Evaluate Innovation,Recognize Excellence

Dr. Max E. Joffe is an Assistant Professor in the Translational Neuroscience Program, Department of Psychiatry at the University of Pittsburgh. His research laboratory investigates how molecular signaling modulates prefrontal cortex function, with the goal of identifying new potential medications for the treatment of alcohol and opioid use disorders. His research is supported by multiple NIH awards, including an NIH Director's Pioneer Award (DP1). Dr. Joffe is an Associate Member of the American College of Neuropsychopharmacology and serves on the Executive Board of the Research Society on Alcohol (RSA). He is passionate about supporting emerging talent in data-driven science and looks forward to bringing his expertise in translational neuroscience to The DataJam.

Matthew Stewart is Lead AI Researcher at Pelago Health, where he leads the research behind the company's clinical AI. He earned his PhD in Engineering Sciences and Data Science from Harvard University and was previously a postdoctoral researcher at Harvard and Chief Technology Officer of an AI startup. His work spans responsible AI, machine learning for healthcare, and building AI that runs efficiently on everyday devices, and his research on algorithmic accountability received a Best Paper Award at the ACM Conference on Fairness, Accountability and Transparency in 2026. He has published in journals including Nature Communications, Nature Machine Intelligence, and Communications of the ACM. He also co-created a HarvardX machine learning course that has reached more than one million learners in over 175 countries, and he cares deeply about opening data science and AI to students everywhere.

I work as a Senior AI Engineer for the Personalization team at Target, where I build machine learning and recommendation pipelines to personalize items, offers, and content on Target's digital platform. I have a Bachelor's degree in Information Technology and a Master's degree in Computer Science, with a specialization in ML and Big Data. I have 7 years of professional experience in designing, developing, testing, and deploying recommendation systems and AI engineering applications, as well as cloud-based Java web services. I am passionate about applying machine learning concepts and my analytical skills to real-world challenges, while continuously learning and adapting to technological advancements in this field. My core strengths are building Microservices, MLOps pipelines, Recommendation Systems Infrastructure, and GenAI solutions.

Dr. Fatma Masmoudi is an Assistant Professor at the College of Computer Studies, Arab Open University, Riyadh, Saudi Arabia. She holds a Ph.D. in Computer Science and has several years of academic and research experience in data science, artificial intelligence, machine learning, computer vision, cloud computing, and networking. She is actively involved in research, student mentoring, and community engagement initiatives that promote data-driven innovation. Fatma serves as a WiDS Worldwide Ambassador and has mentored and judged numerous data science competitions and hackathons. She is passionate about empowering students to apply data science to solve real-world challenges and is committed to advancing STEM education through teaching, research, and outreach.

Elizabeth Ohiokhie is a Data and Systems Specialist dedicated to the intersection of data integrity and actionable insights. With a professional background in IT operations and systems analysis, she specializes in translating complex organizational data into clear, strategic narratives using tools such as Excel, Power BI, Tableau, and Python. Elizabeth focuses on optimizing data workflows, ensuring system reliability, and supporting collaborative research environments within the non-governmental sector. Passionate about evidence-based decision-making and community advocacy, she is a committed mentor who leverages her expertise in statistical modeling to empower the next generation of tech professionals and promote data literacy across the global data science ecosystem.

Passionate about creating a workplace culture that values Diversity, Equity & Inclusion where EVERYONE feels empowered and thrive! Harjeet Virdi is a senior IT leader with more than 25 years of experience delivering regulated, research-focused solutions that improve outcomes for patients and customers. She joined Merck in 2000 as a contractor and became a full-time employee in 2005, advancing through a series of roles with increasing responsibility across Merck IT. From 2018, Harjeet led Development IT for Merck Animal Health, overseeing portfolio governance, regulatory compliance, and value realization across Clinical, Pharmacovigilance, and Regulatory IT domains. In January 2025, she transitioned to lead Research IT, where she now oversees three product teams focused on modernizing laboratory processes and animal farm operations to generate FAIR (Findable, Accessible, Interoperable, Reusable) R&D data. She partners closely with Bio & Pharma R&D stakeholders to help them efficiently create, execute, and share experiments and study results.

Dr. Nikolas Siapoutis is a Teaching Assistant Professor in the Department of Statistics at the University of Pittsburgh, where he has taught since 2022. He earned his Ph.D. and M.S. in Statistics from The Pennsylvania State University and his B.S. in Mathematics and Statistics from the University of Cyprus. His doctoral research focused on shrinkage estimators for mean parameters, including simultaneous estimation across high-dimensional diagonal multivariate natural exponential families. Beyond this theoretical work, Dr. Siapoutis is interested in applying machine learning to real-world problems, and has contributed to the development of a software agent that designs personalized rehabilitation programs for homeless youth affected by opioid addiction. He is also actively involved with Data Jam, serving as both an instructor and judge.

Oyetola Florence Idowu is a Business Analyst and digital transformation professional within the NHS, with expertise in data analytics, artificial intelligence, business analysis, and healthcare transformation. She has a degree in computer engineering and a dual master's in computer science and Big Data Analytics with Distinction. She specialises in using data to improve healthcare services through process optimisation, predictive analytics, dashboard development, process re-engineering, EPR upgrades, and digital innovation. Florence has led several NHS projects involving data analysis, process automation, and business intelligence to improve patient outcomes and operational efficiency. She is passionate about mentoring aspiring data professionals and promoting data literacy through education and community engagement. She serves as an ambassador for women in data science in the United Kingdom and an early career advocate for the British Computer Society (BCS). As a DataJam Judge, she enjoys evaluating innovative student projects, encouraging evidence-based decision-making, and inspiring young people to apply data science to solve real-world challenges. Florence is also an award-winning technology leader, career mentor and advocate for women in STEM, committed to advancing responsible and impactful use of data and AI.

Dr. Priscila Neves Faria is a statistics and data science educator with expertise in data analytics, statistical modeling, and exploratory data analysis. She enjoys connecting research, teaching, and practice to inspire diverse audiences to use data responsibly and effectively to create meaningful impact. Dr. Neves is passionate about making data science accessible through hands-on, real-world learning experiences that prepare students and professionals to solve complex problems with data. As a Women in Data Science (WiDS) Ambassador, she has led initiatives that expand opportunities for students — particularly young women. Dr.Neves leads the Data Science Club for Girls, mentoring high school students and encouraging more young women to pursue careers in Data Science and STEM. She is also a 50CAN Fellow, working to expand equitable access to high-quality Math and Data Science education.

Data Analyst with 5 years of experience executing data driven solutions to increase efficiency, accuracy and utility of internal data processing using Data Analysis techniques. Experienced at creating data visualization, data manipulation, predictive modeling and analyzing data mining algorithms to deliver insights and implement action oriented solutions to complex business problems.

I am a data scientist on the Strategy & Analytics team at LivaNova, a medical device company developing technology in the neuromodulation and cardiac surgery spaces. I have worked on the team for over four years and have worked on projects such as modeling the impact of new drug launches, physician segmentation and targeting, and utilizing medical claims data for patient journey analyses. I received undergraduate and master's degrees in statistics from Carnegie Mellon University in 2021 and 2022, respectively.

Jordan leads the Advanced Analytics team at Armada Supply Chain Solutions which specializes in solving supply chain problems using data science, machine learning, and statistics. Her education is in Mathematics and Industrial Engineering.

I hold an MPH in Epidemiology, where I developed experience using R and SAS for health research. I currently work in Business Insights & Analytics at Merck, supporting immunology teams through patient- and healthcare provider-focused analytics. Prior to this role, I spent three years supporting oncology teams, including women's cancers.

Andy Carlson has a PhD in Machine Learning from Carnegie Mellon University, and has spent 16 years at Google leading teams in Ads and Shopping. Much of this work has involved data analysis to measure and improve the quality of data and predictions at very large scale.

Eric is a data scientist with seven years of experience developing machine learning and AI solutions in the transportation industry. He earned a degree in Physics-Engineering from Washington and Lee University, where he built a strong foundation in mathematics, modeling, and analytical problem-solving. Prior to his work in data science, Eric worked in software engineering at NASA and in the defense industry. His experience spans the full lifecycle of data products, from data engineering and model development to deployment and business adoption.