Computational science uses mathematical models, numerical methods, and high-performance computing to simulate and analyze complex physical, biological, and engineered systems. It enables prediction and understanding where experiments are costly or impractical, driving advances in fields like climate modeling, materials design, and computational biology. Explore our latest research with impacts in healthcare, climate, and more. Use the filters to refine your search.

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  • April 5, 2023
    COVID-19 demonstrated the powerful impact pandemics have on society and the continued need for vigilance and innovation in preparing for and responding to future pandemics. Vast data resources exist that could be helpful for prediction, response, and monitoring of future pandemics. The “Data-Driven Approaches to Prevent the Next Pandemic” series will focus on data driven […]

  • “‘Does This Vehicle Belong to You?’ Processing the Language of Policing for Improving Police-Community Relations” Dan Jurafsky (Stanford)

    September 14, 2018
    Dan Jurafsky, Stanford University Monday, October 22, 2018, 10:30am Networking reception, 10:30-11:00am; Seminar 11:00-12:00pm Kilachand Center, 610 Commonwealth Avenue Boston, MA 02215   "Does This Vehicle Belong to You?" Processing the Language of Policing for Improving Police-Community Relations Abstract: Police body-worn cameras have the potential to play an important role in understanding and improving police-community relations. […]
  • hand holding mobile phone

    “Building and Evaluating Conversational Agents”

    November 28, 2018
    João Sedoc, University of Pennsylvania Monday, December 3, 2018 11:00am – 12:00pm, refreshment & networking at 10:30am Hariri Institute for Computing 111 Cummington Mall Boston, MA 02215 Abstract: There has been a renewed focus on dialog systems, including non-task driven conversational agents (i.e. “chit-chat bots”). Dialog is a challenging problem since it spans multiple conversational turns. […]

  • “Compatible Meshfree Discretization”

    January 18, 2019
    Nathanial Trask, Sandia National Laboratories When: Thursday, January 31st 2019 Networking Reception, 10:45am-11:00am: Seminar 11:00am - 12:00pm Where: BU Hariri Institute for Computing, 111 Cummington Mall, Boston, MA, Room 180 Abstract: Spatially compatible discretization broadly encapsulates discretizations of PDEs possessing mimetic properties of continuum operators, such as conservation, maximum principles, H(div)/H(curl) - conformity, or discrete preservation of an […]

  • “CreativeAI: Machine Learning meets 3D Content Creation”, Niloy Mitra (University College London)

    November 2, 2018
    Niloy Mitra, University College London Monday, November 12, 2018, 10:30am Networking reception, 10:30-11:00am; Seminar 11:00-12:00pm Kilachand Center, 610 Commonwealth Avenue Boston, MA 02215 "CreativeAI: Machine Learning meets 3D Content Creation"   Abstract: A long-standing goal of Computer Graphics is to create high-quality geometric content for a variety of applications including games, movies, product design, and engineering […]

  • “Did You Know You Could…?” Series

    October 21, 2020
    A Meet and Learn Series led by the Graduate Student Fellows The “Did you know you could…?” series provides a venue in which people can quickly get exposed to practical topics either enabling or enabled by computing and data science.  Topics they might not even have realized they wanted to know!  And they can meet other similarly […]

  • “Grounding Natural Language Phrases in Images and Video”

    May 10, 2018
    BU postdoctoral associate <strong>Bryan Plummer</strong> introduces an approach to <strong>improving many image-language tasks</strong> where neural networks learn a set of models, each of which capture a different concept which is useful in the task.

  • “How Can NLP Help Cure Cancer?”

    March 1, 2018
    <strong>Regina Barzilay</strong>, a professor in the Department of Electrical Engineering and Computer Science at MIT, describe a number of tasks where NLP-based models can make a difference in clinical practice and introduces new functionalities such as <strong>interpretable neural models that provide rationales underlying predictions</strong> and semi-supervised methods for information extraction.

  • “Machine Learning: New Challenges and Connections”

    November 22, 2019
    AIR Distinguished Speaker Series Maria-Florina Balcan, Assoc. Prof., Computer Science, Carnegie Mellon Univ. When: Monday, December 2, 2019 10:30am-11:00am networking and reception, 11:00am-12:00pm talk Where: Kilachand Center, 610 Commonwealth Ave, Colloquium Room 101 ABSTRACT: Over the past decades, machine learning has evolved into a highly successful discipline that has significantly influenced several fields, including vision, information […]

  • “New Algorithms for Interpretable Machine Learning” Cynthia Rudin (Duke)

    December 4, 2018
    Cynthia Rudin, Duke University When: Monday, December 10, 2018 Networking reception, 10:30-11:00am; Seminar 11:00-12:00pm Where: Kilachand Center, 610 Commonwealth Ave. Boston, MA, Colloquium Room   Abstract: With the widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed models for medical imaging, and poor […]