ODSC WEST 2023
CONFIRMED SPEAKERS
ODSC hosts a fantastic lineup of some of the best and brightest expert speakers and core contributors to data science
ODSC West will host more than 280 speakers and instructors. Speaker profiles are added weekly. Check back for updates. You’re welcome to check out some speaker blogs here.
Chelsea Finn
Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has pioneered end-to-end deep learning methods for vision-based robotic manipulation, meta-learning algorithms for few-shot learning, and approaches for scaling robot learning to broad datasets. Her research has been recognized by awards such as the Sloan Fellowship, the NSF CAREER Award, the MIT Tech Review 35 Under 35, and the ACM doctoral dissertation award, and has been covered by various media outlets including the New York Times, Wired, and Bloomberg. Prior to Stanford, she received her Bachelor’s degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley.
Neural Networks Make Stuff up. What Should We do About it?(Keynote)
Peter Norvig
Peter Norvig is a Distinguished Education Fellow at Stanford’s Human-Centered Artificial Intelligence Institute and a researcher at Google Inc; previously he directed Google’s core search algorithms group and Google’s Research group. He was head of NASA Ames’s Computational Sciences Division, where he was NASA’s senior computer scientist and a recipient of NASA’s Exceptional Achievement Award in 2001. He has taught at the University of Southern California, Stanford University, and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artifical Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. His publications include the books Data Science in Context (to appear in 2022), Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world’s longest palindromic sentence. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.
Human Centered AI(Keynote)
Jepson Taylor
Jepson has over 18 years of machine-learning experience. After studying chemical engineering Jepson worked for Intel/Micron in applied semiconductor building process control and fault models. After that, he worked as a quant at a hedge fund on a 600 GPU cluster. Taylor then joined a young Sequoia-backed HR startup called HireVue to run their data group where they delivered 14 patents. In 2017 Jepson co-founded Zeff.ai with David Gonzalez to pursue deep learning for image, audio, video, and text for the enterprise. Zeff was acquired by DataRobot in 2020 where Jepson worked as their Chief AI Evangelist until recently joining Dataiku as their Chief AI Strategist. Jepson is very active in the global startup communities.
Dr. Andre Franca
Andre joined causaLens from Goldman Sachs, where he was an executive director in the Model Risk Management group in Hong Kong and Frankfurt. Today he is working with industry leading, global organisations to apply cutting edge Causal AI research in production level solutions that empower individuals and teams to make better decisions. Andre received his PhD in theoretical physics from the University of Munich, where he studied the interplay between quantum mechanics and general relativity in black-holes.
Causal AI: from Data to Action(Workshop)
Matt Harrison
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.
Machine Learning with XGBoost(Workshop)
Idiomatic Pandas(Workshop)
Gaurav Rao
Gaurav is currently the Executive Vice President and General Manager of Machine Learning and AI at AtScale. He is responsible for defining and leading the business that extends the company’s semantic layer platform to address the rapidly expanding set of Enterprise AI and machine learning applications.
Most recently, Gaurav served as VP of Product at Neural Magic – innovators in software acceleration for deep learning utilizing sparse model architectures. Previously, he served in a number of executive roles at IBM spanning product, engineering, and sales that were focused on taking cutting edge data science, machine learning, and AI offerings to market. He specializes in model training and serving, mlops, and trusted AI in the context of driving business outcomes for enterprise applications. He is also an advisor to data and AI companies.
Outside of work Gaurav is an avid sports fan, foodie, traveler, and enjoys spending time with his wife and kids.
Jack McCauley
Jack McCauley an Innovator in Residence at Jacobs Institute for Design Innovation at UC Berkeley, Professor at UC Berkeley, Co-Founder of Oculus, an American engineer, hardware designer, inventor, video game developer and philanthropist. Jack is best known for designing the guitars and drums for the Guitar Hero video game series, and as a co-founder and former chief engineer at Oculus VR. At Oculus, Jack designed and built the Oculus DK1 and DK2 virtual reality headsets. Oculus was acquired by Facebook for $2 Billion. McCauley holds numerous U.S. patents for inventions in software, audio effects, virtual reality, motion control, computer peripherals, and video game hardware and controllers. Jack was awarded a full scholarship to attend University of California, Berkeley where he earned a BSc., EECS in Electrical Engineering and Computer Science in 1986. Jack has authored numerous research papers in the field of artificial intelligence (AI) and mathematical modeling of AI-based systems and is currently pursuing new projects at his private R&D facility and hardware incubator in Pleasanton, California.
Stefanie Molin
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.
Jeffrey Yau, PhD
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.
Lukas Biewald
Lukas Biewald is the CEO and co-founder of Weights & Biases, a developer-first MLOps platform. He also co-founded Figure Eight (formerly CrowdFlower), a pioneer in the ML data-labeling space. Figure Eight was acquired by Appen (APX) in 2019. Lukas has dedicated his career to optimizing ML workflows, teaching ML practitioners, making machine learning more accessible to all, and occasionally tinkering with robots.
Jim Dowling
Jim Dowling is CEO of Hopsworks and an Associate Professor at KTH Royal Institute of Technology. He is lead architect of the open-source Hopsworks Feature Store platform. He is the organizer of the annual feature store summit conference and featurestore.org community, as well as co-organizer of PyData Stockholm.
Personalizing LLMs with a Feature Store(Workshop)
Yaron Haviv
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.
Implementing Gen AI in Practice(Track Keynote)
Evie Fowler
Evie Fowler is a data scientist based in Pittsburgh, Pennsylvania. She currently works in the healthcare sector leading a team of data scientists who develop predictive models centered on the patient care experience. She holds a particular interest in the ethical application of predictive analytics and in exploring how qualitative methods can inform data science work. She holds an undergraduate degree from Brown University and a master’s degree from Carnegie Mellon.
Bridging the Interpretability Gap in Customer Segmentation(Talk)
Robert Crowe
A data scientist and ML enthusiast, Robert has a passion for helping developers quickly learn what they need to be productive. Robert is currently the Senior Product Manager for TensorFlow Open-Source and MLOps at Google and helps ML teams meet the challenges of creating products and services with ML. Previously Robert led software engineering teams for both large and small companies, always focusing on moving fast to implement clean, elegant solutions to well-defined needs. You can find him on LinkedIn at robert-crowe.
MLOps v LMOps – What’s Different?(Talk)
Dominic Bohan
A TEDx speaker, Dom brings a wealth of data storytelling experience to StoryIQ from his career at QBE, one of Australia’s largest insurance companies. At QBE, he was a senior leader in data analytics and business improvement, presenting data-driven strategy recommendations to the company’s senior executives and producing reports for the Group Board of Directors.
Chris Hoge
Chris Hoge is the Head of Community for HumanSignal, where he is helping to grow the Label Studio community. He has spent over a decade working in open source machine learning and infrastructure communities, including Apache TVM, Kubernetes, and OpenStack. He has an M.S. in Applied Mathematics from the University of Colorado, with an emphasis on using high-performance numerical methods for simulating physical systems. He makes his home in the Pacific Northwest, where he spends his free time trail running and playing piano.
An Introduction to Data Labeling(Workshop)
Building an Expert Question/Answer Bot with Open Source Tools and LLMs(Workshop)
Brian Lucena, PhD
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.
Uncertainty Quantification: Approaches and Methods(Training)
Gwendolyn D. Stripling, Ph.D.
Gwendolyn Stripling, Ph.D., is an Artificial Intelligence and Machine Learning Content Developer at Google Cloud. Stripling is author of the widely popular YouTube video, “Introduction to Generative AI” and of the O’Reilly Media book “Low-Code AI: A Practical Project Driven Approach to Machine Learning”. They are also the author of the LinkedIn Learning video “Introduction to Neural Networks”. Stripling is an Adjunct Professor and member of Golden Gate University’s Masters in Business Analytics Advisory Board. Stripling enjoys speaking on AI/ML, having presented at Dominican University of California’s Barowsky School of Business Analytics, Golden Gate University’s Ageno School of Business Analytics, and numerous Tech conferences.
No-Code and Low-Code AI: A Practical Project Driven Approach to ML(Tutorial)
Joep Kokkeler
Joep has more than 12 years experience of developing, engineering, architecting and visualising data products in various markets ranging from energy to clothing manufacturing. He’s focussing on enabling teams to be better at handling data and providing the teams with the tools and the knowledge needed to go live and to stay in production.
He was member of the Teqnation program committee), did a presentation on Kafka and Hue usage during football, developing and deploying on Hololens, Total Devops using Gitlab, Evolution of a datascience product, Using the elastic stack from PoC to Production, Xbox Kinect on a bike at Devoxx London.
Jonas Mueller
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).
How to Practice Data-Centric AI and Have AI improve its Own Dataset(Tutorial)
Nick Schenone
Nick is a passionate machine learning, data science, and MLOps enthusiast with experience across multiple domains including fraud detection, natural language processing, computer vision, and data mining. Nick holds a BSc. in Cognitive Science with a specialization in ML and Neural Computation from University of California, San Diego. He is an AWS Certified Solutions Architect, and has earned certifications in Python, Pytorch, Apache Airflow, PySpark and other frameworks. Currently, Nick acts as pre-sales MLOps Engineer at Iguazio, where he specializes in helping enterprises create real-world impact with their data science initiatives, with expertise in deployments on AWS, GCP, and Azure as well as on-premise Kubernetes architecture. Nick speaks at global industry events and blogs about MLOps, data science and ML Engineering.
Demo Talk Title: Building and Deploying a Gen AI App in 20 Minutes
Abstract:
Generative AI has captured the imagination of many, but building your own Gen AI application is no easy feat. In this session, we’ll demonstrate how you can fine-tune a Gen AI model, build a Gen AI application, and deploy it in 20 minutes. For this exercise, we will use the following open source tools:
a. MLRun – MLOps orchestration framework
b. Langchain – used for building LLM applications
c. Milvus – open-source vector store for indexing documents
We’ll touch upon issues like accelerating the integration of AI/ML applications into existing business workflows, leveraging simple Python SDKs that transform code into a production-quality application, abstracting the many layers involved in the MLOps pipeline, building, testing, and tuning your work anywhere while integrating with other components of their business workflow.
Rajiv Shah, PhD
Rajiv Shah is a machine learning engineer at Hugging Face who focuses on enabling enterprise teams to succeed with AI. Rajiv is a leading expert in the practical application of AI. Previously, he led data science enablement efforts across hundreds of data scientists at DataRobot. He was also a part of data science teams at Snorkel AI, Caterpillar, and State Farm. Rajiv is a widely recognized speaker on AI, published over 20 research papers, and received over 20 patents, including sports analytics, deep learning, and interpretability. Rajiv holds a PhD in Communications and a Juris Doctor from the University of Illinois at Urbana Champaign. While earning his degrees, he received a fellowship in Digital Government from the John F. Kennedy School of Government at Harvard University. He also has a large following on AI-related short videos on Tik Tok and Instagram at @rajistics.
Evaluation Techniques for Large Language Models(Tutorial)
Geeta Shankar
Geeta Shankar is a software engineer who specializes in leveraging data for business success. With expertise in computer science, data science, machine learning, and artificial intelligence, she stays updated with the latest data-driven innovations. Her Indian classical music background has taught her the value of sharp thinking, spontaneity, and connecting with diverse individuals. Geeta uses these skills to translate complex data into meaningful insights that enhance performance and customer experiences.
Hao Zhang, PhD
Hao is currently a postdoctoral researcher at the Sky Lab, UC Berkeley, working with Prof. Ion Stoica. He is recently working on the Alpa project and the Sky project, aiming at democratizing large models like GPT-3. He is an Assistant Professor at Halıcıoğlu Data Science Institute and Department of Computer Science and Engineering (affiliate) at UC San Diego in Fall 2023.
He research is primarily focused on large-scale distributed ML in the joint context of ML and systems, concerning performance, usability, cost, and privacy. His work spans across distributed ML algorithms, large models, parallelisms, performance optimizations, system architectures, ML privacy, and AutoML, with applications in computer vision, natural language processing, and healthcare.
Oliver Zeigermann
Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.
MLOps: Monitoring and Managing Drift(Training)
Jerry Liu
Jerry is the co-founder/CEO of LlamaIndex, an open-source tool that provides a central data management/query interface for your LLM application. Before this, he has spent his career at the intersection of ML, research, and startups. He led the ML monitoring team at Robust Intelligence, did self-driving AI research at Uber ATG, and worked on recommendation systems at Quora. He graduated from Princeton in 2017 with a degree in CS.
Building LLM-powered Knowledge Workers over your Data with LlamaIndex(Workshop)
Dr. Petar Veličković
Petar Veličković is a Staff Research Scientist at Google DeepMind, Affiliated Lecturer at the University of Cambridge, and an Associate of Clare Hall, Cambridge. Petar holds a PhD in Computer Science from the University of Cambridge (Trinity College), obtained under the supervision of Pietro Liò. His research concerns geometric deep learning—devising neural network architectures that respect the invariances and symmetries in data (a topic I’ve co-written a proto-book about). Petar’s research has been used in substantially improving travel-time predictions in Google Maps, and guiding intuition of mathematicians towards new top-tier theorems and conjectures.
Alison Cossette
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.
Bridging the Gap: Light Code Solutions to Uniting Social Science and Modern Knowledge Graphs(Workshop)
Mark Saroufim
Mark Saroufim is an engineer on PyTorch at Meta working on open infrastructure, compilers and community. Mark is fond of hot takes and shares them on his blog https://marksaroufim.substack.com/. Prior to Meta, Mark worked as a Machine Learning engineer at Graphcore, Microsoft and yuri.ai.
Dr. Vinesh Sukumar
Dr. Vinesh Sukumar currently serves as Senior Director – Head of AI/ML product management at Qualcomm Technologies, Inc (QTI). In this role, he leads AI/ML product definition, strategy and translating vision statement into solution deployment across multiple business units (From Mobile to Automotive Domains)
He has about 20 years of industry experience spread across research, architecture, engineering and application deployment. He currently holds a doctorate degree specializing in imaging and vision systems while also completing a business degree focused on strategy and marketing. He is a regular speaker in many AI industry forums and has authored several journal papers and two technical books.
Angad Arora
Angad Arora is a seasoned professional with a wealth of expertise in manufacturing and quality for leading tech companies. With his innovative approach to lean manufacturing using data science, he has successfully saved millions of dollars for consumer electronics manufacturing lines. Currently serving as a Product Quality Manager at Google Inc., Angad continues to drive excellence and ensure top-notch product quality in the dynamic world of technology.
Sinan Ozdemir
Sinan Ozdemir is a mathematician, data scientist, NLP expert, lecturer, and accomplished author. He is currently applying my extensive knowledge and experience in AI and Large Language Models (LLMs) as the founder and CTO of LoopGenius, transforming the way entrepreneurs and startups market their products and services.
Simultaneously, he is providing advisory services in AI and LLMs to Tola Capital, an innovative investment firm. He has also worked as an AI author for Addison Wesley and Pearson, crafting comprehensive resources that help professionals navigate the complex field of AI and LLMs.
Previously, he served as the Director of Data Science at Directly, where my work significantly influenced their strategic direction. As an official member of the Forbes Technology Council from 2017 to 2021, he shared his insights on AI, machine learning, NLP, and emerging technologies-related business processes.
He holds a B.A. and an M.A. in Pure Mathematics (Algebraic Geometry) from The Johns Hopkins University, and he is an alumnus of the Y Combinator program. Sinan actively contribute to society through various volunteering activities.
Sinan’s skill set is strongly endorsed by professionals from various sectors and includes data analysis, Python, statistics, AI, NLP, theoretical mathematics, data science, function analysis, data mining, algorithm development, machine learning, game-theoretic modeling, and various programming languages.
Aligning Open-source LLMs Using Reinforcement Learning from Feedback(Workshop)
Amber Roberts
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.
Troubleshooting Large Language Models in Production with Embeddings and Evals(Talk)
Martin Musiol
Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a founder of generativeAI.net, a lecturer on AI to over 3000 students, and publisher of the newsletter ‘Generative AI: Short & Sweet’. As the lead for GenAI Projects in Europe at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage. -> https://www.linkedin.com/in/martinmusiol1/ and his webpage: https://generativeai.net/
Sanjay Jinturkar
Sanjay Jinturkar is Senior Director, MySQL HeatWave, focused on building machine learning capabilities inside the MySQL HeatWave database. These capabilities enable the user to automatically develop and deploy machine learning models inside the data base using AutoML in a cloud native environment for a variety of use cases, without the need to pull the data or the model outside the database. In past, he has held multiple technology and engineering management positions in systems software, mobile communications software, applications development and diagnostics. His interests include machine learning, cloud computing, database and architecture. Sanjay has a PhD in Computer Science from University of Virginia.
A Unified and User Friendly Approach to Develop ML Solutions in MySQL HeatWave AutoML(Talk)
Sandeep Agrawal, PhD
Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.
A Unified and User Friendly Approach to Develop ML Solutions in MySQL HeatWave AutoML(Talk)
Emmanuel Turlay
Emmanuel Turlay is the founder and CEO of Sematic, an open-source ML infrastructure company. Emmanuel started his career in academia researching particle physics at CERN, before branching out into tech and moving to the US in 2014. He led engineering teams at Instacart, and then Cruise, where he led the ML Infrastructure team for four years. In 2022, Emmanuel founded Sematic to bring learnings from building ML infrastructure for robotaxis to the rest of the industry in an open-source manner.
Battle Scars from the MLOps Trenches of the Robotaxi Industry(Talk)
Doris Lee
Doris Lee is the CEO and co-founder of Ponder (https://ponder.io/). Doris received her Ph.D. from the UC Berkeley RISE Lab and School of Information in 2021, where she developed tools that help data scientists explore and understand their data. She is the recipient of Forbes 30 under 30 for Enterprise Technology in 2023.
Scaling your Data Science Workflows by Changing a Single Line of Code(Talk)
Philip Taylor
Phil Taylor is the product lead for NLP Kensho, which he joined in March 2022. Prior to joining Kensho, he worked at IBM as a senior product manager for their data and AI SaaS platform and as a strategy and operations consultant. He earned his MBA from MIT Sloan in 2019 and previously worked as a economics and data science consultant at firms such as Charles River Associates.
Demo Talk Title: Financial Audio to NLP-based Insights (Kensho solution demo)
Abstract:
In this demo, attendees will learn how to turn raw audio into insights using natural language processing (NLP) techniques. Specific techniques covered include automatic speech recognition (ASR), named entity recognition and disambiguation (NER and NED), and text classification. Financial and business unstructured data are growing exponentially. Advancements in machine learning have made it possible for individuals and organizations to find unique insights and drive value from this data. This demo will show attendees how to do so, using the example of financial earnings calls. Phil will take the audience step-by-step through the process of turning the audio into high fidelity text, applying various NLP techniques to the text, and turning the end result into actionable, valuable insights. This demo pairs with the presentation “Finance Audio and Automated Speech Recognition – The Perfect Marriage,” by Katie Kuzin, Kensho’s Product Lead for Scribe, and represents the type of AI/ML impacts S&P Global is driving within its organization and for its customers.
Jeff Tao
Jeff Tao is the founder and CEO of TDengine. He has a background as a technologist and serial entrepreneur, having previously conducted research and development on mobile Internet at Motorola and 3Com and established two successful tech startups. Foreseeing the explosive growth of time-series data generated by machines and sensors now taking place, he founded TDengine in May 2017 to develop a high-performance time-series database purpose-built for modern IoT and IIoT businesses.
What is a Time-series Database and Why do I Need One?(Workshop)
Michael Auli
Michael Auli is a principal research scientist/director at FAIR in Menlo Park, California. His work focuses on speech and NLP and he helped create projects such as wav2vec/data2vec, the widely used fairseq toolkit, the first modern feed-forward seq2seq models outperforming RNNs for NLP, and several top ranked submissions at the WMT news translation task in 2018 and 2019. Before that Michael was at Microsoft Research, where he did early work on neural machine translation and using neural language models for conversational applications. During his PhD at the University of Edinburgh he worked on natural language processing and parsing. http://michaelauli.github.io
General and Efficient Self-supervised Learning with data2vec(Talk)
Kabir Nagrecha
Kabir Nagrecha is a Ph.D. candidate at UC San Diego, working with Professors Arun Kumar & Hao Zhang. His work focuses on systems infrastructure to support deep learning at scale, aiming to democratize large models and amplify the impact of machine learning applications. He is the recipient of the Meta Research Fellowship, as well as fellowships from the Halicioglu Data Science Institute and Jacobs School of Engineering at UCSD.
Kabir is the youngest-ever Ph.D. student at UCSD, having started his doctorate at the age of 17. He’s previously worked with companies such as Apple, Meta, & Netflix to build the core infrastructure that supports widely-used services such as Siri & Netflix’s recommendation algorithms. Most recently, he’s been working on Saturn, a new system to support automatic parallelization, scheduling, and resource apportioning for training large neural networks.
Democratizing Fine-tuning of Open-Source Large Models with Joint Systems Optimization(Talk)
Sarah Kefayati
Bio Coming Soon!
Driving Success for Sellers by Infusing AI in CRM Platform(Business Talk)
Eli Chen
Eli is CTO and Co-Founder at Credo AI. He has led teams building secure and scalable software at companies like Netflix and Twitter. Eli has a passion for unraveling how things work and debugging hard problems. Whether it’s using cryptography to secure software systems or designing distributed system architecture, he is always excited to learn and tackle new challenges. Eli graduated with an Electrical Engineering and Computer Science degree from U.C. Berkeley.
Alex Liu, Ph.D.
Dr. Alex Liu is the founder and Director of the RMDS Lab, a data and AI ecosystem service provider. From 2013 to 2019, Alex was one of the data science thought leaders and a distinguished data scientist at IBM where he served as a Chief Data Scientist for analytics services. Before joined IBM, Dr. Liu worked as a chief data scientist for a few companies including iSKY and Retention Science. Previously, Dr. Liu taught advanced quantitative methods to PhD candidates in the University of Southern California and the University of California at Irvine, while consulted for many well-known organizations such as the United Nations and Ingram Micro. Alex has a MS of Statistical Computing and a PhD of Sociology from Stanford University.
Completing Knowledge Discovery Fast at High Quality with AI(Talk)
Walid S. Saba
Walid Saba is a Senior Research Scientist at the Institute for Experiential AI at Northeastern University. Prior to joining the institute in 2023, he worked at two Silicon Valley startups, focusing on conversational AI. This work included high-level roles as the principal AI scientist for telecommunications company Astound and CTO of software company Klangoo, where he helped develop its state-of-the-art digital content semantic engine (Magnet).
Saba’s career to date has seen him hold various positions in both the private sector and academia. His resume includes entities such as the American Institutes for Research, AT&T Bell Labs, IBM and Cognos, while he has also spent a cumulative seven years teaching computer science at the University of Ottawa, the New Jersey Institute of Technology (NJIT), the University of Windsor (a public research university in Ontario, Canada), and the American University of Beirut (AUB).
Walid is frequent invited for interviews and as a keynote speaker on AI and NLP has published over 45 technical articles, including an award-winning paper that he presented at the German Artificial Intelligence Conference (KI-2008). Walid received his BSc and MSc in Computer Science from the University of Windsor, and a Ph.D in Computer Science from Carleton University in 1999.
Sandeep Singh
Sandeep Singh is a leader in applied AI and computer vision in Silicon Valley’s mapping industry, and he is at the forefront of developing cutting-edge technology to capture, analyze and understand satellite imagery, visual and location data. With a deep expertise in computer vision algorithms, machine learning and image processing and applied ethics, Sandeep is responsible for creating innovative solutions that enable mapping and navigation software to accurately and efficiently identify and interpret features to remove inefficiencies of logistics and mapping solutions. His work includes developing sophisticated image recognition systems, building 3D mapping models, and optimizing visual data processing pipelines for use in logistics, telecommunications and autonomous vehicles and other mapping applications. With a keen eye for detail and a passion for pushing the boundaries of what’s possible with AI and computer vision, Sandeep’s leadership is driving the future of applied AI forward.
Stable Diffusion: A New Frontier for Text-to-Image Paradigm(Workshop)
Chuying Ma
Chuying (Annie) Ma is a senior data scientist in Walmart Inkiru team, where she works on developing and implementing machine learning models and strategies for real-time fraud detection and risk mitigation. She has a Master of Science degree in Biostatistics from Harvard University and gets her bachelor’s degree in Statistics and Mathematics from University of Michigan – Ann Arbor. In her spare time, she enjoys playing violin and ukulele.
A Semi-Supervised Anomaly Detection System Through Ensemble Stacking Algorithm(Talk)
Suhas Pai
Suhas Pai is a NLP researcher and co-founder/CTO at Bedrock AI, a Toronto based startup. At Bedrock AI, he works on text ranking, representation learning, and productionizing LLMs. He is also currently writing a book on Designing Large Language Model Applications with O’Reilly Media. Suhas has been active in the ML community, being the Chair of the TMLS (Toronto Machine Learning Summit) conference since 2021 and also NLP lead at Aggregate Intellect (AISC). He was also co-lead of the Privacy working group at Big Science, as part of the BLOOM project.
Beyond Demos and Prototypes: How to Build Production-Ready Applications Using Open-Source LLMs(Workshop)
Vincent Granville
Bio Coming Soon!
Massively Speed-Up your Learning Algorithm, with Stochastic Thinning(Tutorial)
Mike Taylor
Mike is a data-driven, technical marketer who built a 50 person marketing agency (Ladder), and 300k people have taken his online courses (LinkedIn, Udemy, Vexpower). He now works freelance on generative AI projects, and is writing a book on Prompt Engineering for O’Reilly Media.
James Phoenix
James is a full-stack engineer that specialises in automating marketing and business processes with AI based solutions.
Anna Jung
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.
Nirmal Budhathoki
Nirmal Budhathoki is a Senior Data Scientist, who is currently working at Microsoft in Cloud Security. Nirmal has over 12+ years of experience in the IT industry, including 5+ years in data science. Nirmal’s strong belief in continuous learning has led him to complete three master’s degrees with majors on: Information Systems, Business Administration, and Data Science. Nirmal loves to help the data science community and has completed over 600+ free mentoring sessions with aspiring data scientists to help them navigate their data science career. Nirmal also conducts mentored learning sessions for MiT’s Data Science and Machine Learning certification program in collaboration with Great Learning. Nirmal has experience working with the US government for the Department of Navy, and he is also a US army veteran. Nirmal yearns to solve data science problems that are aligned with product strategy and business outcomes. In his free time, Nirmal loves using this data science skills in sports analytics.
Neel Kovelamudi
Neel is currently an engineer on the Keras team at Google. His work has been focused on open source development, adding major features to the latest releases of TensorFlow and Keras. Experienced broadly from data science to ML infrastructure, he is responsible for the saving and export of ML models, as well as a developer for cross-framework compatibility at Google. As a key maintainer of keras.io and tensorflow.org, Neel is passionate about educating the data science community at large and helping the latest innovations of AI reach everyone.
Serg Masis
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. He’s an Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Whether concerning leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making. He wrote the bestselling book “Interpretable Machine Learning with Python” and is currently working on a new book titled “DIY AI” with do-it-yourself projects for AI hobbyists and practitioners alike.
Facial Recognition from Scratch with Python and JS(Workshop)
Wes Madrigal
Wes is a machine learning expert with over a decade of experience delivering business value with AI. Wes’s experience spans multiple industries, but always with an MLOps focus. His recent areas of focus and interest are graphs, distributed computing, and scalable feature engineering pipelines.
Using Graphs for Large Feature Engineering Pipelines(Workshop)
Parul Pandey
Parul Pandey has a background in Electrical Engineering and currently works as a Principal Data Scientist at H2O.ai. Prior to this, she was working as a Machine Learning Engineer at Weights & Biases. Parul is one of the co-authors of Machine Learning for High-Risk Applications book, which focuses on the responsible implementation of AI. She is also a Kaggle Grandmaster in the notebooks category and was one of Linkedin’s Top Voices in the Software Development category in 2019. Parul has written multiple articles focused on Data Science and Software development for various publications and mentors, speaks, and delivers workshops on topics related to Responsible AI.
Machine Learning for High-Risk Applications – Techniques for Responsible AI(Tutorial)
Robert Osazuwa Ness, PhD
Robert Osazuwa Ness is a researcher at Microsoft Research and author of the book Causal Machine Learning. He leads the development of MSR’s causal machine learning platform and conducts research into probabilistic models for advanced causal reasoning. He has worked as a machine learning engineer in various machine learning startups. He attended graduate school at both Johns Hopkins SAIS (Hopkins-Nanjing Center) and Purdue University. He received his Ph.D. in Statistics from Purdue, where his dissertation research focused on Bayesian active learning models for causal discovery.
Causality and LLMs(Talk)
Alessandro Romano
Alessandro is a highly experienced data scientist with a Bachelor’s degree in computer science and a Master’s in data science. He has collaborated with a variety of companies and organizations and currently holds the role of senior data scientist at logistics giant Kuehne+Nagel. Alessandro is particularly passionate about statistics and digital experimentation and has a strong track record of applying these skills to solve complex problems. He shares his knowledge regularly, speaking at events like the Data Innovation Summit and DataMass Gdansk Summit.
The Crucial Role of Digital Experimentation and A/B Testing in the AI Landscape(Talk)
Patrick Hall
Patrick Hall is an assistant professor of decision sciences at the George Washington University School of Business, teaching data ethics, business analytics, and machine learning classes. He also conducts research in support of NIST’s AI risk management framework and is affiliated with leading fair lending and AI risk management advisory firms.
Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University. He has been invited to speak on AI and machine learning topics at the National Academies of Science, Engineering, and Medicine, ACM SIG-KDD, and the Joint Statistical Meetings. He has been published in outlets like Information, Frontiers in AI, McKinsey.com, O’Reilly Ideas, and Thompson-Reuters Regulatory Intelligence, and his technical work has been profiled in Fortune, Wired, InfoWorld, TechCrunch, and others. Patrick is the lead author of the book Machine Learning for High-Risk Applications.
Prior to joining the GW School of Business, Patrick co-founded BNH.AI, a boutique law firm focused on AI governance and risk management. He led H2O.ai’s efforts in responsible AI, resulting in one of the world’s first commercial applications for explainability and bias mitigation in machine learning. Patrick also held global customer-facing roles and R&D roles at SAS Institute. Patrick has built machine learning software solutions and advised on matters of AI risk for Fortune 100 companies, cutting-edge startups, Big Law, and U.S. and foreign government agencies.
Adopting Language Models Requires Risk Management — This is How(Talk)
Thomas Nield
Thomas Nield is the founder of Nield Consulting Group and Yawman Flight, as well as an instructor at University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence. At USC he teaches AI System Safety, developing systematic approaches for identifying AI-related hazards in aviation and ground vehicles. He’s authored three books, including Essential Math for Data Science (O’Reilly) and Getting Started with SQL (O’Reilly)
He is also the founder and inventor of Yawman Flight, a company developing universal handheld flight controls for flight simulation and unmanned aerial vehicles.
Introduction to Math for Data Science(Bootcamp)
Michelle Yi
Michelle is a technology leader that specializes in machine learning and cloud computing. She has 15 years of experience in the technology industry, contributed to the original IBM Watson showcased on Jeopardy, and enjoys building and leading teams that develop and deploy AI solutions to solve real-world problems. Michelle is passionate about diversity, STEM education/careers for our minority communities, and serves both on the board of Women in Data and as an avid volunteer for Girls Who Code.
Building Generative AI Applications: An LLM Case Study(Talk)
Nils Reimers
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.
Connecting Large Language Models – Common Pitfalls & Challenges(Talk)
David Mertz, Ph.D.
David is founder of KDM Training, a partnership dedicated to educating developers and data scientists in machine learning and scientific computing. He created the data science training program for Anaconda Inc. and was a senior trainer for them. With the advent of deep neural networks he has turned to training our robot overlords as well.
He was honored to work for 8 years with D. E. Shaw Research, who have built the world’s fastest, highly-specialized (down to the ASICs and network layer), supercomputer for performing molecular dynamics.
David was a Director of the PSF for six years, and remains co-chair of its Trademarks Committee and of its Scientific Python Working Group. His columns, Charming Python and XML Matters, written in the 2000s, were the most widely read articles in the Python world. He has written previous books for Manning, Packt, O’Reilly and Addison-Wesley, and has given keynote addresses at numerous international programming conferences.
Bob Foreman
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.
Cai GoGwilt
Cai GoGwilt has always been passionate about building products that make people more effective at their jobs. He is an MIT graduate in Computer Science and Physics, as well as a former software engineer at Palantir Technologies. By day, he’s a celebrated software engineer and thoughtful leader; by night, he’s a concert cellist and karaoke enthusiast.
Intersection of Gen AI and Legal, or How to Apply LLMs as Agents in Production Features.(Ai X Talk)
Teodora Sechkova
Teodora is an open source software engineer at VMware AI Labs. During her first couple of years in VMware, as part of the Open Source Program Office, she was an active contributor and maintainer of The Update Framework (TUF) – a framework for securing software update systems. Currently, she invests her time in open source projects related to machine learning security.
Security First, Create a Robust Machine Learning Model(Talk)
Alex Watson
Alex Watson, co-founder and chief product officer at Gretel.ai, has been a trailblazer in the technology sector, focusing on data security and innovation. Before founding Gretel, he launched Harvest.ai, a security startup that leveraged NLP and AI to protect cloud data, which was acquired by Amazon in 2016. At AWS, he pioneered Macie, one of the company’s leading security services. Alex’s technological roots run deep, with experience at the NSA and co-founding BTS to revolutionize battlefield communications, leading to the U.S. Army’s first deployment of 3G and 4G networks.
Eddie Zhou
Eddie joined Glean as a founding engineer. He leads a team of engineers and AI experts at Glean who work on challenging problems across Machine Learning, LLMs, NLU, domain adaptation, semantic search, and much more in order to build the world’s best work assistant. Previously, he worked as a research and software engineer for Google, contributing to a number of major projects, including natural language and semantic solutions. His current mission is to leverage AI and NLP technology to create the enterprise solution that speaks the language of each individual workplace.
Jake Bengtson
Jake currently holds the position of Principal Technical Evangelist at Cloudera, where he promotes the strengths of Cloudera’s Lakehouse for delivering trusted AI. His tenure at Cloudera began as a Senior Product Marketing Manager for Cloudera Machine Learning (CML).
Before Cloudera, Jake developed his ML expertise at ExxonMobil, starting as a Data Scientist and later transitioning to a Data Science and Analytics Solution Architect role. He also contributed significantly at FarmersEdge, taking on responsibilities as a Senior Data Scientist and subsequently as a Data Science Manager.
Jake earned both his bachelor’s and master’s degrees from Brigham Young University in Information Systems Management with an emphasis in Statistics.
Outside of work, Jake is passionate about outdoor activities. He enjoys skiing, golfing, rafting, and hiking. However, spending time with his family amidst the mountains remains his most rewarding pastime.
Sandy Ryza
Sandy is a lead engineer, author, and thought leader in the domain of data engineering. Sandy co-wrote “Advanced Analytics with PySpark” and “”Advanced Analytics with Spark”. He led ML and data science teams at Cloudera, Remix, Clover Health, and KeepTruckin.
Sandy is currently the lead engineer on the Dagster project, an open-source data orchestration platform used in MLOps, data science, IOT and analytics. Sandy is a regular speaker at data engineering and ML conferences.
Amy Hodler
Amy Hodler is an evangelist for graph analytics and responsible AI. She’s the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics and Bloomsbury book, AI on Trial. Amy has decades of experience in emerging tech at companies such as Microsoft, Hewlett-Packard (HP), Hitachi IoT, Neo4j, Cray, and RelationalAI. Amy is the founder of GraphGeeks.org promoting connections everywhere.
Philip Wauters
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.
Learn how to Efficiently Build and Operationalize Time Series Models in 2023(Workshop)
Ramon Perez
Ramon is a data scientist, researcher, and educator currently working in the Developer Relations team at Seldon in London. Prior to joining Seldon, he worked as a freelance data professional and as a Senior Product Developer at Decoded, where he created custom data science tools, workshops, and training programs for clients in various industries. Before freelancing, Ramon wore different research hats in the areas of entrepreneurship, strategy, consumer behavior, and development economics in industry and academia. Outside of work, he enjoys giving talks and technical workshops and has participated in several conferences and meetup events. In his free time, you will most likely find him traveling to new places, mountain biking, or both.
Architecting Data: A Deep Dive Into the World of Synthetic Data(Training)
Andrew Dai
Andrew Dai did his PhD at the University of Edinburgh before joining Google Brain 9 years ago in 2014 where he did research on language models, story generation and conversational agents and products including SmartReply. He moved to Google Health in 2017 to research deep learning for medical records. He then returned to continue research at Google Brain (now Google Deepmind) in 2020 and since then has co-led the development and training of LLMs including PaLM 2 and GLaM. Andrew also is a lead for Google SGE modelling, Gemini and data research and is excited by the new abilities we see from LLMs.
A Background to LLMs and Intro to PaLM 2: A Smaller, Faster and More Capable LLM(Tutorial)
Sergey Yurgenson
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.
Integrating Language Models for Automating Feature Engineering Ideation(Talk)
Fabiana Clemente
Fabiana Clemente is the co-founder and CDO of YData, combining Data Understanding, Causality, and Privacy as her main fields of work and research, with the mission to make data actionable for organizations. Passionate for data, Fabiana has vast experience leading data science teams in startups and multinational companies. Host of “When Machine Learning meets privacy” podcast and a guest speaker at Datacast and Privacy Please, the previous WebSummit speaker, was recently awarded “Founder of the Year” by the South Europe Startup Awards.
Missing Data: A Synthetic Data Approach for Missing Data Imputation(Workshop)
Ryohei Fujimaki, PhD
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 industry.
Ryohei received his Ph.D. degree from the University of Tokyo in the field of machine learning and artificial intelligence.
Using Machine Learning to Discover Business Insights(Ai X Talk)
Ayush Thakur
Ayush Thakur is a MLE at Weights and Biases and Google Developer Expert in Machine Learning. He is interested in everything computer vision and representation learning. For the past 8 months he’s been working with LLMs and have covered RLHF and how and what of building LLM-based systems.
Panos Alexopoulos, PhD
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.
Durga Kota
Durga Kota is the Chief Technology Officer for the Americas region and is responsible for leading the strategies to translate Fujitsu technology innovation into differentiated and industry-ready solutions for the region’s customers.
Throughout his 24-year career, Durga has held customer-facing, consultative solution-selling and technology leadership roles. His collaborative spirit and deep technical expertise enable him to build trust, work in true partnership with customers, and provide solution leadership that leverages advanced technologies to solve even the most complex challenges.
Durga holds an MBA from the Indian Institute of Foreign Trade (IIFT), Delhi, and a B.Tech. in Computer Science and Engineering from the Jawaharlal Nehru Technological University, Hyderabad, India.
Durga resides in the San Diego area with his family and likes to spend his free time trekking on one of the many trails in the area, or volunteering for the local food bank.
Katie Kuzin
Katie is the Product Lead for Scribe at Kensho. Scribe is an Artificial Intelligence, Natural Language Processing tool that does voice to text transcription. Katie’s background is in data pipeline management and machine learning – specifically unsupervised machine learning in aerospace to detect satellite collisions. She is a guest lecturer at Johns Hopkins University and graduated with distinction from the University of Virginia.
Hudson Buzby
Hudson Buzby is a dynamic Solutions Architect with a passion for leveraging technology to solve complex challenges. With over a decade of experience in the tech industry, Hudson has made significant contributions to the world of data engineering and cloud solutions. Hudson currently serves as a Solutions Architect at Qwak, where he continues to push the boundaries of what’s possible in the world of cloud computing and data management.
From Raw Data through Vectors to a Comprehensive Recommendation Model(Talk)
Greg Loughnane
Dr. Greg Loughnane is the Founder & CEO of AI Makerspace, where he serves as lead instructor for their LLM Ops: LLMs in Production course. Since 2021 he has built and led industry-leading Machine Learning & AI bootcamp programs. Previously, he has worked as an AI product manager, a university professor teaching AI, an AI consultant and startup advisor, and ML researcher. He loves trail running and is based in Dayton, Ohio.
Chris Alexiuk
Chris Alexiuk, is the Head of LLMs at AI Makerspace, where he serves as a programming instructor, curriculum developer, and thought leader for their flagship LLM Ops: LLMs in Production course. During the day, he’s a Founding Machine Learning Engineer at Ox. He is also a solo YouTube creator, Dungeons & Dragons enthusiast, and is based in Toronto, Canada.
Shashank Prasanna
Shashank is an engineer, educator and doodler. He writes and talks about machine learning, specialized machine learning hardware (AI Accelerators) and AI Infrastructure in the cloud. He previously worked at Meta, AWS, NVIDIA, MathWorks (MATLAB) and Oracle in developer relations and marketing, product management, and software development roles and hold an M.S. in electrical engineering.
Valentina Alto
Valentina is a Data Science MSc graduate and Cloud Specialist at Microsoft, focusing on Analytics and AI workloads within the manufacturing and pharmaceutical industry since 2022. She has been working on customers’ digital transformations, designing cloud architecture and modern data platforms, including IoT, real-time analytics, Machine Learning, and Generative AI. She is also a tech author, contributing articles on machine learning, AI, and statistics, and recently published a book on Generative AI and Large Language Models.
In her free time, she loves hiking and climbing around the beautiful Italian mountains, running, and enjoying a good book with a cup of coffee.
The AI Paradigm Shift: Under the Hood of a Large Language Models(Workshop)
Raghav Bali
Raghav is a seasoned Data Science professional with over a decade’s experience of research & development of large-scale solutions in Finance, Digital Experience, IT Infrastructure and Healthcare for giants such as Intel, American Express, United HealthGroup and DeliverHero. He is an innovator with 10+ patents, a published author of multiple well received books & peer-reviewed papers and a regular speaker in leading conferences on topics in the areas of Generative AI, Recommendation Systems, Computer Vision, NLP, Deep Learning, Machine Learning and Augmented Reality.
Building Robust and Scalable Recommendation Engines for Online Food Delivery(Talk)
Shea Watrin
Bio Coming Soon!
Delivering Gen AI Solutions to Executives: A Nimble Framework for Rapid and Tailored Deployment(Case Study)
Amit Sangani
Amit Sangani is the Director of Partner Engineering leading the Applied AI Platforms team at Meta. Amit has been with Meta for 8+ years and manages developer-facing engineering teams working on Gen AI platforms such as Llama 2 and PyTorch. Amit’s mission is to democratize AI and increase the adoption of these platforms by making it easier for developers to integrate them into their products and spur innovation and increased productivity.
Building Using Llama 2(Workshop)
Krishnaram Kenthapadi
Krishnaram Kenthapadi is the Chief AI Officer & Chief Scientist of Fiddler AI, an enterprise startup building a responsible AI and ML monitoring platform. Previously, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, privacy, and model understanding initiatives in the Amazon AI platform. Prior to joining Amazon, he led similar efforts at the LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. Previously, he was a Researcher at Microsoft Research Silicon Valley Lab. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the senior program committees of FAccT, KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 7000+ citations and filed 150+ patents (70 granted). He has presented tutorials on privacy, fairness, explainable AI, model monitoring, responsible AI, and generative AI at forums such as ICML, KDD, WSDM, WWW, FAccT, and AAAI, given several invited industry talks, and instructed a course on responsible AI at Stanford.
Deploying Trustworthy Generative AI(Tutorial)
Vino Duraisamy
Vino is a Developer Advocate for Snowflake. She started as a software engineer at NetApp, and worked on data management applications for NetApp data centers when on-prem data centers were still a cool thing. She then hopped onto the cloud and big data world and landed at the data teams of Nike and Apple. There she worked mainly on batch processing workloads as a data engineer, built custom NLP models as an ML engineer and even touched upon MLOps a bit for model deployments. When she is not working with data, you can find her doing yoga or strolling the golden gate park and ocean beach.
The Rise of a Full Stack Data Scientist: Powered by Python(Workshop)
Asif Kazi
Asif Kazi has directed global sales engineering, professional services, and technical customer success teams for over 15 years at Foursquare, Ahana, Amazon Web Services, StreamSets, and Couchbase. He is passionate about working with customers and solving their business problems. His core competencies include scaling teams globally, building referenceable client relationships, and accelerating revenue growth.
Change the Game with Graph ML(Training)
Learn to Harness the Power of the Most Intuitive Graph ML Platform(Training)
Arthur Keen, PhD
Arthur Keen, Ph.D. is a Graph evangelist with more than 20 years of experience in Graph AI. His expertise spans Graph Machine Learning, graph analytics, and ontology. Arthur has developed knowledge graph-based solutions in intelligence, cyber, financial services, logistics, retail, and energy. He has led 3 product teams from concept to GA and first customers. He has been awarded 7 patents and has held leadership roles including CTO, Chief Scientist, and VP of Engineering. He has a Computer Science/Industrial Engineering Ph.D. and has been invited to speak at conferences including Data Day TX, NoSQL Now, SemTech, and SXSW.
Change the Game with Graph ML(Training)
Learn to Harness the Power of the Most Intuitive Graph ML Platform(Training)
Vishal Natani
Vishal is an experienced Data science professional with 12+ years of experience. He is currently leading the Ranking and Recommendation topic at DeliveryHero, where he joined in May 2022. Before joining DeliveryHero, he worked at Monotype as a Data science manager, leading the AI Research team in building products like WhatTheFont (identifying fonts from images), Font Similarity and many more. He has a couple of patents filed in his name.
Building Robust and Scalable Recommendation Engines for Online Food Delivery(Talk)
Jörg Schad, PhD
Jörg Schad, PhD is the ArangoDB CTO. Previously, he worked on Machine Learning Infrastructure in health care, distributed systems at Mesosphere, implemented distributed and in-memory databases, and conducted research in the Hadoop and Cloud area. He frequently speaks at meetups, international conferences, and lecture halls. Jörg is fluent in three languages and passionate about science & technology, education, and the environment.
Isha Ghodgaonkar
Isha Ghodgaonkar is a Machine Learning Developer Advocate at Hewlett Packard Enterprise (HPE), working within the HPC, AI & Labs business unit, which includes open source platforms Determined AI and Pachyderm. Prior to HPE, Isha worked at MITRE as an Autonomous Systems Engineer, a Data Science Intern at Genentech, and a technical intern at The Aerospace Corporation. Isha is a graduate of Purdue University.
Leonardo De Marchi
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.
Cal Al-Dhubaib
Cal Al-Dhubaib is a data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin.
Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in numerous publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives.
Sanyam Bhutani
Sanyam Bhutani is a Sr Data Scientist and Kaggle Grandmaster at H2O where he drinks chai and makes content for the community. When not drinking chai, he is to be found hiking the Himalayas, often with LLM Research papers. For the past 6 months, he has been writing about Generative AI everyday on the internet. Before that he has been recognised for his #1 Kaggle Podcast: Chai Time Data Science and also widely known on the internet for “maximising compute per cubic inch of an ATX case” by fixing 12 GPUs into his home office.
LLM Best Practises: Training, Fine-Tuning and Cutting Edge Tricks from Research(Training)
Ian Eisenberg
Ian Eisenberg is Head of AI Governance Research at Credo AI, where he advances best practices in AI governance to support Credo AI’s product and policy strategy. He is also the founder of the AI Salon, an SF-based group supporting conversations on the meaning and impact of AI. His interest in AI started as a cognitive neuroscientist at Stanford, which developed into a focus on the sociotechnical challenges of AI technologies and reducing AI risk. Ian has been a researcher at Stanford, the NIH, Columbia and Brown University. He received his PhD from Stanford University, and BS from Brown University.
Hands-On AI Risk Management: Utilizing the NIST AI RMF and LLMs(Workshop)
Rama Akkiraju
Bio Coming Soon!
Generative AI in Enterprises: Unleashing Potential and Navigating Challenges(Ai X Talk)
Gengliang Wang
Gengliang Wang, a committed Apache Spark PMC Member and Committer, actively works on important Spark projects including ANSI SQL mode, TIMESTAMP_NTZ data type, and data sources. His contributions extend to enhancing the SQL compiler and UI. In addition, he’s engaged in a project using large language models to streamline Spark application development. His work underscores a dedication to improving and making Apache Spark more user-friendly.
Supriya Rao
Supriya is an Engineering Manager working on PyTorch at Meta. Her team works on architecture optimization techniques like quantization, pruning as well as other core components of PyTorch 2.0 whereby enabling users to run AI models on different HW efficiently using native PyTorch. Prior to Meta, she worked as a software engineer at Nvidia on improving their GPU Architecture and accelerating AI models via TensorRT for inference. Supriya has an MS in CSE from University of Michigan, Ann Arbor and a bachelor’s degree from Bits Pilani, India.
Tuli Nivas
Tuli Nivas is a Software Engineering Architect at Salesforce with extensive experience in design and implementation of test automation and monitoring frameworks. Her interests lie in software testing, cloud computing, big data analytics, systems engineering, and architecture. Tuli holds a PhD in computer science with a focus on building processes to set up robust and fault-tolerant performance engineering systems. Her recent area of expertise has been around machine learning and building data analytics for better and faster troubleshooting of performance problems and anomaly detection in production.
Allison Wang
Allison is a Senior Software Engineer at Databricks where she focuses on Spark SQL and PySpark. Before Databricks, she was an early member of Robinhood’s data team. She holds a Bachelor’s degree in Computer Science from Carnegie Mellon University.
Matt White
Matt White is a distinguished expert in artificial intelligence and business, Renowned for successfully deploying large-scale AI platforms across the telecom, gaming, media, and entertainment industries. With over two decades of experience, Matt has consistently demonstrated his ability to stay ahead of the curve in technological innovation, spearheading advancements in AI applications in diverse domains.
Transforming Games, Simulations and the Future Metaverse with Generative and Autonomous AI(Tutorial)
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Participate at ODSC West 2023
As part of the global data science community we value inclusivity, diversity, and fairness in the pursuit of knowledge and learning. We seek to deliver a conference agenda, speaker program, and attendee participation that moves the global data science community forward with these shared goals. Learn more on our code of conduct, speaker submissions, or speaker committee pages.
ODSC Newsletter
Stay current with the latest news and updates in open source data science.
In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London.
And keep a lookout for special discount codes, only available to our newsletter subscribers!