Master of Science
Neuroscience
Affiliated Faculty
J. Srinivasan, Associate Professor, Biology and Biotechnology and Program Director, Neuroscience; Ph.D., University of Tuebingen, Germany; neural networks underlying social behaviors, role of olfactory dysfunction in neurodegenerative disorders, optogenetics & engineering of neural networks.
D. R. Albrecht, Associate Professor, Biomedical Engineering; Ph.D., University of California, San Diego; bioMEMS, microfluidics, quantitative systems analysis and modeling, biodynamics, neural circuits and behavior, optogenetics, high-throughput chemical/genetic screens, tissue engineering, 3-D cell micropatterning, dielectrophoresis.
A. Arnold, Assistant Professor, Mathematical Sciences; Ph.D., Case Western University, 2014. Mathematical biology, bayesian inference, parameter estimation in biological systems.
S. Barton, Associate Professor, Humanities and Arts; Ph.D. University of Virginia, 2012. Human- robot interaction in music composition and performance, design of robotic musical instruments, music perception and cognition, audio production.
F. Bianchi, Professor, Humanities & Arts;
K. L. Billiar, Professor and Department Head, Biomedical Engineering; Ph.D., University of Pennsylvania; Biomechanics of soft tissues and biomaterials, mechanobiology, wound healing, tissue growth and development; functional tissue engineering, regenerative medicine.
S. C. Burdette, Associate Professor, Chemistry and Biochemistry; Ph.D., Massachusetts Institute of Technology; synthesis of fluorescent sensors for iron, photoactive chelators for delivery of metal ions in cells, applications of azobenzene derivatives with unusual optical properties, polymers to detect metal contaminants in the environment.
L. Capogna, Professor and Department Head, Mathematical Sciences; Ph.D., Purdue University, 1996. Partial differential equations.
R. E. Dempski, Associate Professor, Chemistry and Biochemistry; Ph.D., Massachusetts Institute of Technology; molecular mechanism of human zinc transporter, structure-function of light activated channel, optogenetics.
J. Doyle, Associate Professor, Social Science and Policy Studies; Ph.D., University of Colorado-Boulder, 1991. Mental models of complex systems, environmental cognition and behavior.
J. B. Duffy, Associate Professor and Department Head, Biology and Biotechnology; Ph.D., University of Texas; defining signaling pathways that program cellular diversity.
M. Elmes, Professor, School of Business; Ph.D., Syracuse University, 1998. Interpersonal and group dynamics in complex organizations, leading change, leadership ethics.
R. Falco, Assistant Teaching Professor, Humanities & Arts;
N. Farny, Assistant Professor, Biology and Biotechnology; Ph.D., Harvard University, 2009. Translational control of gene expression and cellular stress response in neurodegenerative disease and autism spectrum disorder.
A. Gericke, Professor and Department Head, Chemistry and Biochemistry; Dr.rer.nat., University of Hamburg; biophysical characterization of lipid-mediated protein function, development of vibrational spectroscopic tools to characterize biological tissue.
L. Harrison, Assistant Professor, Computer Science; Ph.D., UNC-Charlotte, 2013. Information visualization, visual analytics, human-computer interaction.
M. Humi, Professor, Mathematical Sciences; Ph.D., Weizmann Institute of Science, 1969. Mathematical physics, applied mathematics and modeling, Lie groups, differential equations, numerical analysis, turbulence and chaos.
S. Ji, Associate Professor, Biomedical Engineering; D.Sc., Washington University in St. Louis; Biomechanics, brain injury, finite element analysis, multi-scale modeling, neuroimaging, medical image analysis, sports medicine.
J. A. King, Professor, Biology and Biotechnology and Peterson Family Dean of Arts and Sciences; Ph.D., New York University; M.S, City University of New York; neuronal plasticity associated with neurological and psychiatric disorders utilizing functional magnetic resonance imaging, molecular biology and behavior.
X. Kong, Associate Professor, Computer Science; Ph.D., University of Illinois, Chicago, IL 2014. Data mining, social networks, machine learning, big data analytics.
D. Korkin, Professor, Computer Science; Ph.D., University of New Brunswick, Canada, 2003. Bioinformatics of disease, big data in biomedicine, computational genomics, systems biology, data mining, machine learning.
K. Lee, Assistant Professor, Biomedical Engineering; Ph.D., Massachusetts Institute of Technology; mechanobiology, cell mechanics, cell morphodynamics, cancer cell migration, quantitative live cell imaging, quantitative cell biology, computational image analysis, data mining, genome engineering, optogenetics.
R. Lopez, Assistant Professor, Social Science and Policy Studies; Ph.D., Dartmouth College; psychology, social neuroscience, functional neuroimaging, longitudinal modeling of behavior.
R. Neamtu, Associate Teaching Professor, Computer Science; Ph.D., Worcester Polytechnic Institute;
S. Olson, Associate Professor, Mathematical Sciences; Ph.D., North Carolina State University 2008. Mathematical biology, computational biofluids, scientific computing.
M. B. Popovic, Assistant Research Professor, Physics; Ph.D., Boston University. Human neurosensory-motor organization, soft robotics, wearable robotics, assistive robotics, human augmentation systems.
A. Rodriguez, Assistant Professor, Social Science & Policy Studies; Ph.D., University of California, Los Angeles;
C. Ruiz, Professor, Computer Science; Ph.D., Maryland, 1996. Data mining, knowledge discovery in databases, machine learning.
E. F. Ryder, Associate Professor, Biology and Biotechnology; Ph.D., Harvard University; M.S., Harvard School of Public Health; bioinformatics and computational approaches to understanding biological systems.
S. F. Scarlata, Professor, Chemistry and Biochemistry; Ph.D., University Illinois Urbana-Champaign; Mechanisms of cell signaling using fluorescence imaging and correlation methods, how mechanical deformation affects calcium fluxes in cells.
J. L. Skorinko, Professor, Social Science & Policy Studies; Ph.D., University of Virginia; soc ial environmental cues, stigmas and stereotyping, perceptions of others
E. T. Solovey, Assistant Professor, Computer Science; Ph.D., Tufts University, 2012. Human-computer interaction, user interface design, novel interaction modalities, human-autonomy collaboration, machine learning.
I. Stroe, Associate Teaching Professor, Physics; Ph.D., Clark University. Experimental biophysics, protein structure, dynamic, and functionality.
D. Tang, Professor, Mathematical Sciences; Ph.D., University of Wisconsin, 1988. Biofluids, biosolids, blood flow, mathematical modeling, numerical methods, scientific computing, nonlinear analysis, computational fluid dynamics.
L. V. Titova, Associate Professor, Physics; Ph.D., University of Notre Dame. THz spectroscopy of nanomaterials for energy applications; optical excitations and ultrafast carrier dynamics in nanomaterials.
L. Vidali, Associate Professor, Biology and Biotechnology; Ph.D., University of Massachusetts-Amherst; understanding the molecular and cellular mechanisms underlying the role of the cytoskeleton in plant cell organization and growth.
C. E. Wills, Professor , Computer Science; Ph.D., Purdue, 1988. Distributed systems, networking, user interfaces.
M. Wu, Visiting Assistant Professor, Mathematical Sciences; Ph.D., University of California, Irvine, 2012. Mathematical biology, modeling of living systems.
Z. Wu, Associate Professor, Mathematical Sciences; Ph.D., Yale University, 2009. Biostatistics, high-dimensional model selection, linear and generalized linear modeling, statistical genetics, bioinformatics.
V. Yakovlev, Research Associate, Mathematical Sciences; Ph.D., Institute of Radio Engineering and Electronics, Russian Academy of Sciences, 1991. Antennas for MW and MMW communications, electromagnetic fields in transmission lines and along media interfaces, control and optimization of electromagnetic and temperature fields in microwave thermal processing, issues in modeling of microwave heating, computational electromagnetics with neural networks, numerical methods, algorithms and CAD tools for RF, MW and MMW components and subsystems.
H. Zhang, Assistant Professor, Biomedical Engineering; Ph.D., Johns Hopkins University; Biomedical robotics, biomedical imaging, ultrasound and photoacoustic instrumentation, functional imaging of brain and cancer, image-guided therapy and intervention.
Program of Study
The Neuroscience program offers graduate studies toward the M.S. degree. This program is designed to provide students with a strong foundation in molecular, psychological, computational, quantitative and interdisciplinary approaches to neuroscience. Neuroscience is a critical and challenging area of human endeavor. Our faculty and students thrive from the synergy of our diverse approaches to understanding the brain and nervous system. The faculty involved in the program have a strong record of extramural funding and provide an excellent research-oriented environment. As a ‘Program’ in Neuroscience, faculty from departments across campus train our students and collaborate on research and projects. The program comprises four broadly defined areas:
- 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.
- Computational Neuroscience: Training in the use of experimental and theoretical methods for the analysis of brain function.
- Psychological Science: Training in how the brain and nervous system interact with development, mental health, cognition, social processes, and behavior.
Master of Science in Neuroscience
Goals:
- Prepare future professional students and industry leaders in the field of neuroscience so that they are ready to help solve the world’s most challenging problems affecting the brain.
- Create a comprehensive educational interdisciplinary program in neuroscience at WPI that distinguishes our program from others typically offered at the master’s level due to the focus on both basic and translational neuroscience coupled with a strong computational base and links to industry partners.
- Development of research areas linking neuroscience to areas like data science and biomedical engineering, in order to train students in a multidisciplinary approach.
Admissions Requirements
Students applying to the M.S. Degree program 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. For example, a student with a bachelor’s degree in biology is expected to have also completed courses in calculus and statistics prior to submitting an application. A strong applicant who is missing background coursework as needed for course requirements may be admitted, with the expectation that he or she will take and pass one or more undergraduate courses in this area of deficiency either during the summer prior to admission or within the first semester after admission. These remedial courses will not count towards meeting the M.S. degree requirements. The determination of what course or courses will satisfy this provision will be made by the Neuroscience Faculty Steering Committee, which consists of faculty members from the participating departments at WPI. The GRE is recommended of all applicants. The GRE is waived for any applicant applying with a bachelor’s degree from an accredited four-year institution in the United States or Canada.
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M.S. in Neuroscience,
Classes
NEU 501: Neuroscience
Department
In this course, students will develop an understanding of neurobiology at several levels, from the physiology of individual neurons, through the functioning of neural circuits, and finally to the behavior of neural systems such as vision, motion, and memory. Topics covered include spatial orientation and sensory guidance, neuronal control of motor output, neuronal processing of sensory information, sensorimotor integration, neuromodulation, circadian rhythms and cellular mechanisms of learning and memory Furthermore, students will learn about artificial intelligence and machine learning approaches to creating computational models of the brain using artificial neural networks and deep learning. The class will be based on lectures accompanied by in-class activities and will include weekly discussion of papers from the scientific literature. The class will focus on a guiding theme, such as neurotransmitter systems, with emphasis on research of human neurological problems, such as schizophrenia, addiction, and neurodegenerative disorders.
NEU 502: Neural Plasticity
Department
Neuronal connections strengthen and weaken with learning, memory, or other events; a phenomenon called synaptic plasticity. In this course, we explore the underlying biological, biophysical and biochemical changes responsible for plasticity. This course covers the structure and organization of neuronal connections, the neurotransmitter receptors that line these structures, the signaling pathways that are mediated in synapses, the mechanical processes that underlie protraction and retraction, and the pharmacological agents that stimulate or block these changes. Students are required to have had an undergraduate level course in biology and biochemistry.
NEU 503: Computational Neuroscience
Department
Computational neuroscience explores the brain at many different levels, from single cell activity, to small local network computation, to the dynamics of large neuronal populations across the brain. This course will introduce students to a multifaceted array of approaches that span biology, physics, mathematics and computer science as well as facilitate the integration of modeling (on both the single molecule and neuron level) and quantitative techniques to investigate neural activity at these different levels. Where possible, this course has a tripartite organization. First, the theory is presented from a text or journal article. Second, students read and critique a paper that uses the technique. Finally, simulations and/or problem sets are assigned to fix the knowledge learned in the course. Pertinent examples will be drawn from research done by WPI students and faculty.
NEU 504: Advanced Psychophysiology
Department
This course will provide an in-depth understanding of what psychophysiology is and the common methods used to understand psychophysiological responses. Common psychophysiological methods will be discussed in-depth, such as sympathetic and parasympathetic nervous system, facial electromyography, electroencephalography (EEG), respiration, blood pressure, pulse rate, skin temperature, electrodermal responses, cortisol, and other neuroendocrine monitoring methods. The social, cognitive, emotional, and motivational responses to different psychological events will be explored in detail. Computational methods will be described from the fields of artificial intelligence, machine learning, and mobile computing for capturing, processing and discovering patterns in physiological and behavioral data. In addition, the course will examine how biofeedback works in educational, clinical, and experimental settings. Students may not receive credit for both PSY 2502 and NEU 504.
NEU 505: Brain-Computer Interaction
Department
This course will explore the current state of brain sensing and its application to human-computer interaction research. This course covers brain function, sensing technology, machine learning methods, and applications of brain-computer interfaces in various domains. This course aims for students to (1) obtain the background to conduct research in brain-computer interaction and human-computer interaction; (2) understand the literature in the field of brain sensing for human-computer interaction research; (2) understand the various tools used in brain sensing, with a focus on functional near-infrared spectroscopy (fNIRS) research; (3) understand the steps required to use real-time brain sensing data as input to an interactive system; (4) understand the domains and contexts in which brain-computer interfaces may be effective; (5) understand the open questions and challenges in brain-computer interaction research today.
NEU 510: Neuroscience Seminar
Department
(0 credits, pass/fail grading) This seminar provides an opportunity for students in the Neuroscience program to present their research work, as well as hear research presentations and talks from guest speakers.
NEU 590: Special Topics in Neuroscience
Department
This course is intended to provide the students of the program a diverse selection of current relevant topics in neuroscience. Prerequisites will vary with topic.
NEU 596: Independent Study in Neuroscience
Department
This course will allow a student to study a chosen topic in Neuroscience under the guidance of a faculty member affiliated with the Neuroscience Masters program. The student must produce a written report at the conclusion of the independent study.
NEU 597: Directed Research in Neuroscience
Department
Directed research conducted under the guidance of a faculty member affiliated with the Neuroscience Program.
NEU 599: M.S. Thesis Research in Neuroscience
Department
A Master’s thesis in Neuroscience consists of a research and development project worth a minimum of 9 graduate credit hours advised by a faculty member affiliated with the Neuroscience Program. A thesis proposal must be approved by the Neuroscience Program Review Board and the student’s advisor before the student can register for more than three thesis credits. The student must satisfactorily complete a written thesis document and present the results to the Neuroscience faculty in a public presentation.
NEU 5900: Graduate Internship in Neuroscience
Department
Graduate internship is carried out in cooperation with a sponsor or industrial partner. It must be overseen by a faculty member affiliated with the Neuroscience Program. The internship will involve development and practice of technical and professional skills and knowledge relevant to different areas of Neuroscience. At the completion of the internship, the student will produce a written report, and will present their work to core and affiliated Neuroscience faculty and internship sponsors.