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When

May 8th, 2026

8:30 AM - 6:30 PM

where

Mercer Island Community & Events Center

Format

In Person

Event Highlights

  • Diverse range of technical & career development talks

  • 2 Panel Discussions

  • 3 Interactive Workshops (Space Limited)

  • Networking Activities

Topic highlights

  • Agentic AI in Practice

  • Applied Data Science Across Industries

  • Career Growth, Leadership, Layoffs and Navigating Uncertainty

  • Data Science for Social Good

    Please visit our speaker page for more details on talks and panels

Early Bird Tickets: $95

Early bird pricing ends April 8. Standard Tickets are $125.

YOUR TICKET includes:

  • Your choice among 20+ sessions, including
    technical talks, professional development, panels, and workshops

  • Networking activities

  • Catered lunch and refreshments

  • Free parking at venue

  • Sponsor booths & resume bank

Get Tickets

REGISTER FOR IN-PERSON CONFERENCE

Our events are open to all, regardless of gender.

We have a limited number of tickets available at a reduced rate for students and unemployed individuals. Contact team@widspugetsound.org to inquire.

The conference highlights data-related work in our region, with an emphasis on the important role of women in data-related fields. We aim to bring together data professionals across the Puget Sound area and provide an opportunity to learn about data science applications and research in our community.

 

2026 Conference Volunteers

Overall Leads

 

I joined the data science field 7 years ago after spending my early career working with environmental nonprofits. Since my passion for volunteering still needs an outlet, I leapt at the opportunity to get involved with Data Circles and the Women in Data Science Puget Sound conference. After DS boot camp and a year of consulting, I joined the small, but mighty team of data scientists at Trupanion. My dog greatly appreciates his insurance benefits. Excited to experience another year of knowledge sharing with WiDS!

Hi everyone! I am a Analytics Consultant at Calligo. I moved to Seattle to complete a MS in Data Science, fell in love with PNW and decided to stay! This will be my 2nd year volunteering with WiDS. I am a big proponent of community and am grateful to be a part of the WiDS community. Being around so many great people in this field is really motivating, and I’m constantly learning. Outside of work I love to cook, do yoga, read mystery novels, and take my dog for hikes.

Hi! I’m Yuqi Zhang, a Data Scientist at Amazon based in the Seattle area. I focus on using data and modeling to improve supply chain decisions and customer experience. I grew up in China and earned my M.S. in Statistics from UIUC before moving to Seattle for work.

Outside of work, I love staying active — especially playing soccer. I play regularly and organize beginner-friendly soccer training and pick-up games for women, which has become another way I enjoy building community. I love the community-driven spirit of WiDS, am grateful to be part of it, and am excited to build and learn together!

 
 

Content Team

 

Cecilia is passionate about diversity and gender inclusion in the technology field. She is the co-founder of the Mozambican Women in Technology Association, a co-organizer of Django Girls, and an active member of PyLadies. Currently pursuing a PhD in Big Data and Artificial Intelligence at Gaston Berger University, her research focuses on Artificial Intelligence and Education, with a special interest in Educational Data Mining and intelligent systems in education.

Cecilia Tivir - CoLead

Hi everyone, my name is Amrapali Samanta. I am originally from India and came here for my Masters few years back. I graduated from Seattle University with MS in Business Analytics and currently working as Business Analyst at Amazon. I have worked previously in various domains like banking, airline and bit of retail. My experience had mostly been around data and analytics however in my current job I do apply quite a few statistical analysis and ml algorithms for advanced predictive analytics. There is a lot to learn in the field of data science as it's emerging quickly and would love to learn from this community.

I was a volunteer last year in content team and was in grad school at that time and so much fun working there. This year I look forward to bring my expertise and bring more ideas to the table to make this event a bigger success as a content team lead.

Amrapali Samanta - CoLead

Tongxin (Joyce) Cai is a Ph.D. candidate at the University of Washington. She studies Physical Oceanography. She earned master’s degrees from the University of Washington and Stanford University. Her research looks at ocean movements and the impacts of climate change. She uses big data and machine learning to build better climate models. She is an expert in Python, SQL, and MATLAB. Joyce also focuses on community service and teaching. She has led groups that focus on environmental protection and sustainable food.

 
 

I am currently a student in the MS in Data Science program at the University of Washington. Prior to returning to school, I worked as a Data Analyst for about three and a half years. I enjoy using data to uncover insights and solve meaningful problems, and I am always excited to continue learning and growing in the field of data science. In my free time, I enjoy playing soccer, hiking, doing pottery, and playing with my puppy!

Sridevi Wagle is a machine learning engineer at the Pacific Northwest National Laboratory, experienced in interpretable machine learning and developing data-driven solutions for research applications. She likes mentoring and supporting learning and collaboration within the data science community.

Richa is a Senior Data Scientist. She currently works for CVS Health. She is passionate about using data science skills for solving interesting and impactful challenges. She is preparing to run a half marathon in June and loves playing tennis.

Richa Gupta

I am a Data Scientist and a Civil Engineer. I hold a PhD in Civil Engineering, with extensive research experience in time-series analysis, system identification, and state-space modeling. My doctoral and postdoctoral work focused on developing and validating predictive models for complex dynamic systems using sparse and noisy temporal data—expertise that directly translates to modeling student academic trajectories over time. Prior to my current role, I worked for three years as a geotechnical engineer at Shannon & Wilson, a consulting firm based in Seattle, where I developed predictive and numerical models for high-visibility, high-impact infrastructure projects across the Puget Sound and San Francisco Bay Area. Currently, I work as a Data Scientist at the University of Oklahoma.

To complement this foundation, I have completed advanced coursework and professional training in data structures, machine learning, deep learning, and artificial intelligence, including certifications from MIT, Purdue University, and the University of Washington. My recent work incorporates supervised learning, clustering, sequence modeling, and attention-based architectures applied to longitudinal student records.

I currently serve as a Data Scientist at the University of Oklahoma, where I routinely analyze academic and institutional data to support institutional decision-making related to student retention, identification of at-risk populations, and technology related operations. I have authored more than ten peer-reviewed journal and conference publications centered on predictive modeling, model validation, and interpretability.

 
 

Marketing Team

 

Angela is an Analytics Engineer at Shields Health Solutions and a co-founder and board member of Women Who Do Data (W2D2), based in Boston, MA. She previously worked as a Data Scientist at Memorial Hermann Health System in Houston, TX, where she spent nearly three years leveraging data to drive meaningful insights and impact. Angela holds a Master’s degree in Data Science from Rice University and a dual Bachelor of Science in Computer Science and Mathematics from The University of Texas at Austin. She is passionate about using data and analytics to generate actionable insights and create real-world impact, both through her professional work and her volunteer leadership efforts.

Angela volunteered with the WiDS Puget Sound 2025 Conference as part of the Experience team and is excited to return as a Marketing Team Lead for the 2026 Conference. In her free time, she enjoys going to the gym, playing sports such as golf and pickleball, exploring around the city, traveling, and watching football, especially cheering on the Philadelphia Eagles and the Los Angeles Chargers.

Angela Cao - CoLead

Carina Chen is a Business Intelligence Engineer at Amazon, where she builds scalable analytics and reporting solutions to support data-driven decision-making across the Devices organization. Prior to Amazon, she conducted public health research at the University of Washington, developing a strong foundation in analytical research and applied data methods. Her work bridges industry and academic data practice, with a focus on metric design, automation, data quality, and delivering actionable insights at scale. She is passionate about using data to solve real-world problems and support better product and operational decisions.

I am a computational biologist with experience working at the intersection of immunology and data science, focusing on identifying molecular signals associated with disease outcomes. I hold an M.S. in Bioinformatics and previously worked as a research consultant at the University of Washington and as a computational biologist at Ozette Technologies. I have found the WiDS community to be a great source of encouragement and practical advice for navigating the industry, and I am happy to volunteer this year.

Malisa Smith - CoLead

Teresa is an environmental data analyst for Alta Science and Engineering, Inc. in Boise, Idaho. Her interest in data science was sparked from rubbing shoulders with post-docs at a research forest station in Olympia, Washington, when she was working as a humble forestry technician. Now Teresa gets to work with scientists and engineers to shape the next best course of action for remediation. When not working, she enjoys taking meandering walks with her dog, attending her friends’ readings, and volunteering for WiDs for the third year.

Sandy is an Earth Scientist at Pacific Northwest National Laboratory, where she contributes to telemetry projects studying fish migration through dams. She earned a B.S. in interdisciplinary computing with a biology focus from the University of Kansas and developed an interest in marine science during a study abroad in Australia. Sandy completed her M.S. in biological oceanography at the Florida Institute of Technology, researching water quality and benthic settlement on oyster restoration mats. This is her first year volunteering with WiDS, and she is thrilled to join the community.

Jaspreet Bhamipuri is an MSIM graduate student at the University of Washington, specializing in Data Science, Business Intelligence, and Program/Product Management & Consulting. She earned her B.S. in Real Estate from UW, complemented by minors in Architecture, Informatics, and Data Science. It was during her undergraduate journey, moving between design, technology, and analysis‑focused work, that she discovered her love for data and the power it has to shape stories, insights, and meaningful change. Driven by curiosity, she is a lifelong learner with more hobbies than she can count and is committed to making data more accessible while creating inclusive spaces where people can learn, connect, and grow..

 
 

Sponsorship Team

 

Prachi is an Applied Scientist at Microsoft, where she has built large-scale ML and AI systems powering real-world products. She is a passionate advocate for community building in tech. She loves volunteering, mentoring, and creating spaces where women and underrepresented groups in data science can thrive. In the past, she has volunteered with and helped organize conferences and events through Women in Big Data, PyLadies, and DataCon LA, supporting inclusive learning and networking opportunities in the data community.

Prachi Agrawal - CoLead

Swapnil Agrawal

Fidan is a quantitative researcher and data scientist with a PhD in Psychology and a background in Mathematics. She has worked across public health and research institutions, generating data-driven insights in health and human behavior. She is passionate about responsible data science and building inclusive communities.

Fidan Howell - CoLead

Holly Simosen

I'm Vedica Bafna, a Master's student in Information Management at the University of Washington, specializing in AI and Data Science. With a background in Computer Engineering, I’ve worked on projects ranging from machine learning models to real-time translation engines. I’m passionate about creating tech solutions that improve lives and leading teams that foster innovation and growth. I believe in the power of emotional intelligence in leadership and am committed to equity and inclusion in tech. When I’m not working on AI or data-driven projects, you’ll find me baking, listening to music, stargazing, or hiking!

 
 

Events Team

 

Ayesha is a data scientist at Boeing where she leverages machine learning and generative AI in the Defense & Space division. Prior to her current role, she evaluated large language models on NLP tasks as a Boeing intern. Ayesha is passionate about supporting women and minorities in STEM fields, particularly in data science and physics. She holds two undergraduate degrees in statistics and physics.

 Ayesha Darekar - CoLead

I am currently completing my Bachelors of Science in Data Science at North Seattle College. This past summer I had the opportunity to work as a Data Science Analytics Intern at Expedia where I developed an interest in data engineering. I am excited to volunteer with WiDS to collaborate with other data enthusiasts! While waiting for my models to train, I like drawing and running with my dog.

Hi! I'm Samridhi Vats, currently working as a Business Analyst at Amazon, where I focus on generating insights to drive operational excellence and customer-centric innovations. I hold a Master’s in Business Analytics from Purdue University and have prior experience in advanced analytics and strategy consulting at ZS Associates, primarily in the healthcare domain.

I’m passionate about using data to tell meaningful stories, solve real-world problems, and create impact—especially in spaces that empower women and underrepresented communities in tech. Outside of work, I enjoy writing, reading (mostly non-fiction) and going for hikes.

I am currently in the Master of Science in Data Science program at the University of Washington and will have graduated at the time of the conference.

I began my data science journey at the start of this graduate program, transitioning from a background in legal editing and digital marketing. I am passionate about storytelling and data visualization. My vision is to leverage data to tell compelling stories that convey powerful insights and drive positive change.

Kamala V J - CoLead

Data-driven professional with an MS in Computer Science (Data Science) and hands-on experience designing scalable data pipelines, building machine learning models, and delivering intelligent, real-world software solutions. I specialize in transforming raw data into actionable insights through applied analytics, ML engineering, and data engineering.

Shachi Sonar

 

Workshops Team

 

Sarita Singh is an Associate Teaching Professor with Khoury College of Computer Science at Northeastern University, USA. She holds a PhD in Computer Science as well as a Doctor of Education (EdD) degree. Her area of research interests includes Internet of Things, Cybersecurity and Computer Science Education.

Sarita Singh - CoLead

Somang Han

Karen Dsouza - CoLead

I am a Masters of Data Science student at Harvard Extension School. After earning my Bachelor of Science in Nursing, I worked as a registered nurse specializing in surgery. Through my experience in the hospital, I recognized numerous opportunities where technology could bridge gaps in healthcare, enhancing patient care and improving the efficiency of healthcare delivery. Working in that environment sparked my interest in the technical side, which led me to data science. I love my job on the clinical side, and I hope to continue serving patients outside the operating room. In my free time, I love to travel and read books.

Sarah is a recent graduate of the University of Washington MS in Data Science program. She currently works at Seattle City Light where she helps leverage data to improve customer experience. She loves continuous learning and the women in technology community and is excited to return to the WIDS conference this year.

Simran Goindani

 
 

2026 Speakers

 

This page is still in progress! Abstracts & More speakers Will be Added Soon.

 
 

Akriti Chadda

Akriti Chadda is an applied machine learning scientist specializing in search, relevance and generative AI systems deployed at scale. Her work focuses on the full lifecycle of AI, from modeling and experimentation to production deployment, monitoring and long-term system reliability. In recent years, she has been deeply involved in agentic and generative AI systems, where non-determinism and autonomy introduce new technical and leadership challenges.

Beyond technical execution, Akriti is passionate about communication, mentorship and helping data professionals grow into thoughtful leaders. She frequently speaks about operating complex AI systems responsibly, aligning stakeholders around uncertainty and translating advanced ML concepts into practical, real-world impact.

From Models to Teammates: Operating, Monitoring and Trusting Agentic AI in Production

 
 

Catherine Nelson

Every LLM Call Counts: The Environmental Cost of AI, and How Data Scientists Can Reduce It

Catherine Nelson is an experienced data scientist and ML engineer, and the author of two O'Reilly books: Software Engineering for Data Scientists (2024) and Building Machine Learning Pipelines (2020). Previously, she was a Principal Data Scientist at SAP Concur, where she deployed NLP models to production and created innovative features including ML-powered carbon emissions analytics. She is currently consulting for startups on AI evaluation and developer relations. Catherine holds a PhD in Geophysics from Durham University and a Masters in Earth Sciences from Oxford University.

 
 

Shaili Guru

How to Work with Your PM (When They Don't Speak AI)

Shaili Guru is an AI product leader and educator with 10+ years of experience building AI products at Amazon, Disney, Nike, and T-Mobile. She currently teaches AI Product Management at the University of Washington's Global Innovation Exchange and runs Bluenox.ai, helping organizations and product teams adopt AI effectively. Her Substack newsletter, AI Product Management Guru, is read by over 4,000 PMs worldwide. Shaili holds a Technology Management MBA from the UW Foster School of Business and a BS in Biology from Baldwin-Wallace University.

 
 

Sridevi Wagle

Leveraging AI to Support Evidence-Based Wildlife and Permit Management

Sridevi Wagle is a Machine Learning Engineer at Pacific Northwest National Laboratory with a master’s degree in Computational Science. She has experience developing AI and machine learning tools for extracting and analyzing information from large-scale, multimodal scientific data. Her work includes building systems for knowledge retrieval and semantic search using advanced language models and data integration techniques. Sridevi’s research interests include explainable AI, uncertainty quantification, and visualization methods to support data-driven decision-making in scientific domains.

 
 

Hoda Soltani

When Time Tells: Using Sequence Modeling to Understand Transfer Student Retention

I am a civil engineer and data scientist with six years of professional engineering experience, three years of Data Science, and eight years of academic research focused on predictive modeling of complex dynamic systems. I hold a PhD in Civil Engineering, where my research applied system identification, time-series analysis, and state-space modeling to large-scale experimental data to study the seismic response of foundations and support infrastructure resilience. My work has been published in peer-reviewed journals and presented at international conferences and workshops.

Following my doctorate, I worked at Shannon & Wilson, a leading geotechnical consulting firm in Seattle, contributing to high-impact projects in the Pacific Northwest and San Francisco Bay Area, including seismic resilience analyses and large-scale numerical simulations for critical infrastructure. I later transitioned into data science, completing advanced training in computer science, machine learning, deep learning, and AI. I currently work as a data scientist in higher education, applying predictive modeling to student success and retention initiatives.

 
 

Erin Zionce and Sandy Rech

A Data Science Approach to Quantifying Fish Passage Through Dams, Assessing Fish Injury, and Advancing Fisheries Research

Erin Zionce is a Data Scientist at the Pacific Northwest National Laboratory with a background in fisheries ecology. Her research contributes to juvenile and adult fish passage studies by integrating ecological expertise with data science through statistical modeling, machine learning, and computational tools to support environmental science and hydropower systems management.

Sandy Rech is an Earth Scientist at Pacific Northwest National Laboratory, where she contributes to fish telemetry projects to study salmonid migration through dams. She has a background in computer science, mathematics, and oceanography, with previous work in mathematical modeling, oyster restoration, and ecological data management. She is passionate about integrating ecology and data science to address complex environmental challenges.

 
 

Rachel Wagner-Kaiser

AI Beyond English: Building Multi-Lingual and Non-English AI Solutions

Rachel Wagner-Kaiser has 15 years of experience in data and AI, entering the data science field after completing her PhD in astronomy. She specializes in building NLP and AI solutions for real-world problems constrained by limited or messy data. Rachel leads technical teams to design, build, deploy, and maintain NLP solutions, and her expertise has helped companies organize and decode their unstructured data to solve a variety of business problems and drive value through automation. Rachel is also the author of the recent book "Teaching Computers to Read" (http://amazon.com/dp/1032484357) and corresponding code companion.

 
 

Riya Joshi

Agentic AI as Your Personal Wellness Coach

As a Data and Applied Scientist at Microsoft AI division, with seven years of industry experience (previously Data Engineer) across multiple geographies, I focus on building machine learning systems that directly improve user experience in the Microsoft Edge browser. My work spans developing on-device ML models, building personalization and content-understanding systems, and designing reliable experimentation and measurement pipelines that help teams make data-informed product decisions. With a Master’s degree in Computer Science and Artificial Intelligence from the University of Massachusetts Amherst, I bring a balance of strong engineering fundamentals and applied research experience.

In my current role, I work end-to-end across the ML lifecycle—from framing product problems, designing lightweight models that run efficiently at the edge, and integrating LLM-driven features, to evaluating performance and shipping improvements at scale. My focus is always on creating practical, efficient, and user-centric ML solutions, which has become especially important as the industry moves toward more agentic and intelligent browser experiences.

My career has been defined by a dual passion: advancing AI innovation and fostering an inclusive tech community. I've had the honor of sharing my knowledge as a speaker at premier conferences including PyData Global (2024), PyLadies Con (2024) and Women in Data Science (2023) and (2024), and as a featured guest on prominent podcasts like Women in Data and Women in STEM. These platforms have allowed me to advocate for greater diversity in our field while demonstrating AI's transformative potential.

My commitment to mentorship runs deep. As a Career Advisor in the prestigious Women in Data Science Career Catalysts program, I've guided aspiring technologists from over 12 countries, helping shape the next generation of data leaders. This work, which earned me recognition as a top advisor on the platform, reflects my belief that technology advances furthest when we lift others as we climb. Whether through technical innovation or community building, I remain dedicated to creating AI solutions that are as impactful as they are inclusive.

 
 

Emma Rosenthal & Stephanie Chen

From Bots to Bookings: Agentic AI in the Real World @ Expedia

Emma Rosenthal is a Data Scientist at Expedia Group where she works on the Checkout team, focusing on AI Driven Insights, AI integrations into the checkout flow with ChatGPT, A/B testing, and data-driven product optimization. Prior to Expedia, Emma received her Master’s in Computer Science and Bachelor’s of Economics from the University of Chicago.

 
 

Shikha Verma

From Individual Contributor to Data Leader: How to Unblock your team & Influence Strategy

Ph.D. in Machine Learning with 5+ years of industry experience working in high-performance, worldwide scale projects on fraud detection, warehouse management & promotion targeting across fintech, e-commerce & healthcare. She is skilled in supervised & unsupervised machine learning algorithms, building end-to-end ML pipelines, applied statistics, Python & SQL.

She has presented her research at various academic and practitioner conferences like Grace Hopper Celebrations (India), the Women in Machine Learning workshop at NeurIPS & ICML, and ACM Conference on Machine Learning and Human-Computer Interaction (2020). She has served as a visiting faculty for courses on AI, ML, and business analytics across management institutes in India.

 
 

Anastasia Bernat

GeoAI for the Built Environment: Siting and Permitting

I am a Senior Data Scientist at the Pacific Northwest National Laboratory (PNNL) specialized in processing and modeling energy and Earth system data for impactful decision-making science. At PNNL, I am the lead architect of novel GeoAI data pipelines and manage several AI-driven and/or cloud-native applications for U.S. energy and environmental mission areas. This includes six research, agentic, and generative AI applications to streamline federal permitting reviews, a techno-economic simulator for advanced geothermal systems (GeoCLUSTER), and a U.S. energy feasibility mapper (GRIDCERF). Combining data science, computational modeling, and environmental science, I am also deft in geographic information systems (GIS) and statistical modeling used to better enable intelligent mapping and environmental monitoring analyses. My leadership has guided data product teams to deliver impact and value to sponsors across the Department of Energy, including earning project-level recognition by the White House in the “AI for Good” space and in “America’s AI Action Plan”.

 
 

Swapnil Agrawal

Soft Skills Are Not Optional: Why Early-Career Data Professionals Need Them Most

Hello, I’m Swapnil. I was born and raised in India and moved to the United States in 2018. I earned my BTech from the Indian Institute of Technology, Delhi, and my master’s degree from Carnegie Mellon University in Pittsburgh.

I’ve built my career as a Data Scientist across diverse organizations. I began at a startup in Pittsburgh, then spent nearly three years at Lubrizol Corporation in Houston, Texas. Currently, I work as a Data Scientist at Microsoft, specializing in product data science.

Outside of work, I enjoy painting, reading, and cooking. I’m very outdoorsy and love cycling, kayaking, hiking, and camping. I also have a five-year-old German Shepherd who keeps life busy, active, and joyful.

 
 

Nandita Krishnan

AI As Your Personal Data Science Intern

Nandita Krishnan is a Consultant-turned-Data Scientist who brings a unique blend of strategic thinking and technical expertise to her work. Currently part of Adobe's team, she focuses on enhancing user experience for flagship products such as Premiere Pro by uncovering user needs hidden within complex data.

Beyond her day-to-day role, Nandita is deeply curious about the evolving tech landscape and is constantly exploring and experimenting with the latest tools and technologies, expanding her skill-set and staying at the forefront of data science innovation.

Passionate about creating pathways for others, Nandita is also an active advocate for women in STEM. She regularly mentors aspiring Data Scientists and participates in speaking engagements to inspire the next generation in tech.

 
 

Ojasvi Khanna

Forecasting You: How Data Science Powers Personalized Marketing

I have been doing AI and ML-modeling for Xbox for the past 4 years as a Data Scientist. My work helps create better marketing outputs, helping gamers play their next game faster! I went to UC Berkeley, enjoy skiing, tennis and biking when the weather allows.

 
 

Sneha Sivakumar

Beyond the Prompt: Building Autonomous AI Agents for High-Stakes Adversarial Environments- such as finance, fraud & abuse

Sneha Sivakumar is a Product leader at Amazon, where she leads Content Risk Moderation (CRM) for the self-publishing books business, Kindle Direct Publishing. Prior to Amazon, she worked at KPMG where she led the Technology Risk practice advising large public companies on mechanisms to quantify and mitigate technology, financial and social risk. She is experienced in building consumer and enterprise products that help organizations manage risk through the use of ML, automation and human inputs. Sneha holds a BS in Engineering from Anna University, India, an MS in Industrial and Systems Engineering from USC and an MBA from Kellogg School of Management.

 
 

Erin Wilson

Biomanufacturing for a better world

I am a data scientist pursuing a career at the intersection of computing, biology, and sustainability. My experience includes working at biotech companies like Amyris (engineer yeast to convert sugar into alternatives to petroleum based products) and LanzaTech (convert carbon emissions to ethanol with bacteria), and completing a PhD in the Computer Science program at UW (using ML techniques to model DNA patterns in methane-eating bacteria). When I'm not nerding out about climate biotech, you can find me enjoying fresh air on PNW trails or rolling dice to explore D&D fantasy realms.

 
 

Harsheeta Venkoba Rao

Designing Reliable Agentic AI Systems: Design Patterns for Production

Harsheeta Venkoba Rao is a Founding software engineer at Gone.com with extensive experience in agentic AI, machine learning, and building reliable end-to-end software systems. She holds a master’s degree in Electrical and Computer Engineering, specializing in machine learning and data science.

 
 

Neelam Koshiya

Model Context Protocol (MCP): The Next Frontier of Generative AI

I'm a Principal Applied AI Architect at AWS with 17+ years of experience in architecture, including 10+ years focused on cloud and AI. As a thought leader in AI-driven solutions, I regularly speak at global tech conferences including AWS re:Invent, AWS re:Inforce, AWS Summits, NRF, and Grace Hopper Celebration (GHC), where I share insights on bridging the gap between AI and real-world business applications.

My expertise spans cloud architecture, generative AI, and retail innovation, making me a recognized voice in the industry. I'm the author of the published book AWS Solutions Architect Associate Certification Guide and a contributor to the Responsible AI Lens for the AWS Well-Architected Framework.

My work has been recognized with several prestigious awards, including the Advancing Women in Technology (AWT) 2023 Rising Stars Award, Success Quarterly 2025, the Globee Award for thought leadership in artificial intelligence, and the Global Recognition Award as a standout leader in the industry.

I'm passionate about helping organizations unlock the transformative potential of AI through practical, scalable solutions—from property inspection and document processing to customer experience enhancement and workforce productivity. I've successfully identified and prioritized AI use cases across diverse industries, including finance and real estate.

 
 

Somang (So) Han

Surfacing Hidden Potential: ML-Driven Selection and Causal Inference for Rare Event Prediction in Partner Ecosystems

I am a Data Scientist with over six years of experience building production machine learning models at Amazon, spanning partner prioritization strategy, marketing channel attribution, and advertising measurement. I hold a Master's in Data Science from the University of Pennsylvania and a Bachelor's in Mathematics and Statistics from St. Olaf College. Beyond data science, I was a member of the South Korea Junior National Alpine Skiing Team and now pursue amateur baking in my spare time.

 
 

Ayushi Das

Taxonomy-Agnostic Hybrid Recommendation System for Procurement Classification

I am from Kolkata and have a strong academic foundation in mathematics and applied sciences. I completed my undergraduate and master’s degrees in Mathematics from Banaras Hindu University, followed by an M.Tech in Cryptology and Security from the Indian Statistical Institute, Kolkata. In 2023, I was selected for a six-month internship at Amazon, where I worked on applied machine learning problems at scale. In January 2024, I joined Amazon as a full-time Data Scientist in the AFT organization and subsequently transitioned to the FinAuto team. My current work focuses on building production-grade data science and Generative AI systems, including taxonomy-agnostic classification and supplier-aware recommendation solutions for enterprise procurement. Besides work, I love to cook, dance, and spend time with animals.

 

2026 Datathon Contest

 
 
 

Art generated with Adobe firefly

Register Here


​Key Dates

  • ​Registration opens in March

  • Slide Submission Deadline: April 5, 2026

  • ​Final presentation (In Person): April 8, 2026, Seattle

​The WiDS Worldwide Datathon deadline is May 1, 2026. Team merges are permitted before April 24.

​Submission & Presentations

  • Submit the predictions generated by your top-performing model.

  • Submit a 1-slide ‘poster’ by April 5, 2026.

    • The poster should summarize your approach to solving the datathon challenge

    • Some examples of relevant topics include:

      • Exploratory Data Analysis - The steps you took to examine the dataset, and what relevant information you learned.

      • Data Cleaning & Feature Engineering - Outline the process of preparing your data for modeling.

      • ML Architecture - What types of model did you experiment with? What factors influenced your choice?

      • Hyperparameter Optimization - How did you determine what values to use for hyperparameters?

      • Cross-validation - Describe your steps for evaluating & comparing model performance.

      • Challenges encountered - Did you experience difficulties at any of the steps above?

  • Deliver a 10-minute in person presentation on April 8.

To enter the WiDS Worldwide competition, you must submit your final solution and code repository/link via Kaggle.

​Eligibility

  • ​Open to all skill levels -  beginners and experienced practitioners welcome!

  • ​Participants must be local and able to attend the in-person final presentation on April 8 in Seattle

  • ​A Kaggle account is required to access the dataset

  • ​Compete individually or in a team of 2 - form your own team before registering

  • ​As always, our events are open to all genders.

​If you aren’t local to the greater Seattle area, you should still check out the global competition linked above!

​Prize

​🏆 The top presenters will be invited to present their work at the WiDS Puget Sound Annual Conference on May 8, a great opportunity to showcase your project to data science professionals and enthusiasts in the community!

​Support & Office Hours

​You won't be on your own! We'll be hosting weekly office hours throughout the datathon to provide mentorship and guidance as you work toward your final presentation. Details on scheduling will be shared after registration.​


FAQ

​What is the team size?
Teams can be 1 or 2 people. Please form your team before registering. Further collaboration is allowed, but the presentation and prize are limited to 2 people. 

​The global Kaggle competition allows larger teams.

​Do all team members have to register?
Yes, we ask that all team members complete the registration form so we have accurate information on file.

​What is the problem statement?
When a wildfire ignites, emergency managers must decide which communities to warn and where to position resources - before certainty is available. This competition turns that operational need into a survival analysis challenge: generate calibrated probability forecasts across multiple time horizons to help prioritize the most urgent fires and support real-world evacuation decisions.


More info here: https://www.widsworldwide.org/learn/datathon/

​Where can I find information on the data?
Register on Kaggle to be able to view the complete dataset, metadata and evaluation criteria.
All information can be found here: https://www.kaggle.com/competitions/WiDSWorldWide_GlobalDathon26/overview

​When and where is the final event?
The in-person final presentation is on April 8, 2026 at the Queen Anne Library, Seattle. The event is expected to run approximately 1–2 hours (tentative).

​Who is eligible to participate?
The datathon is open to all experience levels. Participants must be local to Seattle and able to attend the in-person final presentation. Travel reimbursement is not provided.

​What do I need to submit?

  • Submit a 1-slide ‘poster’ by April 5, 2026. (Google Slide or PDF)

  • Submit the predictions generated by your top-performing model.

  • Deliver a 10-minute in person presentation on April 8.

To enter the WiDS Worldwide competition, you must submit your final solution and code repository/link via Kaggle.

What should I include on my poster and in my presentation?

We want to hear about your approach to solving the datathon problem statement. Your model metrics matter, but your methods matter more. Summarize the steps you took and the tools you used. Explain the reasons behind your decisions.

Some examples of relevant topics include:

  • Exploratory Data Analysis - The steps you took to examine the dataset, and what relevant information you learned.

  • Data Cleaning & Feature Engineering - Outline the process of preparing your data for modeling.

  • ML Architecture - What types of model did you experiment with? What factors influenced your choice?

  • Hyperparameter Optimization - How did you determine what values to use for hyperparameters?

  • Cross-validation - Describe your steps for evaluating & comparing model performance.

  • Final Model Metrics - Report the metrics for your best performing model.

  • Challenges encountered - Did you experience difficulties at any of the steps above?

​What criteria will be used to evaluate presentations and posters?

Judges will consider these factors:

  • Thorough methods

  • Sound reasoning

  • Articulate presentation

  • Strong model performance

What is the format for the poster?
Here is an example template for poster.  It is a Google Slide, sized 36” wide and 24” tall. This size will allow us to print winning entries onto a poster for display at the WiDS Puget Sound conference.

​Is there mentorship available?
Yes! We will hold weekly office hours throughout the datathon period to provide basic guidance and mentorship. Details will be shared after registration.

​Who are the judges?
Presentations will be evaluated by a panel of professionals from the data science and machine learning industry. More details to be announced.

​More questions?
Email us at events@widspugetsound.org