At the Harvard John A. Paulson School of Engineering and Applied Sciences, IACS is the home for students and faculty who are tackling major challenges in science and the world through the use of computational methods.
We train graduate students to solve real-world problems and conduct innovative research by using mathematical models, algorithms, systems innovations and statistical tools. Embedded within a large liberal arts research University, IACS serves as a focal point for interdisciplinary collaborations in computational science and data science at Harvard and the Boston area community. Click HERE to read the 2018 - 2019 IACS Annual Report.
Our Commitment to Diversity, Inclusion and Belonging
As an educational community within Harvard University, the Institute for Applied Computational Science has a responsibility to provide education and encourage scholarship that advances ethical data science, exposes bias in the way data and data science is used, and advances research into fair and responsible data science. We also have a responsibility to build a diverse, inclusive, and representative community offering opportunities to those who have been historically marginalized.
We can have the greatest impact on students with intentional work in our role as educators. Our measure of success will be the students we educate who, by interacting and learning with other community members, and in their coursework and research projects, will be able to take their knowledge and experience into the world and help address systemic racism at the companies and organizations they will join.
IACS Team & Staff
Teaching & Research Staff
Faculty Director, Institute for Applied Computational Science
Lecturer in Computational Science
Weiwei is a machine learning researcher in the Data to Actionable Knowledge (DtAK) lab at Harvard. Her PhD is in pure mathematics and her current work focuses on building machine learning models with guaranteed properties that align with task-specific desiderata, such as human interpretability, risk-awareness and satisfaction of domain-specific constraints. Weiwei is dedicated to supporting women, first-generation students and students from historically minoritized backgrounds in STEM through equalizing access to opportunities in higher education. Weiwei has experience organizing community outreach programs, undergraduate mentoring/research programs and summer REU programs for underrepresented and underserved students in mathematics. At Harvard, Weiwei served as a research mentor in IACS summer REU programs, an organizer of the Women in Data Science (WiDS) Cambridge workshops as well as an organizer in the Stanford Global WiDS Datathon Challenge. Weiwei created and teaches the Diversity, Inclusion and Leadership in Tech seminar at IACS and is the faculty advisor for the IACS Graduate Advisory Committee - a student group focused on community building through diversity, inclusion and belonging. Weiwei supervises undergraduate and graduate research, interested students are welcomed to reach out for research opportunities.
In 2019, Chris completed his PhD from Brown University in computer science. His research lies within natural language processing (NLP), specifically discourse, semantics, and understanding, and the persistent theme is trying to better understand, within any body of text, what is being said, what exactly is happening, and who is who? Toward this, most of his projects concern the tasks of coreference resolution, commonsense knowledge, adversarial NLP, and American Sign Language (ASL) translation. He is always interested in collaborating with and mentoring students on new, exciting projects. His other hobbies include traveling,
photography, woodworking, and going on challenging hikes.
Data Science master’s programs - advising students about courses and other academic matters, running the admissions process, and maintaining a network of program alumni. He also advises PhD students in the CSE and Data Science Secondary Fields. Daniel is also a lecturer on Computation Science and teaches AC 298r “Interdisciplinary Seminar in Applied Computation” a course built around the IACS Seminar Series of public talks. In the course students read and discuss background material related to the topic of each talk in the seminar series and have an opportunity to interact with the invited academic and industry speakers after their presentations. Daniel has a PhD in theoretical and computational chemistry from Boston University. Before coming to Harvard, Dr. Weinstock was at Rutgers University - initially as a postdoc working in computational biophysics and later as the Associate Director for Graduate Education at the BioMaPS Institute for Quantitative Biology. He has been at IACS since the Fall of 2012 when he began work by answering questions from the first group of applicants to the jury launched CSE master’s program.