Julia Lintern currently works as a Director of Data Science at Gartner. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.

Daniel Gerlanc has worked as a data scientist for more than decade and been writing software for nearly 20 years. He frequently teaches live trainings on oreilly.com and is the author of the video course Programming with Data: Python and Pandas. He has coauthored several open source R packages, published in peer-reviewed journals, and is a graduate of Williams College.

Eric Eager is the VP of Research and Development at SumerSports, a football analytics startup founded by Paul Tudor Jones and Jack Jones. Prior to joining Sumer, he held similar roles at Pro Football Focus, and is responsible for many of the insights that have grown the game of American football to this day. Eric holds a PhD in Mathematical Biology from the University of Nebraska, and has taught at Wharton, DataCamp and the University of Wisconsin – La Crosse, publishing over 25 academic papers during his career.

Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.

Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.

Tamilla Triantoro is an Associate Professor of Computer Information Systems at Quinnipiac University and a leader of the Masters Program in Business Analytics. She was previously an Academic Director of Data Analytics at the University of Connecticut. Dr. Triantoro is an author, speaker, researcher, and educator in the fields of artificial intelligence, data analytics, user experience with technology, and the future of work. She received her Ph.D. from the City University of New York where she researched online user behavior. Dr. Triantoro presents her research around the world, attempting to demystify the complexity of today’s digital world and to make it understandable and relevant to business professionals and the general audience.

Thomas J. Fan is a Staff Software Engineer at Quansight Labs and is a maintainer for scikit-learn, an open-source machine learning library for Python. Previously, Thomas worked at Columbia University to improve interoperability between scikit-learn and AutoML systems. He is a maintainer for skorch, a neural network library that wraps PyTorch. Thomas has a Masters in Mathematics from NYU and a Masters in Physics from Stony Brook University.

Matt Harrison has been using Python since 2000. He runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage.

He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.

Nikolay is an experienced Data Science professional who currently leads the EMEA Data Science team at Domino Data Lab. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King’s College London. His area of expertise is Statistics, Mathematics, and Data Science in general, and his research interests are in Neural Networks with emphasis on biological plausibility. He writes articles and blogs regularly and speaks at various European conferences (ODSC, Big Data Spain, Strata, Big Data London etc.) to build awareness about data science and artificial intelligence. He is also the organizer of the London Data Science and Machine Learning meetup and recipient of several technical mastery awards like the Oracle ACE Award and the IBM Outstanding Technical Achievement Award.

A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern workforce to wisely communicate and handle a data-driven future at the nation’s first Master of Science in Analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks. He now works in Thomson Reuters as VP of Labs, and also provides consultancy and training for small and large companies. His previous experience includes being Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through acquisition and an IPO.

Dr Hezekiah O Babatunde is a faculty at the University of Virginia’s College at Wise, VA, USA and a Machine Learning Consultant. He completed his PhD degree in Computer Science from the School of Computer and Security Science, Edith Cowan University, Perth, WA, Australia in 2015. He is a certified Big Data Consultant and Scientist from Arcitura Certification. He also holds a B.Sc degree in Mathematical Sciences (computer major) and three MSc degrees in Applied Mathematics, Computer Sciences and Organizational Leadership from FUNNAB, University of Ibadan and Charleston Southern University (USA) respectively. He worked as a Postdoctoral Research Associate in Systems Biology at Professor John Yin’s Laboratory at the university of Wisconsin, Madison, USA.

Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks. He now works in Thomson Reuters as VP of Labs, and also provides consultancy and training for small and large companies. His previous experience includes being Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through acquisition and an IPO.

Laura Skylaki is a Manager of Applied Research in Thomson Reuters Labs, where she leads advanced machine learning projects in the domain of Legal and Tax AI.With a career spanning more than a decade at the intersection of research and practical application, she has contributed technical expertise in diverse fields such as bioinformatics and stem cell biology, image processing and natural language processing. She holds a doctorate in stem cell bioinformatics from the University of Edinburgh, UK, and has been publishing on machine learning applications in leading academic journals since 2012.

Alison Cossette is a dynamic Data Science Strategist, Educator, and Podcast Host. As a Developer Advocate at Neo4j specializing in Graph Data Science, she brings a wealth of expertise to the field. With her strong technical background and exceptional communication skills, Alison bridges the gap between complex data science concepts and practical applications.

Alison’s passion for responsible AI shines through in her work. She actively promotes ethical and transparent AI practices and believes in the transformative potential of responsible AI for industries and society. Through her engagements with industry professionals, policymakers, and the public, she advocates for the responsible development and deployment of AI technologies.

Alison’s academic journey includes pursuing her Master of Science in Data Science program, specializing in Artificial Intelligence, at Northwestern University and research with Stanford University Human-Computer Interaction Crowd Research Collective. Alison combines academic knowledge with real-world experience. She leverages this expertise to educate and empower individuals and organizations in the field of data science.

Overall, Alison Cossette’s multifaceted background, commitment to responsible AI, and expertise in data science make her a respected figure in the field. Through her role as a Developer Advocate at Neo4j and her podcast, she continues to drive innovation, education, and responsible practices in the exciting realm of data science and AI.

Akash Tandon is co-founder and CTO of Looppanel where he builds software to help product teams record, store and analyze user research data. He is a co-author of Advanced Analytics with PySpark, published by O’Reilly. Previously, Akash worked as a senior data engineer at Atlan, SocialCops and RedCarpet where he built data infrastructure for enterprise, government and finance use-cases. He has also been a participant and mentor in the Google Summer of Code program with the R Project for Statistical Computing.

Panos Alexopoulos has been working since 2006 at the intersection of data, semantics, and software, building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, he currently works as Head of Ontology at Textkernel, in Amsterdam, Netherlands, where he leads a team of Data Professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Panos holds a PhD in Knowledge Engineering and Management from National Technical University of Athens, and has published more than 60 papers at international conferences, journals and books. He is the author of the book “Semantic Modeling for Data – Avoiding Pitfalls and Breaking Dilemmas” (O’Reilly, 2020), and a regular speaker and trainer in both academic and industry venues.

Daniel Whitenack (aka Data Dan) is a Ph.D. trained data scientist working with SIL International on NLP and speech technology for local languages in emerging markets. He has more than ten years of experience developing and deploying machine learning systems at scale. Daniel co-hosts the Practical AI podcast, has spoken at conferences around the world (Applied Machine Learning Days, O’Reilly AI, QCon AI, GopherCon, KubeCon, and more), and occasionally teaches data science/analytics at Purdue University.

Ben is a Senior Data Scientist at the Institute for Experiential AI at Northeastern University. He obtained his Masters in Public Health (MPH) from Johns Hopkins and his PhD in Policy Analysis from the Pardee RAND Graduate School. Since 2014, he has been working in data science for government, academia and the private sector. His major focus has been on Natural Language Processing (NLP) technology and applications. Throughout his career, he has pursued opportunities to contribute to the larger data science community. He has presented his work at conferences, published articles, taught courses in data science and NLP, and is co-organizer of the Boston chapter of PyData. He also contributes to volunteer projects applying data science tools for public good.

Matt Harrison has been using Python since 2000. He runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage.

He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.

Daniel J. Smith, PhD, MBA has worked at WGU for 3 years. He has experience in several industries in analytics through the director level in insurance, health care administration, and higher education. His experience is in AI and machine learning applications in industry using R, Tableau, SAS and Python. He enjoys working with students to improve their analytical, programming, and communication skills.

Leticia Rabor worked as a professional Software and Systems Engineer in the Defense and Aerospace industries for over 13 years. She has designed, implemented, and tested various image formation subsystem components for ground system development.

She has also worked in Academia since 2012. Her roles include program chair and instructor. Leticia is currently an adjunct professor at Fort Hays State University and a full-time senior instructor at Western Governor University.

She has a Master of Science degree in Information Assurance and a bachelor’s degree in Computer Science. Her yearly activities include conducting an external one hour workshop in both mobile development and JavaScript at the Geek Girls Tech Conference at University of San Diego (USD). She participated as one of the panel experts for “The future of mobile development” at the Geek Girls Tech Conference in San Diego, California. She is a member of the Women Who Code (WWC) and a recipient for “Faculty of the Year” award in 2017.

Mohammad Soltanieh-ha is a Clinical Assistant Professor in the Information Systems department at Boston University’s Questrom School of Business. He specializes in data science programming, big data analytics, and business applications. He earned his Ph.D. in computational physics from Northeastern University and currently focuses his research on computer vision applications in cancer diagnosis, macroeconomic forecasting, and high-performance computing. Mohammad holds leadership roles at Google and the American Physical Society (APS). He founded APS’s data science unit in 2019 and serves as a Faculty Expert at Google Cloud, where he supports cloud computing education and best practices for fellow faculty members.

Florian Jacta is a specialist of Taipy, a low-code open-source Python package enabling any Python developers to easily develop a production-ready AI application. Package pre-sales and after-sales functions. He is data Scientist for Groupe Les Mousquetaires (Intermarche) and ATOS. He developed several Predictive Models as part of strategic AI projects. Also, Florian got his master’s degree in Applied Mathematics from INSA, Major in Data Science and Mathematical Optimization.

Albert has skills in machine learning and big data to solve (financial) optimization problems. He developed projects of different skill levels for Taipy’s tutorial videos. He got his degree from McGill University – Bachelor of Science. Major in Computer Science & Statistics. Minor in Finance.

Jeffrey Yau is currently Chief Data & A.I. Officer at Fanatics Collectibles. Most recently, he served as Global Head of Data Science, Analytics & Engineering at Amazon Music where he oversaw multiple teams who developed both insights-packed analytics and end-to-end statistical and machine learning systems. Prior to Amazon, Jeffrey worked at WalmartLabs as the VP of Data Science & Engineering where he led the team responsible for powering Walmart store mobile apps and the entire store finance system. Further, his team created end-to-end machine learning systems for key business initiatives and had a multi-billion dollar impact annually on Walmart U.S.

Over the years, he has held various senior level positions in quantitative finance at global investment management firm AllianceBernstein, consulting firm Data Science at Silicon Valley Data Science, multinational financial services company Charles Schwab Corporation, and the world’s leading professional services firm KPMG. He began his career as a tenure-track Assistant Professor of Economics at Virginia Tech, and he was an adjunct professor at UC Berkeley, Cornell, and NYU, teaching machine learning and advanced statistical modeling for finance and business.

Nick Singh is an Ex-Facebook & Google Engineer turned best-selling author of Ace the Data Science Interview, and founder of SQL Interview Platform DataLemur.com. His career advice on LinkedIn has earned him 100,000 followers, and he’s successfully career coached 578 people to land their dream job in data!

Bio Coming Soon!

Rajeev Prabhakar is a Machine Learning Platform Engineer at Lyft. Currently he is focused on building model observability at scale for a wide range of ML applications across teams at Lyft. Prior to Lyft, he worked at Quantcast on the ML platform team. Enabling distributed computing with spark and notebooks on k8s, building control systems for optimal spend budget allocation and optimising real time prediction latency in a low latency serving environment are some of the things he worked on.

Anindya Saha is a Staff Machine Learning Platform Engineer @Lyft, focusing on distributed computing solutions for machine learning and data engineering. He led and implemented the Spark on Kubernetes support on ml platform for feature engineering at scale with ephemeral Spark clusters on k8s. He is currently working on enabling scalable distributed model training on the ML platform.

James Demmel is the Dr. Richard Carl Dehmel Distinguished Professor of Computer Science and Mathematics at the University of California at Berkeley, and former Chair of the EECS Dept. He also serves as Chief Strategy Officer for the start-up HPC-AI Tech, whose goal is to make large-scale machine learning much more efficient, with little programming effort required by users. Demmel’s research is in high performance computing, numerical linear algebra, and communication avoiding algorithms. He is known for his work on the widely used LAPACK and ScaLAPACK linear algebra libraries. He is a member of the National Academy of Sciences, National Academy of Engineering, and American Academy of Arts and Sciences; a Fellow of the AAAS, ACM, AMS, IEEE and SIAM; and winner of the IPDPS Charles Babbage Award, IEEE Computer Society Sidney Fernbach Award, the ACM Paris Kanellakis Award, the J. H. Wilkinson Prize in Numerical Analysis and Scientific Computing, and numerous best paper prizes.

Yang You is a Presidential Young Professor at National University of Singapore. He is on an early career track at NUS for exceptional young academic talents with great potential to excel. He received his PhD in Computer Science from UC Berkeley. His advisor is Prof. James Demmel, who was the former chair of the Computer Science Division and EECS Department. Yang You’s research interests include Parallel/Distributed Algorithms, High Performance Computing, and Machine Learning. The focus of his current research is scaling up deep neural networks training on distributed systems or supercomputers. In 2017, his team broke the world record of ImageNet training speed, which was covered by the technology media like NSF, ScienceDaily, Science NewsLine, and i-programmer. In 2019, his team broke the world record of BERT training speed. The BERT training techniques have been used by many tech giants like Google, Microsoft, and NVIDIA. Yang You’s LARS and LAMB optimizers are available in industry benchmark MLPerf. He is a winner of IPDPS 2015 Best Paper Award (0.8%), ICPP 2018 Best Paper Award (0.3%) and ACM/IEEE George Michael HPC Fellowship. Yang You is a Siebel Scholar and a winner of Lotfi A. Zadeh Prize. Yang You was nominated by UC Berkeley for ACM Doctoral Dissertation Award (2 out of 81 Berkeley EECS PhD students graduated in 2020). He also made Forbes 30 Under 30 Asia list (2021) and won IEEE CS TCHPC Early Career Researchers Award for Excellence in High Performance Computing. For more information, please check his lab’s homepage at https://ai.comp.nus.edu.sg/

Mélissa is a data scientist engineer. Over the past 7 years working at Quinten Health in the healthcare sector as a Project Manager in data science, she has participated in the development of several decision support solutions powered by AI, e.g. for rare disease diagnosis, disease progression modelling and endotyping, or evaluation of population heterogeneity. She leds multiple studies of real-world data using advanced analytics methods to characterize phenotypes and disease progression for neurological conditions, cardiovascular diseases, and oncology, for pharma companies, research organizations and care providers. Currently, she is managing at Quinten Health the development of AI-powered solutions to support R&D decisions using RW data for our client.

Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.

Mingo is a Senior Sales Engineer at Plotly. After graduating from Bowdoin College with a degree in computer science, he started working with organizations in the master data management and data science spaces. Throughout his career, Mingo has partnered with large financial institutions, life sciences organizations, retail companies, and government agencies to help them better understand their data and more effectively serve their customers. Mingo enjoys building relationships with people to understand their pain points and help them solve their most challenging business and technical problems.

A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern workforce to wisely communicate and handle a data-driven future at the nation’s first Master of Science in Analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.

Jacob Andreas is the X Consortium Assistant Professor at MIT. His research aims to build intelligent systems that can communicate effectively using language and learn from human guidance. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. As a researcher at Microsoft Semantic Machines, he founded the language generation team and helped develop core pieces of the technology that powers conversational interaction in Microsoft Outlook. He has been the recipient of Samsung’s AI Researcher of the Year award, MIT’s Kolokotrones teaching award, and paper awards at NAACL and ICML.

Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.

Robert teaches machine learning for SAS and specializes in neural networks. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University where he built models pertaining to student success, faculty development and resource management. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.

Ari Zitin holds bachelor’s degrees in both physics and mathematics from UNC-Chapel Hill. His research focused on collecting and analyzing low energy physics data to better understand the neutrino. Ari taught introductory and advanced physics and scientific programming courses at UC-Berkeley while working on a master’s in physics with a focus on nonlinear dynamics. While at SAS, Ari has worked to develop courses that teach how to use Python code to control SAS analytical procedures.

Dr. Avi Pfeffer is Chief Scientist at Charles River Analytics. Dr. Pfeffer is a leading researcher on a variety of computational intelligence techniques including probabilistic reasoning, machine learning, and computational game theory. Dr. Pfeffer has developed numerous innovative probabilistic representation and reasoning frameworks, such as probabilistic programming, which enables the development of probabilistic models using the full power of programming languages, and statistical relational learning, which provides the ability to combine probabilistic and relational reasoning. He is the lead developer of Charles River Analytics’ Figaro™ probabilistic programming language. As an Associate Professor at Harvard, he developed IBAL, the first general-purpose probabilistic programming language. While at Harvard, he also produced systems for representing, reasoning about, and learning the beliefs, preferences, and decision making strategies of people in strategic situations. Prior to joining Harvard, he invented object-oriented Bayesian networks and probabilistic relational models, which form the foundation of the field of statistical relational learning. Dr. Pfeffer serves as Action Editor of the Journal of Machine Learning Research and served as Associate Editor of Artificial Intelligence Journal and as Program Chair of the Conference on Uncertainty in Artificial Intelligence. He has published many journal and conference articles and is the author of a text on probabilistic programming. Dr. Pfeffer received his Ph.D. in computer science from Stanford University and his B.A. in computer science from the University of California, Berkeley.

Ms. Sanja Cvijic is a Senior Scientist at Charles River Analytics who leads our Probabilistic AI Representations and Reasoning Systems group and has pioneered the application of Scruff to real-world problems in ISR and maintenance. Dr. Cvijic’s research activities are centered around applications of probabilistic programming to condition monitoring, fault detection and prediction systems. She developed a prognostic health management tool for assessing health and status of power transformers in Scruff. She also developed a probabilistic tool in Scruff for improved space domain awareness for assessing risks to satellites in space. Previously, she worked as a Director of Software and a Consultant in power industry at New Electricity Transmission Software Solutions. She earned her Doctoral degree in Electrical and Computer Engineering, Power Systems, at Carnegie Mellon University in 2013. She earned her Bachelors in Electrical and Computer Engineering at the University of Belgrade, Serbia, in 2008.

Dr. Joshua Gordon is a data scientist specializing in statistical modeling, machine learning, and deep learning technologies. While earning his Ph.D. in Statistics from the University of California Los Angeles, he published six articles focused on geospatial time series algorithms for earthquake prediction as well as residual analysis for spatial temporal models.
Joshua has over 10 years of experience working as a Data Scientist in the insurance and manufacturing industries prior to joining dotData as a Senior Data Scientist. In dotData, he leads customer-facing data science projects and is responsible for customers’ success with dotData technologies.

Tejaswini Pedapati works at IBM Research. Her research is focused on interpretability and automating deep learning. To that end, she was involved in developing tools and algorithms to provide these capabilities for IBM products. She has a masters’ degree from Columbia University.

Andrew Lamb is the chair of the Apache Arrow Program Management Committee (PMC) and a Staff Software Engineer at InfluxData. He works on InfluxDB IOx, a time series database engine written in Rust, that heavily uses the Apache Arrow ecosystem. He actively contributes to many open source software projects including the Apache Arrow Rust implementation and the Apache Arrow DataFusion query engine.

Thomas J. Fan is a Staff Software Engineer at Quansight Labs and is a maintainer for scikit-learn, an open-source machine learning library for Python. Previously, Thomas worked at Columbia University to improve interoperability between scikit-learn and AutoML systems. He is a maintainer for skorch, a neural network library that wraps PyTorch. Thomas has a Masters in Mathematics from NYU and a Masters in Physics from Stony Brook University.

Andrew Zaldivar is a member of the Responsible AI & Human-Centered Technology organization in Google Research. His role is to advocate for the responsible development and use of AI by disseminating and democratizing research findings from his organization. Andrew works with researchers and designers that are examining and shaping the socio-technical processes underpinning AI technologies through participatory, culturally-inclusive, and intersectional equity-oriented approaches. Before joining Google Research, Andrew was a Senior Strategist in Google’s Trust and Safety team, protecting the integrity of some of Google’s key products by utilizing machine learning to scale, optimize, and automate abuse fighting efforts. Andrew also holds a doctorate in cognitive neuroscience from the University of California, Irvine and was an Insight Data Science fellow.

Mahima Pushkarna is a design lead at the People + AI Research Initiative at Google. She brings design thinking and human-centered design into Human-AI Research. Her work explores advanced technologies, including generative AI, and draws from a mix of human-centered, participatory, and speculative design practices to bridge the gap between upstream developer practices and their impact on end user experiences and society. Mahima has designed tools and frameworks for explainability and interpretability that are widely used across industries and academia. She believes design can be a powerful tool for understanding and addressing the needs of people impacted by technology. Mahima is also interested in exploring the intersection of design, technology, and society, and is always looking for new ways to use design to make the world a better place. Mahima holds a masters degree in Information Design and Data Visualization from Northeastern University, Boston, MA. She has published in leading academic journals and conferences, including IEEE Vis, FAccT, and workshops at NeuRIPS. Prior to Google, Mahima worked as a product designer at Innovation by Design, a global think-tank, consulted at MIT’s Design Lab, and designed visualization tools at Ion Interactive. This bio was written with assistance from a language-driven model.

Melanie Veale, Ph.D. is a recovering Astrophysicist, currently working as a Data Solutions Architect at Anomalo. Her Ph.D. research on galaxy dynamics introduced her to statistical and computational python, as well as other languages and tools like C++, Fortran, IDL, R, bash, SLURM, and others. She has also dabbled in AWS infrastructure, Kubernetes, Docker, Spark, Ray, Dask and more as a Field Engineer and Field Data Scientist at Domino Data Lab, helping analytics and machine learning teams modernize their collaboration and deployment workflows. Nowadays she is a troubleshooting enthusiast anywhere on the Data, Analytics, and MLOps tech stacks, and enjoys melding her passions for crisp technical communication, good visualizations, and first-principles thinking into helping organizations get the most out of their data.

Swagata is a Data Professional with over 6 years experience in Healthcare, Retail and Platform Integration industry. She is an avid blogger and writes about state of the art developments in the AI space. She is particularly interested in Natural Language Processing, and focuses on researching how to make NLP models work in practical setting. In her spare time, she loves to play her guitar, sip masala chai and find new spots for doing Yoga. Connect with her here – https://www.linkedin.com/in/swagata-ashwani/

Guillaume Moutier is a Sr. Principal Data Engineering Architect at Red Hat, focusing his work on data services, AI/ML workloads and data science platforms. Former Project Manager, Architect and CTO for large organizations, he is constantly looking for and promoting new and innovative solutions, always with a focus on usability and business alignment brought by 20 years of IT architecture and management experience. When he’s not tinkering with IT, electronics or other high tech toys, Guillaume plays music (guitar, bass, drums, keyboards), is a video games enthusiast, and a reading addict.

Dr. Sagar Samtani is an Assistant Professor and Grant Thornton Scholar in the Department of Operations and Decision Technologies at Indiana University. Dr. Samtani graduated with his Ph.D. from the AI Lab from University of Arizona. Dr. Samtani’s research interests are in AI for Cybersecurity, developing deep learning approaches for cyber threat intelligence, vulnerability assessment, open-source software, AI risk management, and Dark Web analytics. He has received funding from NSF’s SaTC, CICI, and SFS programs and has published over 40 peer-reviewed articles in leading information systems, machine learning, and cybersecurity venues. He is deeply involved with industry, serving on the Board of Directors for the DEFCON AI Village and Executive Advisory Council for the CompTIA ISAO.

Philip Wauters is Customer Success Manager and Value engineer at Tangent Works working on practical applications of time series machine learning at customers from various industries such as Siemens, BASF, Borealis and Volkswagen. With a commercial background and experience with data engineering, analysis and data science his goal is to find and extract the business value in the enormous amounts of time-series data that exists at companies today.

Jenna Reps is a Director at Janssen Research and Development where she is focusing on developing novel solutions to personalize risk prediction. Jenna’s areas of expertise include applying machine learning and data mining techniques to develop solutions for various healthcare problems. She is currently working within the patient level prediction OHDSI workgroup with the aim of developing open source and user friendly software for developing risk models using data sets in the OMOP Common Data Model format. Prior to joining Janssen Research and Development, Jenna was a Senior Research Fellow at the University of Nottingham where she developed supervised learning techniques to signal adverse drug reactions using UK primary care data and acted as a data consultant to other researchers within the University. Jenna received her BSc in Mathematics and MSc in Mathematical Biology at the University of Bath and her PhD in Computer Science at the University of Nottingham.

Connor Shorten is a Research Scientist at Weaviate, an Open-Source Vector Search Database. Connor has had a role in the development of Ref2Vec, Hybrid Search, Generative Search, Weaviate’s Pipe API, and Re-Ranking. Connor has also hosted 34 episodes of the Weaviate podcast featuring guests from OpenAI, Cohere, You.com, MosaicML, Jina AI, Deepset, Neural Magic and many others! Connor also co-hosts Weaviate meetups in Boston and New York City! Prior to Weaviate, Connor has earned a Ph.D. in Computer Science from Florida Atlantic University. Connor’s Ph.D. was primarily focusing on Data Augmentation in Deep Learning and Applications of Deep Learning for COVID-19. Connor’s publication “A survey on image data augmentation in deep learning” has achieved over 5,000 citations.

Noah Giansiracusa (PhD in math from Brown University) is a tenured associate professor of mathematics and data science at Bentley University, a business school near Boston. His research interests range from algebraic geometry to machine learning to empirical legal studies. After publishing the book How Algorithms Create and Prevent Fake News in July 2021, Noah has gotten more involved in public writing and policy discussions concerning data-driven algorithms and their role in society. He’s written op-eds for Barron’s, Boston Globe, Wired, Slate, and Fast Company and is currently working on a second book, Robin Hood Math: How to Fight Back When the World Treats You Like a Number, with a Foreword by Nobel Prize-winning economist Paul Romer.

Freddy Boulton started his career as a data scientist for Nielsen where he built predictive models of television viewing behavior to make television ratings more accurate. This gave him a first hand-view of one of the biggest challenges faced by industry data scientists – being able to easily communicate and share machine learning models with stakeholders. He is currently solving that problem by working on Gradio, an open-source python library that lets data scientists create fully interactive demos of machine learning models with just a few lines of code.

Audrey is a Sr. Principal Software Engineer in the Red Hat Cloud Services – Red Hat OpenShift Data Science team focusing on helping customers with managed services, AI/ML workloads and next-generation platforms. She holds a degree in Computer Information Systems and has been working in the IT Industry for over 20 years in full stack development to data science roles. Audrey is passionate about Data Science and in particular the current opportunities with AIML at the Edge and Open Source technologies.

Hagay Lupesko is the VP of Engineering at MosaicML, where he focuses on making generative AI training and inference efficient, fast, and accessible. Prior to MosaicML, Hagay held AI engineering leadership roles at Meta, AWS, and GE Healthcare. He shipped products across various domains: from 3D medical imaging, through global-scale web systems, and up to deep learning systems that power apps and services used by billions of people worldwide.

Jay is a VP of our Artificial Intelligence and Machine Learning organization at Oracle Cloud. He completed a degree in neuroscience and started his career in technology at Oracle, maintaining an idea that these two paths would converge.

Raluca Ada Popa is the Robert E. and Beverly A. Brooks associate professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab and SkyLab at UC Berkeley, as well as a co-founder of Opaque Systems and PreVeil, two cybersecurity companies. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of the 2021 ACM Grace Murray Hopper Award, a Sloan Foundation Fellowship award, Jay Lepreau Best Paper Award at OSDI 2021, Distinguished Paper Award at IEEE Euro S&P 2022, Jim and Donna Gray Excellence in Undergraduate Teaching Award, NSF Career Award, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT CS doctoral thesis.

Pedro Domingos is a professor emeritus of computer science and engineering at the University of Washington and the author of The Master Algorithm. He is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. He is a Fellow of the AAAS and AAAI, and has received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Pedro received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon, and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas. He is a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. He was program co-chair of KDD-2003 and SRL-2009, and has served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. I’ve written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. He helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.

Cansu joined the Institute of Experiential AI as the ethics lead and a research associate professor. She also has an affiliation with the Department of Philosophy and Religion and the Ethics Institute in the College of Social Sciences and Humanities. She has a doctorate in philosophy specializing in applied ethics.

Cansu is the founder of AI Ethics Lab, one of the first initiatives focusing exclusively on advising practitioners and conducting multidisciplinary research on the ethics of artificial intelligence. She remains the director of the AI Ethics Lab, where she leads teams of computer scientists, philosophers, legal scholars, and other experts in research, the development of toolkits, and consulting.

Cansu developed the Puzzle-solving in Ethics (PiE) Model, a dynamic and collaborative model for integrating ethics into the AI innovation cycle that organizations have implemented through consulting work with the AI Ethics Lab. She brings the PiE Model to EAI along with her industry experience and her background in philosophy.

Cansu serves as an ethics expert in various ethics, advisory, and editorial boards. She is a founding editor for the international peer-reviewed journal AI & Ethics (Springer Nature), serves as an ethics expert for EU-funded research projects focusing on the ethics of AI, robotics, human enhancement, and law enforcement AI technologies, and chairs the Institute of Electrical and Electronics Engineers (IEEE) AI Experts Network Criteria Committee.

Danica Fine is a Senior Developer Advocate at Confluent where she helps others get the most out of Kafka and their event-driven pipelines. In her previous role as a software engineer on a streaming infrastructure team, she predominantly worked on Kafka Streams- and Kafka Connect-based projects to support computing financial market data at scale. She can be found on Twitter, tweeting about tech, plants, and baking @TheDanicaFine.

Tomasz Adamusiak MD Ph.D. is a Chief Scientist in the Clinical Insights & Innovation Cell at MITRE. He leads a multi-disciplinary group driving high-impact contributions to private and public sectors in Clinical and Genomic Data Science. Before MITRE, Tomasz was the Head of Data Science in the Pfizer Innovation Research (PfIRe) Lab. His team was responsible for developing novel digital endpoints, designing decentralized approaches for clinical trials, and applying AI/machine learning methods to generate novel insights from clinical data. Tomasz served in leadership and advisory roles in the American Medical Informatics Association, the SNOMED International, and the Epic Research Data Network.

Matt Bezdek is a Senior Data Scientist at Elder Research. In his work, he empowers commercial clients to make better business decisions, with expertise in machine learning, forecast modeling, natural language processing, and visualization. He has a PhD in Cognitive Psychology from Stony Brook University and has conducted neuroimaging research at Georgia Tech and Washington University in St. Louis.

Matt is Director of Data Science with over a decade of experience solving complex business problems with data, modeling and simulation. Over the past year in his tenure at project44, Matt has been scaling the data science team from a few disparate efforts to a full department of 30 team members around the globe. The data science team at project44 uses the billions of shipments that are tracked through project44’s platform to extract insights that help customers made data-driven decisions: everything from “estimated time of delivery” to “impact of the latest disruptions”. Project44’s data science team uses state-of-the-art Machine Learning techniques to capture the dynamic trends and patterns of today’s supply chain. Despite the pandemic and global nature of Matt’s team – the data scientists at project44 routinely hold “virtual whiteboarding sessions” where they brainstorm, trade ideas about statistical techniques, and also discuss their latest Netflix favorites.

Ted Kwartler is the Field CTO at DataRobot. Ted sets product strategy for explainable and ethical uses of data technology. Ted brings unique insights and experience utilizing data, business acumen and ethics to his current and previous positions at Liberty Mutual Insurance and Amazon. In addition to having 4 DataCamp courses, he teaches graduate courses at the Harvard Extension School and is the author of “Text Mining in Practice with R.” Ted is an advisor to the US Government Bureau of Economic Affairs, sitting on a Congressionally mandated committee called the “Advisory Committee for Data for Evidence Building” advocating for data-driven policies.

Rebecca Vislay-Wade is a Principal Data Scientist at Moderna, where she leads a team of scientists developing AI applications for clinical operations, regulatory science, and pharmacovigilance. Prior to Moderna, she worked as Senior Research Data Scientist at Highmark Health. Rebecca holds a PhD in biochemistry from Harvard University and did postdoctoral work in neuroscience at the NIH and Children’s National Medical Center in Washington, DC. She currently lives in the Boston area with her family.

Jesse Johnson is Vice President of Data Science and Data Engineering at Dewpoint Therapeutics, a drug development Biotech startup founded in 2019 around a scientific field called biomolecular condensates. In this role, Jesse’s diverse set of experiences from academic math departments, engineering teams at Google, and data science teams at large, medium and small life science companies provide a unique perspective on the ways that data and wet lab teams communicate differently, or sometimes don’t communicate at all.

Afrah Shafquat is a Senior Data Scientist II at Medidata, a Dassault Systemès company where she leads synthetic data solutions in clinical trials. At Medidata, her work focuses on innovative solutions to generate synthetic data, synthetic data evaluation (fidelity and privacy metrics), and new use cases for synthetic data. She has a Ph.D. in Computational Biology from Cornell University and an S.B. in Biological Engineering from Massachusetts Institute of Technology.

Minsoo is a Senior Product Manager at Microsoft Azure Machine Learning designing and building out Responsible AI tools for data scientists. She’s worked with OSS tools such as InterpretML, Fairlearn, Responsible AI Toolbox and contributed to the UX of the Responsible AI dashboard now released in Azure Machine Learning. She has bachelor’s degrees in Applied Mathematics and Painting from Brown University and Rhode Island School of Design (RISD). Coming from an interdisciplinary background with experience in building machine learning models and products, analyzing data, and designing UX, she is always finding work at the intersection of AI/ML, design, and social sciences to empower data and ML practitioners to work ethically and responsibly end-to-end.

Mehrnoosh Sameki is a principal PM manager at Microsoft, where she leads emerging Responsible AI technology and tools and for the Azure Machine Learning platform. She has cofounded Error Analysis, Fairlearn and Responsible AI Toolbox and has been a contributor to the InterpretML offering. She earned her PhD degree in computer science at Boston University, where she currently serves as an adjunct assistant professor, offering courses in responsible AI. Previously, she was a data scientist in the retail space, incorporating data science and machine learning to enhance customers’ personalized shopping experiences.

Nils Reimers is an NLP / Deep Learning researcher with extensive experience on representing text in dense vector spaces and how to use them for various applications. During his research career, he created sentence-transformers that were the foundation for many today’s semantic search applications.

In 2022, Nils joined Cohere.com to lead the team on smarter semantic search technologies and how to connect LLMs to enterprise data. Here, his teams develop new foundation models that can understand and reason over complex data.

A multi-faceted leader with in-depth experience in technology, marketing, finance, and operations, Vatsala strives to connect the dots between strategy and execution. She works at Stanford University’s Graduate School of Business as the Managing Director of Technology and Finance for Executive Education. As an ICF-credentialed leadership coach, she works with people–especially women executives, entrepreneurs, and youth–to uncover possibilities within themselves that even they are surprised by. She is an award-winning speaker and panel moderator, and has most recently spoken at the Women in Technology Conference, Argyle Forum, Dreamforce, and Stanford’s IT Unconference.

Originally from Cambridge, Matt now helps clients move to a data centric ML approach having worked with clients across autonomous vehicles, green energy and fintech whilst providing meaningful work in the developing world. Away from work Matt has a passion for photography, traveling and unusual cars. In fact his passion for unusual cars bought him to import a Nissan Stagea from Japan to the UK.

Liran Hason is the Co-Founder and CEO of Aporia, a full-stack ML observability platform used by Fortune 500 companies and data science teams across the world to ensure responsible AI. Prior to founding Aporia, Liran was an ML Architect at Adallom (acquired by Microsoft), and later an investor at Vertex Ventures. Liran created Aporia after seeing first-hand the effects of AI without guardrails. In 2022, Forbes named Aporia as the “Next Billion-Dollar Companies”.

Temilade (“Temi”) Oyeniyi, CFA is Vice President at S&P Global Market Intelligence’s Quantamental Research Group, which is responsible for building global equity strategies for institutional investors.

Max Cembalest is a researcher at Arthur focused on simplifying and explaining machine learning models. Previously, he received an M.S. in Data Science from Harvard University, where he concentrated on interpretability and graph-based models. He is particularly excited about recent advances in applying abstract algebra, topology, and category theory to neural network design.

Amber Roberts is a ML Growth Lead at Arize AI, a ML observability company built for maintaining models in production. Previously, Amber was a product manager of AI at Splunk and the Head of Artificial Intelligence at Insight Data Science. A Carnegie Fellow, Amber has an MS in Astrophysics from the Universidad de Chile.

Florian Jacta is a specialist of Taipy, a low-code open-source Python package enabling any Python developers to easily develop a production-ready AI application. Package pre-sales and after-sales functions. He is data Scientist for Groupe Les Mousquetaires (Intermarche) and ATOS. He developed several Predictive Models as part of strategic AI projects. Also, Florian got his master’s degree in Applied Mathematics from INSA, Major in Data Science and Mathematical Optimization.

Albert has skills in machine learning and big data to solve (financial) optimization problems. He developed projects of different skill levels for Taipy’s tutorial videos. He got his degree from McGill University – Bachelor of Science. Major in Computer Science & Statistics. Minor in Finance.

Ido Michael, a seasoned data engineering and science professional, co-founded Ploomber & JupySQL with the mission of empowering data scientists to build faster and more efficient solutions. Prior to this, he led data engineering and science teams at Amazon Web Services (AWS), where he played an instrumental role in building hundreds of data pipelines during various customer engagements, working closely with his team.

A proud alumnus of Columbia University, Ido moved to New York to pursue his Master’s degree in Computer Engineering. It was during his time at Columbia that he identified the challenges in working with multiple data sources and Jupyter notebooks for reliable model development. This realization inspired him to concentrate on building Ploomber, a platform designed to address these issues and streamline the data science workflow.

Elliott is an expert in data engineering, data warehousing, information management, and technology innovation with a passion for helping transform data into powerful information. He has more than a decade of experience implementing cutting-edge, data-driven applications. He has a passion for helping organizations understand the true potential in their data by working as a leader, architect, and hands-on contributor.

Elliott has built nearly a dozen cloud-native data platforms on AWS, ranging from data warehouses and data lakes, to real-time activation platforms in companies ranging from small startups to large enterprises.

Allen Downey is a curriculum designer at Brilliant.org and professor emeritus at Olin College. He is the author of several books — including Think Python, Think Bayes, and Probably Overthinking It — and a blog about data science and Bayesian statistics. He received a Ph.D. in computer science from the University of California, Berkeley; and Bachelor’s and Masters degrees from MIT.

Aaron is currently the Vice President of Data Science and Solutions at dotData. As a data science practitioner with 14 years of research and industrial experience, he has held various leadership positions in spearheading new product development in the fields of data science and business intelligence. At dotData, Aaron leads the data science team in working directly with clients and solving their most challenging problems.

Prior to joining dotData, he was a Data Science Principle Manager with Accenture Digital, responsible for architecting data science solutions and delivering business values for the tech industry on the West Coast. He was instrumental in the strategic expansion of Accenture Digital’s footprint in the data science market in North America.

Aaron received his Ph.D. degree in Applied Physics from Northwestern University.

Nick Singh is an Ex-Facebook & Google Engineer turned best-selling author of Ace the Data Science Interview, and founder of SQL Interview Platform DataLemur.com. His career advice on LinkedIn has earned him 100,000 followers, and he’s successfully career coached 578 people to land their dream job in data!

Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in AI, cloud, data and networking to leading startups and enterprises since the late 1990s. As the Co-Founder and CTO of Iguazio, Yaron drives the strategy for the company’s MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases. He also initiated and built Nuclio, a leading open source serverless framework with over 4,000 Github stars and MLRun, a cutting-edge open source MLOps orchestration framework. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA – NASDAQ: NVDA), where he led technology innovation, software development and solution integrations. He also served as the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007 and was later acquired by Mellanox (NASDAQ:MLNX). Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He sits on the Data Science Committee of the AI Infrastructure Alliance (AIIA), of which Iguazio is a founding member. He is co-authoring a book on Implementing MLOps in the Enterprise for O’Reilly. Yaron presents at major industry events worldwide and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.

Bill Franks is the Director of the Center for Statistics and Analytical Research at Kennesaw State University. He is also Chief Analytics Officer for The International Institute For Analytics (IIA) and serves on several corporate advisory boards. Franks is also the author of the books Winning The Room, Taming The Big Data Tidal Wave, The Analytics Revolution, and 97 Things About Ethics Everyone In Data Science Should Know. He is a sought after speaker and frequent blogger who has over the years been ranked a top global big data influencer, a top global artificial intelligence and big data influencer, a top AI influencer, and was an inaugural inductee into the Analytics Hall of Fame. His work, including several years as Chief Analytics Officer for Teradata (NYSE: TDC), has spanned clients in a variety of industries for companies ranging in size from Fortune 100 companies to small non-profit organizations. You can learn more at http://www.bill-franks.com.

Christina Qi is the CEO of Databento, an on-demand market data platform. She formerly founded Domeyard LP, a hedge fund focused on high frequency trading (HFT) that traded up to $7.1 billion USD per day. Failing to earn a job offer after a Wall Street internship, Christina started Domeyard from her dorm room with $1000 in savings, about 9 years ago. Her fund was a tiny minnow amongst the tigers of the hedge fund world, but after Michael Lewis’s Flash Boys came out in 2014 and HFT firms hid from the spotlight, Domeyard accidentally found itself in the center of the ring. Over the next decade, her company’s story was featured on the front page of Forbes and Nikkei, and quoted in the Wall Street Journal, Bloomberg, CNN, NBC, and the Financial Times as a result of the controversy and fascination with HFT. By a series of accidents, Christina became a voice in her industry, contributing to the World Economic Forum’s research on AI in finance, guest lecturing at dozens of universities, and teaching Domeyard’s case study at Harvard Business School. She is grateful to be able to open up about her mistakes, and to help people turn failures into opportunities.

No amount of therapy has quashed Christina’s impostor syndrome, but she will always be proud of her non-profit volunteer work. Christina was elected as a Member of the MIT Corporation, MIT’s Board of Trustees. She is Co-Chair of the Board of Invest in Girls, bringing financial literacy education to underserved populations across the US. Christina also sits on the Board of Directors of The Financial Executives Alliance (FEA) Hedge Fund Group, drives entrepreneurship efforts at the MIT Sloan Boston Alumni Association (MIT SBAA), and served on the U.S. Non-Profit Boards Committee of 100 Women in Finance. Although “X Under X” lists are a gimmick, she’ll admit that Forbes 30 Under 30 made a positive impact on her life by giving her a community – friends who dragged her out of bed during the lowest days of her life. Christina holds a Bachelor of Science in Management Science from MIT and is a CAIA Charterholder.

Dean has a background combining physics and computer science. He’s worked on quantum optics and communication, computer vision, software development, and design. He’s currently CEO at DagsHub, where he builds products that enable data scientists to work together and get their models to production, using popular open-source tools.

He’s also the host of the MLOps Podcast, where he speaks with industry experts about ML in production.

Moran is a machine learning manager at booking.com, researching and developing computer vision and NLP models for the tourism domain. Moran is a Ph.D candidate in information systems engineering at Ben Gurion University, researching NLP aspects in temporal graphs. Previously worked as a Data Science Team Leader at Diagnostic Robotics, building ML solutions for the medical domain and NLP algorithms to extract clinical entities from medical visit summaries.

Iryna Gurevych (PhD 2003, U. Duisburg-Essen, Germany) is professor of Computer Science and director of the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University (TU) of Darmstadt in Germany. Her main research interests are in machine learning for large-scale language understanding and text semantics. Iryna’s work has received numerous awards. Examples are the ACL fellow award 2020 and the first Hessian LOEWE Distinguished Chair award (2,5 mil. Euro) in 2021. Iryna is co-director of the NLP program within ELLIS, a European network of excellence in machine learning. She is currently the president of the Association of Computational Linguistics. In 2022, she received an ERC Advanced Grant to support her vision for the next big step in NLP “InterText – Modeling Text as a Living Object in a Cross-Document Context”.

Haritz Puerto is a Ph.D. candidate in Machine Learning & Natural Language Processing at UKP Lab in TU Darmstadt, supervised by Prof. Iryna Gurevych. His main research interests are reasoning for Question Answering and Graph Neural Networks. Previously, he worked at the Coleridge Initiative, where he co-organized the Kaggle Competition Show US the Data. He got his master’s degree from the School of Computing at KAIST, where he was a research assistant at IR&NLP Lab and was advised by Prof. Sung-Hyon Myaeng.

Chandra Khatri is a prominent figure in technology, best known for developing state-of-the-art AI products such as the world’s first fully autonomous Conversational AI technology, the Alexa Prize (a ChatGPT-like voice experience for Alexa users 5 years before ChatGPT), ELMAR (the first Enterprise Language Model Architecture), and Truth Checker AI, the first and currently only model to detect hallucinations generated by language models such as GPT-4.

He is the co-founder of Got It AI. Under his leadership, Got It AI is pushing the boundaries of the conversational AI ecosystem and delivering the next generation of automation products. In addition to developing products, he invests in and serves on the boards of cutting-edge technology companies. Prior to Got-It AI, Chandra established or led a number of AI teams at Amazon (Alexa AI, Alexa Prize), Uber (Multimodal AI, Conversational AI), and eBay (Recommendation Systems). Furthermore, he is well-known for leading efforts at Amazon to develop the first consumer-facing, large-scale, open-domain conversational system, which is regarded as the holy grail of conversational AI and one of the unsolved challenges in artificial intelligence.

Anna Jung is a Senior ML Open Source Engineer at VMware, leading the open source team as part of the VMware AI Labs. She currently contributes to various upstream ML-related open source projects focusing on the project’s overall health, adoption, and innovation. She believes in the importance of giving back to the community and is passionate about increasing diversity in open source. When away from the keyboard, Anna is often at film festivals supporting independent filmmakers.

Katie is a Data Science Solution Architect at Neo4j. She completed her degree in Cognitive Neuroscience at Harvard University. Passionate about people and problem solving, she transitioned to focusing on helping people and businesses leverage data for impactful outcomes. As a customer-facing data scientist, she has had the opportunity to work with large and small organizations across a variety of industries. At Neo4j she helps teams up-level their data science practice with graph data science.

Timo Walther is a Principal Software Engineer at Confluent and a long-time member of Apache Flink’s management committee. He studied Computer Science at TU Berlin and was part of the Database Group there – the origins of Apache Flink. He worked as a software engineer at DataArtisans and led SQL team at Ververica. He was a Co-Founder of Immerok which was acquired by Confluent in 2023. In Flink, he is working on various topics in the Table & SQL ecosystem to make stream processing accessible for everyone.

Hakan is a staff software engineer in ML Platform team at Lyft. They build ML development, training and serving systems helping 40+ teams. Previously, Hakan was a staff engineer in Box. He helped build cloud content management applications focused on security and also scaled kubernetes clusters, service meshes in an on-premise infrastructure. He started his career at the hardware level, building ASICs and transitioned to distributed systems software in a startup experience. Hakan is passionate about wearing many hats, switching abstraction levels, operational excellence and mentorship, and loves challenges and solving problems that take the whole team to address.

Mihir Mathur is the lead Product Manager for Machine Learning at Lyft, where he works on building ML/AI tools that power Lyft’s automated intelligent decisions across realtime pricing, ETAs, fraud detection, safety classification etc. In the past Mihir has worked on building delightful products for millions of users at Quora, Houzz, and Thomson Reuters and spoken about his work at conferences such as MLOps World and ODSC. Mihir graduated magna cum laude from UCLA with a Bachelor’s and Master’s in Computer Science.

Ruth Yakubu is a Principal Cloud Advocate at Microsoft. Ruth specializes in Java, Advanced Analytics, Data Platforms and Artificial Intelligence (AI).

In addition, she’s been a tech speaker at several conferences like Microsoft Ignite, O’reilly velocity, Devoxx UK, Grace Hopper Dublin, TechSummit, Websummit and numerous other developer conferences. Prior to Microsoft, She has also worked for great companies like UNISYS, ACCENTURE and DIRECTV over the years where she gained a lot of experience with software architectural design and programming. She’s awarded Dzone.com’s Most Valued Blogger.

Ali Rossi is a Data Science Tech Lead at Foursquare, working closely with their first-party foot traffic panel to deliver insights against a broad range of client business questions. She is passionate about consumer behavioral data, with experience building consumer panels, researching normalization methodologies, and developing methods to derive actionable insights. Previously, she worked in product management at Foursquare, Amazon and Nielsen, mainly focused on building analytics products using consumer-sourced data. She studied chemistry and mathematics at the University of Connecticut and is currently pursuing a Master of Science in computer science at the Georgia Institute of Technology.

As the Head of Machine Learning at Abnormal Security, Dan builds cybercrime detection algorithms to keep people and businesses safe. Before joining Abnormal Dan worked at Twitter: first as an ML researcher working on recommendation systems, and then as the head of web ads machine learning. Before Twitter Dan built smartphone sensor algorithms at TrueMotion and Computer Vision systems at the Serre Lab.

Sergey is a data scientist with a background in physics and neurobiology. FeatureByte is Sergey’s second startup. He was one of the first employees at DataRobot where he created and led a professional services group and helped the company grow into a unicorn. Sergey is widely known for being a Kaggle Grandmaster and holding the #1 rank on Kaggle in the past. Multiple times he was mentioned as one of the top data scientists by various publications. Sergey’s passion is in machine learning, predictive modeling and inventive feature engineering.

Allen is a Principal Machine Learning Architect and AI Researcher working for Oracle Cloud Infrastructure.

Shan is a data analytics, AI, and management consulting practitioner with over 20 years of experience in solving complex business problems for clients through innovative technology solutions. Shan joined Fujitsu in 2017 to grow and lead the data analytics business at Fujitsu North America. In his current role as the Head of AI offerings, Shan is responsible for shaping the AI go-to-market strategy, and product offerings, and for promoting AI adoption amongst Fujitsu’s clients. Shan has published thought leadership articles/whitepapers for the Fujitsu Global blog on technology and industry topics such as AI Enabled Trusted Society, AI and Advanced Analytics, Mobility Industry, and Smart Cities. Shan lives in Dallas, Texas, and is an avid NBA fan and motorcyclist.

Isaac is a co-founder and Principal Data Scientist at DrivenData, Inc, where he leads client engagements and spearheads development of the data science competition platform. He holds a master’s in Computational Science and Engineering from Harvard’s School of Engineering and Applied Sciences and a BS in Operations Research from the U.S. Coast Guard Academy, and previously spent seven years as a Coast Guard officer serving in a variety of operational and quantitative roles.

Colleen M. Farrelly is a lead data scientist whose expertise spans generative AI, topological data analysis, network science, and NLP, among others. She’s recently focused her research on the geometry of generative AI models and how this impacts their performance on tasks such as bias detection, and her volunteer work includes mentoring African machine learning students. She and Dr. Yae Gaba are the authors of The Shape of Data, an overview of machine learning from a geometric perspective.

Dan Roth is the Eduardo D. Glandt Distinguished Professor at the Department of Computer and Information Science, University of Pennsylvania, a VP/Distinguished Scientist at Amazon AWS, and a Fellow of the AAAS, the ACM, AAAI, and the ACL.

In 2017 Roth was awarded the John McCarthy Award, the highest award the AI community gives to mid-career AI researchers. Roth was recognized “for major conceptual and theoretical advances in the modeling of natural language understanding, machine learning, and reasoning.”

Roth has published broadly in machine learning, natural language processing, knowledge representation and reasoning, and learning theory, and has developed advanced machine learning based tools for natural language applications that are being used widely. Until February 2017 Roth was the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). Roth has been involved in several startups; most recently he was a co-founder and chief scientist of NexLP, a startup that leverages the latest advances in Natural Language Processing (NLP), Cognitive Analytics, and Machine Learning in the legal and compliance domains. NexLP was acquired by Reveal in 2020. Prof. Roth received his B.A Summa cum laude in Mathematics from the Technion, Israel, and his Ph.D. in Computer Science from Harvard University in 1995.

Jeff Clune is an Associate Professor of Computer Science at the University of British Columbia and a Faculty Member at the Vector Institute and a Senior Research Advisor at DeepMind.
Previously, he was a Research Team Leader at OpenAI. Before that he was a Senior Research Manager and founding member of Uber AI Labs, which was formed after Uber acquired a startup our startup. Prior to Uber, he was the Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming.

He conducts research in three related areas of machine learning (and combinations thereof):
– Deep Learning: Improving our understanding of deep neural networks, harnessing them in novel applications, and advancing deep reinforcement learning
– Evolving Neural Networks: Investigating open questions in evolutionary biology regarding how intelligence evolved and harnessing those discoveries to improve our ability to evolve more complex, intelligent neural networks
– Robotics: Making robots more like animals in being adaptable and resilient

A good way to learn about Jeff’s research is by visiting the Google Scholar page, which lists all of his publications.

Alex Sherstinsky is a staff machine learning and data products engineer on the team developing the core platform of Great Expectations, the leading open source data quality platform. Previously, Alex developed augmented intelligence systems that harness machine learning and gig work models to transform and scale customer service at Directly, Inc. He was a product and technical co-founder at GrowthHackers.com and Qualaroo, and a product/engineering executive at other venture capital-backed startups. Alex earned his Ph.D. in machine learning from MIT, with research conducted at the Media Lab. His scientific publications appear in refereed journals and conference proceedings; he holds 5 U.S. patents.

Bhakti is a Responsible AI Tech Lead at Google Research, where she develops fair, safe, and robust AI systems. She has spearheaded numerous projects at Google, including YouTube, Maps, Android, and Ads, making significant advancements to ensure that ML in these applications is fair, transparent, and safe for all. She is also a strong supporter of open-source technology and is the maintainer of several offerings in the TF Responsible AI toolkit, used globally by developers in the industry to make their ML workflows more responsible.

Zairah is a Data Scientist at you.com, the AI search engine, where she leverages her expertise in statistical and machine-learning techniques to build analytics and experimentation platforms. She recently spoke at NeurIPS 2022 and shared her expertise on data-driven decision-making in a privacy-focused AI-first startup. Previously, Zairah was a Data Scientist at IBM Research, researching Natural Language Processing (NLP) and AI Fairness topics. She has published research and holds patents in these domains. Zairah obtained her M.S. in Computer Science from the University of Pennsylvania, where she researched scikit-learn model performance. Her findings have since been used as guidelines for applying machine learning to supervised classification tasks. Zairah has published her work in top AI conferences such AAAI and has over 300 citations. Aside from work, Zairah enjoys adventure sports and poetry.

Kshetrajna is a Staff Data Scientist at Shopify working in the Merchant Services Org. Over the last 10 years of his career he has built and productionalized many ML models in various domains including retail, ad-tech and healthcare. His interests are mainly applied ML and ML systems and enjoys solving complex problems to help use machine learning at scale. Outside of work, Kshetrajna loves to spend time with his dogs, play music on his guitar, and is an avid gamer.

Jonas Mueller is Chief Scientist and Co-Founder at Cleanlab, a software company providing data-centric AI tools to efficiently improve ML datasets. Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms which now power ML applications at hundreds of the world’s largest companies. In 2018, he completed his PhD in Machine Learning at MIT, also doing research in NLP, Statistics, and Computational Biology.

Jonas has published over 30 papers in top ML and Data Science venues (NeurIPS, ICML, ICLR, AAAI, JASA, Annals of Statistics, etc). This research has been featured in Wired, VentureBeat, Technology Review, World Economic Forum, and other media. He has also contributed open-source software, including the fastest-growing open-source libraries for AutoML (https://github.com/awslabs/autogluon) and Data-Centric AI (https://github.com/cleanlab/cleanlab).

Jordan is an associate professor in the University of Maryland Computer Science Department (tenure home), Institute of Advanced Computer Studies, iSchool, and Language Science Center. Previously, he was an assistant professor at Colorado’s Department of Computer Science (tenure granted in 2017). He was a graduate student at Princeton with David Blei.

His research focuses on making machine learning more useful, more interpretable, and able to learn and interact from humans. This helps users sift through decades of documents; discover when individuals lie, reframe, or change the topic in a conversation; or to compete against humans in games that are based in natural language.

Meg is currently the Lead UXR for Intrinsic.ai, where she focuses her work on making it easier for engineers to adopt and automate with industrial robotics. She is a “Xoogler”, and prior to Intrinsic worked on the Explainable AI services on Google Cloud. Meg has had a varied career working for start-ups and large corporations alike, and she has published on topics such as user research, information visualization, educational-technology design, voice user interface (VUI) design, explainable AI (XAI), and human-robot interaction (HRI). Meg is also a proud alumnus of Virginia Tech, where she received her Ph.D. in Human-Computer Interaction.

Eric Eager is the VP of Research and Development at SumerSports, a football analytics startup founded by Paul Tudor Jones and Jack Jones. Prior to joining Sumer, he held similar roles at Pro Football Focus, and is responsible for many of the insights that have grown the game of American football to this day. Eric holds a PhD in Mathematical Biology from the University of Nebraska, and has taught at Wharton, DataCamp and the University of Wisconsin – La Crosse, publishing over 25 academic papers during his career.

Peter Wang is the CEO and co-founder of Anaconda, Inc. Prior to founding Anaconda (formerly Continuum Analytics), Peter spent 15 years in software design and development across a broad range of areas, including 3D graphics, geophysics, large data simulation and visualization, financial risk modeling, and medical imaging. As a creator of the PyData community and conferences, he devotes time and energy to growing the Python data science community and advocating for increasing data literacy around the world. Peter holds a BA in Physics from Cornell University.

Madhav is a Senior Data Scientist at Shopify where he focuses on building/evaluating recommendation systems. His role includes prototyping potential solutions and scaling them for production. Prior to Shopify, Madhav was a data science consultant where he focused on NLP projects for pharmaceutical companies. He then transitioned to Disney to develop personalized movie recommendations which sparked his passion for recommendation systems. In his free time, Madhav hosts free Q&A sessions for aspiring data scientists who are looking to get into this space.

Andrew is the head of data science at Bigeye, a data observability company. Prior to joining Bigeye, Andrew built ML-powered tools for Citi and (as a consultant) a range of top consumer banks; he specialized in pricing and underwriting problems. In his free time, Andrew enjoys cooking, travel, and using his TVR Chimaera to escape New York.

Hajime is a data professional with five years of expertise in marketing, retail, and eCommerce, working across Japan and the United States.

As a Data Analyst at Procter and Gamble and MIKI HOUSE Americas, Hajime has led data-driven strategy formulation and implemented technology initiatives such as e-commerce expansion, advertising optimization, and the identification of growth opportunities.

As an organizer of PyData NYC, Hajime is dedicated to fostering a vibrant community centered around the exchange of knowledge on open-source technologies in New York. Additionally, Hajime lends his expertise as a contributing technical writer for Towards Data Science.

Dr. Mohammed Taboun is a Principal Data Scientist at Precisely, where he uses his experience in analytics, optimization, and machine learning to drive innovation and business growth. Over the past 15 years, Mohammed has consistently demonstrated exceptional expertise in his field applied to various industries including technology, oil and gas, energy and utilities and telecommunications. With a strong academic background, Mohammed holds a PhD in Mechanical Engineering, specializing in Intelligent Control Systems, as well as a Master of Applied Science (MASc) and a Bachelor of Applied Science (BASc) in Industrial Engineering, focusing on Operations Research.

Dr. Blank is Professor Emeritus at Bryn Mawr College and Head of Research at Comet ML. Doug has 30 years of experience in Deep Learning and Robotics, was one of the founders of the area of Developmental Robotics, and is a contributor to the open source Jupyter Project, a core tool in Data Science. He currently lives in San Francisco, California, along with his family and animals.

Bio Coming Soon!

Bonny is a geospatial analyst and self described human geographer and social anthropologist. Exploring geographic properties that capture complex interactions, dynamic shifts in ecosystem balance and how activities influence eco-geomorphic conceptual frameworks across a wide variety of environments are the topics of popular public talks and panel discussions.

The ability to apply advanced data analytics, including data engineering and geo-enrichment, to poverty, race, and gender discussions targets judgments about structural determinants, racial equity, and elements of intersectionality to illuminate the confluence of metrics contributing to poverty.

Bonny is the author of the books Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence (publisher, O’Reilly Media) and Geospatial Analysis with SQL: A hands on guide to performing geospatial analysis by unlocking the syntax of spatial SQL published by Packt Press. Current projects include a new book in progress with Locate Press, Geospatial Data Science & the Art of Storytelling.

Dr. Adam Ross Nelson, is a career coach and a data science consultant. As a career coach he helps others enter and level up in data related professions. As a data science consultant he provides research, data science, machine learning, and data governance services. Previously, he was the inaugural data scientist at The Common Application which provides undergraduate college application platforms for institutions around the world. He holds a PhD from The University of Wisconsin – Madison in Educational Leadership & Policy Analysis. Adam is also formerly an attorney with a history of working in higher education, teaching all ages, and working as an educational administrator. Adam sees it as important for him to focus time, energy, and attention on projects that may promote access, equity, and integrity in the field of data science. This commitment means he strives to find ways for his work to challenge system oppression, injustice, and inequity.

Han Wang is the tech lead of Lyft Machine Learning Platform, focusing on distributed computing solutions. Before joining Lyft, he worked at Microsoft, Hudson River Trading, Amazon and Quantlab. Han is the creator of the Fugue project, aiming at democratizing distributed computing and machine learning.

Joshy George is a bioinformatics researcher with a Ph.D. in Bioinformatics from the University of Melbourne, Australia, and a Master's in Computer Science from the Indian Institute of Science. With his background in data science and machine learning, Dr. George has co-authored over 100 peer- reviewed scientific articles, showcasing expertise in developing principled methods to solve complex biological problems. In his current role, he leads a team that is focused on building predictive models for cancer precision medicine and understanding the molecular mechanisms leading to diseases.

Tendü Yoğurtçu, Ph.D., is the Chief Technology Officer (CTO) at Precisely. In this role, she directs the company’s technology strategy and innovation, leading all product research, and development programs.

Prior to becoming Chief Technology Officer, Tendü served as General Manager of Big Data for Syncsort, the precursor to Precisely, leading the global software business for Data Integration, Hadoop, and Cloud. She previously held several engineering leadership roles at the company, directing the development of the Integrate family of products.

Tendü has over 25 years of software industry experience, with a focus on Big Data and Cloud technologies. She has also spent time in academics, working as a Computer Science Adjunct Faculty Member at Stevens Institute of Technology.

In 2019, Tendü was named CTO of the Year at the prestigious Women in IT Awards, and in 2018 was recognized as an Outstanding Executive in Technology by Advancing Women in Technology (AWT).

Tendü received her Ph.D. in Computer Science from Stevens Institute of Technology, NJ, a Master of Science in Industrial Engineering, and a B.S. in Computer Engineering from Bosphorus University in Istanbul.

Tamilla Triantoro is an Associate Professor of Computer Information Systems at Quinnipiac University and a leader of the Masters Program in Business Analytics. She was previously an Academic Director of Data Analytics at the University of Connecticut. Dr. Triantoro is an author, speaker, researcher, and educator in the fields of artificial intelligence, data analytics, user experience with technology, and the future of work. She received her Ph.D. from the City University of New York where she researched online user behavior. Dr. Triantoro presents her research around the world, attempting to demystify the complexity of today’s digital world and to make it understandable and relevant to business professionals and the general audience.

Irina Rish is an Associate Professor in the Computer Science and Operations Research Department at the Université de Montréal (UdeM) and a core faculty member of MILA – Quebec AI Institute. She holds Canada Excellence Research Chair (CERC) in Autonomous AI and a Canadian Institute for Advanced Research (CIFAR) Canada AI Chair. She received her MSc and PhD in AI from University of California, Irvine and MSc in Applied Mathematics from Moscow Gubkin Institute. Dr. Rish’s research focus is on machine learning, neural data analysis and neuroscience-inspired AI. Before joining UdeM and MILA in 2019, Irina was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She received multiple IBM awards, including IBM Eminence & Excellence Award and IBM Outstanding Innovation Award in 2018, IBM Outstanding Technical Achievement Award in 2017, and IBM Research Accomplishment Award in 2009. Dr. Rish holds 64 patents, has published over 80 research papers in peer-reviewed conferences and journals, several book chapters, three edited books, and a monograph on Sparse Modeling.

Bio Coming Soon!

David Talby is the Chief Technology Officer at John Snow Labs, helping companies apply artificial intelligence to solve real-world problems in healthcare and life science. David is the creator of Spark NLP – the world’s most widely used natural language processing library in the enterprise.

He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK.

David holds a Ph.D. in Computer Science and Master’s degrees in both Computer Science and Business Administration. He was named USA CTO of the Year by the Global 100 Awards and GameChangers Awards in 2022.

Arvind Neelakantan is a Research Lead and Manager at OpenAI working on deep learning research for real-world applications. He got his PhD from UMass Amherst where he was also a Google PhD Fellow. His work has received best paper awards at NeurIPS and at Automated Knowledge Base Construction workshop.

Elijah Meeks is a co-founder and Chief Innovation Officer of Noteable, a startup focused on evolving how we analyze and communicate data. He is known for his pioneering work in the digital humanities while at Stanford, where he was the technical lead for acclaimed works like ORBIS and Kindred Britain. He was Netflix’s first Senior Data Visualization Engineer, and while at Netflix and Apple worked to develop the charting library Semiotic as well as bring cutting-edge data visualization techniques to analytical applications for stakeholders across the organization including A/B testing, conversation flows, algorithms, membership, people analytics, content, image testing and social media. He is a prolific writer, speaker and leader in the field of data visualization and the co-founder and first executive director of the Data Visualization Society.

Carol Willing is the VP of Engineering at Noteable, a three-time Python Steering Council member, a Python Core Developer, PSF Fellow, and a Project Jupyter core contributor. In 2019, she was awarded the Frank Willison Award for technical and community contributions to Python. As part of the Jupyter core team, Carol was awarded the 2017 ACM Software System Award for Project Jupyter’s lasting influence. She’s also a leader in open science and open-source governance serving on Quansight Labs Advisory Board and the CZI Open Science Advisory Board. She’s driven to make open science accessible through open tools and learning materials.

Dr. Hongxia Yang, PhD from Duke University, led the team to develop AI open sourced platforms and systems such as AliGraph, M6, Luoxi. Dr. Yang has published nearly 100 top conference and journal papers, and held more than 20 patents. She has been awarded the highest prize of the 2019 World Artificial Intelligence Conference, Super AI Leader (SAIL Award), the second prize of the 2020 National Science and Technology Progress Award (China’s Top tech award), the first prize of Science and Technology Progress of the Chinese Institute of Electronics in 2021, and the Forbes China Top 50 Women in Science and Technology in 2022. She used to work as the Senior Staff Data Scientist and Director in Alibaba Group, Principal Data Scientist at Yahoo! Inc and Research Staff Member at IBM T.J. Watson Research Center, joint adjunct professor at Zhejiang University Shanghai Advanced Research Institute respectively.

Bio Coming Soon!

Bio Coming Soon!

Pete joined Elementl as head of engineering in early 2022, and took over the reins as CEO in November of that year. Pete was previously co-founder and CEO of Smyte, an anti-abuse provider that was acquired by Twitter. Prior to his Pete led Instagram’s web team, built Instagram’s business analytics products, and helped to open source Facebook’s React.js.

RJ He is Zoox’s Director of Perception, where he is responsible for Zoox robotaxi’s ability to see and understand the world around them. He also leads the Zoox Boston office to assemble a world-class team of AI engineers. RJ was previously co-founder & CEO of Strio AI, an agriculture robotics startup, as well as VP Eng at Optimus Ride, an AV startup. RJ has also commanded a mechanized infantry unit, and holds an MIT PhD in autonomous systems.

Roozbeh Davari is a highly experienced data scientist and technology leader with a diverse background in research, development, software engineering, and product management. He has a track record of developing and deploying innovative solutions that leverage AI and data science to solve complex business problems. He holds a Ph.D. in Astrophysics from the University of California, Riverside and Carnegie Observatories. Currently, he serves as the Director of Data Science at Aisera, a software company that provides an AI-driven service management platform. Prior to joining Aisera, he worked as a Data Scientist at The Honest Company and Happy Money, where he built predictive models and data analytics solutions for various business applications.

Rehgan Avon is the co-founder & CEO of AlignAI, a Knowledge Management Platform helping companies sustainably transform their organizations to effectively work with data & Artificial Intelligence. With a background in Integrated Systems Engineering and a strong focus on building technology to support analytics and machine learning, Rehgan has worked on architecting solutions and products to operationalize machine learning models at scale within the large enterprise. Rehgan’s previous experience has been fueled by a passion for early-stage startups and product development.

Rehgan has built an extensive community of analytics & data experts through Women in Analytics, a global organization she founded in 2016 to provide more visibility to diverse individuals making an impact in this space. She hosts a global annual conference that has put over 250 women on the stage. The community has over 5000 members from around the world that participate in tutorials, learning groups, discussion boards, and mentorship programs. She was also inducted into the inaugural class of Columbus CEO’s Future 50.

Gary Nakanelua is a professional technologist with over 17 years of experience and the author of Experiment or Expire. Gary is the Managing Director of Innovation at Blueprint, a data intelligence company based in Bellevue, WA. He’s responsible for the experimentation and creation of Blueprint’s transformative solutions and accelerators. With his diverse background, Gary brings a different perspective to problems that businesses are facing today to create quantifiable solutions driven through a high level of collaborative thought processing, strategic planning, and cannibalization.

Tom Shafer works as a Lead Data Scientist at Elder Research, a recognized leader in data science, machine learning, and artificial intelligence consulting since its founding in 1995. As a lead scientist, Tom contributes technically to a wide variety of projects across the company, mentors data scientists, and helps to direct the company’s technical vision. His current interests focus on Bayesian modeling, interpretable ML, and data science workflow. Before joining Elder Research, Tom completed a PhD in Physics at the University of North Carolina, modeling nuclear radioactive decays using high-performance computing.

Emily is a Staff MLOps Engineer at Intuit Mailchimp, meaning she gets paid to say “it depends” and “well actually.” Professionally she leads a crazy good team focused on helping Data Scientists do higher quality work faster and more intuitively. Non-professionally she paints huge landscapes and hurricanes in oils, crushes sweet V1s (as long as they’re not too crimpy), rides her bike, reads a lot, and bothers her cats. She lives in Atlanta, GA, which is inarguably the best city in the world, with her husband Ryan who’s a pretty darn cool guy.

Alex Antony is a senior staff data scientist at GE Aerospace where he leads modeling, analytic development, reporting, and forecasting for the market intelligence function. He has 10 years of experience in the data science field and 15 years of experience working with the Department of Defense. He holds a MS in Applied Statistics and a PhD from Indiana University where he focused on Computational and Quantitative Social Science.

Erika Cardenas is a Developer Advocate at Weaviate, an open-source vector database. She has two master’s degrees in economics and data science from Florida Atlantic University. Erika was part of the NSF-NRT program, where she published a paper on predicting house prices using structured and unstructured text data. She has written several blog posts such as vector database versus vector library, hybrid search, and integrating LangChain and Weaviate for generative search applications.

Bernard is a Director in the AI Center of Excellence at Fidelity Investments working on personalization and recommender systems. His work is primarily concentrated in recommender systems and optimization, and he regularly presents on these topics, most recently at IJCAI’21 and CPAIOR’21 conferences. He is the lead developer of the open-source libraries Selective, MABWiser and Mab2Rec. He holds a MS in Computational Science and Engineering from Harvard University.

Joe Dery joined Western Governors University’s College of IT as the VP & Dean of Data Analytics in summer, 2022. At WGU, Joe is working to help more than 3,000 current analytics students learn how to effect change in their professional roles – surgically balancing a combination of mathematics, data management, programming, and business influence skills. Prior to joining academia full-time, Joe spent much of his corporate career working for EMC – and later, Dell Technologies – where he joined as a “hands-on-keyboard” Data Scientist in 2011. Joe went on to hold leadership positions in Dell’s Sales, Finance, and Supply Chain organizations driving efforts in Data Science, Business Intelligence, Digital Strategy, and Digital Transformation. Across these domains, Joe’s efforts touched a wide variety of business problems, including ML-driven sales quota allocations, sales forecasting & opportunity prioritization, customer cross-sell/whitespace targeting, addressable marketing opportunity sizing, sales territory optimization, supply chain planning optimization, data/analytics literacy training, and self-service BI. Building from his experiences, Joe is often invited to speak on the crucial role of decision intelligence frameworks, change management, and “improv” in bringing analytics solutions to life. Joe holds a Ph.D in Business Analytics & an M.S. in Marketing Analytics, both from Bentley University.