Computational Biology
AI methods for biological data, omics-informed analysis, and translational research questions.
PhD Student | University of North Dakota
Samarth Singhal
I am a PhD student in Clinical and Translational Science at the University of North Dakota and a Graduate Research Assistant working on AI-driven computational analysis. My current work sits around computational biology, bioimaging, spatial biology, and omics-informed AI, while my broader interests are in useful, reliable AI systems that connect research ideas with working software.
Before starting my PhD, I completed a B.Tech in Computer Science and Engineering at Delhi Technological University and worked as a Software Development Engineer at Fareportal, where I built and maintained production systems including API features for travel products.
Research Focus
Areas that currently shape my academic and applied work.
Bioimaging and Spatial Biology
Models and tools for imaging-based biological data, digital pathology, and spatially resolved measurements.
AI in Healthcare
Practical machine learning systems for biomedical workflows, clinical research, and decision-support contexts.
General AI Systems
Reliable AI applications that combine model development, evaluation, deployment, and usable software design.
Projects
Selected AI and software projects. See the project page for expanded notes.
AI Bioinformatics Platform
S-BIOS
An AI-based bioinformatics platform for modern analysis of imaging-based data, including cell typing, gene expression prediction, and computational workflows around spatial and pathology data.
Human-Centered AI Application
BloomScroll
A feed-control app aimed at reducing doom scrolling by helping users shape what they consume, with a more intentional and gamified content experience.
Applied Deep Learning
Deep Learning AI Proctoring Framework
A remote proctoring framework using detection, facial landmarks, gaze cues, head-pose estimation, and suspicious task monitoring.
Media Forensics
Deepfake Detection
A detection system combining EfficientNet with engineered video features such as blinking rate, frame correlation, and video quality.
Reinforcement Learning
Chrome Dino AI
A reinforcement learning project built around a Pygame game environment and trained from obstacle-distance state values.
Applied Machine Learning
Fairgrades
A grade prediction project exploring ML-based alternatives for awarding grades when exams were disrupted.
Presentations, Awards, and Service
Presentations
- Oral Presentation, Pediatric Centers of Excellence in Nephrology Symposium 2026
- Poster Presentation, AI and Human Innovation Showcase 2026
- Poster Presentation, UND GRAD 2026
- Poster Presentation, CVPR 2025
- Oral Presentation, North Dakota Academy of Science Annual Meeting
Awards
- UND Graduate Conference Travel Grant recipient
- 2nd Place, Chester Fritz Library Data Visualization Contest 2026
- 1st Place, Bit-by-Bit Hackathon, IIIT-B
- 1st Place, Hack-With-MAIT 2021
- Runner Up, Innovathon, DTU 2021
Service
- Judge Volunteer, Northeast Regional Science and Engineering Fair 2025, 2026
- Judge Volunteer, North Dakota State Science and Engineering Fair 2025, 2026
Recognition
- Amazon ML Summer School 2022 attendee
- Google ML Bootcamp 2022 attendee
- Codeforces Expert, peak rating 1707
- Google Kick Start 2022 Round G, rank 176
- Google Hash Code 2021, global rank 216
Contact
For research conversations, collaborations, or academic inquiries, I am easiest to reach by university email.