Competitions

The SIIM-ACR Pneumothorax Segmentation competition, hosted on Kaggle by the Society for Imaging Informatics in Medicine (SIIM) and the American College of Radiology (ACR), challenged participants to develop AI models capable of detecting and segmenting pneumothorax (collapsed lung) in chest X-ray images. Accurate identification of pneumothorax is critical for timely medical intervention, and this competition aimed to enhance diagnostic tools to assist radiologists and improve patient outcomes.

The Avito Demand Prediction Challenge, hosted on Kaggle, tasked participants with predicting the demand for online classified ads on Avito, Russia's largest classifieds website. Competitors utilized various data modalities—including textual descriptions, images, and contextual information—to forecast the likelihood of an ad leading to a successful transaction. Accurate demand prediction is crucial for optimizing user experience and platform efficiency, helping sellers set appropriate expectations and pricing. This competition aimed to enhance Avito's ability to match supply and demand effectively, thereby improving overall user satisfaction.

The Home Credit Default Risk competition, hosted on Kaggle, invited participants to develop machine learning models to predict the repayment capabilities of clients using alternative data sources, such as telecommunications and transactional information. The goal was to enhance financial inclusion by enabling Home Credit to offer safe borrowing experiences to individuals with limited or no credit histories. Accurate predictions would ensure that clients capable of repayment are approved for loans with appropriate terms, while minimizing the risk of default.

Awarded the ML Competition Progress Award at the annual Open Data Science conference in Moscow, recognizing consistent achievements and contributions to the machine learning competition community.

The Asia Pacific Mathematical Olympiad (APMO) is an annual high-level competition that challenges high-school students across the Asia–Pacific region to solve advanced problems in algebra, geometry, number theory, and combinatorics. Regularly participating countries include Australia, Canada, China, Hong Kong, Indonesia, Japan, Korea, Malaysia, Mexico, New Zealand, the Philippines, Russia, Singapore, Taiwan, Thailand, the United States, and Vietnam. Over the years, top-performing teams have often hailed from China, Japan, Korea, Russia, and the United States.

The Freesound Audio Tagging 2019 competition, hosted on Kaggle by Freesound and Google's Machine Perception team, challenged participants to develop multi-label audio tagging systems capable of recognizing 80 diverse sound categories from real-world environments. Participants utilized both curated and noisy datasets, applying machine learning techniques to automatically tag audio clips. The primary evaluation metric was label-weighted label-ranking average precision (lwlrap), measuring the precision of ranked label lists for each test clip.

The iMaterialist (Fashion) 2019 competition, part of the Fine-Grained Visual Categorization Workshop (FGVC6) at CVPR, challenged participants to develop algorithms for accurate segmentation and attribute labeling of fashion images. Visual analysis of clothing has received increasing attention in recent years, with applications that could enhance shopping experiences for consumers and increase work efficiency for fashion professionals. This competition introduced a novel fine-grained segmentation task that unified both categorization and segmentation of rich and complete apparel attributes.

Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring rapid and often intensive medical treatment. For example, intracranial hemorrhages account for approximately 10% of strokes in the U.S., where stroke is the fifth-leading cause of death. Identifying the location and type of any hemorrhage present is a critical step in treating the patient. In this competition, hosted by the Radiological Society of North America (RSNA), participants were challenged to build an algorithm to detect acute intracranial hemorrhage and its subtypes from head CT scans, aiming to help reduce the time to diagnosis.

Google Research featured prediction competition focused on building a better classifier for the Quick, Draw! dataset. The task was to improve pattern recognition solutions for handwriting recognition with robust applications in OCR (Optical Character Recognition), ASR (Automatic Speech Recognition) & NLP (Natural Language Processing). By advancing models on this dataset, participants could improve pattern recognition solutions more broadly.

Microsoft challenged the data science community to develop techniques to predict if a machine will soon be hit with malware. With more than one billion enterprise and consumer customers, Microsoft takes this problem very seriously and is deeply invested in improving security. As one part of their overall strategy, Microsoft provided Kagglers with an unprecedented malware dataset to encourage open-source progress on effective techniques for predicting malware occurrences.

TalkingData, China's largest independent big data service platform, covers over 70% of active mobile devices nationwide. They handle 3 billion clicks per day, of which 90% are potentially fraudulent. Participants were challenged to build an algorithm that predicts whether a user will download an app after clicking a mobile app ad. The competition provided a generous dataset covering approximately 200 million clicks over 4 days.

A regional Olympiad-style contest for high-school students from countries along the historic Silk Road, including Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan, Turkey, Azerbaijan, and China. The competition featured challenging problems in algebra, geometry, number theory, and combinatorics, promoting mathematical excellence and fostering collaboration among participating countries.