Prateek Verma

PhD. Machine Learning Scientist.

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deviceswww.prateekverma.com

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Seeking opportunities where I can leverage my industrial and academic experience to drive differentiation and innovation. I am a machine learning scientist with cross-functional expertise in chemicals, materials, health, and environment. My key strengths lie in visionary and complex problem solving, interdisciplinary research, leadership, and research advising.

Work experience

Manager, Data Science Core, University of Arkansas

Arkansas Integrative Metabolic Research Center (AIMRC) group, 2023 – present

  • Evaluation, fine-tuning, and prompt engineering of large (vision) language models (LLMs) using multimodal data
  • Led machine learning and data science support for 50+ researchers focusing on multimodal, multi-omic medical, chemical and materials research
  • Manage high-performance computing and data-storage servers
  • Example projects: AI for microscopy, AI for materials science, cell movement and division tracking, cell viability prediction, AI for women's health, combining multi-omics and ANOVA, animal pose tracking and behavior classification, data mining and analysis from PubMed

Postdoctoral Fellow, University of Arkansas

Nayani, Nakarmi and Wu groups, 2021 – 2023

  • Built an end-to-end CNN ML pipeline for scientific images
  • Built graph and generative algorithms for molecular discovery and finding functional groups on molecules and macromolecules
  • Applications: predicting onset of diseases, sensors for bacteria and viruses, drug and molecular design, medical diagnosis

Postdoctoral Fellow, Georgia Institute of Technology

Shofner and Russo groups, 2018 – 2021

  • Developed multivariable deep neural network regression to split, interpolate, and predict total signal into constituents
  • Developed CNN and regression algorithms for noise detection in signals
  • ML applications: Extract pollution composition (expensive measurement) from total PM2.5 (inexpensive) data; noise detection in light scattering data
  • Fabricated metamaterial composites using tensegrity/auxetic approaches
  • Executive Director for OPALL (Open Polymer Active Learning Laboratory)

Senior Coating Chemist, Kimoto Tech

2016 – 2018

  • Team leader for 5 R&D chemists
  • Led scale-up and production of 15+ lab-to-market products
  • Development of flexible & protective coatings, conductive coatings, and pressure sensitive adhesives

Internships

U Akron (2011), UMass Amherst (2010), U Minnesota (2009)

23  papers published or submitted
14  first author papers
21  conference presentations
21  manuscripts reviewed

Education

Georgia Institute of Technology

PhD, 2011 – 2015

Materials Science and Engineering

GPA 4.0/4.0

Indian Institute of Technology Roorkee

BS and MS, 2006 – 2011

Polymer Science and Technology

GPA 8.5/10.0

New skills

dashboards
large language models
BioPython
vision language models
high performance computing
data mining
graph neural networks
chemical informatics
Django
RDKit
GPU computations

Select awards

2024 – NISBRE Merit Awardee

2021 – Lead scientist on $300,000 award

2021 – MSE 5 year mentorship award

2019 – Hightower Fellow, OPALL

2017 – Chairman, Tech. Conference, Kimoto

2014 – Second prize, auxetic conference

Skillsets

Machine Learning

large language models
vision language models
graph neural networks
chemical informatics
ML pipelines
RESNET
support vector machines
k-Means clustering
convolutional neural networks
image preprocessing
logistic regression
linear regression

Computer languages

Python
MATLAB
PHP
SQL
C/C++

Computational

BioPython
Django
RDKit
Pandas
Tensorflow & PyTorch
Numpy
Scikit
Matplotlib
LAMMPS
AWS
MATLAB
MySQL

Data Infrastructure

dashboards
high performance computing
data mining
GPU computations
data pipelines
ML workflows
cloud

Chemistry

polyurethane synthesis
thermal & UV curing
LCE synthesis
FR polymerization
silanes & silicones

Materials

liquid crystals
polymer processing
viscoelasticity
auxetics
metamaterials
biopolymers
nanotechnology
characterization
structure-property relationships
thermal analysis

Industry

adhesives
process development
chemical mixing
chemical formulations
protective coatings
scale-up operations
thermal & UV curing

Interpersonal

mentoring
research advising
leadership
team building
teaching
illustration

Lab Techniques

isothermal titration calorimetry
environmental testing
electron microscopy
FTIR
micro-CT
viscometry
AFM
DSC, TGA, DMA
mechanical testing

Select Publications

  • P Verma, A Poe, X Wu; Machine Learning Meets Computational Ethology: Comparative Analysis of Algorithms and Validation Methods for Pose-Based Behavior Recognition; NeurIPS; 2025; (submitting next)
  • P Verma, C Kuebler, MH Van, et al.; Machine learning powered tracking of cells, midlines, cell divisions and their angles during embryo development; eLife; 2025; (submitting next)
  • MH Van, P Verma, X Wu; A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools; arXiv; 2025 link
  • P Verma, MH Van, X Wu; Beyond human vision: The role of large vision language models in microscope image analysis; IEEE BigData; 2025 link
  • P Verma, E Adeogun, ES Greene, et al.; Machine-learning based sensing of heat-stress in organisms; RSC Sensors & Diagnostics; 2024 link
  • MH Van, P Verma, X Wu; On large visual language models for medical imaging analysis: an empirical study; IEEE/ACM CHASE; 2024 link
  • D Ansari, P Verma, T Ansari; Promise of machine learning techniques towards retrieving aerosol chemical composition from temporal variations of total PM mass concentrations; Journal of Research in Atmospheric Science; 2023 link
  • X Fang, H Sun, ..., P Verma, et al.; Ag nanoparticle-thiolated chitosan composite coating reinforced by Ag–S covalent bonds with excelle…; ACS Applied Materials & Interfaces (IF=10.4); 2023 link
  • H Sun, X Fang, ..., P Verma, et al.; An ultra-sensitive and stretchable strain sensor based on micro-crack structure for motion monitoring; Nature Micro Nano (IF=9.9); 2022 link
  • P Verma, C Smith, AC Griffin, et al.; Towards textile metamaterials: A pathway to auxeticity and tensegrity in a needle-punched nonwoven stiff felt; Materials Advances (RSC) (IF=5.0); 2022 link
  • P Verma, C He, AC Griffin; Implications for auxetic response in liquid crystalline polymers; Physica Status Solidi B; 2020; (appeared in Wiley's 'Hot Topics: Liquid Crystals') link
  • N Jappar, P Verma, J Holmes; Development of functional films in roll-to-roll manufacturing; RadTech; 2018; (conference paper) link
  • P Verma, ML Shofner, AC Griffin; Deconstructing the auxetic behavior of paper; Physica Status Solidi B; 2013 link

Select Presentations

  • Evaluation of large vision language models on scientific images; Washington DC (USA); 2024 link
  • CNN based rapid sensing of heat-stress in organisms; Orlando (USA); 2023 link
  • Pathways to Commodity Mechanical Metamaterials – Auxeticity in Nonwoven Fiber Networks; College Station (USA); 2022; Invited talk link
  • Constructing out-of-plane auxetic response in paper; Denver (USA); 2020 link
  • OPALL: The open polymer active learning laboratory at Georgia Tech; Orlando (USA); 2019
  • X-ray scattering from LC polymers: Implications for auxetic response; Bedlewo (Poland); 2019 link
  • Auxetic liquid crystalline polymers; Crete (Greece); 2017 link
  • Elastic moduli of polymeric thin films of nanocomposites and blends via buckling on elastomeric substrates; Boston (USA); 2012 link

Professional service

Peer reviewing

Reviewed more than 21 manuscripts for journals such as:

Advanced Materials Technologies (Wiley), Computational Materials Science (Elsevier), Industrial & Engineering Chemistry Research (ACS), International Conference on Machine Learning and Applications (Microsoft), Journal of Engineered Fibers and Fabrics (Sage), Journal of Micromechanics and Microengineering (IOP), etc.

Mentoring

Served as a mentor for Mentor Jackets, MSE Industry Mentoring and IITR's Alumni Mentorship Program since 2016 for:

 9  Bachelor's students

 7  Doctoral students

 2  Master's students

Research advising

Advised the research of 17 (direct supervisor for 14) industry members / graduates / undergraduates in the following broad areas:

  • convolutional neural networks
  • machine learning for molecules
  • linear and logistic regression
  • auxetics and metamaterials
  • structure-property relationships

Select leadership

  • DEI council representative for MSE staff, Georgia Tech, 2019–2021
  • Co-launched Postdoc Chats, Georgia Tech, 2019–2023
  • Advisor/mentor for OPALL members, Georgia Tech, 2019–2022
  • Team leader, Kimoto Tech, 2016–2018
  • Co-manager for thermal analysis lab, Georgia Tech, 2013–2015

References

Anselm C Griffin

Professor Emeritus, Georgia Tech

mail_outlineanselm.griffin@mse.gatech.edu

Meisha L Shofner

Professor, Georgia Tech

mail_outlinemeisha.shofner@mse.gatech.edu

Paul S Russo

Professor, Georgia Tech

mail_outlinepaul.russo@mse.gatech.edu

Bin Li

Senior Research Chemist, Koppers

mail_outlinebinli415@gmail.com

Karthik Nayani

Assistant Professor, U Arkansas

mail_outlineknayani@uark.edu

Extracurriculars

  • Gets way too excited about graphics design and web development
  • Is the best table tennis player in the break room
  • Paints and draws