Jan 2022 – Jan 2022 · 0 mo · Cambridge, Massachusetts, United States

• Selected for Amgen's competitive Future Operations Leader (FUEL) program—MIT LGO partnership placing top PhD-MBA candidates on strategic AI/ML projects at the $27B biotech leader.
• AI FOR DRUG SYNTHESIS
• Developed graph neural network models achieving state-of-the-art prediction of reaction impurities in small molecule drug synthesis
• Built impurity propagation framework tracking contaminants across multi-step synthesis pathways
• Impact: $25K+ savings and 120+ hours eliminated per synthesis step
• INVERSE STRUCTURE ELUCIDATION
• Adapted AI method to propose chemical structures from analytical data (mass spec, NMR)
• Reduced unknown compound identification from weeks → hours
• Impact: 80+ hours and $120K+ saved per identification
• STAKEHOLDER ENGAGEMENT
• Conducted 70+ interviews with scientists, engineers, and leadership to build project roadmap
• Aligned technical capabilities with operational requirements across Process Development, Manufacturing, and Quality
• RECOGNITION
• Presented at internal Amgen Science Symposium and NeurIPS 2022 Workshop
• Published pre-print within 4 months; work became MIT Sloan MBA thesis

Jan 2020 – Jan 2020 · 0 mo · Mountain View, California, United States

• Research position at Google Accelerated Sciences (Sequin team)—elite internal group applying ML to breakthrough scientific problems.
• Hosts: Lucy Colwell (Senior Staff) & Suhani Vora.
• CRISPR THERAPEUTICS
• Developed ML model for CRISPRi guide RNA design across all 20,000 human genes
• Achieved state-of-the-art results: 20%+ improvement over previous best models
• Identified top-2 guide RNA candidates per gene for therapeutic development
• Enables precise gene expression control for disease treatment
• ML INFRASTRUCTURE
• Co-developed software for automated model building, hyperparameter tuning, and feature attribution
• Built architecture search for optimal model discovery
• Impact: 20+ hours saved per model development task
• Google Accelerated Sciences (now Google DeepMind) applies AI to drug discovery, genomics, and materials - including contributions to AlphaFold.

Jan 2018 – Jan 2022 · 4 yrs · Cambridge, Massachusetts, USA

• Pioneering research at intersection of machine learning and molecular science. Developed AI methods accelerating drug discovery and materials design by 10-100x.
• Advisor: Prof. Rafael Gómez-Bombarelli
• RESEARCH IMPACT
• Led 8+ projects with 30+ researchers across 5 labs at MIT and UIUC
• Achieved 10-250% property improvements using interpretable deep learning
• Established AI → synthesis → experiment loops, increasing throughput 10x
• Generated $1M+ annual savings across collaborating labs
• PUBLICATIONS: 15+ papers in Nature Chemistry (2x), Nature Machine Intelligence, ACS Central Science (4x), NeurIPS, ICML, ICLR | 1 U.S. Patent
• TECHNICAL CONTRIBUTIONS
• Graph Neural Networks, RNNs, Gaussian Processes for molecular design
• Multi-objective optimization for competing molecular properties
• Open-source: GLAMOUR & Peptimizer (GitHub)
• FUNDING ENABLED: $2.27M+ grants to PI (Novo Nordisk $2.1M, JClinic $70K, MIT-SenseTime $100K)
• MENTORSHIP: 9 students → placements at Google, Meta, top PhD programs
• RECOGNITION: TEDx Boston speaker, Google seminar, 10+ conference presentations
• Thesis: "Designing Macromolecules Using Machine Learning and Simulations"

Jan 2018 – Jan 2020 · 2 yrs

• Co-founded AI startup serving manufacturing industries. Scaled from 2 founders to 30-person team with $500K+ ARR.
• COMPANY BUILDING
• Built and led 30-person team across engineering, sales, operations (US + India)
• Established hiring processes, org structure, and performance management
• Created culture of technical excellence and customer obsession
• REVENUE & BUSINESS DEVELOPMENT
• Led sales strategy and closed ~$300K contracts (ARR >$500K)
• Managed full cycle: prospecting → demos → negotiations → closure
• Built customer success function for retention and expansion
• PRODUCT & CLIENT IMPACT
• Developed RPA/ML solutions for manufacturing automation
• Flagship: Automated customer inquiry processing for oil and gas pipeline industry
• Client savings: $3M+ annually in operational efficiency
• P&L OWNERSHIP
• Full responsibility: revenue, costs, margins, cash flow
• Managed scaling, vendor relationships, legal/compliance
• Transitioned to MIT PhD/MBA (2018); company continued under co-founder. This experience informs how I now build and lead teams at enterprise scale.

Jan 2018 – Jan 2018 · 0 mo · Singapore

• Quantitative research role at Singapore-based proprietary trading firm, applying machine learning to high-frequency trading strategies.
• TRADING MODEL OPTIMIZATION
• Increased average performance of HFT ML models by 20%+ through systematic, data-driven feature elimination
• Identified and removed noise features degrading model signal-to-noise ratio
• Models operated on millisecond-level execution timeframes
• SENTIMENT ANALYSIS INTEGRATION
• Initiated novel project coupling NLP-based sentiment analysis with technical indicators
• Reduced prediction uncertainty by 15% through multi-modal signal fusion
• Demonstrated value of alternative data in quantitative strategies
• SKILLS DEVELOPED
• Real-time ML systems with strict latency requirements
• Financial time series analysis and feature engineering
• Production ML in high-stakes, low-latency environments

Jan 2017 – Jan 2018 · 1 yr · New Delhi, India

• Technical role at UIDAI operating Aadhaar—world's largest biometric ID system (1.4B users, 80B+ authentications).
• PROGRAM DELIVERY
• Implemented $3M programs for authentication analytics and mAadhaar iOS development
• Led 9 EOIs/RFPs for software, storage, networking, and data center procurement
• Reduced bid evaluation by 40+ hours through process integration
• PRIVACY LEADERSHIP
• Led executive committee analyzing global privacy frameworks (PATRIOT Act, GDPR precursors, OECD)
• Submitted report to Supreme Court of India representative informing data protection legislation

Jan 2017 – Jan 2017 · 0 mo · Okinawa, Japan

• Computational genomics research at OIST, a premier graduate university in Japan.
• Modeled effective population size for pre-Columbian Native American bovine populations using SNP datasets
• Developed approximate Bayesian computation (ABC) models for genomic analysis
• Applied statistical genetics methods to ancient DNA research questions
• International research experience through competitive OIST Fellowship.

Jan 2016 – Jan 2016 · 0 mo · Cambridge, Massachusetts

• Cancer immunotherapy research at Harvard Medical School as SN Bose Fellow (top ~50 nationally selected).
• Studied supramolecular nanoparticles for tumor-targeting immunotherapy
• Conducted chemical synthesis, in vitro binding studies, and characterization
• Developed Monte Carlo simulations modeling experimental optimization
• SN Bose Fellowship: Prestigious India-US research exchange by Dept. of Science & Technology, Govt. of India.

Jan 2014 – Jan 2017 · 3 yrs · IIT Roorkee

• Research across 3 labs during undergrad. Graduated as Gold Medalist (Rank 1/110), completing 4-year degree in 3 years.
• SMART MATERIALS LAB
• Synthesized magnetic nanocomposites for cancer hyperthermia therapy
• FEM simulations for biomedical treatment optimization
• Publications in Journal of Materials Science, J. Biotechnology and Biomaterials
• COMPUTATIONAL BIOLOGY LAB
• Built pipelines for medicinal plant screening and TB biomarker identification
• Applied ML clustering to chemical feature analysis
• UNDERGRADUATE THESIS (Biomaterials Lab)
• Developed magneto-hyperthermic scaffolds for cartilage regeneration
• Integrated materials synthesis with FEM modeling
• Recognition: Institute Gold Medal | 3 publications | National Chemistry Olympiad Finalist