Lulu Kang
Office: LGRT RM 1436 710 N. Pleasant Street
Amherst, MA 01003-9305
Phone: +1 413-545-9212
About Me
Welcome to my website! I'm Lulu Kang, an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst.
My research pursuits revolve around the creation of novel theories and methodologies in fields such as Statistical Learning/Machine Learning, Statistical Design of Experiments, Uncertainty Quantification, Bayesian Statistical Modeling, Approximate Inference, and Optimization. I've dedicated myself to interdisciplinary research, where the transformative power of data science intersects with realms as diverse as mechanical engineering, manufacturing, chemistry, material sciences, healthcare, and social sciences. I invite you to explore the comprehensive details available on the Research and Publications pages. On the educational front, I have had the privilege of crafting and delivering a range of enlightening courses on statistics at my previous institute, which are outlined in the Teaching page.
Thank you for visiting my website – I look forward to sharing knowledge, exploring innovative ideas, and making meaningful contributions to the worlds of data science and mathematics.
Recent News
08/2023, My collaborators, Dr. Chun Liu (Illinois Tech) and Dr. Yiwei Wang (UC Riverside), and I received a Supplement Award of $59,998 to our existing NSF Award # 2153029. We will work on "Physics-Informed Energetic Variational Models for Tumor Growth".
08/2023, My collaborator Dr. Wei Chen (University at Buffalo) and I received an EAGER Award from NSF DMR. Details.
08/2023, I am starting my new position as an Associate Professor in the Department of Mathematics and Statistics at UMass Amherst in Fall 2023!
08/2023, My Ph.D. student Yindong Chen is among the four finalists of the 2023 ASA SPES Best Student Paper Competition. He presented our joint work at the 2023 JSM in Toronto Canada.
08/2023, Check out the session Design and Analysis of Experiments in Emerging Areas organized by me, Dr. Qiong Zhang, and Dr. Xinwei Deng at 2023 JSM.
Education
Ph.D. in Industrial Engineering and M.S. in Operations Research from H. Milton Stewart School of Industrial and System Engineering at the Georgia Institute of Technology.
B.S. in Mathematics from Nanjing University, Nanjing, China.
Academic Appointments
University of Massachusetts Amherst
Associate Professor, 2023--Present
Illinois Institute of Technology
Associate Professor, 2016--2023
Assistant Professor, 2010-2016
Associate Director of Master of Data Science program, 2013--2021.
Awards
Statistical Partnerships Among Academe, Industry, and Government (SPAIG) Award, by American Statistical Association, 2020. Official Announcement.
Early Career Scholarship, ASA/IMS Spring Research Conference on Statistics in Industry and Technology, 2015.
Project NExT Fellow, Mathematical Association of America (MAA), 2012.
Best Student Paper Award, Quality, Statistics, and Reliability (QSR) Section of Institute for Operations Research and the Management Sciences (INFORMS), San Diego, 2009.
Kiplinger Fellowship, Georgia Institute of Technology, 2005.
Professional Activities
Lead of the Organizing Committee for the Uncertainty Quantification and AI for Complex Systems long program hosted by Institute for Mathematical and Statistical Innovation in Spring 2025.
Associate Editor, SIAM/ASA Journal on Uncertainty Quantification, 01/01/2019--present.
Associate Editor, Technometrics, 10/01/2019--present.
Committee Chair for the 2022 Best Student Paper Competition of the Quality, Statistics, and Reliability Section of INFORMS.
Chair and Chair-Elect of Section on Physical and Engineering Sciences of the American Statistical Association, 2021-2022.
Council Member of the Quality, Reliability, and Statistics (QSR) Section of INFORMS, 2018/11-2020/11.
Volume Co-Editor of INFORMS Editor’s Cut volume on “Harnessing Value Through Streaming Data Analytics”.
To Students
I am looking for undergraduate and graduate students to work on exciting research projects on data science. If you are interested in topics such as statistical/machine learning, uncertainty quantification, design and analysis of experiments, applications of data science, etc, please feel free to contact me.