Table of Contents
- Education
- Scholarships, Medals & Distinctions
- Technical Skills
- Professional Experience
- Research Experience
- Extracurricular Activities
- International Exposure & Conferences
Education High-achieving computer science graduate from ETH Zürich (5.25/6 GPA) and IIT Bombay (9.6/10 GPA, Gold Medalist)
Education
ETH Zürich
- GPA: 5.25/6
- Major: Machine Intelligence
- Minor: Data Management Systems
- Courses: Large Scale AI Engineering, Cloud Computing Architecture, Big Data, Machine Learning, Deep Learning, Machine Perception, Computational Intelligence Lab, System Security
Indian Institute of Technology Bombay (IIT Bombay)
- GPA: 9.6/10 - Highest Cumulative GPA in batch
- Activities: Journalism, Public Speaking, Tech Competitions and Debating
Scholarships, Medals & Distinctions Recognized for academic excellence and leadership through multiple awards and scholarships
Scholarships, Medals & Distinctions
Department Rank 1 & Gold Medalist
Graduated with Rank 1 out of 165 students - The Highest CGPA in the 2022 class of Bachelors in Mechanical Engineering
Institute Academic Award
Awarded to only 2 students for the highest grade point scores of the academic year
KC Mahindra Scholarship
Prestigious scholarship awarded to meritorious Indian students for graduate studies abroad. Associated with ETH Zurich
Heyning-Roelli Scholarship
Recipient of ETH Zurich's merit and need-based international exchange scholarship
Mensa International High IQ Society
Member of the high IQ society
AB InBev 'Pint' & 'Pitcher' Award
Award for Excellence during one year of work experience as a data scientist
OPJEMS Scholarship
Awarded to only 3 top-performing, entrepreneurial students from among 1100+ students at IIT Bombay
IIT Bombay Journalism Color Award
Awarded the Institute's most prestigious Journalism Award for exemplary contribution. One of only 2 students selected from among 10,000+
Technical Skills Proficient in machine learning, data engineering, and cloud technologies
Technical Skills
Programming & Development
Advanced: Python (ML/Data Engineering), SQL (Snowflake/Postgres/Oracle), Bash/Linux CLI
Proficient: C/C++ (CUDA/high-perf computing), R (statistical analysis), JavaScript/TypeScript (dashboards)
Machine Learning & AI
Frameworks: PyTorch, TensorFlow/Keras, scikit-learn, Hugging Face Transformers
Specialized ML: XGBoost/LightGBM/CatBoost, Prophet/statsforecast (Time-Series), BERTopic/UMAP (Topic Modeling)
NLP & LLMs: OpenAI API, LangChain, RAG Pipelines, Sentence-Transformers, BERT
Data Engineering & Analysis
Data Processing: Pandas, NumPy, Polars, PySpark, PyArrow/Parquet, DuckDB
Statistical Analysis: SciPy, statsmodels, Bayesian inference (PyMC), hypothesis testing
Databases: Snowflake, PostgreSQL, BigQuery, Redshift, SQLite
MLOps & Deployment
Experimentation: MLflow, Weights & Biases, TensorBoard, experiment design
Containerization & Cloud: Docker, Kubernetes, AWS (EC2/S3/Lambda), Azure ML
Automation: GitHub Actions, CI/CD pipelines, Airflow, Prefect
Optimization & Quantitative Methods
Mathematical Optimization: Gurobi, PuLP, OR-Tools, Linear/Integer Programming
Time-Series Forecasting: ARIMA/SARIMAX, Prophet, Ensemble methods
High-Performance Computing: SLURM, parallel processing, concurrent.futures, asyncio, Ray
Visualization & Domain Tools
Data Visualization: Plotly/Dash, Matplotlib, Seaborn, Tableau
Specialized Tools: GeoPandas (Geospatial), BeautifulSoup/Scrapy (Web Scraping), FastAPI/Flask (API Development)
Developer Tools: Git, pytest, black/flake8, JupyterLab, VS Code, LaTeX
Professional Experience Experienced in delivering business impact through advanced machine learning
Experience
AB InBev — Growth Analytics Center
Led data science solutions for supply chain optimization in the US market, focusing on forecasting and logistics cost reduction.
Logistics Forecasting & Cost-to-Serve Optimizer
- Engineered an ensemble forecasting pipeline combining Prophet, SARIMAX, XGBoost, and traditional time series models
- Developed a Gurobi-powered integer programming optimizer using PuLP for route optimization across 2,000+ US distribution routes
- Built an interactive scenario planning simulator with parameterized carrier behaviors and demand shock modeling
- Impact: Achieved 15% forecast accuracy improvement, delivering $2.4M annual savings
- Leadership: Led a 3-analyst enhancement squad, managing weekly refreshes and documentation
- Tech: Python, Gurobi, PuLP, Plotly, Snowflake, AWS CodePipeline/EC2
AB InBev — Growth Analytics Center
Product Recommendation Engine
- Designed a latent-factor recommender system using SVD and NMF collaborative filtering for cross-selling opportunities among 50 SKUs to 18,500+ Tanzanian retailers
- Created 15 custom interaction scoring mechanisms combining recency, frequency, monetary values, and product affinities
- Developed comprehensive evaluation framework with metrics for precision, recall, diversity, novelty, and coverage
- Impact: Outperformed existing recommendation models by 30%, supporting $6M projected annual sales growth
- Recognition: Selected as Best Intern among 100+ peers and offered full-time position
- Tech: Python, scikit-learn, SciPy, Pandas, Azure
Glenmark Pharmaceuticals — Demand Planning
Global Demand-Planning Automation
- Reengineered 5 KPI calculation frameworks for improved outlier resilience and accuracy
- Automated reporting pipelines for 5,000+ SKUs across 20+ countries with cloud-based data storage and validation
- Developed 3 Tableau dashboards with executive and business unit drill-downs
- Impact: Reduced reporting cycle time by 75%, saving approximately 1,000 hours annually
- Recognition: Received full-time offer upon project completion
- Tech: Python, SQL, Tableau, Dropbox API
Growth Source Financial Technologies (Protium)
Debt-Refinancing Optimizer & Loan Recommender
- Formulated a linear programming optimization framework to consolidate MSME debt, leveraging collateral appreciation
- Developed a loan recommendation system that ranks feasible refinancing options based on dual-benefit scoring
- Prototyped a sales territory realignment system using Google Maps API and K-means clustering for 6 metropolitan areas
- Built analytics pipeline for yield tracking across 600+ bond securities
- Impact: Successfully validated approach on 350+ loan portfolios
- Recognition: Received partner-signed Letter of Recommendation
- Tech: Python, PuLP, NumPy, Pandas, scikit-learn, Google Maps API
Rephrase.AI
Contributed to this Lightspeed-funded, Forbes 30-Under-30 startup specializing in AI-generated synthetic video from text input.
- Represented Rephrase.AI at the Amazon AWS AI Conclave ‘19, presenting to an audience of 100+ CXOs and 50+ startups
- Recorded 20+ hours of training audio to develop a custom-voice Text-to-Speech engine
- Designed and executed a comprehensive feature validation study on Amazon Mechanical Turk with 160+ respondents
- Tech: Voice synthesis, Natural Language Processing, Amazon Mechanical Turk
- Exposure: AI video synthesis, public speaking, user testing methodologies
Research Experience Applied ML researcher at ETH Zurich
Research
Medical Data Science Group, D-INFK, ETH Zurich
Developed a lightweight self-supervised framework for sleep stage classification from EEG data, achieving 80%+ accuracy with just 200K parameters. This research addressed the challenge of limited labeled medical data through innovative contrastive representation learning approaches.
Key Contributions:
- Systematically evaluated 13 domain-specific EEG signal augmentations across 5 categories (amplitude, frequency, masking-cropping, noise-filtering, temporal)
- Discovered optimal augmentation combinations and severity levels that maximize downstream performance
- Designed an extremely lightweight CNN architecture optimized for edge deployment (<1MB)
- Created a modular framework with clean separation between pretraining and fine-tuning components
Technologies: PyTorch, TensorBoard, Slurm HPC, Configuration Management, Advanced Signal Processing
Impact: Demonstrated that targeted self-supervised learning can dramatically reduce the annotation burden in medical contexts while maintaining high classification performance. The compact model architecture enables potential deployment to resource-constrained medical devices.
ETH Zurich
Led a systematic benchmark study comparing different self-supervised learning paradigms and neural architectures for EEG-based sleep stage classification, establishing clear guidelines for optimal model design in this domain.
Key Contributions:
- Evaluated three self-supervised learning paradigms (Contrastive, Masked Prediction, Hybrid) across multiple neural backbone architectures
- Conducted comprehensive ablation studies on CNN, CNN+Attention, and Transformer architectures
- Designed novel metrics for latent space quality assessment specific to neurophysiological signals
- Demonstrated that CNN+Attention architectures paired with contrastive learning objectives create the most discriminative latent representations for this task
Technologies: Python, PyTorch, TensorBoard, Advanced Neural Architectures, Latent Space Analysis
Impact: Established clear evidence-based guidelines for selecting optimal combinations of self-supervised learning paradigms and backbone architectures for EEG analysis. Findings suggest CNN+Attention with contrastive or hybrid learning objectives consistently outperform alternatives for short EEG segments.
Chair of Technology and Innovation Management, ETH Zurich
Enterprise Machine Learning Research Project Large-scale collaboration between Zühlke and ETH Zurich examining ML/AI adoption patterns across 600+ enterprises
Key Contributions:
- Designed end-to-end data processing pipeline integrating multiple survey sources with robust cleaning, standardization, and anonymization processes
- Developed automated statistical analysis framework executing 10,000+ tests (t-tests, ANOVA, Chi-Square, etc.) for comprehensive pattern detection
- Created dynamic visualization system generating 2,600+ charts intelligently selected based on data characteristics
- Integrated NLP techniques and OpenAI’s LLM APIs for text categorization and analysis
- Transformed complex statistical outputs into business-friendly reporting for non-technical stakeholders
Impact: Reduced analysis time by 80%, identified significant ML adoption patterns across regions/industries, and created a reproducible research framework now used for ongoing studies at the Chair.
Technologies: Python, pandas, scipy, statsmodels, matplotlib, seaborn, OpenAI API
Unicode Technical Consortium Document Analysis Project Comprehensive analysis of 20,000+ standardization documents spanning a decade of technical development
Key Contributions:
- Engineered robust web crawlers with concurrent HTTP handling achieving 94% download success rate
- Built multi-format document processing pipeline for PDF, HTML, and plaintext with specialized parsing
- Developed hierarchical document classifier with 96% accuracy across 30+ categories and 100+ subcategories
- Created parallel keyword extraction framework producing 150,000+ unique technical terms
- Implemented LSA-based document summarization with length-adaptive output
- Engineered optimized LLM integration reducing API costs by 42% while maintaining extraction quality
Impact: Enabled unprecedented analysis of Unicode standardization patterns, revealing emoji adoption trends and contributor influence networks across a decade of technical development.
Technologies: Python, BeautifulSoup4, PyPDF2, NLTK, OpenAI API, concurrent.futures, matplotlib
Patent Analysis and Technological Shift Detection Project Identification of technological trends across 10,000+ semiconductor industry patents
Key Contributions:
- Built high-performance patent data acquisition system with asyncio achieving 20× faster processing
- Engineered intelligent token optimization pipeline preserving critical technical information within embedding constraints
- Deployed BERTopic modeling with Sentence Transformers identifying 80+ distinct technology clusters
- Created sophisticated patent selection criteria with LLM integration (GPT-4o-mini) achieving 95% accuracy
- Implemented visualization and analysis of topic trends revealing 5 major technological shifts over 40+ years
Impact: Provided unprecedented visibility into semiconductor industry innovation patterns, enabling strategic research direction planning and competitive technology landscape analysis.
Technologies: Python, aiohttp, pandas, BERTopic, Sentence Transformers, UMAP, HDBSCAN, PyTorch, GPT-4o-mini
World Bank Group x KPMG India
Led strategic research on capital investment opportunities in green hydrogen technologies across three nations as part of an international collaboration between the World Bank Group and KPMG India.
- Demand Forecasting: Developed comprehensive 10-year hydrogen demand models by analyzing 10 end-use industries across manufacturing, energy, and mobility sectors
- Alternative Fuel Analysis: Conducted comparative assessment of 5 conventional fuels against 4 cleaner alternatives, evaluating technical feasibility and economic viability
- Policy & Readiness Assessment: Synthesized national renewable energy policies, planned industrial capacity expansions, and corporate sustainability initiatives to quantify green hydrogen technology adoption readiness
Skills: Energy Market Analysis, Economic Modeling, Quantitative Analytics, Policy Research, Sustainability Assessment
Capital Foods Pvt. Ltd.
Spearheaded research on food and beverage warehousing automation technologies, culminating in executive presentations to C-suite leadership.
- Competitive Intelligence: Analyzed modern warehousing practices of global food and beverage FMCG companies through comprehensive review of 5+ industry conferences, 10+ market reports, and 50+ specialist articles
- Strategic Recommendations: Developed and presented 50+ automation proposals tailored to Capital Foods’ operational needs and growth strategy
- Internal Publications: Authored two comprehensive reports documenting findings and implementation frameworks for warehouse modernization
Skills: Industry Research, FMCG Supply Chain, Automation Technology Assessment, Executive Communication
Indian School of Business
Conducted advanced modeling of SARS-CoV-2 transmission dynamics in agricultural markets under guidance of Prof. Sarang Deo, Executive Director of Max Institute of Healthcare Management.
- Data Analysis: Performed exhaustive exploratory analysis on grain movement patterns across 3,200 agricultural markets serving 12,800 villages
- Geospatial Mapping: Created custom geographical dataset of village boundaries and centroids using Google Maps API to enable spatial epidemiological modeling
- Mathematical Modeling: Implemented polynomial regression to quantify grain procurement volumes and adapted SIR (Susceptible, Infectious, Recovered) models to predict viral transmission patterns
- Impact: Research informed safer grain procurement strategies for public institutions and policymakers during COVID-19 pandemic
- Recognition: Received Letter of Recommendation for exceptional research contributions
Skills: Epidemiological Modeling, Geospatial Analysis, Python, Statistical Modeling, Research Methodology
Various Institutions
- Vertical Farming Optimization (2021): Designed computational model for biomass optimization in vertical farms using computer vision, hydroponics variables, and spectral lighting analysis
- Biomedical Materials Research (2021): Conducted comparative analysis of manufacturing techniques for metal-polymer biomedical stents
- PPE Manufacturing Analysis (2020): Researched and documented production processes for N-95-certified respirators during global shortage
- Urban Digital Archive (2020): Developed digital catalogue of performing arts venues in Milan using Foursquare APIs and geospatial mapping
- Vehicle Dynamics Simulation (2019): Engineered 2-wheel simulation model using Simulink for IITB Racing Team
- Robotics Competition (2018): Designed and built remote-controlled obstacle-navigating robot, achieving 4th place among 100+ teams in XLR8 competition
Technical Skills: Mathematical Modeling, API Integration, Simulink, Robotics, Computer Vision, Statistical Analysis
Extracurricular Activities Active student leader across journalism, analytics, with creative pursuits and Mensa membership.
Extracurricular Activities
Leadership
- Editor, Student Journalism Wing, IIT Bombay: Led 200+ student journalists in official body serving 10,000+ campus community with 0.9M+ global readership; published author at ETH D-INFK’s Visionen magazine
- Project Leader, Data Analytics Team: Published influential institute-level reports on graduate admissions and COVID-19 impact; developed interactive online dashboard
- Student Alumni Relations Cell: Core team member organizing flagship “Alumination” event connecting 200+ alumni with 1,000+ students
Mentorship & Community Impact
- Corporate Mentorship: Guided 70+ AB-InBev interns; conducted analytics interview preparation workshop (100+ attendees)
- Academic Mentorship: Currently mentoring 2 undergraduates during ETH Zurich exchange; led undergraduate team in building remote-control rover for XLR8 competition
- National Service Scheme: Dedicated 80+ hours teaching sustainability to underprivileged students; created educational content for NSS YouTube channel
Communication & Cultural Exchange
- Institute Newsletter: Co-authored “The Knowledge Tree” and 18 articles reaching 60,000+ global alumni
- Public Speaking: Interviewed nobroker.com CEO; secured 3rd in Debating Championship; organized “Indian Night” cultural showcase in Zurich
- Leadership Role: Elected School Vice-Captain, coordinated 11 captains and 1,000+ students for annual events
Achievements & Creative Pursuits
- Member, Mensa International: Active member of the High IQ society, India chapter
- Entrepreneurship: Designed computer vision-optimized vertical farm with hydroponics and spectral lighting
- Performing Arts: Active spoken word artist and stand-up comedian
International Exposure & Conferences Handpicked for international exchanges and global conferences
International Exposure & Conferences
Swiss Federal Institute of Technology (ETH Zurich)
- Pursued 6-month long International Exchange at the Department of Mechanical and Process Engineering; QS Rank 8
- One of only 2 Indian students handpicked, recommended by Dean Prof. Agrawal and HoD Prof. Sheshadri
- Interacted with 200+ international students; Achieved A1 German proficiency; visited 10+ EU countries
International Conferences 2021
Handpicked to contribute as an International Student Delegate from India to the following global conferences: - Harvard Project for Asian & International Relations - South American Business Forum - Princeton University Business Today Conference - AUA and ICGS Academic Conference