2026 MSI STEM Faculty Mentors
Dr. Heather Bradshaw
Email: hbbradsh@iu.edu
Neuroscience
Research Area: Endocannabinoids and endogenous lipids in the CNS
Research Link: https://c3a.indiana.edu/
Research Area keywords: endocannabinoids, cannabis, mass spectrometry, lipid signaling
Research Description: We use mass spectrometric techniques to study lipid signaling molecule regulation in a variety of disease and drug abuse models.
Dr. Justin Greaves
Email: jcgreave@iu.edu
Environmental Health
Research Area: Environmental Microbiology and Virology
Research Link: https://greaveslab.publichealth.indiana.edu/index.html
Research Description: Our lab studies viral persistence and transport in stagnant and flowing waters. We develop new methods to quantify infectious viruses then use it to understand the risk of infection to the public. Summer students will learn novel viral culture and measurement techniques and perform studies to understand flow of viruses in environmental waters
Dr. Lei Jiang
Email: jiang60@iu.edu
Computer Engineering
Research Area: Hardware design
Research Link: www.jianglei.org
Research Area keywords: machine learning accelerator
Research Description: Designing low-power hardware accelerators for machine learning applications.
Dr. Travis O'Brien
Email: obrienta@iu.edu
College of Arts and Sciences
Research Area: Extreme Weather and Climate Change
Research Link: https://cascade.lbl.gov/
Research Area keywords: extreme weather, climate change, machine learning
Research Description: The Calibrated and Systematic Characterization, Attribution, and Detection of Extremes (CASCADE; https://cascade.lbl.gov/) project focuses on four questions aimed at ultimately improving confidence in future projections of low-likelihood, high-impact (LLHI) extreme events: 1. How can new approaches –including ML-based emulation of ESMs, new model ensembles, and new experiments – suggest promising ESM development pathways for greater fidelity of simulated LLHIs? 2. How can the observational record be leveraged to improve statistical and physical understanding of LLHIs? 3. What are the sources of LLHI predictability that can provide early warning of LLHIs at seasonal to decadal timescales? 4. What machine learning and computational tools are needed to advance understanding of LLHIs? Students working on CASCADE can expect to learn how analyze extreme weather in climate datasets and use machine learning and dynamical models to simulate extreme weather.
Dr. Austin Robinson
Email: ausrobin@iu.edu
Kinesiology
Research Area: Human Performance and Physiology
Research Link: TBD
Research Area keywords: dietary salt, hypertension, health behaviors, social determinants, cardiovascular disease prevention, cardiovascular physiology, exercise physiology, nutrition
Research Description: Students will have the opportunity to assist with biochemical assays to measure various biomarkers in urine, serum, and plasma samples from ongoing studies in our laboratory. Projects include testing the effects of ketone supplementation in offsetting the negative effects of high dietary salt, as well as our current CHANGE study examining how the COVID-19 pandemic affected health behaviors (diet, physical activity, sleep) and cardiovascular health. Students will have the the opportunity to gain hands-on experience with applied human physiology data collection, nutrient analysis, data entry, and the use of statistical and graphical software to create high-quality figures and tables. We’ll also provide guidance and support for presenting their work at a scientific meeting if that's of interest.
Dr. Selma Sabanovic
Email: selmas@iu.edu
Informatics, Cognitive Science
Research Area: Human-Robot Interaction Research Link: https://luddy.indiana.edu/contact/profile/?Selma_Sabanovic
Research Area keywords: social robotics, user-centered design, cultural studies of computing, assistive robotics
Research Description: Our lab — R-House Laboratory for Human-Robot Interaction (HRI) — studies the design, use and consequences of social robots in various contexts, including homes, schools, assistive and therapeutic applications, and interpersonal communication. We often work with members of the community, including older adults, caregivers, students and teachers, to identify promising uses and designs for robotic technologies that can benefit users. There are several ongoing projects in the lab. One involves the design of communication and interaction capabilities for the Haru robot prototype (https://spectrum.ieee.org/automaton/robotics/home-robots/honda-research-institute-haru-social-robot), particularly focusing on how the robot can be used to support intercultural communication and socio-emotional learning for groups of middle and high-school children. We are also continuing to develop the capabilities of IRIS, our Interactive Robot for Ikigai Support, to assist older adults to reflect on, develop, and maintain their sense of purpose and meaning and life as they age. We are also exploring how we can use machine learning to tailor conversational activities to older adults’ cognitive and social needs. Finally, we are working with care partners of older adults living with dementia to see how we can support them. We are also working on several studies exploring how people perceive robot sociality, and evaluating how these perceptions affect trust in robots and AI. Research activities for students on these projects include learning how to work with, program and control robots, recruiting and scheduling participants, running participants for studies in and outside the lab, going to relevant field sites with robots to observe human-robot interaction, collecting and managing textual, audio, and video data, discussing study design, results, and implications, attending regular lab meetings, and working closely with other faculty and students engaged in the project. If you are interested in such topics, we invite you to join us!
Dr. Patricia Silveyra
Email: psilveyr@iu.edu
Environmental and Occupational Health
Research Area: Air pollution effects on lung health
Research Link: http://silveyralab.com/
Research Area keywords: lung disease, lung physiology, e-cigarettes, air pollutants, inhalation toxicology
Research Description: The laboratory of Patricia Silveyra studies the cellular and molecular mechanisms underlying sex differences in lung inflammation and lung disease triggered by various environmental exposures. With an interdisciplinary focus on respiratory physiology, molecular endocrinology, and cellular and molecular immunology, the Silveyra laboratory investigates the effects of air pollutants, cigarette smoke, e-cigarettes, and other environmental exposures in the male and female lung.
Dr. Sara Skrabalak
Email: sskrabal@iu.edu
Materials Chemistry
Research Area: Nanoscience
Research Link: https://skrablab.sitehost.iu.edu/ and https://csennd.iu.edu/
Research Area keywords: Nanoscience, Materials Chemistry, Energy, Chemical Sensing, Catalysis
Research Description: Students will participate in the newly formed NSF-sponsored Center for Single-Entity Nanochemistry and Nanocrystal Design, making nanoparticles for catalysis in fuel cell applications and for chemical sensing applications.
Dr. Paul Sokol
Email: pesokol@iu.edu
Physics
Research Area: Condensed Matter Physics
Research Area keywords: Quantum Liquids, Low Dimensional Materials, Neutron and X-ray scattering, Materials Properties, Synthesis and characterization
Research Description: Our work focuses on the study of the structure and dynamics of condensed matter systems under confinement at the nanoscale. Confinement in nanometer size pores or on surfaces can drastically affect the properties of confined systems ranging from classical solids to quantum liquids and can even lead to new phases not present in the bulk. We use neutron and x-ray scattering to probe the microscopic structure and dynamics of these confined systems.
Dr. Da Yan
Email: yanda@iu.edu
Computer Science
Research Area: Data Science & Artificial Intelligence
Research Link: https://homes.luddy.indiana.edu/yanda
Research Area keywords: geospatial artificial intelligene (GeoAI), 2D/3D computer vision
Research Description: Our laboratory is currently training effective GeoAI models for monitoring natural disasters, such as flooding, tornado/hurricane, and wildfire, using remote sensing data. A key objective is to segment objects of interest from Earth imagery obtained from satellites and drones, including flood extent, fire scar, flare, smoke, fallen trees (along with their rootballs and ground pit), damaged buildings, etc. The data types include both 2D imagery (that can be converted to 3D by integrating DEM) and 3D point clouds. We aim to curate annotated datasets and train several types of advanced models including geo-foundation model, and multi-modal LLM that can directly take text prompts from users for conducting vision tasks with reasoning capabilities. Prior research on flooding imagery can be found at the publication page of PhD student Saugat Adhikari: https://saugatadhikari.github.io/publications/