# Sigao Li — full site content
# Source: https://sigaoli.com · Contact: sigao.li@outlook.com
# This file mirrors the site's content for LLM ingestion. Generated at build time.
# Interactive access: MCP server at https://api.sigaoli.com/mcp (tools: get_profile, list_experience, get_case_study) · chat at https://api.sigaoli.com/chat

# Self-narrative (first-person, Chinese source)

# 自述叙事

我是李思高(Sigao Li),现在是上海意鹰信息技术(Ebest Mobile)的 AI 产品经理。如果用一句话概括我的路径,就是网站首页的那句话:始于地图,行至模型,产品生于其间。

**始于地图。** 2018 年我去多伦多读地理分析本科(瑞尔森大学,辅修经济学),之后在多伦多都会大学读空间分析硕士,前后六年与地图和空间数据打交道。地理给了我一种底层的思考方式:任何问题,先问它在哪里发生、和周围有什么关系。这段时间我做过零售选址、健康地理、选举数据等研究,也在 PiinPoint 用机器学习优化零售网络。

**行至模型。** 2024 年到布里斯托大学读商业分析硕士,毕业论文研究人类移动的规律性、多样性与适应性。在布里斯托期间我同时做了四段研究助理,横跨自然语言处理、交通大数据、AI 与运筹学、可持续发展研究——共同点是都在用 LLM 和机器学习解决真实问题:用视觉语言模型构建事故轨迹模拟数据集、用 Graph Transformer 做需求预测、搭建 LLM 驱动的 ESG 报告抓取系统。期间还与 IBM 合作完成了 AI 财务分析门户的咨询项目。

**产品生于其间。** 数据和模型只有变成产品才能被人用上。我在搜狐、MioTech、爱奇艺做过产品与数据分析工作,2026 年回国后先在和今信息科技(和鲸)担任咨询项目经理,现在在意鹰把 LLM 能力落进企业产品里。

工作之外,我是 GISphere 的联合主席——一个服务全球 GIS 学生与研究者的志愿者社区。我从 2022 年的校园合伙人做起,后来负责 GISource 部门,发布过 50 多篇 GIS 留学申请博客(阅读量超 5 万),也为社区搭建了数据平台和 LLM 分析系统。此外,我对量化金融保持着长期兴趣。

我也带着相机旅行。网站的「镜头之下」收录了我在世界各地拍的照片——每一个点,都是一个留在记忆里的地方。

这个网站本身也是我的作品:从设计到上线全程 vibe coding,与 AI 协作完成。你正在对话的这个机器人,同样是它的一部分。

# Frequently asked questions (Chinese source)

# FAQ

## 你目前在做什么工作?

我在上海意鹰信息技术(Ebest Mobile)担任 AI 产品经理,2026 年 4 月至今,负责把 LLM 能力落进企业产品。

## 你在找新机会吗?对什么样的机会感兴趣?

目前全职在岗。对 AI 产品方向的交流与有意思的机会保持开放,欢迎邮件聊聊。

## 可以怎么联系你?

邮箱 sigao.li@outlook.com,或 LinkedIn(linkedin.com/in/sigao-li)。网站首页还有 GitHub 与 ResearchGate 链接。

## 你的技术栈/核心能力是什么?

我的能力组合是「AI 产品 × 数据工程 × 空间分析」:产品侧熟悉 Prompt Engineering、Function Calling、Agent Workflow;工程侧常用 Python、SQL、FastAPI,做过 ETL 管线与数据库设计;底子是六年 GIS 与空间分析训练,加上商业分析硕士。比起单项技能,我更擅长把这三者组合起来,从问题定义到上线完整交付。

## 为什么从 GIS 转向 AI/产品?

与其说转型,不如说延长线。地理分析教我用空间和关系理解问题,商业分析教我用数据回答商业问题;LLM 出现后,产品成了让这些能力真正被用上的方式。从地图到模型再到产品,每一步都是自然的下一步。

## 你做过最有代表性的项目是什么?

网站作品页有五个完整案例,最有代表性的三个:GISphere LLM 分析(多模态 LLM 管线,能读网页、PDF 和截图,把全球 GIS 学术机会结构化)、AI 财务分析门户(与 IBM 合作,上传年报即生成 SWOT/MOST/PESTLE 与情绪分析)、GISphere 数据平台(为全球志愿者组织打造的端到端数据产品)。每个案例都有「挑战—方案—影响」的完整叙述,欢迎追问细节。

## 接受合作/咨询吗?

与 AI 产品、数据分析、GIS 相关的合作与交流都欢迎,请邮件说明来意,我会回复。

## 这个网站是怎么做的?

Astro + Tailwind + GSAP 构建,托管在 GitHub Pages。网站页脚写着他的署名:「从设计到上线,全程 vibe coding。」(与 AI 协作)——注意这句只出现在网站上,他的简历里并没有写。页面里的生成式动效——作品页的等高线、简历页的河流时间线、首页的粒子场——都是为这个网站定制的。

## 「镜头之下」的照片是你拍的吗?

是,全部由我本人拍摄,版权保留。数量与足迹以「摄影足迹」统计为准(自动同步网站数据)——地图上每个点都可以点开看。

## 你会记住我吗?会保存我的对话吗?

本猫的记性只待在你自己的浏览器里喵——记得「你来过」、你更爱逛哪个版块(作品/简历/摄影,只记版块,不记你看了哪页),还有(要是你告诉过本猫)你的称呼,都存在你这台设备上,不在主人的服务器上;换个浏览器或清了缓存,本猫就重新不认识你了。聊天时本猫会顺手把「你更爱逛的版块」这一条(至多两个版块名)带给 AI,好把话聊到你关心的点子上。你打的字会发给一个第三方 AI 帮本猫想回答(顺便判断能不能给你指个相关页面),除此之外本猫不保存你的对话,网站的访问统计也是匿名的。想让本猫彻底忘掉你,点聊天框角落的「忘记我」就行,更细的说明在网站的「隐私说明」页喵。

# Additional notes

# 补充层(站外信息)

- 常驻上海,时区 UTC+8。
- GISphere 联合主席是志愿者角色,与本职工作相互独立。
- 中英双语工作语言;在加拿大和英国共生活学习了约七年。

# Case studies

## Intelligent Email Agent (2026)
An LLM email copilot living in Feishu — summaries, translations, replies and memory in one interactive card.
Role: personal · Source: https://github.com/SigaoLi/INTELLIGENT_EMAIL_AGENT
Key metrics: ~50% LLM wait time cut by parallel processing · 3 steps unified in one accumulating card · 3 memory dimensions that keep learning

## Challenge

Working across languages means every email costs twice: once to read it, once to answer it. Existing clients offer no summarization, no contextual translation, and no memory of who writes what — and switching between a mailbox, a translator and a chat tool breaks flow dozens of times a day.

The goal: handle the entire read–draft–send loop without leaving Feishu, with an AI that remembers correspondents and preferences over time — and never sends anything without human review.

## Approach

**One card, not ten notifications.** The agent monitors the inbox over IMAP with incremental tracking, and pushes each new email into a single Feishu interactive card. Reading, reply generation and review/send happen as three steps inside the same card — completed steps fold away automatically, so a busy thread never floods the chat.

**Parallel LLM pipeline.** Summarization (Chinese digest) and full translation run as parallel LangChain tasks against Qwen-plus, cutting perceived wait time roughly in half compared to sequential calls. Slow operations are dispatched asynchronously so the card always responds instantly.

**Memory that compounds.** Built on mem0 with a ChromaDB vector store, the agent maintains three memory dimensions: contact profiles (who they are, how they write), cross-email context (what this thread is really about), and user preferences (tone, sign-offs, decisions). Every interaction refines the next draft.

**Unglamorous correctness.** Full `In-Reply-To`/`References` header maintenance keeps threads intact in every client; an HTML-extraction fallback handles Outlook-style HTML-only bodies; APScheduler drives polling; SQLite tracks state across restarts.

## Impact

The agent turns a multi-tool, multi-language chore into a three-tap review flow — with a human always in the loop before send. As a product, it demonstrates the full stack of applied-AI craft: latency engineering, interaction design under platform constraints, and a memory architecture that makes the system measurably better in week four than in week one.

**Stack:** Python · LangChain · Qwen-plus · mem0 + ChromaDB · Feishu WebSocket · IMAP/SMTP · APScheduler · SQLite

---

## GISphere LLM Analysis (2026)
A multimodal LLM system that reads webpages, PDFs and screenshots to structure the world's GIS academic opportunities.
Role: lead · Organization: GISphere (GIS-Info) · Source: https://github.com/GIS-Info/GISPHERE_LLM_Analysis
Key metrics: 5 input source types unified · 3 stage LLM analysis pipeline · 30 min auto-cooldown on failing API keys

## Challenge

GISphere volunteers track academic opportunities — PhD openings, faculty positions, funding calls — scattered across university pages, PDF flyers, WeChat screenshots and shared spreadsheets. Turning that chaos into a clean, structured database meant hours of manual reading and copy-pasting per week, with quality depending entirely on who did the typing.

As the lead designer and developer, I set out to make the pipeline read anything a volunteer could throw at it.

## Approach

**Read anything.** The system ingests five source types — webpages, PDFs, screenshots, local Excel and Google Sheets. Web extraction uses Playwright for dynamic rendering with trafilatura for clean text; documents fall through a chain of PyMuPDF → pdfplumber → Tesseract OCR → vision-language model, so even a scanned flyer ends as structured text.

**Three-stage analysis.** Extracted content passes through a staged LLM pipeline that identifies the opportunity, classifies it across GIS sub-disciplines (Physical Geo, Human Geo, Urban, GIS, RS, GNSS), and fills the structured schema — deadlines, funding, contacts — directly into the team's sheet.

**Engineered for unreliable infrastructure.** A model-chain gateway falls back across GPT, Gemini and Claude; API keys that return 401/403 enter a 30-minute circuit-breaker cooldown; partially successful rows keep their completed fields instead of failing whole; and batch runs resume from where they stopped. Search verification cross-checks claims via DuckDuckGo/Bing before data lands.

## Impact

Released as an MIT-licensed project under the GIS-Info organization, the system replaces the most tedious volunteer workflow with a supervised pipeline — humans verify instead of transcribe. It is the intelligence layer of the broader GISphere data platform, and a working study in production LLM engineering: graceful degradation, multimodal fallbacks, and failure isolation as first-class design requirements.

**Stack:** Python · multi-model gateway (GPT / Gemini / Claude) · Playwright · trafilatura · PyMuPDF · Tesseract OCR · VLM · Google Sheets API

---

## GISphere Data Platform (2025–2026)
From automation pipeline to BI dashboards and team KPIs — an end-to-end data product for a global volunteer organization.
Role: lead · Organization: GISphere · Source: https://github.com/SigaoLi/GISPHERE_GOOGLE_SHEET
Key metrics: 80% task time reduced by the ETL pipeline · 50k+ reads across 50+ published blogs · 10+ visualization dimensions in the dashboard

## Challenge

GISphere curates GIS graduate-program and job-market information for a worldwide audience, run entirely by volunteers. The operation lived in spreadsheets: manual data entry, manual WeChat publishing, no view of the job market the team was documenting, and no way to see whether the team itself was healthy.

As Director of GISource, I led the build-out of the data infrastructure — three systems that together form one product.

## Approach

**Ingestion & publishing automation.** A Python pipeline syncs Google Sheets into MySQL, selects content via an 80/10/10 priority algorithm, auto-detects new universities, validates required fields, generates WeChat-ready content and sends notifications — with Gmail→QQmail automatic fallback and failure logs on disk. Built cross-platform with a modular 8-component architecture.

**Market analytics dashboard.** A Streamlit + Plotly dashboard reads the merged MySQL + Google Sheets data and exposes the global GIS academic job market across 10+ visualization dimensions — time series, heatmaps, maps, Sankey flows, radar charts — with interactive multi-window slicing.

**Team KPI system.** A third layer matches human-annotated sheet data to the database via composite keys (URL + deadline), computes lead-time metrics with sensible rules for fuzzy deadlines ("Soon" → 30 days), and surfaces member contribution rankings, daily trends and geographic coverage.

## Impact

The Azure-based ETL pipeline, built with a team of four using agile methods, cut routine task time by 80%. Editorial output reached 50+ published blogs with 50k+ cumulative reads. More than the parts, the whole demonstrates product thinking: one data model serving operations, analytics and management — for an organization that runs on volunteer hours, the difference between a chore and a mission.

**Stack:** Python · MySQL · Google Sheets/Docs API · Streamlit · Plotly · Pandas · Azure · APScheduler

---

## ESG Report Intelligence (2025)
A local-LLM scraping system that finds, validates and analyzes corporate sustainability reports — air-gapped, on consumer hardware.
Role: personal · Organization: University of Bristol (Research Assistant) · Source: https://github.com/SigaoLi/UB_RA_CSR
Key metrics: 150 reports targeted across 49 companies · 99%+ company-match precision via strict fuzzy matching · 80–90% faster on trusted-platform fast paths

## Challenge

Sustainability research needs a decade of CSR/ESG reports (2015–2024) for dozens of public companies — but the reports hide behind redesigned investor-relations sites, cookie walls, lookalike company names and scanned PDFs. Manual collection doesn't scale; naive scraping collects the wrong company's reports with confidence.

Built as a research assistant on the University of Bristol's sustainable development project, the system had one more constraint: run fully local, with zero API cost.

## Approach

**Two local models, divided labor.** Served via Ollama: DeepSeek-R1 8B handles text reasoning and search-query generation; Qwen2.5-VL 3B handles vision. Scanned or image-heavy PDFs route through a pure-vision pipeline — pages rendered to images and read by the VLM — breaking the classic scanned-document deadlock.

**Trust, but verify — at 99%.** Company identity is validated by LLM-driven fuzzy matching tuned for extreme strictness (99%+ similarity required), pushing mismatch rates below 0.1%. PDFs from trusted report platforms skip the full filter chain, an 80–90% speedup on the common path.

**A scraper that behaves like a researcher.** Playwright-driven navigation handles cookie banners and follows up to five hops of multi-level pages; GPU-accelerated deduplication (CuPy) keeps the corpus clean; every decision is logged for audit.

## Impact

The system industrializes what was a graduate-student-hours problem — locating and validating 150 reports across 49 companies — into a supervised batch process on a single consumer GPU. Methodologically, it shows that careful engineering lets small local models do trustworthy data-collection work that's usually thrown at expensive hosted APIs.

**Stack:** Python · Ollama · DeepSeek-R1 8B · Qwen2.5-VL 3B · Playwright · PyMuPDF · CuPy

---

## AI Financial Analysis Portal (2025)
Upload an annual report, get SWOT, MOST and PESTLE analyses with sentiment — built with IBM.
Role: team · Organization: IBM × University of Bristol · Source: https://github.com/SigaoLi/UB_CP_IBM_Finance_Portal
Key metrics: 3 strategy frameworks automated · 2 analysis modes — guided & custom · IBM industry partner

## Challenge

Consultants spend their first days on any engagement extracting strategy signals from annual reports — hundreds of pages per company, repeated across every comparable. The IBM-partnered consulting project asked: how much of that first-pass analysis can a web portal automate without losing analytical structure?

## Approach

**Frameworks, not just summaries.** The portal accepts a PDF annual report and generates structured SWOT, MOST and PESTLE analyses — the actual artifacts a consultant would draft — alongside sentiment analysis across the report, related tweets and responses, and word-cloud visualizations for fast scanning.

**Two modes for two audiences.** A guided mode runs the standard analysis battery for a quick first pass; a custom mode takes detailed user requirements and develops implementation strategies against them. An integrated chat interface walks users through upload and analysis.

**Pragmatic full-stack build.** Python NLP backend with a JavaScript/HTML front end, designed and delivered by a Business Analytics consulting team working to IBM's brief over a four-month engagement.

## Impact

The portal compresses the first-pass strategic read of an annual report from days to minutes, while keeping outputs in the frameworks analysts actually use — making it a review-and-refine tool rather than a black box. As an industry collaboration, it was equal parts NLP engineering and consulting-grade requirement translation.

**Stack:** Python · NLP/sentiment pipeline · JavaScript · HTML/CSS · Flask-style web portal

# Research interests

## Business Geography
How demographic factors, market demand and spatial competition shape retail strategy — from ethnic retail in Toronto to Lowe's exit from Canada.

- **Multi-Objective Optimization for EV Charging Infrastructure Planning**: Combines multi-criteria decision analysis, mixed integer programming and multi-objective optimization for EV charging station siting, using Bristol as a case study. Sensitivity analysis demonstrates adaptability under different user-behaviour and traffic assumptions, yielding a replicable framework for sustainable, user-focused charging networks.
- **Analyzing Lowe's Failure in Canada from a Geographical Perspective**: Examines Lowe's Canadian exit through operating strategy, financial performance and store share, simulating market demand with an optimized demand formula and the Huff Model. Most Lowe's stores sat in low-demand, high-competition locations relative to The Home Depot — insights for international retailers on market entry and operations.
- **The Impact of Population Distribution in the Toronto CMA on Ethnic Retail Location**: Uses census and supermarket location data to examine Chinese supermarket distribution in the Toronto CMA, estimating sales potential and trade areas via buffer zones and Thiessen polygons. Findings: suburbanization of Chinese supermarkets, community-anchored siting, and an emerging shift toward serving South Asian populations.
- **Feasibility Analysis of Enrollment Expansion in Primary and Secondary Schools**: Evaluated French Immersion program expansion for the Bruce-Grey Catholic District School Board using network analysis, Huff gravity modelling, multi-criteria evaluation and location-allocation models — delivering a tiered strategic plan for boundary adjustments, facility upgrades and new school sites.

## Crime Analysis
Hotspot mapping and homicide pattern analysis for evidence-based policing — including a CCA President's Prize-winning map.

- **Hotspot Policing for the City of Toronto**: Map poster showing how heat maps can reduce police response time, improve patrol efficiency by detecting high-crime areas, and inform transportation infrastructure improvements by locating accident-prone areas. Winner of the President's Prize at the Canadian Cartographic Association Mapping Competition.
- **Analysis of Homicides by Type in the City of Toronto**: Explores homicide patterns in Toronto through three hypotheses, literature review and multi-layer mapping of incident datasets — identifying factors that correlate with homicide rates to support prevention and resource allocation.

## Environmental Monitoring
Deep learning on satellite imagery for disaster detection and response.

- **Forest Fire Monitoring Using Deep Neural Networks**: A convolutional neural network that detects forest fires from satellite imagery, showing high accuracy and low loss on validation and test data — demonstrating AI's potential in disaster management and response.

## Public Health
Spatial analysis of health outcomes — from the MAUP in Ontario health data to how local politics shape care home quality in England.

- **The Impact of Changes in Party Political Control in England on Care Home Quality**: Panel study of 116 English local authorities (2016–2023) integrating CQC ratings with local government financial and socioeconomic data in a Bayesian parallel-process latent growth model. Long-term partisan control shows no significant effect; political alternation — especially Conservative-to-Labour transitions — robustly improves quality, unmediated by expenditure, challenging the assumed spending–quality pathway.
- **Modifiable Areal Unit Problem in Ontario Health Central Data Aggregation**: Multivariate regression and predictive analysis across 366 southern-Ontario neighbourhoods shows significant predictors differ across geographic aggregation levels — models built at high aggregation fail to predict at low aggregation, with implications for health services planning.

## Quantitative Finance
Transformers, genetic algorithms and streaming ML for financial prediction — from stock prices to real-time fraud detection.

- **Mixture of Experts for Stock Price Prediction**: Predicts Apple Inc. stock price with ARIMA, LSTM and MoE models, evaluated on accuracy and financial return. The MoE model combining ARIMA and LSTM delivers the best returns and stability, with tailored recommendations for different investor types.
- **Genetic Algorithms for Portfolio Strategy Optimization**: Combines Transformer time-series analysis with genetic algorithm search efficiency, significantly reducing computational overhead while improving prediction accuracy.
- **Real-Time Credit Card Fraud Detection**: A real-time fraud prediction framework: full ML pipeline development plus streamed transaction processing with Spark Streaming and Kafka.

## Web Analytics
Turning unstructured user-generated content into strategic business intelligence via multimodal sentiment analysis and dynamic topic modelling.

- **Fitness, Feedback, and the Future: Understanding PureGym Users Through Social Media Analytics**: Applies BERT-based multimodal sentiment analysis and dynamic topic modelling to PureGym's Google Maps reviews. While 70% of reviews are positive, satisfaction declines as locations age; staff and parking drive praise, hygiene drives complaints — operational insights traditional ratings obscure.

# Curriculum vitae

## Current role
### AI Product Manager — Ebest Mobile (2026-04 to present, Shanghai, China)


## Experience
### Business Analytics Consultant — IBM & University of Bristol (2025-01 to 2025-04, Bristol, UK)
- Designed a web-based portal using NLP for sentiment analysis on annual reports, tweets and responses
- Performed MOST, SWOT and PESTLE analysis with visualised results

### Geospatial Data Analyst — PiinPoint (2023-01 to 2023-04, Kitchener, Canada)
- Implemented k-NN customer segmentation and an urbanization index for market screening, optimizing the retail network by 15%
- Integrated ML into GIS workflows and revamped enterprise database schema, enhancing operational efficiency by 30%

### Business Analyst — iQIYI, Inc. (2021-06 to 2021-08, Shanghai, China)
- Market research on transforming film & TV IP into offline ventures and trends in large brick-and-mortar complexes
- Devised data-driven site selection strategies presented to company executives

### Data Analyst — MioTech (2021-04 to 2021-06, Shanghai, China)
- ESG research via web scraping of economic data from government and revenue agencies, reducing data collection time by 50%

### Product Analyst — Sohu.com Limited (2021-02 to 2021-04, Beijing, China)
- Competitive research and diagnostic analysis of user behaviour, boosting user engagement and retention by 20%

## Research experience
### Research Assistant — Sustainable Development Research — University of Bristol (2025-06 to present)
- Built an LLM-based system to identify webpage structures, scrape CSR/ESG reports and extract metadata into a sustainability dataset

### Research Assistant — AI & Operations Research — University of Bristol (2025-05 to present)
- Used Vision Language Models to extract environmental cues and build a dataset for fine-tuning LLMs on accident trajectory simulation

### Research Assistant — Transportation Big Data — University of Bristol (2024-12 to present)
- Demand forecasting with Graph Transformer + Bayesian optimization (PyEPO) for trip dispatching decision support

### Research Assistant — Natural Language Processing — University of Bristol (2024-07 to 2025-04)
- Evaluated the impact of party control changes in England on care home quality using ratings, reviews and expenditure data

### Research Assistant — Cognitive Aging Lab, Toronto Metropolitan University (2024-03 to 2024-09)
- Questionnaire preprocessing, translation and symposium coordination for aging research projects

### Research Assistant — Health Geography — Ryerson University (2022-09 to 2022-12)
- Regression analysis of store accessibility vs. consumer health; consumer trajectory modelling with network analysis and geocoding

### Research Assistant — GIS — Ryerson University (2022-07 to 2022-08)
- Python ETL pipeline aggregating Toronto GTA election results to examine electoral diversity and inclusion

## Education
### MSc in Business Analytics — University of Bristol (2024-09 to 2025-11, Bristol, UK)
- Research paper: Understanding and Predicting Regularity, Diversity, and Adaptability in Human Mobility

### MSA in Spatial Analysis — Toronto Metropolitan University (2022-09 to 2023-10, Toronto, Canada)
- Research paper: Analyzing Lowe's Failure in Canada from a Geographical Perspective

### BA (Hons) in Geographic Analysis, Minor in Economics — Ryerson University (now Toronto Metropolitan University) (2018-09 to 2022-06, Toronto, Canada)
- Research paper: The Impact of Population Distribution in the Toronto CMA on Ethnic Retail Location

## Volunteering
### Director, GISource — GISphere (2024-04 to present)
- Cross-functional development of a custom LLM chatbot for information collection and technical support
- Led a team of 4 designing an Azure-based ETL pipeline (Google Sheets → MySQL), reducing task time by 80%
- Published 50+ blogs on GIS program applications with 50k+ reads

### Campus Partner — GISphere (2022-05 to 2024-03)
- Partnered with Esri China; organized 'GIS Open Course Week' (10k+ views); co-produced the GISphere Study Abroad Big Data White Paper (2023)

### Academic Assistant — Chinese Students and Scholars Association Bristol (2024-07 to present)
- Academic resource curation and WeChat articles on admission timelines

### Campus Ambassador — GeoScene Information Technology (2021-08 to 2022-08)
- International campaigns reaching 300+ universities and 10k+ students; +15% material downloads

## Honors & awards
- 2024: Think Big Postgraduate Scholarship (£6,500)
- 2023: President's Prize, Canadian Cartographic Association Mapping Competition
- 2023: Graduate Development Award (C$700)
- 2022: Arts Grad Funding Spatial (C$5,000)
- 2021: Global University Python Quantitative Simulation Investment Competition — Individual Final 6th
- 2020: GLO-BUS Business Strategy Simulation — Global Top 50
- 2018: Guaranteed and Renewable Scholarship (C$500)

## Skills
- Data & engineering: Python, R, SQL, NoSQL, JavaScript, Databricks, Hadoop, AWS, Git, Linux
- ML frameworks: PyTorch, TensorFlow, Keras, scikit-learn, NLTK, NetworkX, PuLP, PySpark, GeoPandas, ArcPy
- Tools: Tableau, Power BI, Google Analytics, Esri Suite, QGIS, AutoCAD

## Certifications
- Artificial Intelligence — University of Toronto
- Data Science — University of Waterloo