Innvoation Corps Winners

Fall 2013: NYC, NY Cohort

Winner: Team EndoInsight

EL: Byron Smith

PI: Pietro Valdastri

About: EndoInSight is a disposable CO2 insufflation system for the colonoscopy market that will help clinicians improve patient outcomes while reducing procedure and recovery times.


Spring / Summer 2014: Ann Arbor, MI Cohort

Winner: Team Filtergraph

EL: Dan Burger, Rachel-Chloe Gibbs

PI: Keivan Stassun

About: Filtergraph (ww.filtergraph.vanderbilt.edu) is a web application designed to flexibly and rapidly visualize large datasets. The user loads a dataset in a variety of supported file types into Filtergraph, which automatically generates an interactive data portal that can be easily shared with others.


Fall 2014: Austin, TX Cohort

Winner: Team PinPtr

EL: Will Hedgecock

PI: Ákos Lédeczi

About: Cloud-based positioning system using our own patent-pending localization methodology and a network of stationary base stations.


Fall 2014: Austin, TX Cohort

Winner: Team VenoStent

EL: Timothy Boire

PI: Hak-Joon Sung

About: VenoStent is designed to prevent intimal hyperplasia so that the initial coronary artery bypass grafting procedure is more successful and does not require re-do operations.


Winter 2015: Berkeley, CA Cohort

Winner: Team SMAC

EL: Ekawahyu Susilo

PI: Pietro Valdastri

About: The STORM Lab Modular Architecture for Capsules (SMAC) is a modular open-source architecture for building capsule robots aiming to provide the users with a tool to shorten capsule robot design and development time.


Spring 2015: Reston, VA Cohort

Winner: Team INCA

EL: Lara Jazmin

PI: Jamey Young

About: INCA is a software that enables Metabolic Flux Analysis (MFA). Metabolic flux analysis (MFA) allows for the determination of biochemical reaction rates inside of living cells, which are otherwise impossible to measure directly. Similar to how Google Maps reports traffic flows within highway networks, MFA can identify cellular pathway bottlenecks (roadblocks) and wasteful metabolic processes (detours) within biochemical networks. This information can be used to engineer improved production systems (e.g., host organisms and/or cultivation platforms) for use in industrial bioprocesses or agriculture.