Sparsh Kudrimoti
Hi, I'm Sparsh

I build things across robotics, machine learning, and business AI products.

I studied mechanical engineering at Georgia Tech, with a focus on robotics and automation, and then earned my M.S. in Computer Science there. As an undergraduate I built machine learning tools in two research labs: one analyzed muscle ultrasound for exoskeleton research, and another helped produce a peer-reviewed leukemia study. I also designed medical devices at OXOS Medical and Ecolab, and helped create MediFlex Bell, a nurse call bell with a provisional patent.

Today I'm a Senior Associate Product Manager at Capital One in New York, where I use experiments and AI tools to grow the web and mobile apps. Outside of work I keep building. I developed Splor, a social travel app for iOS, and I'm an angel investor and board member at Demotu.

Portrait of Sparsh Kudrimoti
Explore by discipline
Projects & experience

Mechanical Engineering

CAD, FEA, and hardware — from a patent-pending medical device to a sensor mount for a robot dog.

Senior Design Capstone

MediFlex Bell: a nurse call bell that adapts to the patient

About 5.4 million people in the US live with paralysis or limited mobility, yet 43% of nurse call systems still rely on a standard push-button. Hospitals buy a separate specialty cord for each patient's needs. We designed one modular bell that adapts to each patient instead.

Role
Founding Mechanical Engineer
Team
5 engineers · Team Mechatronics
Advisor
Dr. Tequila Harris, Georgia Tech
Outcome
US provisional patent filed, Apr 2024
4Swappable input mechanisms in one device
555×Safety factor on the incline-pad mechanism
3.9×10¹²Estimated fatigue-life cycles, incline pad
18 VLow-voltage system on a custom 2-layer PCB
One bell, four ways to call a nurse
Breath callGooseneck-mounted mouthpiece with a pneumatic sensor, for patients with no hand mobility
Incline padA force-sensing tilted pad that activates with about 0.5 N of pressure
Soft buttonA silicone button with a trigger mechanism, for patients with limited grip
Remote buttonsA standard button pad for patients with typical hand function
From CAD to working prototype

Objective

Design and prototype a specialty call bell that reliably connects nurses with limited-mobility patients, is configurable and easy to use, and plugs into a facility's existing nurse call system.

Design

The enclosure is about 5 × 5 inches, with removable casings, a rotating turntable base that stays connected to power as the bell is reconfigured, a built-in power cord path, and speaker and microphone outlets for two-way talk. Inside, a snap-fit base plate holds the PCB, with storage space for the gooseneck and electronics.

A custom two-layer PCB links the force and pneumatic sensors, microphone, and speaker to a microcontroller over I²C.

Material selection

In Ansys Granta EduPack, I built material indices to keep mass and cost low while staying tough, water-resistant, and latex-free. I compared PC, PET, and ABS, and chose ABS for the frame, casings, and turntable because it's cheap to make and holds up to hospital disinfectants. I picked silicone elastomer for the soft buttons and FR-4 for the PCB, which is flame-retardant and about 2.5× cheaper than the alternatives.

My contributions

  • Designed and iterated the modular assembly in CAD, integrating the gooseneck, turntable, and sensor housings
  • Ran material selection and FEA with mesh-convergence studies
  • Wrote the FMEA risk assessment and mapped the design to medical device standards

Structural analysis (FEA)

ComponentLoad caseResult
Incline pad0.5 N repeatedFoS 555 · 3.89×10¹² cycles
Breath-call mouthpiece1 psi internalFoS 247 · 4.26×10¹¹ cycles
Casing10 N axialFoS 464
Bell frame250–400 N compressionYields at 380–400 N
Gooseneck (steel)Axial pullYields at 80 N

Top FMEA risks

Failure modeSODRPN
Incline plane deformation854160
Bell frame deformation953135
Mouthpiece lifespan744112
Soft button lifespan744112

Standards addressed

  • IEC 60601-1 — safety of electrical medical devices (stress analysis on the frame and mechanisms)
  • UL 1069 — reliability of nurse call systems
  • AAMI HE75 — usability and product life cycle
  • IEC 80601-2-49 — patient monitoring devices
  • NFPA 99 — fire and electrical safety (PCB and power supply placement)
Final capstone presentation · April 2024 Open PDF in new tab ↗

Invention Studio

2021 – 2024
Prototyping Instructor · Georgia Tech makerspace
  • Staffed Georgia Tech's student-run makerspace
  • Taught students to use equipment like 3D printers, laser cutters, and CNC machines to build their own projects

OXOS Medical

Aug – Dec 2022
Mechanical Engineering Intern · Atlanta, GA
  • Cut electromagnetic sensor tracking error in the company's flagship x-ray device by 24% through sensor position testing in Unity
  • Revised engineering specifications in Onshape to secure FDA clearance, avoiding a ~$30,000 loss
  • Sped up team workflow by 15% to hit quality assurance deadlines

Ecolab

May – Aug 2021
Software Engineering Intern · Alpharetta, GA
  • Built a material tracking database with macros, reducing inventory errors and speeding time to market by 33%
  • Wrote 23 verification and validation reports in Python for FDA and EU medical device compliance
  • Ran 4 feasibility tests and 7 design iterations on a medical-grade monitor drape

Agile Locomotion & Manipulation Lab

Jan – May 2021
Mechanical Engineer · Vertically Integrated Projects, Georgia Tech
  • Designed a visual-inertial-lidar sensor box that lets the lab's Unitree A1 robot dog map its surroundings and track its own position in real time (SLAM)
  • 3D printed and tested 15 parts across 3 assemblies to validate the box's structural stability and sensor placement
  • Mounted the box to the robot's rail with T-slotted framing, using a hollow mount to cut weight without losing strength
  • Worked with the electrical and design leads to fit the electronics into one integrated system
Final CAD design of the SLAM sensor box: a closed, vented enclosure with the lidar sensor mounted on its lid, sitting on the robot's base plate
Final design: lidar mounted on the lid of a vented enclosure.
The final sensor box with its lid lifted, showing the electronics packed inside
Lid lifted to show the electronics inside.
The final sensor box without its lid, showing the internal electronics bay and cooling vents along the sides
Electronics bay with cooling vents along the sides.
Exploded CAD view separating the lidar lid, the enclosure, and the base plate
Exploded view: lid, enclosure, and base plate.
Earlier CAD iteration of the SLAM sensor box: a lidar sensor sits on top of an open enclosure holding a small onboard computer, mounted on an aluminum rail above the robot's base plate
Earlier iteration: lidar, onboard computer, rail, and mount.
CAD model of the sensor box enclosure resting on a length of T-slotted aluminum rail
Enclosure on aluminum rails that slide onto the A1, Cassie, and Mini Cheetah robots.
CAD model of the redesigned mount with hollowed-out pockets and screw holes
Hollow mount redesign that cuts weight while keeping strength.
Cross-section of T-slotted aluminum framing with a screw and nut seated in the top slot
T-slot attachment: screws run through the box bottom into the rail.

3D Printing Side Projects

2020 – 2021
Personal projects
  • Designed and 3D printed a birdhouse feeder, then set it up outdoors on a deck railing
  • Printed a multi-part box with a Georgia Tech logo lid
Gray 3D-printed birdhouse with a round entrance hole and a feeding tray, mounted on a deck railing
Birdhouse feeder, front.
The 3D-printed birdhouse feeder on a deck railing with trees behind it
Set up on the deck railing.
The birdhouse feeder with its roof open, filled with birdseed
Roof open, filled with seed.
3D-printed gray box and lid with a gold and navy Georgia Tech logo, laid out with small printed parts on a wooden table
Multi-part box with a Georgia Tech lid.
Projects & experience

Computer Science

Machine learning, data science, and software — models, apps, and a peer-reviewed publication.

Venture

Splor: a social travel app for iOS

A full-stack iOS app for planning trips with friends, discovering places, and staying motivated through streaks and friendly competition.

Role
Founding Developer
When
Oct 2025 – May 2026
Stack
Swift, SwiftUI, Supabase, Gemini

What I built

  • A Swift/SwiftUI app backed by a Supabase database and storage with real-time sync
  • Apple Sign-In via OAuth
  • An AI concierge search feature, powered by Gemini, for personalized recommendations

Social features

  • Gamification with streaks and leaderboards
  • Activity feeds and trip collaboration tools
  • Pair-programmed with Claude Code to ship faster
Physiology of Wearable Robotics Lab

Automating ultrasound muscle analysis with machine learning

Measuring muscle in ultrasound images used to mean tracing every frame by hand. I built a MATLAB deep-learning tool that finds the top and bottom muscle borders (aponeuroses) automatically — and that other labs can quickly retrain for their own studies.

Role
Machine Learning Engineer
Lab
PoWeR Lab, Georgia Tech (Prof. Sawicki)
When
Aug 2020 – May 2024
Stack
MATLAB, Deep Learning Toolbox, ImageJ
99.5%Global pixel accuracy (top border)
0.99Weighted IoU, top border model
490Hand-labeled training images, 7 participants
~800 hrsManual labeling time saved
Pipeline
Collect raw ultrasound Label borders into masks Crop & resize to 512×512 80/20 train/validation split Train two U-Nets Validate & apply to new images

Problem

Ultrasound is a cheap, non-invasive way to see muscles and tendons moving. But the images are noisy, results vary between researchers, and rules-based tools break when new data looks different. Even UltraTrack, the field's standard tool, needs manual correction and can't follow the long muscle fibers in the thigh.

Approach

I traced the top and bottom borders of the rectus femoris (a thigh muscle) in 490 images, then built two separate networks from Cronin's U-Net design. I imported the model into MATLAB, replaced its output with a pixel classification layer, and trained each network for 60 epochs on an RTX 2060 GPU using the Adam optimizer. Each took about an hour.

Broader lab work

  • Wrote an award-winning undergraduate thesis on ML-enhanced post-processing for real-world biomechanical signals
  • Combined IR motion capture data with ML to improve safety assessment accuracy by 42% in an exoskeleton study
Raw thigh ultrasound images next to model predictions highlighting the top and bottom muscle borders in cyan
Raw ultrasound image (left) next to the model's predicted top and bottom borders (right).
BorderGlobal acc.Mean IoUWeighted IoUMean BF
Top0.99520.91570.99070.9926
Bottom0.99070.84650.98250.9447
Full report · PURA Final Report, May 2022 Open PDF in new tab ↗

Grand Challenges: NeuroPrep

Aug 2020 – May 2021
Team Member · Grand Challenges, Georgia Tech · 5-person team

NeuroPrep is a concept app that helps neurodivergent young adults build interview skills and social confidence for the job search.

  • Designed the app's features: finding mentors, events and workshops, building strengths, eye contact practice, managing anxiety, and applying to open roles
  • Created a step-by-step process to help unemployed autistic adults strengthen their job applications
  • Interviewed and surveyed 10 mental health professionals to ground the team's research
  • Positioned NeuroPrep against tools like Pramp, Proloquo2Go, and Simugator as both personalized and affordable
  • Pitched the app at the Grand Challenges Demo Day
1 in 68Children diagnosed with autism
53%Of autistic young adults unemployed after high school
50,000Autistic teens turning 18 every year
52%Of people the team surveyed wanted help with interviews
NeuroPrep website homepage showing phone mockups of the app's welcome, sign-in, and profile screens
The NeuroPrep website.
Pitch slide mapping the app's six main features along a winding road
Six main features, from mentors to open roles.
Pitch slide showing four phone mockups of the app's landing, registration, sign-in, and dashboard screens
Landing, sign-up, and dashboard screens.
Pitch slide showing the eye contact practice exercise across three phone screens
Eye contact practice exercise.

Pathology Dynamics Lab

May 2018 – May 2021
Data Scientist · Georgia Tech
  • Found 3 clinical trends in cancer patients using unsupervised ML, including K-means and DBSCAN clustering
  • Text-mined 56 PubMed articles and curated the extracted patient data into a database
Peer-reviewed publication · Co-author

Meta-Analysis of Gastrointestinal Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia

Mohanavelu P, Mutnick M, Mehra N, White B, Kudrimoti S, Hernandez Kluesner K, Chen X, Nguyen T, Horlander E, Thenot H, Kota V, Mitchell CS. Cancers (Basel). 2021;13(7):1643.

Tyrosine kinase inhibitors (TKIs) are the first-line drugs for chronic myeloid leukemia, but their digestive side effects affect patients' quality of life. The study pooled 43 studies covering 10,769 patients to compare how often four TKIs cause nausea, vomiting, and diarrhea. Unsupervised clustering showed that treatment outcomes depend mostly on how severe the disease is, not on which TKI is used. Doctors can therefore pick a TKI based on its side effects and the patient's health and lifestyle.

Share of patients with digestive side effects, by TKI
Bosutinib
52.9%
Imatinib
24.2%
Dasatinib
20.4%
Nilotinib
9.1%
Differences between drugs were statistically significant (p < 0.001). Across all four, diarrhea was most common (22.5%), then nausea (20.6%) and vomiting (12.9%).

Mentra Startup Project

Aug 2020 – Jan 2021
Data Scientist · Data Science @ GT
  • Helped Mentra, a startup that connects neurodivergent job seekers with careers suited to their strengths, improve its job matching
  • Built NLP matching algorithms in Python that paired 100 neurodivergent clients with employers from a test database
  • Designed 20 ranking features to score candidates on skills, experience, education, and job interests
  • Grouped job listings with K-means clustering on TF-IDF text features, and mapped O*NET skills to relevant occupations

Mental Health & Wellbeing

Aug – Dec 2020
Undergraduate Researcher · Vertically Integrated Projects, Georgia Tech
  • Collected posts from Georgia Tech's subreddit by scraping Reddit with the Python Reddit API Wrapper (PRAW)
  • Measured emotions tied to anxiety and depression with sentiment analysis, and visualized them with word clouds
  • Logged weekly individual and team progress in a shared research journal
Word cloud of frequent words in Georgia Tech subreddit posts, with mad, nice, and doesn't among the largest
Word cloud from Georgia Tech subreddit posts.
Second word cloud from Georgia Tech subreddit posts, with use, basis, and students among the largest words
A second sample, highlighting school and campus themes.
Projects & experience

Business & Finance

Product growth, monetization, and market research.

Product Management

AI-driven growth and monetization at Capital One

Product work across the Capital One web and mobile apps and Capital One Shopping — running experiments, building data pipelines, and deploying AI tools that drive revenue.

Roles
Sr. Associate PM, Growth · Associate PM, Monetization
When
Aug 2024 – Present
Where
New York, NY
+4.23%Conversion lift from A/B testing
$8.34MAnnual present value from a re-evaluated customer group
$1.8M/moNew revenue from an AI upsell tool
$92MRevenue supported across 212 client meetings

Growth — Web & Mobile App

  • Led A/B testing work with product and design teams, lifting conversion rate by 4.23%
  • Added AI-driven analysis to daily monitoring pipelines to surface product suggestions and KPI shifts in real time
  • Used root cause analysis to re-evaluate a previously restricted customer group, adding $8.34M in annual present value

Monetization — Capital One Shopping

  • Deployed an algorithmic AI tool that informs upsell strategy in real time, adding $1.8M in monthly revenue
  • Led a cross-functional effort with product and engineering to bring category-level ETL data into the sales team's CRM
  • Delivered rate and flat-fee recommendations for 212 client meetings, enabling $92M in revenue

Demotu

2025 – Present
Angel Investor & Board Member
  • Invested early in Demotu and serve on its board

Equity Research Boutique @ GT

Jan 2023 – May 2024
Analyst
  • Researched UBER, LYFT, CART, and DASH using the Bloomberg Terminal
  • Built DCF models forecasting 5-year stock growth for UBER and CART, with 92% and 88% accuracy
  • Presented quarterly earnings updates and predictions for Uber and Instacart

Science Olympiad @ GT

2021 – 2024
Vice President · previously Finance Director
  • Raised $10,000 for the state tournament through department sponsorships and student organization grants
  • Co-hosted the first-ever flighted Georgia State Science Olympiad Competition, splitting it into a division for emerging schools and one for experienced schools
  • Organized Georgia Tech's first-ever Yellow Jacket Invitational Tournament
Education
M.S. Computer ScienceGeorgia Institute of Technology · GPA 4.00
B.S. Mechanical EngineeringGeorgia Institute of Technology · GPA 3.97 · Robotics & Automation, Minor in CS (AI)
Technical
Python, SQL, Swift, MATLAB, TensorFlow, PyTorch, NLP, LLMs (Claude Code / Codex), Optimizely, JIRA, Figma
Analytical
ETL creation, dashboarding, data product development, financial modeling, client-facing work, root cause analysis
Mechanical
CAD (Onshape, SolidWorks), Ansys Granta, FEA (Siemens NX), 3D printing, laser cutting, water jet, lathe, CNC