Center for Comparative Neuroimaging.

Erin Solovey

Associate Professor, Computer Science

My research is in human-computer interaction. One focus of my research is on next-generation interaction techniques, such as brain-computer interfaces, physiological computing, and reality-based interaction. My students and I design, build and evaluate interactive computing systems that use machine learning approaches to adapt and support the user’s changing cognitive state and context. I also investigate novel paradigms for designing with accessibility in mind, particularly for the Deaf community.

Jagan Srinivasan

Associate Professor-Biological Science, Biology & Biotechnology

It has been my lifelong dream to become a professor in the field of Biology. Being a faculty member provides a great opportunity to teach and interact with students. Students by nature are highly inquisitive and motivated, and as teachers, we have the responsibility to guide our students to explore and think in new ways. I believe that teaching is a two-way interaction between teachers and students. I come from India and my parents, both of whom were teachers, taught me to strive for excellence in my scholarly pursuits.

Admissions Requirements for Master's in Neuroscience

Curriculum

Graduate Studies Series

Faculty Profiles