Pushkar Ghanekar
Data aesthete at heart, I am a Ph.D. candidate in the Davidson School of Chemical Engineering at Purdue University, advised by Jeffrey P. Greeley. Understanding molecular-level behavior of the catalyst through data and science is at the heart of my Ph.D. research.
Besides research, I enjoy teaching and making scientific ideas accessible to the public. I have taught introductory chemistry and mathematics to middle-schoolers, and have assisted in designing modules that introduce critical thinking, logical reasoning, and coding in high-school curricula.
Outside of work, I enjoy cooking (link to some latest experiments), gaming, diving deep in arcane internet rabbit holes, reading philosophy, and going on longer-than-necessary bike rides.
Skills
- Kinetic modeling
- Atomistic model development
- High-throughput screening
- Theory/Experiment collaborations
- Python
- Shell
- Julia
- html/css
- Scikit-learn
- Tableau/Plotly
- NumPy
- SciPy
- Pandas
- Matplotlib
- Git
- Beautifulsoup
- Selenium
- High Performance Computing (CPU/GPU)
- PyTorch
- XGBoost
- RDKit
- SIMCA
- Dask
- JMP
- RAPIDS.ai
- MATLAB
- VASP
- Aspen Plus
- Adobe Creative Suite
- Blender
- MongoDB
- Docker
Journal article - Promoting a Safe Laboratory Environment Using the Reactive Hazard Evaluation and Analysis Compilation Tools
Journal article - Catalysis at Metal/Oxide Interfaces: Water Gas Shift at Pt/MgO Boundaries
Journal article - Electronic and Structural modification of Pt-V alloy and its consequences for Propane Dehydrogenation Catalysis
Experience
Chemometric & AI Intern
Research and Development intern in Chemometric & AI (M&E) and Cheminformatics (Core R&D) department.
- Developed and deployed ML model for molecular discovery. Scaled-up model inference capabilities resulting 30-fold improvement in compute time, increasing capability to screen potential molecules from millions to billions. Benchmarked state-of-the-art chemistry-based deep-learning models for ligand design. Collaborated with Business R&D to validate model predictions.
- Performed multivariate historical data analysis to troubleshoot complex manufacturing problems in collaboration with plant experts. Proposed key variables driving the manufacturing process upsets to detect anomaly at least 12 hours in advance, improving plant reliability & safety.
Process Engineering Intern
Worked as Process Engineering intern for the Oil & Gas sector. Worked on following projects:
- Designing and optimisation of proprietary LNG liquefaction processes
- Unit Operation design and life-cycle analysis.
Technical Head @ Vortex 2014
Responsible for developing and managing the festival online registration and landing portal. Lead designing of promotion and publicity material. Lead organizer of IDP (Industry Defined Problem), one of the primary events of the festival with approximate audience of 1000.
Formulation R&D Intern
Worked in the manufacturing and formulation department. Handled following projects:
- Mechanical design of chemical equipment.
- Process development for production of APIs (collaborated with the R&D dept.)
Research & Development Intern
Worked as an intern in the manufacturing (R&D) and distribution (supply-chain) facility. Main project was on scaling up manufacturing protocols for an API
Graphic Design Lead @ Freshers 2012
In charge of designing merchandise and promotional media for the festival
Education
Purdue University
Thesis topic: Structural and Kinetic Modeling of Heterogeneous Catalysts Using First-Principles and Machine Learning
Insitute Of Chemical Technology (ICT)
Dr. S. Radhakrishnan International School
Simple Graph Neural Network
Simple Dropout using PyTorch
Analysis on Spice Use in Indian Food
Bollywood Movie Rating Analysis
Web scraping S&P 500 data
Bayesian Optimization using Gaussian Processes
Central limit theorem
Property Price Prediction ML-model
Wait! There's more..
See all Blogs for more examples!