JANUARY 2025 HACKATHON

HACKATHON AGENDA

"Harnessing Machine Learning to Improve Subseasonal-to-Seasonal Climate Predictions"

LEAP, in collaboration with Columbia University’s Data Science Institute, Amazon Web Services (AWS), and NVIDIA, invites applications to participate in our January 2025 Hackathon: “Harnessing Machine Learning to Improve Subseasonal-to-Seasonal Climate Predictions.”

  • Wednesday, January 15 – Thursday, January 16, 2025
  • In-person at Columbia University (New York, NY)


For participants who would like to learn more about coding prior to participating in the Hackathon, please consider joining LEAP’s optional pre-Hackathon event, the Momentum Bootcamp.

HACKATHON GOAL

This Hackathon challenges teams of data science students, professionals working in the climate sector, and interested community members to build innovative demonstrations and machine-learning solutions for sub-seasonal climate modeling and prediction. Participants will use a newly released benchmarking dataset, ChaosBench (please read here and here for a description), to:

  1. illustrate the skill and limitations of current predictive tools,
  2. explore the value of such predictions for downstream applications, or
  3. improve current models by integrating machine learning, physics, and other domain knowledge.


Teams can supplement this dataset with additional data sources to enrich their project. Projects will be evaluated on real-world relevance, innovative integration of machine learning and domain science, clear presentation, and the effective use of external data.

HACKATHON ELIGIBILITY

This Hackathon is open to participants at all skill levels and from a range of disciplines and data needs in support of LEAP’s commitment to broadening participation in climate data science and making LEAP data and code broadly accessible. We aim to support travel to the Hackathon for a limited number of participants from the Global South and areas most gravely impacted by the climate crisis.

Judges

LEAP offers a special “thank you” to the following judges for volunteering their time and insights:

  • Perry Beaumont, PhD (Senior Product Manager, AWS): Perry is a data scientist with AWS, and works on topics related to the environment. His research interests include geospatial analytics, as well as machine learning, AI, and Generative AI. He has authored four texts related to business applications of data science, and received a patent related to innovative ways of combining statistical distributions. Perry was a founder of two successful startups (FinTech and InsurTech), and he is an occasional lecturer with the DSI at Columbia University as well as at Yale in the department of Statistics and Data Science.
  • Pranay Dharmale (Engineering Team Leader, Bloomberg): As an Engineering Team Leader at Bloomberg, Pranay spends his days immersed in the world of data, leading a team that builds cutting-edge data engineering solutions. A proud Columbia University alum (MS ’10 SEAS), Pranay brings his love of technology and innovation to the LEAP Hackathon judging panel. He’s excited to see the creative solutions that will emerge from this hackathon and to share his passion for technology with the participants.
  • Hina Gandhi (Senior Software Engineer, Cisco Systems): Hina is a technical leader with extensive experience in designing and developing scalable, high performance applications. She holds Master’s and Bachelor’s degrees in computer science engineering and has demonstrated her technical expertise in roles at Cisco Systems, VMware and Cloudhealth technologies, excelling in areas like cloud-based microservices, big data platforms and building SaaS solutions.
  • Patrick Lizaso (Global Account Manager, AWS): Patrick is a seasoned AWS Global Account Manager with over a decade of experience at Amazon Web Services. Currently working with one of the big three major music labels in the world, Patrick is focused on driving innovation in the music industry. Patrick’s previous engagements include Fortune 500 companies across financial services, healthcare, and the automotive sectors. He specializes in helping organizations leverage AI/ML and Generative AI technologies to solve complex business challenges, regularly advising C-suite executives and engineering teams on their artificial intelligence strategies and implementation.
  • Som Tripathy (Analytical Lead, Google): Som has 7 years of experience, working in the field of data science and analytics. As a Columbia University alumnus, holding a Master’s in Business Analytics, he’s excited to see students apply machine learning to climate predictions. He anticipates an inspiring event showcasing the power of data science for addressing environmental challenges!

Reach out to leap@columbia.edu with any questions!

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