The Office of Arts & Sciences is pleased to announce the application process for the 2017 Clare Boothe Luce Research Scholar awards. Since its inception in 1989, the Clare Boothe Luce Program has been one of the most significant sources of support for women seeking to study science, engineering, and mathematics. WPI is offering up to 8 Luce Research Scholar awards for the 2017-2018 academic year to support student research projects that will be conducted under the guidance of Luce faculty mentors. Each award is $6,000. Awards can be used to support summer research projects; students with interests in summer research are strongly encouraged to apply.
Deadline: Applications must be submitted through the online application by Friday, January 27, 2017.
To read about the 2016 WPI Clare Boothe Luce Scholars, including their research projects, please visit the Luce Scholars page.
Students should begin the application process only after speaking with a Clare Boothe Luce faculty mentor (applicants will be asked to list their two top choices for mentors). Please see the information below for bios, contact information, and research interests for mentors.
Assistant Professor, Mathematical Sciences
Dr. Olson is an Assistant Professor in the Mathematical Sciences and enjoys mentoring and working with students on research projects. These projects have been in diverse areas where mathematical and computational skills are required. She has been actively mentoring females on summer research projects and MQPs since coming to WPI. Many of these projects have been funded by the National Science Foundation (including a recent CAREER grant) and involve collaborations with researchers in the biological sciences. Dr. Olson's research interests include scientific computing, mathematical biology, and biomechanics. Below is a list of potential projects. Dr. Olson is excited to work on many areas beyond/outside of the list below.
Associate Professor, Computer Science
Dr. Ruiz's research interests are in data mining, machine learning, and artificial intelligence. Together with her undergraduate and graduate students, colleagues in computer science and biology, and medical doctors, Dr. Ruiz investigates and develops general-purpose data mining algorithms as well as data mining algorithms for clinical medicine and behavioral psychology. In addition to being a faculty member in computer science, she is a founder and active member of the bioinformatics and computational biology program and of the data science program at WPI. She enjoys teaching courses, and advising undergraduate and graduate research projects. More than 60 (including 12 female) undergraduates have conducted or are conducting research on data mining and machine learning related topics under Dr. Ruiz's supervision. Several of these projects have involved research collaboration with the Univ. of Massachusetts Medical School and Boston College. Potential projects are described below (the detailed scope of each project will be determined between the prospective scholar and Dr. Ruiz):
Research Projects on Computational Tools and Algorithms to Investigate Human Behavior
These projects involve the design, implementation and use of algorithms and computational tools that investigate human behavior. These tools, including health-support mobile apps, enable users to track their own behavior, receive automatic context-sensitive feedback and advice, and adopt healthier behaviors. This set of projects covers a wide range of computational aspects (including the design and implementation of mobile apps, cyber-human symbiosis, social media, automatic data collection, data mining and predictive analytics), as well as medical and behavioral psychology aspects (including healthy behaviors, additions, behavioral change, feedback and interventions). Human behaviors under investigation include sleep habits and eating habits.
Research Projects on Artificial Intelligence in Clinical Medicine
These projects involve the investigation of human diseases using artificial intelligence. Specific diseases under consideration are sleep disorders and stroke. Artificial intelligence approaches, including machine learning and data mining, are used to learn patterns from clinical data. These patterns provide insights into the disease that could be used for prevention and treatment, as well as the basis to design predictive algorithms that aim at enhancing clinicians’ ability to anticipate health outcomes from treatment options.
Research Projects on Data Mining and Machine Learning
These projects involve the investigation of general-purpose data mining and machine learning algorithms. Topics include but are not limited to feature selection, clustering, sequence mining, predictive analytics, and meta-learning. In addition, prospective scholars are encouraged to propose data mining and machine learning related projects that fit their own research interests.
Assistant Professor, Physics
After getting a PhD in experimental condensed matter physics at University of Notre Dame, Dr. Titova spent two years as a postdoctoral fellow in a Nano Materials Physics group of L.M. Smith and H.E. Jackson at University of Cincinnati, where she studied optical properties of single nanowires. She then moved to Ultrafast Nanotools Lab of F.A. Hegmann at University of Alberta in Canada where she was awarded an Avadh Bhatia Postdoctoral Fellowship. There, Dr. Titova used ultrafast laser techniques to study processes in nanostructures with sub-picosecond time resolution. In 2014, Dr. Titova joined WPI and started an Ultrrafast Terahertz Physics Lab. Several projects with Dr. Titova are possible, depending on interests of a student, including:
Associate Professor, Mathematical Sciences
Dr. Weekes is a faculty member in the Department of Mathematical Sciences at WPI. Her research interests are, broadly speaking, in mathematical and computational modeling. She has advised student research on cancer growth, pattern formation, groundwater remediation, traffic flow, dynamic materials, and course scheduling. As a member of the Center for Industrial Mathematics and Statistics (CIMS), she enjoys tackling problems that come directly from business, industry, and government that have a real-world impact. She encourages students to look at the Preparation for Industrial Careers in Mathematical Sciences (PIC Math) “Solving Real World Problems” videos to learn more about career options for students well-trained in mathematics and statistics. Dr. Weekes is also interested in learning more about predictive analytics and would welcome a research advisee who would want to explore this topic with her.