Data Science | Kisaco Research

Data Science

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Artificial intelligence (AI) stands at the forefront of enterprise innovation, offering unparalleled opportunities for growth, efficiency, and competitive advantage. However, the journey toward AI optimization is fraught with challenges, from legacy infrastructures to strategic alignment and workforce readiness. This keynote speech will navigate the complex landscape of AI integration, presenting a comprehensive roadmap tailored for Fortune 500 companies ready to harness the power of generative AI.

Technologist Deep-Dive (Gen AI & Data Science) Track
Retail
Business Leader
Data Science
AI Technologists

Author:

Dr. Astha Purohit

Director - Product (Tech) Ops
Walmart

Astha is a global leader in Retail with extensive expertise in artificial intelligence and generative AI. She has advised Fortune 500 companies and senior C-suite leaders on driving innovation and growth in Retail to deliver amazing customer experiences.

She is a former McKinsey consultant and an MBA graduate from MIT Sloan. Currently she is a Director at Walmart spearheading AI and ML model development and deployment across the Walmart product eco-system.

Dr. Astha Purohit

Director - Product (Tech) Ops
Walmart

Astha is a global leader in Retail with extensive expertise in artificial intelligence and generative AI. She has advised Fortune 500 companies and senior C-suite leaders on driving innovation and growth in Retail to deliver amazing customer experiences.

She is a former McKinsey consultant and an MBA graduate from MIT Sloan. Currently she is a Director at Walmart spearheading AI and ML model development and deployment across the Walmart product eco-system.

Technologist Deep-Dive (Gen AI & Data Science) Track
AI Safety
AI Technologists
Data Science
C-Suite
Moderator

Author:

Sarah Luger

Co-Chair, Data Sets Working Group
MLCommons

Sarah Luger host of the AI Artifacts podcast (www.aiartifacts.net) and the Co-Chair of the Data Sets Working Group for AI benchmarking organization, MLCommons. Data Sets Working Group continues the research initiated with the Rigorous Evaluation of AI Systems workshop series at AAAI Human Computation and AAAI conferences. The goal is to develop robust schemas and infrastructure supporting the Open Source hosting of benchmark evaluation data sets. The group aims to provide free storage for researchers who have human-generated data (spoken word data is the current focus) of generally high quality.

Sarah is a Contributing Member of the MLCommons AI Safety Stakeholder Engagement, Benchmarks and Tests, and Platform Technology working groups. This nonprofit engineering consortium guides the ML industry by developing benchmarks, public datasets, and best practice.

Her current AI Safety work focuses on building LLM Safety Test Sets, Creating Scoring System, and Running Benchmarks. Sarah is leading the subsequent work automating the translation of safety test prompts into in low-resource languages.

Sarah Luger

Co-Chair, Data Sets Working Group
MLCommons

Sarah Luger host of the AI Artifacts podcast (www.aiartifacts.net) and the Co-Chair of the Data Sets Working Group for AI benchmarking organization, MLCommons. Data Sets Working Group continues the research initiated with the Rigorous Evaluation of AI Systems workshop series at AAAI Human Computation and AAAI conferences. The goal is to develop robust schemas and infrastructure supporting the Open Source hosting of benchmark evaluation data sets. The group aims to provide free storage for researchers who have human-generated data (spoken word data is the current focus) of generally high quality.

Sarah is a Contributing Member of the MLCommons AI Safety Stakeholder Engagement, Benchmarks and Tests, and Platform Technology working groups. This nonprofit engineering consortium guides the ML industry by developing benchmarks, public datasets, and best practice.

Her current AI Safety work focuses on building LLM Safety Test Sets, Creating Scoring System, and Running Benchmarks. Sarah is leading the subsequent work automating the translation of safety test prompts into in low-resource languages.

Panelists

Author:

Jonathan Bennion

AI Engineer
Rackspace

Jonathan Bennion

AI Engineer
Rackspace

Author:

Sergey Davidovich

Co-Founder & Chairman
SparkBeyond

Sergey is an entrepreneur, technological visionary and machine intelligence enthusiast, who continually strives to bridge the gap between human and machine reasoning and interaction. He’s passionate about computational knowledge representation, acquisition, storage, reasoning, and processing.
 

Sergey has served in a range of executive technological positions in disruptive startup companies. Prior to co-founding SparkBeyond, Sergey served as GM and SVP of R&D for NewBrandAnalytics, a social business intelligence pioneer. He’s also served as VP R&D of SemantiNet, a semantic reasoning engine, and co-founded Delver, a social search engine that was acquired by Sears, where he served as CTO. Prior to founding Delver, Sergey was the architect of a large-scale award-winning predictive maintenance system.

Sergey Davidovich

Co-Founder & Chairman
SparkBeyond

Sergey is an entrepreneur, technological visionary and machine intelligence enthusiast, who continually strives to bridge the gap between human and machine reasoning and interaction. He’s passionate about computational knowledge representation, acquisition, storage, reasoning, and processing.
 

Sergey has served in a range of executive technological positions in disruptive startup companies. Prior to co-founding SparkBeyond, Sergey served as GM and SVP of R&D for NewBrandAnalytics, a social business intelligence pioneer. He’s also served as VP R&D of SemantiNet, a semantic reasoning engine, and co-founded Delver, a social search engine that was acquired by Sears, where he served as CTO. Prior to founding Delver, Sergey was the architect of a large-scale award-winning predictive maintenance system.

Author:

Vipul Raheja

Applied Research Scientist
Grammarly

Vipul Raheja is an Applied Research Scientist at Grammarly. He works on developing robust and scalable approaches centered around improving the quality of written communication, leveraging Natural Language Processing and Deep Learning. His research interests lie at the intersection of large language models and controllable text generation. He has published several papers at top-tier Machine Learning and Natural Language Processing conferences and is also an organizer of the workshops on Intelligent and Interactive Writing Assistants held at ACL and CHI conferences. He obtained an MS in Computer Science from Columbia University.

Vipul Raheja

Applied Research Scientist
Grammarly

Vipul Raheja is an Applied Research Scientist at Grammarly. He works on developing robust and scalable approaches centered around improving the quality of written communication, leveraging Natural Language Processing and Deep Learning. His research interests lie at the intersection of large language models and controllable text generation. He has published several papers at top-tier Machine Learning and Natural Language Processing conferences and is also an organizer of the workshops on Intelligent and Interactive Writing Assistants held at ACL and CHI conferences. He obtained an MS in Computer Science from Columbia University.

Technologist Deep-Dive (Gen AI & Data Science) Track
AI Technologists
Data Science
Digital Infrastructure
MLOps

Author:

Aayush Mudgal

Senior Machine Learning Engineer
Pinterest

Aayush Mudgal is a Senior Machine Learning Engineer at Pinterest, currently leading the efforts around Privacy Aware Conversion Modeling. He has a successful track record of starting and executing 0 to 1 projects, including conversion optimization, video ads ranking, landing page optimization, and evolving the ads ranking from GBDT to DNN stack. His expertise is in large-scale recommendation systems, personalization, and ads marketplaces. Before entering the industry, Aayush conducted research on intelligent tutoring systems, developing data-driven feedback to aid students in learning computer programming. He holds a Master's in Computer Science from Columbia University and a Bachelor of Technology in Computer Science from Indian Institute of Technology Kanpur. 

Aayush Mudgal

Senior Machine Learning Engineer
Pinterest

Aayush Mudgal is a Senior Machine Learning Engineer at Pinterest, currently leading the efforts around Privacy Aware Conversion Modeling. He has a successful track record of starting and executing 0 to 1 projects, including conversion optimization, video ads ranking, landing page optimization, and evolving the ads ranking from GBDT to DNN stack. His expertise is in large-scale recommendation systems, personalization, and ads marketplaces. Before entering the industry, Aayush conducted research on intelligent tutoring systems, developing data-driven feedback to aid students in learning computer programming. He holds a Master's in Computer Science from Columbia University and a Bachelor of Technology in Computer Science from Indian Institute of Technology Kanpur. 

Technologist Deep-Dive (Gen AI & Data Science) Track
AI Technologists
Data Science
Hallucination Prevention
AI Optimizations

Author:

Dat Ngo

Machine Learning Engineer
Arize AI

Dat Ngo is a data scientist and machine learning engineer who works directly with Arize AI users to evaluate and troubleshoot generative AI applications. Before Arize, Ngo led strategic data science efforts at PointPredictive, alliantgroup, and Wood Mackenzie. Ngo has a Master of Science in Applied Statistics from Texas A&M University.

Dat Ngo

Machine Learning Engineer
Arize AI

Dat Ngo is a data scientist and machine learning engineer who works directly with Arize AI users to evaluate and troubleshoot generative AI applications. Before Arize, Ngo led strategic data science efforts at PointPredictive, alliantgroup, and Wood Mackenzie. Ngo has a Master of Science in Applied Statistics from Texas A&M University.

In an era where artificial intelligence is not just an asset but a necessity, understanding the intricacies of Large Language Models (LLMs) has become paramount for enterprises. This session, 'Understanding and Mitigating Hallucinations in Large Language Models', offers a deep dive into the phenomenon of LLM hallucinations – a critical challenge in the deployment of AI technologies in business environments.


We will explore the mechanics behind LLM hallucinations, shedding light on how these AI models, despite their sophistication, can generate inaccurate or misleading information. From the subtlety of input-conflicting hallucinations to the complexity of context and fact-conflicting errors, we will dissect various types of hallucinations with real-world examples, including notable instances from prominent LLMs.

This talk will not only focus on the identification and detection of such hallucinations but will also present effective strategies for mitigation. We will discuss the role of data quality, model fine-tuning, and advanced techniques like Reinforcement Learning with Human Feedback (RLHF) in reducing the risks of inaccuracies. Furthermore, the session will highlight the importance of balancing the creative potential of LLM hallucinations with the need for factual accuracy, especially in high-stakes business decisions.

Attendees will leave with a comprehensive understanding of the challenges and opportunities presented by LLM hallucinations. This knowledge is crucial for enterprises looking to leverage AI responsibly and effectively, ensuring that their use of these powerful tools aligns with the highest standards of accuracy and reliability in the business world.

Technologist Deep-Dive (Gen AI & Data Science) Track
Retail
Business Leader
Data Science
AI Technologists

Author:

Ved Upadhyay

Senior Data Scientist
Walmart Global Tech

Ved Upadhyay is a seasoned professional in the realm of data science and artificial intelligence (AI). With a focus on addressing complex challenges in data science on an enterprise scale, he boasts over 7 years of hands-on experience in crafting AI-powered solutions for businesses. Ved’s expertise spans diverse industries, including retail, e-commerce, pharmaceuticals, agrotech, and socio-tech, where he has successfully productized multiple machine learning pipelines. Currently serving as a Senior Data Scientist at Walmart, Ved spearheads multiple data science initiatives centered around customer propensity and responsible AI solutions at enterprise scale. Prior to venturing into the industry, Ved earned his master’s degree in Data Science from the University of Illinois at Urbana-Champaign and contributed as a Deep Learning researcher at IIIT Hyderabad. His research contributions are reflected in multiple publications in the field of applied AI. 

Ved Upadhyay

Senior Data Scientist
Walmart Global Tech

Ved Upadhyay is a seasoned professional in the realm of data science and artificial intelligence (AI). With a focus on addressing complex challenges in data science on an enterprise scale, he boasts over 7 years of hands-on experience in crafting AI-powered solutions for businesses. Ved’s expertise spans diverse industries, including retail, e-commerce, pharmaceuticals, agrotech, and socio-tech, where he has successfully productized multiple machine learning pipelines. Currently serving as a Senior Data Scientist at Walmart, Ved spearheads multiple data science initiatives centered around customer propensity and responsible AI solutions at enterprise scale. Prior to venturing into the industry, Ved earned his master’s degree in Data Science from the University of Illinois at Urbana-Champaign and contributed as a Deep Learning researcher at IIIT Hyderabad. His research contributions are reflected in multiple publications in the field of applied AI. 

This presentation explores the integration of generative AI in healthcare and pharmacology, highlighting advancements in prompt engineering and its impact on decision-making. The session will examine the complexities and variability of AI responses and the difficulties in establishing a reliable ground truth, emphasizing the need for structured and reproducible outputs to support clinical and business processes efficiently.

Application & Gen AI Integration (Business Leaders) Track
Healthcare
Pharma
Data Science
AI Technologists

Author:

Zoran Krunic

Principal Product Manager
Amgen

Since joining Amgen R&D in 2018, Zoran Krunic has been at the forefront of applying Machine Learning to enhance patient outcomes and streamline clinical trial enrollment processes, utilizing comprehensive Electronic Health Records and clinical datasets. His pioneering work in the Quantum Machine Learning space, in collaboration with IBM's Quantum team, has been instrumental in integrating machine learning with quantum computing through IBM’s Qiskit platform.

Prior to his tenure at Amgen, Zoran developed Machine Learning algorithms at Optum to predict hardware and software failures within complex enterprise architectures. He has a strong background in data engineering and systems development, having contributed significantly to large-scale projects at renowned organizations such as Capital Group and ARCO Petroleum.

In his current full and part-time endeavors, Zoran is leading the efforts in embracing generative AI technologies, with a particular focus on OpenAI's GPT and Anthropic's Claude-2 models. His work is focused on prompt engineering and its application to code generation, advanced document analysis, and process management, with a commitment to ethical AI practices and data privacy.

A recognized voice in quantum computing circles, Zoran is a regular presenter at industry conferences and has served on numerous panels discussing the integration of quantum computing and generative AI within the Health Sciences sector.

With a Master of Science in Electrical Engineering & Computer Science, Zoran continues to explore and contribute to the evolving relationship between quantum computing and artificial intelligence, fostering groundbreaking advancements in healthcare technology.

Zoran Krunic

Principal Product Manager
Amgen

Since joining Amgen R&D in 2018, Zoran Krunic has been at the forefront of applying Machine Learning to enhance patient outcomes and streamline clinical trial enrollment processes, utilizing comprehensive Electronic Health Records and clinical datasets. His pioneering work in the Quantum Machine Learning space, in collaboration with IBM's Quantum team, has been instrumental in integrating machine learning with quantum computing through IBM’s Qiskit platform.

Prior to his tenure at Amgen, Zoran developed Machine Learning algorithms at Optum to predict hardware and software failures within complex enterprise architectures. He has a strong background in data engineering and systems development, having contributed significantly to large-scale projects at renowned organizations such as Capital Group and ARCO Petroleum.

In his current full and part-time endeavors, Zoran is leading the efforts in embracing generative AI technologies, with a particular focus on OpenAI's GPT and Anthropic's Claude-2 models. His work is focused on prompt engineering and its application to code generation, advanced document analysis, and process management, with a commitment to ethical AI practices and data privacy.

A recognized voice in quantum computing circles, Zoran is a regular presenter at industry conferences and has served on numerous panels discussing the integration of quantum computing and generative AI within the Health Sciences sector.

With a Master of Science in Electrical Engineering & Computer Science, Zoran continues to explore and contribute to the evolving relationship between quantum computing and artificial intelligence, fostering groundbreaking advancements in healthcare technology.

This engaging panel discussion delves into the critical differences between proprietary and public data, emphasising the distinct advantages and disadvantages associated with each. Explore how the accessibility and vast quantities of public data facilitate robust generalisation within AI models, contrasting with the nuanced strengths of proprietary data.

Public data's accessibility and abundance offer significant advantages, enabling broad generalisation within AI models. Conversely, proprietary data boasts higher quality, enhanced control, and minimal risk of contamination, catering specifically to niche topics with detailed coverage.

Delve into the advantages of public data, its scalability, and the challenges it poses, juxtaposed against the precise and controlled nature of proprietary data. Gain valuable insights into navigating the trade-offs between the two, understanding their impacts on model performance, ethical and regulatory considerations, and innovation within the realm of AI.

Technologist Deep-Dive (Gen AI & Data Science) Track
AI Technologists
Data Science
Digital Infrastructure
MLOps
Moderator

Author:

Tom Kersten

R&D Engineer
Royal NLR - Netherlands Aerospace Centre

Tom is a distinguished R&D Engineer specialising in AI within the aerospace sector. Armed with a background in computer science and AI, Tom possesses a comprehensive understanding of AI systems. Within his company, he stands out as a leading visionary delving into the integration of generative AI in space, in particular to support the efforts of the Dutch government and its military in this domain. His pioneering work involves exploring and harnessing the potential of GenAI models to revolutionise satellite operations, mission planning, earth observation and space exploration. Tom's dedication to pushing the boundaries of AI in aerospace extends to leveraging generative AI's capabilities, envisaging transformative applications that could redefine the landscape of space technology.

Tom Kersten

R&D Engineer
Royal NLR - Netherlands Aerospace Centre

Tom is a distinguished R&D Engineer specialising in AI within the aerospace sector. Armed with a background in computer science and AI, Tom possesses a comprehensive understanding of AI systems. Within his company, he stands out as a leading visionary delving into the integration of generative AI in space, in particular to support the efforts of the Dutch government and its military in this domain. His pioneering work involves exploring and harnessing the potential of GenAI models to revolutionise satellite operations, mission planning, earth observation and space exploration. Tom's dedication to pushing the boundaries of AI in aerospace extends to leveraging generative AI's capabilities, envisaging transformative applications that could redefine the landscape of space technology.

This talk delves into the forefront of AI reliability, presenting sophisticated strategies that address core challenges in the field. Our focus encompasses the intricacies of hallucination prevention, the refinement of data batching processes, and the criticality of compliance in AI development. Leveraging deep insights from cutting-edge research and practice, we offer a comprehensive perspective on enhancing AI systems' accuracy and ethical integrity. This discourse is designed to equip practitioners and researchers with advanced methodologies, fostering the next wave of AI innovations grounded in robustness and responsibility.

Technologist Deep-Dive (Gen AI & Data Science) Track
Finance
Data Science
AI Technologists
AI Integration

Author:

Sai Teja Akula

Senior Director, Data Science
LTX Trading

With over a decade of dedicated experience in the field of Artificial Intelligence, Sai stands at the forefront of implementing and leveraging Data Science within organizations. A respected figure in the AI community, Sai has played an instrumental role in the transformation of traditional business models by seamlessly integrating advanced data-driven solutions. His expertise extends from developing sophisticated machine learning algorithms to strategizing the holistic implementation of AI within organizational infrastructures

Sai Teja Akula

Senior Director, Data Science
LTX Trading

With over a decade of dedicated experience in the field of Artificial Intelligence, Sai stands at the forefront of implementing and leveraging Data Science within organizations. A respected figure in the AI community, Sai has played an instrumental role in the transformation of traditional business models by seamlessly integrating advanced data-driven solutions. His expertise extends from developing sophisticated machine learning algorithms to strategizing the holistic implementation of AI within organizational infrastructures

Developer Efficiency
Enterprise AI
Data Science
Software Engineering
Systems Engineering
Moderator

Author:

Carlos Guestrin

Professor, Computer Science
Stanford

Carlos Guestrin is a Professor in the Computer Science Department at Stanford University. His previous positions include the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington, the Finmeccanica Associate Professor at Carnegie Mellon University, and the Senior Director of Machine Learning and AI at Apple, after the acquisition of Turi, Inc. (formerly GraphLab and Dato) — Carlos co-founded Turi, which developed a platform for developers and data scientist to build and deploy intelligent applications. He is a technical advisor for OctoML.ai. His team also released a number of popular open-source projects, including XGBoost, LIME, Apache TVM, MXNet, Turi Create, GraphLab/PowerGraph, SFrame, and GraphChi. Carlos received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, and IBM Faculty Fellowship, and was named one of the 2008 ‘Brilliant 10’ by Popular Science Magazine. Carlos’ work received awards at a number of conferences and journals, including ACL, AISTATS, ICML, IPSN, JAIR, JWRPM, KDD, NeurIPS, UAI, and VLDB. He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA.

Carlos Guestrin

Professor, Computer Science
Stanford

Carlos Guestrin is a Professor in the Computer Science Department at Stanford University. His previous positions include the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington, the Finmeccanica Associate Professor at Carnegie Mellon University, and the Senior Director of Machine Learning and AI at Apple, after the acquisition of Turi, Inc. (formerly GraphLab and Dato) — Carlos co-founded Turi, which developed a platform for developers and data scientist to build and deploy intelligent applications. He is a technical advisor for OctoML.ai. His team also released a number of popular open-source projects, including XGBoost, LIME, Apache TVM, MXNet, Turi Create, GraphLab/PowerGraph, SFrame, and GraphChi. Carlos received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, and IBM Faculty Fellowship, and was named one of the 2008 ‘Brilliant 10’ by Popular Science Magazine. Carlos’ work received awards at a number of conferences and journals, including ACL, AISTATS, ICML, IPSN, JAIR, JWRPM, KDD, NeurIPS, UAI, and VLDB. He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA.

Author:

Sakyasingha Dasgupta

Founder & CEO
EdgeCortix

Sakya is the founder and Chief Executive officer of EdgeCortix. He is an artificial intelligence (AI) and machine learning technologist, entrepreneur, and engineer with over a decade of experience in taking cutting edge AI research from ideation stage to scalable products, across different industry verticals.  He has lead teams at global companies like Microsoft and IBM Research / IBM Japan, along with national research labs like RIKEN Japan and the Max Planck Institute Germany. Previously, he helped establish and lead the technology division at lean startups in Japan and Singapore, in semiconductor technology, robotics and Fintech sectors. Sakya is the inventor of over 20 patents and has published widely on machine learning and AI with over 1,000 citations. 

Sakya holds a PhD. in Physics of Complex Systems from the Max Planck Institute in Germany, along with Masters in Artificial Intelligence from The University of Edinburgh and a Bachelors of Computer Engineering. Prior to founding EdgeCortix he completed his entrepreneurship studies from the MIT Sloan School of Management.

Sakyasingha Dasgupta

Founder & CEO
EdgeCortix

Sakya is the founder and Chief Executive officer of EdgeCortix. He is an artificial intelligence (AI) and machine learning technologist, entrepreneur, and engineer with over a decade of experience in taking cutting edge AI research from ideation stage to scalable products, across different industry verticals.  He has lead teams at global companies like Microsoft and IBM Research / IBM Japan, along with national research labs like RIKEN Japan and the Max Planck Institute Germany. Previously, he helped establish and lead the technology division at lean startups in Japan and Singapore, in semiconductor technology, robotics and Fintech sectors. Sakya is the inventor of over 20 patents and has published widely on machine learning and AI with over 1,000 citations. 

Sakya holds a PhD. in Physics of Complex Systems from the Max Planck Institute in Germany, along with Masters in Artificial Intelligence from The University of Edinburgh and a Bachelors of Computer Engineering. Prior to founding EdgeCortix he completed his entrepreneurship studies from the MIT Sloan School of Management.

Author:

Luis Ceze

Co-founder and CEO
OctoML

Luis Ceze is Co-founder and CEO at OctoML, Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and Venture Partner at Madrona Venture Group. His research focuses on the intersection between computer architecture, programming languages, machine learning and biology. His current focus is on approximate computing for efficient machine learning andDNA-based data storage. He co-directs the Molecular Information Systems Lab (MISL), the Systems and Architectures for Machine Learning lab (SAMPL) and the Sampa Lab for HW/SW co-design. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, the IEEE TCCA young Computer Architect Award and UIUC Distinguished Alumni Award.

Luis Ceze

Co-founder and CEO
OctoML

Luis Ceze is Co-founder and CEO at OctoML, Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and Venture Partner at Madrona Venture Group. His research focuses on the intersection between computer architecture, programming languages, machine learning and biology. His current focus is on approximate computing for efficient machine learning andDNA-based data storage. He co-directs the Molecular Information Systems Lab (MISL), the Systems and Architectures for Machine Learning lab (SAMPL) and the Sampa Lab for HW/SW co-design. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, the IEEE TCCA young Computer Architect Award and UIUC Distinguished Alumni Award.

Author:

Jian Zhang

Director, Machine Learning
SambaNova Systems

Jian Zhang

Director, Machine Learning
SambaNova Systems

Transformers are in high demand, particularly in industries like BFSI and healthcare, for language processing, understanding, classification, generation and translation. The parameter counts for models like GPT, that are fast becoming the norm in the world of NLP, are mind-boggling, and the cost involved in training and deploying even more so. If the vast potential for LLMs is to extend beyond the wealthiest companies and research institutions on the planet, then there is a need to evaluate how to lower the barriers of entry for experimentation and research on models like GPT. There's also a need to discuss the extent to which bigger is better, in the field of practical and commercial NLP.

This panel will look at the state of play of how enterprises are using large language models today, what their plans are for future research in NLP, and how hardware & systems builders and organizations like HuggingFace can help bring state-of-the-art performance into production in smaller, more resource-constrained enterprises and labs.

Developer Efficiency
Enterprise AI
ML at Scale
NLP
Novel AI Hardware
Systems Design
Data Science
Hardware Engineering
Software Engineering
Strategy
Systems Engineering

Author:

Phil Brown

VP, Scaled Systems Product
Graphcore

Phil leads Graphcore’s efforts to build large scale AI/ML processing capability using Graphcore unique Intelligence Processing Units (IPUs) and IPU-Fabric and Streaming Memory technology. Previously he has held a number of different roles at Graphcore including Director of Applications, leading development of Graphcore’s flagship AL/ML models, and Director of Field Engineering, which acts as the focal point for technical engagements with our customers. Prior to joining Graphcore, Phil worked for Cray Inc. in a number of roles, including leading their engagement with the weather forecasting and climate research customers worldwide and as a technical architect. Phil holds a PhD in Computational Chemistry from the University of Bristol.

Phil Brown

VP, Scaled Systems Product
Graphcore

Phil leads Graphcore’s efforts to build large scale AI/ML processing capability using Graphcore unique Intelligence Processing Units (IPUs) and IPU-Fabric and Streaming Memory technology. Previously he has held a number of different roles at Graphcore including Director of Applications, leading development of Graphcore’s flagship AL/ML models, and Director of Field Engineering, which acts as the focal point for technical engagements with our customers. Prior to joining Graphcore, Phil worked for Cray Inc. in a number of roles, including leading their engagement with the weather forecasting and climate research customers worldwide and as a technical architect. Phil holds a PhD in Computational Chemistry from the University of Bristol.

Author:

Selcuk Kopru

Director, Engineering & Research, Search
eBay

Selcuk Kopru is Head of ML & NLP at eBay and is an experienced AI leader with proven expertise in creating and deploying cutting edge NLP and AI technologies and systems. He is experienced in developing scalable Machine Learning solutions to solve big data problems that involve text and multimodal data. He is also skilled in Python, Java, C++, Machine Translation and Pattern Recognition. Selcuk is also a strong research professional with a Doctor of Philosophy (PhD) in NLP in Computer Science from Middle East Technical University.

Selcuk Kopru

Director, Engineering & Research, Search
eBay

Selcuk Kopru is Head of ML & NLP at eBay and is an experienced AI leader with proven expertise in creating and deploying cutting edge NLP and AI technologies and systems. He is experienced in developing scalable Machine Learning solutions to solve big data problems that involve text and multimodal data. He is also skilled in Python, Java, C++, Machine Translation and Pattern Recognition. Selcuk is also a strong research professional with a Doctor of Philosophy (PhD) in NLP in Computer Science from Middle East Technical University.

Author:

Jeff Boudier

Product Director
Hugging Face

Jeff Boudier is a product director at Hugging Face, creator of Transformers, the leading open-source NLP library. Previously Jeff was a co-founder of Stupeflix, acquired by GoPro, where he served as director of Product Management, Product Marketing, Business Development and Corporate Development.

Jeff Boudier

Product Director
Hugging Face

Jeff Boudier is a product director at Hugging Face, creator of Transformers, the leading open-source NLP library. Previously Jeff was a co-founder of Stupeflix, acquired by GoPro, where he served as director of Product Management, Product Marketing, Business Development and Corporate Development.

Author:

Morteza Noshad

Senior ML/NLP Scientist
Vida Health

Morteza Noshad is a senior ML/NLP scientist at Vida health. He is skilled at designing large scale NLP models for different healthcare applications such as automated clinical documentation, symptom detection and question answering. Morteza was a research scientist at Stanford University focusing on graph neural networks for clinical decision support systems where he received the SAGE Scientist Award for his research. Morteza received his Ph.D. in Computer Science from University of Michigan where he contributed to the theory of information bottleneck in deep learning. 

Morteza Noshad

Senior ML/NLP Scientist
Vida Health

Morteza Noshad is a senior ML/NLP scientist at Vida health. He is skilled at designing large scale NLP models for different healthcare applications such as automated clinical documentation, symptom detection and question answering. Morteza was a research scientist at Stanford University focusing on graph neural networks for clinical decision support systems where he received the SAGE Scientist Award for his research. Morteza received his Ph.D. in Computer Science from University of Michigan where he contributed to the theory of information bottleneck in deep learning.