WPI’s MS program in Bioinformatics and Computational Biology (BCB) will prepare you to become a truly interdisciplinary scientist with expertise in computer science, informatics, and statistics on the one hand, and life sciences on the other.
While other schools offer Bioinformatics as a concentration within a Biology program, WPI’s BCB department draws equally from three disciplines—Biology, Computer Science, and Mathematics. You will become versed in each and specialize in one, working alongside expert researchers to translate staggering volumes of biological data into new knowledge and uncover meaningful ways to improve health care and the environment.
Through our well-rounded curriculum, you will be expected to develop a base of knowledge In Biology, Computer Science, and Mathematics, but how you do so is up to you—choose courses from virology to environmental challenges; design of software systems to artificial intelligence; and Bayesian statistics to regression analysis.
You can also take unique interdisciplinary courses in topics including biovisualization, biomedical database mining, simulation in biology, and statistical methods in genetics and bioinformatics.
No matter what topics you choose, you will have many chances to apply your growing knowledge to solve real problems through hands-on projects. You will also synthesize your learning with a research-based thesis or industrial internship.
WPI’s research in Bioinformatics and Computational Biology is as diverse as our wide-ranging course selection. You will have many opportunities to work alongside faculty and student researchers who are solving biological problems using mathematical and computational analysis in these areas:
The strong interdisciplinary nature of our program means that you get access to facilities in multiple departments across campus, from our dedicated Bioinformatics and Computational Biology Computer Lab to the state-of-the-art laboratories and equipment at Gateway Park, our thriving life sciences research and development center.
My research is interdisciplinary and spans the fields of bioinformatics of complex disease, computational genomics, systems biology, and biomedical data analytics. We bring expertise in machine learning, data mining and massive data analytics to study molecular mechanisms underlying genetic disorders, such as cancer, diabetes, and autism, and deadly infections, such as pandemic flu. Our approaches benefit from integrating Next Generation Sequencing, high-throughput interactomics, and structural biology data.
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I have a long-standing interest in applying computer science and mathematics to solve biological problems. I am currently the Associate Director of WPI’s Program in Bioinformatics and Computational Biology, and I am always looking for students with interests in this exciting interdisciplinary area. One of my goals in teaching biology is to help students to think more quantitatively about biological questions. A few years ago, my colleague Dr. Brian White of UMass Boston and I were awarded a grant from the NSF to develop a course, “Simulation in Biology”.
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Work in my lab is focused on defining the cellular mechanisms that maintain genome stability in normal cells and understanding how these pathways are corrupted in cancer cells.
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Carolina Ruiz's research interests are in machine learning, artificial intelligence and data mining. Together with her graduate and undergraduate students, Dr. Ruiz has worked on numerous interdisciplinary research projects with clinicians from the University of Massachusetts Medical School on developing and using machine learning algorithms over clinical and behavioral patient data.
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I have a passion for understanding how living systems work, as well as for sharing my love of biology and research with the next generation of scientists and informed citizens.
The central goal of my lab is to understand the regulatory mechanisms that underlie mycobacterial stress tolerance. We combine genetics, genomics, transcriptomics and biochemistry to understand how mycobacteria respond to, and ultimately survive, stressful conditions.
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Professor Wu's research interest lies in applying the power of statistical science to promote biomedical researches. In statistical genetics, he is developing novel statistical theory and methodology to analyze genome-wide association (GWA) data and deep (re)sequencing data to hunt new genetic factors for complex human diseases. In epigenetics, he is studying gene expression regulation mechanisms through chromatin interaction, and RNA silencing pathways in the developmental stages of germ-line cells.
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