2026 Speakers

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.