ODSC East 2021

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 and Manchester United, and with large social networks, like Justgiving.

He now works as Head of Data Scientist and Analytics in Badoo, the largest dating site with over 420 million users. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning, and Machine Learning training.

Lara is a Data Science Manager at EY and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science bootcamp and built risk models for large financial institutions at McKinsey & Co.

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.

Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

Eric is an Investigator at the Novartis Institutes for Biomedical Research, where he solves biological problems using machine learning. He obtained his Doctor of Science (ScD) from the Department of Biological Engineering, MIT, and was an Insight Health Data Fellow in the summer of 2017. He has taught Network Analysis at a variety of data science venues, including PyCon USA, SciPy, PyData, and ODSC, and has also co-developed the Python Network Analysis curriculum on DataCamp. As an open-source contributor, he has made contributions to PyMC3, matplotlib, and bokeh. He has also led the development of the graph visualization package nxviz, and a data cleaning package pyjanitor (a Python port of the R package).

Daniel Gerlanc has worked as a data scientist for more than decade and been writing sofware 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.

Matt is currently Growth Manager at Roboflow, a YCombinator-backed startup focusing on responsibly democratizing computer vision. Matt is also Managing Partner + Principal Data Scientist at BetaVector. His full-time professional data work spans finance, education, consumer-packaged goods, and politics and he earned General Assembly’s 2019 “Distinguished Faculty Member of the Year” award. Matt earned his Master’s degree in statistics from Ohio State. Matt is passionate about mentoring folx in data and tech careers. Matt also volunteers with Statistics Without Borders and currently serves on their Executive Committee as the Marketing & Communications Director.

Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.

Joseph Nelson is co-founder and CEO of Roboflow, a computer vision developer tool. Roboflow enables anyone to build better computer vision models quickly. Joseph previously co-founded Represently (acq. 2018). He has been named Distinguished Faculty at General Assembly and worked at companies big (Facebook) and small (failed startups). Joseph is a managing partner at BetaVector, a data science consultancy he co-founded. He’s easily reached on Twitter: @josephofiowa

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.

Coming Soon!

Rajiv Shah is a data scientist at DataRobot, where his primary focus is helping customers improve their ability to make and implement predictions. Previously, Rajiv has been part of data science teams at Caterpillar and State Farm. He has worked on a variety of projects from a wide ranging set of areas including supply chain, sensor data, acturial ratings, and security projects. He has a PhD from the University of Illinois at Urbana-Champaign.

Jordan Bakerman holds a Ph.D. in statistics from North Carolina State University. His dissertation centered on using social media to forecast real-world events, such as civil unrest and influenza rates. As an intern at SAS, Jordan wrote the SAS Programming for R Users course for students to efficiently transition from the R to SAS using a cookbook style approach. As an employee, Jordan has developed courses demonstrating how to integrate open source software within SAS products. He is passionate about statistics, programming, and helping others become better statisticians.

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, including machine learning and optimization.

Laura Seaman is a Senior Machine Intelligence Scientist at Draper where she applies machine learning and bioinformatics algorithms to a variety of applications. Dr. Seaman’s graduate work focused on using genetic data to identify alterations to the genomic structure in cancer. She is currently using data science for many applications including analysis of financial networks and identification of genetically engineered organisms. Dr. Seaman has a Bachelor of Science in Biological Engineering from the Massachusetts Institute of Technology, a Masters of Arts in Statistics from the University of Michigan, and a Doctor of Philosophy in Bioinformatics from the University of Michigan.

Julie Josse is a senior researcher at Inria and a scientific collaborator and teacher at at Ecole Polytechnique (Institut Polytechnique de Paris) in France. Julie Josse’s is an expert in handling missing values (inference, multiple imputation, matrix completion, missing non at random data, supervised learning with missing values) and has created a platform to collect works and give resources to users https://rmisstastic.netlify.app/, has organized workshops on the topic. Her vocation is to push methodological innovation to bring useful application of her research to the user in particular in bio-sciences and health. Her curent research focuses on causal inferences techniques for personalized medicine. She leads an important project with the Traumabase group dedicated to the management of polytraumatized patients to help emergency doctors making decisions. Julie Josse is dedicated to reproducible research with the R statistical software: she has developed packages including missMDA and FactoMineR to transfer her work, she is a member of the R foundation and of Rforwards to increase the participation of minorities in the community.

Dr. Freddy Lecue is the Chief Artificial Intelligence (AI) Scientist at CortAIx (Centre of Research & Technology in Artificial Intelligence eXpertise) at Thales in Montreal – Canada. He is also a research associate at INRIA, in WIMMICS, Sophia Antipolis – France. Before joining the new R&T lab of Thales dedicated to AI, he was AI R&D Lead at Accenture Labs in Ireland from 2016 to 2018. Prior to joining Accenture he was a research scientist, the lead investigator in large-scale reasoning systems at IBM Research from 2011 to 2016, a research fellow at The University of Manchester from 2008 to 2011, and a research engineer at Orange Labs from 2005 to 2008.

Noemi Derzsy is a Senior Inventive Scientist at AT&T Chief Data Office within the Data Science and AI Research organization. Her research is centered on understanding and modeling customer behavior and experience through large-scale consumer and network data, using machine learning, network analysis/modeling, spatio-temporal mining, text mining and natural language processing techniques. She is an organizer of Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group, and she is a NASA Datanaut.
Prior to joining AT&T, Noemi was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. She holds a PhD in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.

Robert didn’t start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.

After switching to the tech industry, Robert’s interests shifted to modeling data. He attained his Ph.D. in mathematical statistics from Purdue University, and then he worked as a research engineer in various AI startups (he is currently a ML research scientist at Gamalon). He has published in journals and venues across these spaces, including RECOMB and NeurIPS, on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. In addition to startup work, he is a machine learning professor at Northeastern University.

Mona is a Senior Data Scientist at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology, and are passionate about contributing to the ethical use of data science methodology in the public and private sector.

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.

The researcher first, developer second, in the last 5 years Ariel worked on various projects from the realms of quantum chemistry, massively-parallel supercomputing, and deep-learning computer-vision. With AllegroAi, he helped build an open-source R&D platform (Allegro Trains) and later went on to lead a data-first transition for a revolutionary nanochemistry startup (StoreDot). Answering his calling to spread the word on state-of-the-art research best practices, He recently took up the mantle of Evangelist at ClearML. Ariel received his Ph.D. in Chemistry in 2014 from the Weizmann Institute of Science. With a broad experience in computational research, he made the transition to the bustling startup scene of Tel-Aviv, and to cutting-edge Deep Learning research.

Christopher Kanan is an Assistant Professor at the Rochester Institute of Technology (RIT), a Visiting Assistant Professor at Cornell Tech, and a Senior AI Scientist at Paige. At RIT, his lab works on lifelong machine learning and language driven computer vision, which has been supported by awards from NSF, AFOSR, ONR, DARPA, Adobe Research, and other industrial partners. He is also Associate Director of RIT’s Center for Human-aware AI and he is a member of RIT’s McNair Scholars advisory board. At Paige, a startup that has raised $95M to improve the diagnosis of cancer, he led the AI R&D team during its first 1.5 years and continues to advise its AI teams. He received a PhD in computer science from the University of California at San Diego, where he worked on brain-inspired algorithms for object recognition, neural networks, active vision, and cognitive modeling. He received an MS in computer science from the University of Southern California. Before joining RIT, he was a postdoctoral scholar at the California Institute of Technology, and later worked as a Research Technologist at NASA’s Jet Propulsion Laboratory, where he used deep learning to develop vision systems for autonomous ships. He is the recipient of the 2016 Rising Star Award and the 2019 Distinguished Scholarship Award in RIT’s College of Science. He is an IEEE Senior Member and has published over 50 refereed papers, many of which are in top venues across AI such as CVPR, ICCV, NeurIPS, AAAI, ICLR, ACL, etc.

Tyler Hayes is a PhD candidate in the Chester F. Carlson Center for Imaging Science at the Rochester Institute of Technology (RIT) in Rochester, NY. She works in Professor Christopher Kanan’s laboratory to advance online continual learning. Her current research interests include lifelong machine learning, computer vision, and computational mathematics. Previously, she earned a BS in Applied Mathematics from RIT in 2014 and an MS in Applied and Computational Mathematics from RIT in 2017.

Brian Lucena is a Principal at Lucena Consulting and a consulting Data Scientist at Agentero. An applied mathematician in every sense, he is passionate about applying modern machine learning techniques to understand the world and act upon it. In previous roles, he has served as SVP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.

Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.

Pieter Gijsbers is a Ph.D. student at the Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open-source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.

Rahul Agarwal is currently working as a Machine Learning Engineer at Facebook. Before this he has worked at WalmartLabs and Citi India.
In his free time, Rahul likes to write about Data Science and Machine Learning and you could find him at Medium, his own Website, or linkedin profile which is the place where he is the most active

In two decades in the data management industry, Paige Roberts has worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant.
She has built data engineering pipelines and architectures, documented and tested large scale open source analytics implementations, spun up Hadoop clusters from bare metal, picked the brains of some of the stars in the data analytics and engineering industry, championed data quality when that was supposedly passé, worked with a lot of companies in a lot of different industries, and questioned a lot of people’s assumptions.
Now, she promotes understanding of Vertica, MPP data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.

Pranjal Singh has been a Data Scientist at Vertica since May 2020. Prior to joining Vertica, Pranjal received his Bachelor’s degree in Data Science from Northeastern University in Boston, MA. He has experience with Software Engineering, Data Analytics, and Machine Learning. Pranjal has a passion for ML and Predictive Analytics, and helping organizations make better decisions with data. He’s an avid sports fan, with a special interest in sports analytics and data.

Francesco received his MS degree in Computational Mathematics in 2017 from the Department of Mathematics at the Technical University of Berlin. After a research experience in Bayesian machine learning, he pivoted into deep learning with generative models and computer vision. In his current position as a machine learning research engineer, he works on NLP and speech synthesis and has presented and authored a few open-source projects related to these topics.

Christian Schäfer is driving AI research within the Axel Springer group and helps to integrate machine learning systems in production. His goal is to create products that are both smart and pretty so that everyone will like them. Having an academic background in theoretical physics, Chris is interested in understanding complex phenomena, and his professional interests moved from network theory over neuroscience to deep learning, which all share some interesting similarities.

Andras Zsom is a Lead Data Scientist in the Center for Computation and Visualization group at Brown University, Providence, RI. He works with high-level academic administrators to tackle predictive modeling problems, he collaborates with faculty members on data-intensive research projects, and he was the instructor of a data science course offered to the data science master students at Brown.

Teal Guidici is a Senior Machine Intelligence Scientist at Draper where she uses statistical techniques and machine learning algorithms to develop creative solutions for interesting data-driven problems in areas including biomedicine, finance, and remote sensing. Prior to Draper, she did graduate work creating new methods to analyze patterns of co-variation in complex datasets and applied these methods applied to high throughput metabolomics data. She has additional experience in survey design and data analysis in consumer marketing research. Dr. Guidici has a B.S. in Theoretical Mathematics from MIT, a M.S. in Bioinformatics and a Ph.D. in Statistics from the University of Michigan.

Joy Payton is a cloud engineer, data scientist, and adjunct professor who specializes in helping biomedical professionals conduct reproducible computational research. In addition to moving medicine forward through principles of open science and reproducibility, Joy also enjoys teaching citizen scientists how to use public data repositories to understand their own communities better and advocate for change from a data-centric perspective. Her various roles allow Joy to lead efforts to teach people how to write their first line of code and help anyone who’s interested climb the data science learning curve. Currently employed by the Children’s Hospital of Philadelphia and Yeshiva University, Joy is always open to hearing about open-source, data-centric volunteer opportunities for herself and her students.

Dr. Clair Sullivan is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she began her career in nuclear emergency response at Los Alamos National Laboratory where her research involved signal processing of spectroscopic data. She spent 4 years working in the federal government on related subjects and returned to academic research in 2012 as an assistant professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign. While there, her research focused on using machine learning to analyze the data from large sensor networks. Deciding to focus more on machine learning, she accepted a job at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. Additionally, she founded a company, La Neige Analytics, whose purpose is to provide data science expertise to the ski industry. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers. Dr. Sullivan was the recipient of the DARPA Young Faculty Award in 2014 and the American Nuclear Society’s Mary J. Oestmann Professional Women’s Achievement Award in 2015.

Shagun Sodhani is a Research Engineer in the Facebook AI Research Group. He is primarily interested in lifelong reinforcement learning – training AI systems that can interact with and learn from the physical world and consistently improve as they do so without forgetting the previous knowledge. He did his MS from Mila, University of Montreal., where he was supervised by Dr. Yoshua Bengio and Dr. Jian Tang.

A proven leader in Data Sciences & Applied AI. Experience in managing large scale projects and teams in data engineering, ML operations, and ML modeling. Helped in developing and implementing enterprise AI strategy; I have lead Cloud Migration and Integration efforts. Good understanding of State of the Art (SOTA) ML tools, framework, and algorithms. Experience in building distributed machine learning workflows using Apache Spark, DASK, Rapids and Kubernetes.

Vamsi enjoys reading the latest ML papers and staying up-to-date with relevant trends in the field.
Leading research in SOTA in areas such as Natural Language Inference, Text Entailment, Q&A, Text Classification, and Semantic Similarity using BERT, RoBERTa, Fairseq, Huggingface, LSTM, Transformers and others.

In the past established highly talented engineering teams at various enterprises. Vamsi has a successful track record of tactical execution in fast-paced software and technology development environments.

Chandra Khatri is the Chief Scientist and Head of AI at Got It AI, wherein, his team is transforming the AI space by leveraging state-of-the-art technologies in order to deliver Self-Discovering, Self-Training, and Self-Optimizing products. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various kinds of applied research projects at Uber AI such as Conversational AI, Multi-modal AI, and Recommendation Systems.

Prior to Uber AI, he was the founding member of the Alexa Prize Competition at Amazon, wherein he was leading the R&D and got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. Prior to Alexa AI, he was driving NLP, Deep Learning, and Recommendation Systems related Applied Research at eBay. He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India.

His current areas of research include Artificial and General Intelligence, Democratization of AI, Reinforcement Learning, Language Understanding, Conversational AI, Multi-modal and Human-agent Interactions, and Introducing Common Sense within Artificial Agents.

Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She completed her Ph.D. in Statistics at the University of California, Berkeley in 2014. Previously, she received an AB in Mathematics from Princeton University (2007), a Master of Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge (2008), an MPhil by research in Physics from the University of Cambridge (2009), and an MS in Computer Science from the University of California, Berkeley (2013). Her recent research has focused on developing and analyzing models for scalable Bayesian machine learning. She has been awarded an Early Career Grant (ECG) from the Office of Naval Research (2020), an AISTATS Notable Paper Award (2019), an NSF CAREER Award (2018), a Sloan Research Fellowship (2018), an Army Research Office Young Investigator Program (YIP) award (2017), Google Faculty Research Awards, an Amazon Research Award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods), the Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the Berkeley campus showing the greatest promise in statistical research), the Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average).

William Falcon is the creator of the popular open-source project PyTorch Lightning, and the recently announced Grid AI. William created Lightning while doing his PhD at NYU and as a PhD researcher at Facebook AI; Lightning allows users to scale models without the boilerplate and Grid enables large-scale training on the cloud. Previously he co-founded the now acquired NextGenVest and spent time at Goldman Sachs. His PhD (currently on leave to focus on Lightning), is funded by Google Deepmind and NSF Foundation. His research interest is in unsupervised learning and the intersection of AI and neuroscience. William is a native of Venezuela and holds a BA from Columbia University in Computer Science and Statistics, with a minor in Math.

Louvere Walker-Hannon is a MathWorks Senior Application Engineer, who provides direction and recommendations on technical workflows for various applications. Specifically, she assists with the following topics image processing, computer vision, machine learning, deep learning, geospatial analysis, and data analytics when discussing technical workflows. She has a bachelor’s degree in Biomedical Engineering and a master’s degree in Geographic Information Technology with a specialization in Remote Sensing. Louvere has worked in three different engineering roles throughout her 20-year career while at MathWorks. She was also a Program Presenter; in this role she was the lead educator of STEM topics for Cahners ComputerPlace within the Boston Museum of Science. A prominent theme in her career is communication of technical concepts to various audiences and being involved with STEM education. Louvere is a member of the Society of Women Engineers (SWE), the National Society of Black Engineers (NSBE), the Society of Hispanic Professional Engineers (SHPE), and other organizations. She has a long history of serving as a STEM mentor. Louvere volunteers with Black Girls Code and the Society of Women Engineers. She is a Curriculum Lead for the Boston Chapter of Black Girls CODE, a Co-Lead of the SWE Latinos Affinity Group, and a Society Engagement Lead for the SWE African American Affinity Group. Louvere has presented and continues to present at several STEM related conferences on various topics. She presented her work on a Natural Language Processing application at the 100th American Meteorological Society in January 2020 and at the American Association of Geographers (AAG) virtual conference in April 2020. She presented content on Augmented Reality, Artificial Intelligence, and their challenges at the virtual Grace Hopper Celebration in October 2020. She served as one of the Virtual Chairs for the thirty-seventh International Conference on Machine Learning (ICML) in July 2020 and thirty-fourth conference on Neural Information Processing Systems (NeurIPS) which occurs in December 2020. Louvere is scheduled to serve as one of the MeetUp Chairs for the upcoming thirty-fifth conference on Neural Information Processing Systems (NeurIPS) in 2021. She is helping her company begin an initiative regarding STEM Outreach with a DEI focus.

Sarah Mohamed is a Software Engineer at MathWorks working on deep learning interoperability. She develops tools that allow AI models to be exchanged and deployed among frameworks such as MATLAB, Pytorch, TensorFlow, and ONNX. In the past, she has also worked as an Application Support Engineer, where she interacted directly with customers developing deep learning applications. She obtained her M.Sc. in Computational Science and Engineering from Harvard University, and holds dual bachelor’s degrees in Computer Science and Biology from the University of Virginia. She has experience organizing and delivering hands-on workshops at the Grace Hopper Celebration, the world’s largest conference for women in computing, as well as TechTogether Boston, the largest student-led hackathon for women and nonbinary individuals in Boston.

Shruti Karulkar is the Quality Engineering Manager MATLAB Hardware at MathWorks. Shruti’s team of highly creative engineers work to design and implement cutting-edge tools to qualify MATLAB and Simulink support for hardware ranging from simple – Arduino/Raspberry Pi/iOS and Android phones – to high-end RF Signal Generators. Shruti has a long history of serving as a STEM mentor and is a STEM Ambassador for MathWorks. Shruti was a speaker at GHC18, GHC19 and GHC20 delivering successful and popular hands-on AI workshops.

Scott Bailey is the Digital Research and Scholarship Librarian at the NC State University Libraries, where he collaborates with faculty and other scholars in applying digital and computational tools and methods to open new possibilities in research and learning. He regularly teaches workshops using programming languages like Python and R to introduce data analysis and visualization, machine learning, and computational approaches to text data. He was previously the Head of Social Science Data and Software in the Center for Interdisciplinary Digital Research (CIDR) at Stanford Libraries, where he oversaw a group of Ph.D. students in delivering expert consultation on statistical computing, organized and taught in the CIDR workshop series, and collaborated with colleagues across Stanford University to provide better access to data and support for data-driven research.

Emma Ning is a Senior Product Manager in the AI Framework team under Microsoft Cloud + AI Group, focusing on AI model operationalization and acceleration with ONNX/ONNX Runtime for open and interoperable AI. She has more than five years of product experience in search engines taking advantage of machine learning techniques and spent more than three years exploring AI adoption among various businesses. She is passionate about bringing AI solutions to solve business problems as well as enhancing product experience.

Guoyu Wang is a senior software engineer at Microsoft, where he works on Onnx Runtime to bring accelerated inferencing to mobile platforms. In the past, he has also worked on Microsoft Office and shipped multiple versions of Microsoft PowerPoint. Guoyu earned his master’s degree in Computer Science from University of Toronto.

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Dr. Boardman has over a decade of experience as a scientist, professor, and consultant in various sectors and disciplines, including digital marketing, health tech, intelligence, entrepreneurship, economic development, and health/medical policy. Her knowledge and experience includes A/B testing, machine learning, agent-based modeling, statistics, and intelligence analysis. In her work, she places heavy emphasis on clearly and empathetically communicating insights derived from small, qualitative data up to the big data she currently works with in digital marketing. Invigorated by messy, unconventional data and problems, she has also designed and modified several techniques and approaches to work with this. She believes that science is an ongoing process and we should adapt our approach to the problems and data, not find problems and data that work well with what we know. Currently, Dr. Boardman is a Senior Data Scientist at TI Health and holds a B.B.A. in International Business from the University of Oklahoma, an M.P.P. in Public Policy from Pepperdine University, a PhD in Public Policy from George Mason University, and a Masters of Information and Data Science from Berkeley.

Thushan is a Senior Data Scientist at QBE Insurance, Australia, where he works on marrying artificial intelligence with business acumen to solve business problems. He has extensive knowledge of deep learning techniques, that has been poured into two books and a video course on deep learning (TensorFlow). In his free time, Thushan answers questions on StackOverflow creates YouTube content or presents at local meetups. He obtained his Ph.D. from the University of Sydney, specializing in deep learning and reinforcement learning. When not indulging in the world of data science, Thushan enjoys meditation, swimming, and hiking.

James Lamb is a software engineer at Saturn Cloud, where he works on a managed data science platform built on Dask and Kubernetes. Before Saturn Cloud, James worked on the industrial internet of things (IIoT) problems as a data scientist at AWS and Chicago-based Uptake. He is a core maintainer on LightGBM, and has contributed on other open-source data science and data engineering projects such as XGBoost and prefect. James holds Masters degrees in Applied Economics (Marquette University) and Data Science (University of California, Berkeley).

James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web, and Cognitive Sciences at RPI. He also is acting director of the RPI-IBM Artificial Intelligence Research Collaboration and serves on the Board of the UK’s charitable Web Science Trust. Hendler has authored over 400 books, technical papers, and articles in the areas of Semantic Web, artificial intelligence, agent-based computing, and high-performance processing. Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS, and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. He is also the first computer scientist to serve on the Board of Reviewing Editors for Science. In 2010, Hendler was named one of the 20 most innovative professors in America by Playboy magazine and was selected as an “Internet Web Expert” by the US government. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee. In 2016, became a member of the National Academies Board on Research Data and Information and in 2018 became chair of the ACM’s US technology policy committee and was elected a Fellow of the National Academy of Public Administration.

Ritika Gunnar is the Vice President of Data and AI Expert Labs. In this role, she and her team of ‘ninjas’ work with clients on their transformation journey through a data & AI first methodology and help implement Data & AI solutions using deep knowledge-based skills to accelerate the adoption of data & AI enterprise capabilities. Prior to this role, Ritika was the Vice President of Offerings for IBM Watson. In this role, she has responsibility for all of Watson’s Data and AI portfolio where she is responsible for defining the portfolio strategy, execution of product offerings, and driving business results. Previous to this, as Vice President of IBM’s World-Wide Cloud and Cognitive GTM organization she drove IBM’s mission to apply deep cloud, data, and AI expertise to our clients’ most pressing needs Ritika has also been the Vice President of IBM’s Data and Analytics Business where she was responsible for setting the strategy and execution of IBM’s data platform, data science, and analytics practices. Prior to this, she led and managed IBM’s Master Data Management & Information Integration and Governance business and IBM’s Data Warehousing & Analytics business. Ritika joined IBM as a software engineer in 1999 and has held numerous development and support roles in her career. Ritika holds a Bachelor of Science in Computer Science and an Executive Masters in Business from the University of Texas at Austin. She currently resides in New York.

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards.

In 2019, she was identified by National Endowment for Science, Technology, and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning, and AI.

Mihaela’s research focus is on machine learning, AI, and operations research for healthcare and medicine.

In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

Mike Piech is Vice President and General Manager of the Cloud Storage and Data Services organization, managing a team responsible for the product management, strategy, and marketing of Red Hat’s portfolio of data services offerings. These include cloud-native capabilities for storing, moving, transforming, and combining data for such applications as machine learning and edge computing. Prior to Red Hat, Piech held executive product roles at Magnet Systems (mobile app server) and Engine Yard (PaaS), after Oracle and BEA with product responsibility for WebLogic, Java™, and other enterprise platform technologies. He holds several patents.

As Executive Vice President and Chief Technology Officer, Bryan Harris is responsible for setting the technology direction for SAS and working with the executive leadership team to translate the organization’s strategic objectives and priorities into products and solutions. Harris has more than 20 years of experience researching and developing analytic techniques, enterprise search technologies, distributed computing and cloud architectures, and user experiences for both the federal and commercial industries. For nearly 10 years, he has been a critical senior leader of SAS R&D. Most recently, as Senior Vice President and Head of Engineering, Harris was instrumental in releasing the latest advancement of SAS® software, SAS® Viya®

Chris Wright is Senior Vice President and Chief Technology Officer (CTO) at Red Hat. He leads the Office of the CTO, which is responsible for incubating emerging technologies and developing forward-looking perspectives on innovations like artificial intelligence, cloud and edge computing, distributed storage, software-defined networking and network functions virtualization, containers, automation and continuous delivery, and distributed ledgers. During his 25 years as a software engineer, Wright has worked in the telecommunications industry and beyond on high-availability and distributed systems. He also has experience in the Linux® industry on security, virtualization, and networking. Wright has been a Linux developer for more than 15 years, with most of this time spent working deep in the Linux kernel. He is passionate about open source software serving as the foundation for next-generation IT systems.

Diego Oppenheimer, founder and CEO of Algorithmia, is an entrepreneur and product developer with an extensive background in all things data. Prior to founding Algorithmia he designed, managed and shipped some of Microsoft’s most used data analysis products including Excel, Power Pivot, SQL Server and Power BI.

Diego holds a Bachelors degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University.

Ryohei is the Founder & CEO of dotData. Prior to founding dotData, he was the youngest research fellow ever in NEC Corporation’s 119-year history, the title was honored for only six individuals among 1000+ researchers.

During his tenure at NEC, Ryohei was heavily involved in developing many cutting-edge data science solutions with NEC’s global business clients and was instrumental in the successful delivery of several high-profile analytical solutions that are now widely used in the industry.

Ryohei received his Ph.D. degree from the University of Tokyo in the field of machine learning and artificial intelligence.

Ayodele Odubela is a Data Science Advocate for Comet ML. She combines her background in marketing and passion for data and analytics to educate Data Scientists on model reproducibility and experiment tracking. She earned her Master’s degree in Data Science from Regis University after working in various digital marketing roles at agencies and startups. She’s passionate about data justice, kayaking, and hockey.

Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.

Dr. Stonebraker has been a pioneer of database research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty-five years. More recently at M.I.T., he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system.
Presently he serves as Chief Technology Officer of Paradigm4 and Tamr, Inc.

Marko is the Head of Quantitative Research for the Americas at RavenPack with over 10 years of experience in the finance industry. He focuses on exploring novel approaches and techniques for combining fundamental drivers with big data quantitative frameworks to identify alpha opportunities from a wide universe of securities across multiple asset classes. Previously, as the head trader/investment analyst at an event-driven hedge fund in New York, he was responsible for macro research, idea generation, and risk management. Marko has experience in utilizing quantitative methods in portfolio construction, developing hedging strategies, and trading structured derivative instruments.