Competitions

Kaggle · Atlantis Data Solutions (SEC fundamentals) · Apr – Jul 2026

• Recognized the task as identity resolution, not modeling: an honest model on the 33 fundamentals tops out around 15,000 MSE while the leaderboard sat three orders of magnitude below; only reconstructing each masked company's real 2024 return closes that gap
• Fingerprinted masked companies to real tickers on exact reported dollar values (revenue, assets, net income), requiring at least two independent field agreements and a company-level Hungarian match across quarters to kill costly collisions
• Nailed the host's return convention (price return, not total return) by using raw split-adjusted Close instead of dividend-adjusted prices (correlation 0.998 vs. training labels), worth more than any modeling
• Pulled as-originally-filed values from SEC companyfacts rather than latest-only APIs to recover rows lost to amended and restated 10-Ks
• Read the full write-up on how it was solved

Kaggle · AI Fest 2026 · Jun 2026

• Classified real photos against six generators (DALL·E 3, Midjourney, SDXL, SD3, SD2.1, GAN) under a metric that punishes real-vs-fake flips far more than confusing one generator for another
• Ensembled a fine-tuned ConvNeXt-Base with a frozen CLIP ViT-B/16 probe, averaging the pixel-level and semantic signals 50/50
• Derived a decision rule matched to that asymmetric metric, committing to "real" only when confident, plus flip-TTA and validation-tuned blend weights

Kaggle · AI Fest 2026 · Jun 2026

• Reconstructed 1080p video frames from 540p inputs plus per-frame depth and motion vectors, scored on combined SSIM, GMSD, and PSNR
• Built a recurrent supersampler that motion-warps the previous high-res frame and blends it into the current one through a learned per-pixel mask
• Killed ghosting at object edges with depth-dilated motion warping, so foreground motion wins at boundaries instead of smearing into the background
• Recovered 20 hidden HDR test samples other teams had mean-filled, worth most of the final margin

Kaggle · Omnilex (Swiss legal-tech) · Apr – May 2026

• Built a dense plus cross-encoder retrieval pipeline over 1.18M German-language Swiss legal documents in about three weeks
• Reframed retrieval as citation extraction, pulling references straight from the text and expanding them to canonical form for an order-of-magnitude F1 gain
• Added a HyDE query-enrichment pass with Mistral-7B that declares the relevant legal codes before drafting, lifting Macro F1 by 22%
• Ran 15+ validation ablations and reverted every regression, so only score-improving changes shipped