Curriculum Vitae

Ahmedabad, Gujarat, India · fenilmmodi@gmail.com · linkedin.com/in/fenilmodi823 · github.com/fenilmodi823

Profile

Computer Engineering graduate (degree conferral pending) seeking research positions in space situational awareness and large-scale scientific data processing. First author and presenter of an IEEE SMC-sponsored paper on probabilistic conjunction assessment, with experience across the full analysis chain — acquisition, preprocessing, exploratory analysis, probabilistic modelling, uncertainty quantification and validation. Currently Lead Backend & Data Engineer while leading a six-member orbital-analysis research team. Available for international relocation.

Publication

Probabilistic Space Debris Conjunction Assessment using Machine Learning and Covariance Intersection Analysis — F. M. Modi, S. V. Khara, G. D. Tivari, J. Patel, P. Patel, G. Kumawat. 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 1957–1962. Paper ID 1849, technically sponsored by the IEEE SMC Society. Presented online, 28–30 July 2026. Published in IEEE Xplore — DOI: 10.1109/ICSSIT69151.2026.11656410

First author and presenter. Showed that a nominal miss distance can remain statistically consistent with collision, as measured by overlap of 3σ covariance ellipses projected into the B-plane, by fusing state estimates of unknown correlation through covariance intersection and screening candidate conjunctions with machine learning.

Experience

Lead Backend & Data Engineer, Hubbl — Jul 2026 to present

Technology Community Platform · Full-time

• Defined the backend and data architecture for a platform consolidating four regional technology user groups into one ecosystem, as measured by 8+ core backend services and the PostgreSQL schema now underpinning all community, event and speaker records, by setting service boundaries and data contracts at platform inception.
• Turned fragmented per-group records into a single queryable dataset, as measured by four previously separate user-group datasets normalised into one relational schema, by designing the data model and building the ingestion path.
• Kept community and event data current without manual entry, as measured by mail-synchronisation and event-scraping jobs running unattended on schedule, by building Node-based background workers against external community and event sources.
• Delivered the registration data pipeline for a 120-capacity national analytics event with the India AI + Tableau User Group and Queen’s University Belfast (India GIFT City), as measured by a fully pre-registered cohort requiring no on-the-spot fallback, by owning participant records and registration analytics end to end.

Team Lead & Lead Analyst, ORCAS — Jan 2024 to present

Silver Oak University

• Cut catalogue preparation from a manual multi-hour task to a single automated pass, as measured by end-to-end runtime over a ∼10,000-object TLE catalogue, by building a Python ingestion pipeline that validates checksums, resolves epoch drift and de-duplicates records.
• Narrowed the conjunction search space before expensive computation, as measured by the count of candidate object pairs surviving each filter stage, by implementing a coarse-to-fine screening cascade using perigee–apogee and geometric pre-filters.
• Replaced single point estimates with quantified confidence, as measured by 95% confidence intervals on probability of collision, by running Monte Carlo sampling over positional covariance in a batch-parallel workload.
• Established propagator correctness before publishing any result, as measured by position residuals against published reference ephemerides across a multi-day horizon, by cross-validating the SGP4 implementation against independent reference outputs.
• Kept a six-member team shipping reproducibly, as measured by full reconstruction of every published figure from versioned source, by instituting Git-based code review and parameterised analysis notebooks.

Selected projects

Quantum-Random Key Generation — Statistical Evaluation · Python, Qiskit, SciPy · 2023

• Determined whether quantum-derived keys outperform pseudo-random streams, as measured by pass rates across the 15 statistical tests of NIST SP 800-22, by designing a controlled comparison over matched-length key sets.
• Characterised diffusion behaviour with stated significance rather than headline claims, as measured by χ² goodness-of-fit and avalanche-effect distributions, by implementing entropy and bit-correlation analysis in NumPy/SciPy.

Education

Silver Oak University, Ahmedabad — B.Tech, Computer Engineering — coursework completed, degree conferral pending · Aug 2022 – Aug 2026

Coursework: Statistical Methods, Machine Learning, Data Structures & Algorithms, Cryptography, Computer Networks, Database Systems, Scientific Computing.

Technologies

Scientific computing & analysis Python (NumPy, SciPy, Pandas), exploratory data analysis, time-series analysis, hypothesis testing, Monte Carlo methods, uncertainty quantification, numerical integration

Large-scale & HPC batch pipelines, catalogue-scale datasets, scheduled background jobs, performance profiling, parallel workloads, Linux/CLI

Orbital & space data SGP4/SDP4, TLE handling, Skyfield, ephemeris validation, conjunction screening, covariance intersection, B-plane geometry, coordinate frame transformations

Machine learning Scikit-learn, TensorFlow, PyTorch, cross-validation, residual analysis, model selection

Backend & data engineering REST API design, schema design, query optimisation, JWT authentication, Node.js, Express.js, PostgreSQL, Prisma, FastAPI, MongoDB, React

Research practice Git/GitHub, reproducible analysis, LaTeX, Jupyter, Matplotlib, Tableau, technical documentation

Additional

Languages: English (full professional), Hindi (native), Gujarati (native) Interests: space situational awareness, orbital debris environment modelling, Kessler syndrome dynamics

Ahmedabad, Gujarat, India · fenilmmodi@gmail.com · linkedin.com/in/fenilmodi823 · github.com/fenilmodi823

Research interests

Orbital data analysis and space situational awareness; probabilistic conjunction assessment, covariance fusion and uncertainty quantification; large-scale scientific data processing and reproducible analysis pipelines; statistical evaluation of randomness and cryptographic primitives; scientific data visualisation.

Education

Silver Oak University — Ahmedabad, Gujarat Bachelor of Technology in Computer Engineering · Aug 2022 – Aug 2026

• Status: all coursework and examinations completed August 2026; degree conferral pending.
• Relevant coursework: Statistical Methods, Machine Learning, Artificial Intelligence, Data Structures & Algorithms, Cryptography, Computer Networks, Database Management Systems, Scientific Computing.
• Self-directed study: orbital mechanics (two-body dynamics, Keplerian elements, SGP4/SDP4 propagation), numerical methods, probabilistic modelling and Bayesian inference (in progress).
• Final-year work: probabilistic conjunction assessment under positional uncertainty — basis of the published ICSSIT 2026 paper.

Publications

Probabilistic Space Debris Conjunction Assessment using Machine Learning and Covariance Intersection Analysis F. M. Modi, S. V. Khara, G. D. Tivari, J. Patel, P. Patel, G. Kumawat 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 1957–1962. Paper ID 1849. Technically sponsored by the IEEE Systems, Man and Cybernetics Society. Published in IEEE Xplore; presented online 28–30 July 2026. DOI: 10.1109/ICSSIT69151.2026.11656410

Contribution (first author): led the study design and analysis — fusing state estimates of unknown cross-correlation via covariance intersection and applying machine learning to conjunction screening, showing through B-plane projection of 3σ covariance ellipses that a nominal miss distance can remain statistically consistent with collision. Validated against the Iridium 33 / Cosmos 2251 event.

Conference presentations

Probabilistic Space Debris Conjunction Assessment using Machine Learning and Covariance Intersection Analysis. Oral presentation delivered by F. M. Modi on behalf of all authors, ICSSIT 2026, 28–30 July 2026 (online).

Research experience

ORCAS — Orbital Risk and Conjunction Assessment System — Jan 2024 to present

Team Lead & Lead Analyst, Silver Oak University

• Cut catalogue preparation from a manual multi-hour task to a single automated pass, as measured by end-to-end runtime over a ∼10,000-object TLE catalogue, by building a Python ingestion pipeline validating checksums, resolving epoch drift and de-duplicating records.
• Characterised the tracked-object population by orbital regime, as measured by element distributions across LEO/MEO/GEO and inclination bands, by performing exploratory data analysis on the cleaned catalogue.
• Narrowed the conjunction search space ahead of expensive computation, as measured by candidate object pairs surviving each filter stage, by implementing a coarse-to-fine screening cascade with perigee–apogee and geometric pre-filters.
• Replaced point estimates with quantified confidence, as measured by 95% confidence intervals on probability of collision, by running Monte Carlo sampling over positional covariance as a batch-parallel workload.
• Established propagator correctness before publication, as measured by position residuals against published reference ephemerides over a multi-day horizon, by cross-validating the SGP4 implementation against independent reference outputs.
• Coordinated a six-member research team — the co-author group of the ICSSIT 2026 paper — keeping work reproducible, as measured by full reconstruction of every published figure from versioned source, through Git-based code review and parameterised analysis notebooks.
• Stack: Python, SGP4/Skyfield, NumPy, SciPy, Pandas, Scikit-learn, FastAPI, React.

Quantum-Enhanced AES Encryption using Quantum Random Walks — Aug 2023 to Dec 2023

Independent Research Project, Silver Oak University

• Determined whether quantum-derived keys outperform pseudo-random streams, as measured by pass rates across the 15 statistical tests of NIST SP 800-22, by designing a controlled comparison over matched-length key sets.
• Characterised diffusion behaviour with stated significance levels, as measured by χ² goodness-of-fit and avalanche-effect distributions, by implementing entropy and bit-correlation analysis in NumPy/SciPy.
• Made the study independently repeatable, as measured by full regeneration of results from a documented seed and parameter set, by recording experimental methodology and data-collection procedure alongside the code.
• Stack: Python, Qiskit, NumPy, SciPy, Matplotlib.

Professional experience

Hubbl — Technology Community Platform — Jul 2026 to present

Lead Backend & Data Engineer, Full-time

• Defined the backend and data architecture at platform inception — service boundaries, data contracts and the relational schema underpinning all community, event and speaker records — as measured by 8+ core backend services, for a platform consolidating four regional technology user groups (Gujarat Tableau, Gujarat Databricks, Ahmedabad Tableau, India AI + Tableau) into one ecosystem.
• Turned fragmented per-group records into one queryable dataset, as measured by four previously separate user-group datasets normalised into a single PostgreSQL schema, by designing the data model and building the ingestion path with Prisma and Zod-validated contracts.
• Kept community and event data current without manual entry, as measured by mail-synchronisation and event-scraping jobs running unattended on schedule, by building Node-based background workers against IMAP inboxes and external community sources.
• Made privileged actions traceable across the platform, as measured by an audit log covering role-stage and role-review state transitions, by building role management and profile change-request handling into the service layer.
• Delivered the registration data pipeline for a 120-capacity national analytics event run with the India AI + Tableau User Group and Queen’s University Belfast (India GIFT City) on 17 August 2026, as measured by a fully pre-registered cohort requiring no on-the-spot fallback, by owning participant records and registration analytics end to end.
• Stack: Node.js, Express.js, PostgreSQL (Supabase), Prisma, Zod, JWT + bcrypt, REST APIs, SMTP/IMAP, scheduled background jobs; deployed on Render behind Cloudflare.

Additional projects

Diablex — Clinical Time-Series Analytics Platform — 2024 to present

Full-Stack Developer, Independent

• Reduced noise-driven false anomaly flags in continuous glucose data, as measured by flag counts before and after filtering across ∼1,000 readings per day, by implementing outlier detection, resampling and normalisation for irregular time series.
• Enabled clinicians to distinguish signal from instrumentation artefact, as measured by data-quality flags attached to every derived statistic, by surfacing provenance and confidence alongside each reported trend.
• Stack: Node.js, React, MongoDB, Python.

Retail Sales Performance Dashboard — 2026

Data Analysis Project, Independent

• Delivered an executive dashboard covering four KPI tiles and three linked views, as measured by drill-down coverage across region, category and customer segment, by modelling table relationships and wiring dashboard actions for cross-filtering.
• Surfaced under- and over-performing segments at a glance, as measured by variance against target in a diverging bar view, by designing the encoding around deviation rather than absolute value.
• Stack: Tableau, data modelling, dashboard actions.

Technical skills

Scientific computing & data analysis Python (NumPy, SciPy, Pandas), exploratory data analysis, time-series analysis, statistical hypothesis testing, Monte Carlo methods, uncertainty quantification, numerical integration

Large-scale & high-performance computing batch data-processing pipelines, catalogue-scale dataset handling, scheduled background jobs, parallel workloads, performance profiling, Linux/CLI workflows

Orbital & space data SGP4/SDP4, TLE ingestion and validation, Skyfield, ephemeris cross-validation, conjunction screening, covariance intersection, B-plane geometry, coordinate frame transformations

Machine learning Scikit-learn, TensorFlow, PyTorch; cross-validation, residual analysis, hyperparameter tuning, model selection and evaluation

Programming Python, C/C++, Java, JavaScript, SQL

Backend & data engineering REST API design, service architecture, database schema design, indexing and query optimisation, JWT/bcrypt authentication, scheduled workers, third-party integration; Node.js, Express.js, PostgreSQL, Prisma, Zod, FastAPI, MongoDB, React

Visualisation Matplotlib, Seaborn, PyVista, Tableau, D3.js

Research practice Git/GitHub version control, reproducible analysis, LaTeX, Jupyter, technical documentation, collaborative development in multi-member teams

Professional development

India AI + Tableau User Group — Data & Analytics Day (with Queen’s University Belfast, India GIFT City, and Hubbl). Attendee, 17 August 2026. Hands-on analytics sessions and industry-mentor networking; 120-attendee capacity, pre-registration only.

Presented project work in departmental reviews and college innovation challenges.

Leadership & service

Team Lead, ORCAS research team, Silver Oak University (Jan 2024 – Present) — task allocation, code review and reproducibility standards across a six-member team.

Organised and participated in college-level technical events and hackathons.

Additional

Languages: English (full professional proficiency), Hindi (native), Gujarati (native)

Availability: open to research positions, graduate traineeships and postgraduate study internationally. Available for relocation.

References: available on request.