Oct 31st – Nov 3rd, 2023
Research Frontiers
The Most Advanced Active Research, Summarized
Research Frontiers Track
Rapid Pace of Advancement
Data Science is a broad field and advancing at a tremendous pace. Every few months new research, models, and advances are announced. For data science practitioners, it’s essential to keep abreast of the latest advances. However, given the demands on our time this can be a daunting task.
The Most Advanced Research, Summarized
The Research Frontiers track is the first of its kind. You don’t have to parse the contents of countless papers or attend academic conferences; instead, we bring the most relevant information to you. World-class academics, researchers, and professionals summarize the latest research across focus areas, and detail what’s important.
Some of Our Previous Research Frontiers Speakers
Guy Van den Broeck, PhD
Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.
Artificial Intelligence Can Learn from Data. But Can It Learn to Reason?(Talk)
Kira Radinsky, PhD
Dr. Kira Radinsky is the CEO and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. In the past, she co-founded SalesPredict, acquired by eBay in 2016, and served as eBay director of data science and IL chief scientist. One of the up-and-coming voices in the data science community, she is pioneering the field of medical data mining. Dr. Radinsky gained international recognition for her work at Microsoft Research, where she developed predictive algorithms that recognized the early warning signs of globally impactful events, including political riots and disease epidemics. In 2013, she was named to the MIT Technology Review’s 35 Young Innovators Under 35, in 2015 as Forbes 30 under 30 rising stars in enterprise technology, and in 2016 selected as “woman of the year” by Globes. She is a frequent presenter at global tech events, including TEDx, Wired, Strata Data Science, Techcrunch and academic conferences, and she publishes in the Harvard Business Review. Radinsky serves as a board member in: Israel Securities Authority, Maccabi Research Institute, and technology board of HSBC bank. Dr. Radinsky also serves as visiting professor at the Technion, Israel’s leading science and technology institute, where she focuses on the application of predictive data mining in medicine.
Aaron Roth, PhD
Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science, in the Computer and Information Sciences department at the University of Pennsylvania, with a secondary appointment in the Wharton statistics department. He is affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. He is also an Amazon Scholar at Amazon AWS. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and research awards from Yahoo, Amazon, and Google. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory, learning theory, and machine learning. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.” Together with Michael Kearns, he is the author of “The Ethical Algorithm”.
Raluca Ada Popa, PhD
Raluca Ada Popa is the Robert E. and Beverly A. Brooks associate professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab and SkyLab at UC Berkeley, as well as a co-founder of Opaque Systems and PreVeil, two cybersecurity companies. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of the 2021 ACM Grace Murray Hopper Award, a Sloan Foundation Fellowship award, Jay Lepreau Best Paper Award at OSDI 2021, Distinguished Paper Award at IEEE Euro S&P 2022, Jim and Donna Gray Excellence in Undergraduate Teaching Award, NSF Career Award, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT CS doctoral thesis.
Oswald Campesato
Oswald is a former PhD Candidate (ABD) in Mathematics, an education fanatic (5 degrees), and an author of 40 technical books. He has worked for Oracle, AAA, and Just Systems of Japan as well as various startups. He has lived/worked in 5 countries on three continents, and in a previous career he worked in South America, Italy, and the French Riviera, and has traveled to 70 countries on five continents. He has worked from C/C++/Java developer to CTO, comfortable in 4 languages, and currently he is an AI (ML,DL,NLP,DRL) adjunct instructor at UCSC and works on NLP-related tasks in a start-up in the Bay Area.
Mosharaf Chowdhury, PhD
Mosharaf Chowdhury is a Morris Wellman associate professor of CSE at the University of Michigan, Ann Arbor, where he leads the SymbioticLab. His work improves application performance and system efficiency of machine learning and big data workloads. He is also building software solutions to monitor and optimize the impact of machine learning systems on energy consumption and data privacy. His group developed Infiniswap, the first scalable software solution for memory disaggregation; Salus, the first software-only GPU sharing system for deep learning; FedScale, the largest federated learning benchmark and a scalable and extensible federated learning engine; and Zeus, the first GPU energy-vs-training performance tradeoff optimizer for DNN training. In the past, Mosharaf did seminal works on coflows and virtual network embedding, and he was a co-creator of Apache Spark. He has received many individual awards and fellowships, thanks to his stellar students and collaborators. His works have received seven paper awards from top venues, including NSDI, OSDI, and ATC, and over 22,000 citations. Mosharaf received his Ph.D. from UC Berkeley in 2015.
Kirstin Aschbacher, PhD
Kirstin Aschbacher is a Data Scientist, with a background in PsychoNeuroImmunology Research from her days as an Associate Professor at the University of California, San Francisco (UCSF), Department of Psychology, Weill Institute for Neurosciences, and the Division of Cardiology. She has a PhD in Clinical Psychology and is also a licensed Psychologist with a certificate in HRV Biofeedback. She uses her cross-functional skill-sets to drive innovative, AI-based products that enhance user well-being and stress-resilience. In her current role as Senior Director of Health Data Science at Meru Health, she has focused on HRV Biofeedback and Precision Care algorithms.
Arun Verma, PhD
Arun heads the Bloomberg Quantitative Research Solutions Team. Arun’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing/hedging and more generally around asset pricing using traditional quantitative finance methods. More recently, he has enjoyed working at the intersection of diverse areas such as data science, innovative quantitative finance models and using AI/Machine Learning methods to help reveal embedded signals in traditional & alternative data such as Company Financials, ESG, News/Social, Supply Chain, Geolocational & Extreme Weather and their potential impact on capital markets. Most recently in an attempt to complete a full circle, he has been exploring use of ML methods in asset pricing , e.g. Derivatives pricing and illiquid instrument pricing.
Prior to joining Bloomberg, he earned his Ph.D from Cornell University in the areas of computer science and applied mathematics and a B. Tech in Computer Science from IIT Delhi, India. Arun is also an editorial board member of The Journal of Financial Data Science.
Machine Learning Models for Quantitative Finance and Trading(Talk)
Victor Zitian Chen, PhD, CFA
Dr. Victor Zitian Chen, CFA, is a believer and action-taker on the idea of a world brain. Dr. Chen is currently the Director of Data Analytics and Insights, Experimental Design and Causal Inference at Fidelity Investments. He leads the causal analytics efforts across the personal investing business at the Fidelity, including experimentation, prescriptive analytics, and causal knowledge graph-based applications. Before joining Fidelity, Dr. Chen was a tenured professor in management and data science at the University of North Carolina, Charlotte, and a visiting professor in international business at Copenhagen Business School, Denmark. He led two major National Science Foundation (NSF) grants focusing on causal knowledge graph-based explainable AI and analytics applications. He founded and led the Global OpenLabs for Performance Enhancement-Analytics and Knowledge System (GoPeaks) – a startup to advance and commercialize knowledge synthesis and causal/prescriptive analytics solutions for business decisions.
Causal/Prescriptive Analytics in Business Decisions(Business Talk)
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.
Advanced Gradient Boosting (I): Fundamentals, Interpretability, and Categorical Structure(Training)
Advanced Gradient Boosting (II): Calibration, Probabilistic Regression and Conformal Prediction(Training)
Swasti Kakker
Swasti Kakker is a senior software development engineer on the data analytics and infrastructure team at LinkedIn, where she worked on the design and implementation of Darwin – a hosted Jupyter notebook solution. She has worked on features like scheduling notebooks based on a cron expression, creating publishable reports from executions of a notebook, introducing Language servers in notebooks and integrating notebooks with various apps at LinkedIn. She works closely with stakeholders to understand the expectations and requirements of the platform that would improve developer productivity. Her passion lies in increasing and improving developer productivity by designing and implementing scalable platforms. She has also spoken previously at international conferences like Grace Hopper, Orlando and O’reilly Strata, New York in 2019.
Unified Data Science Platform for Accelerating Data Insights(Talk)
Manu Ram Pandit
Manu Ram Pandit is a Staff software engineer on the data analytics and infrastructure team at LinkedIn, where he’s influenced the design and implementation of hosted notebooks, providing a seamless experience to end users. Manu has worked on setting up multiple features in the platform like sharing/choosing custom docker environments & recently is involved with visualization efforts to effectively view big data visualizations.He works closely with customers, engineers, and product to understand and define the requirements and design of the system. He has extensive experience in building complex and scalable applications. Previously, he was with Paytm, Amadeus, and Samsung, where he built scalable applications for various domains.
Unified Data Science Platform for Accelerating Data Insights(Talk)
Balaji Lakshminarayanan, PhD
Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.
Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning(Tutorial)
ODSC WEST 2023 - Oct 31st – Nov 3rd
RegisterActive Research Focus Areas
Data science is a broad and expanding field with many areas of study. Here are some of the main areas that our presenting researchers will address:
Neural Networks
Machine Learning
Transfer Learning
Machine Vision
Natural Language Processing
Predictive Analytics
Pattern Recognition
Quantitative Finance
Speech Recognition
Time Series Analysis
Graph Theory
Network Analysis
Data Visualization
Anomaly Detection
You Will Meet
Experienced data scientists
Software engineers and architects
Business professionals interested in data science advancements
Experts from other domains looking to leverage data science
Researchers from academia and industry
Industry professionals
Technologists interested in new data science applications
Industry experts looking to access the impact of data science
Why Attend?
Hear from world-class researchers and academics about the top areas of active research
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science
Be the first amongst your peers to grasp changes that will affect the field over the next few years
Take advantage of another 120 talks, tutorials, and workshops at ODSC West
Learn directly from top researchers what works and what doesn’t
Connect and network with academics, research, and fellow professionals
Meet with peers and professionals looking to learn, connect, and collaborate
Get access to other focus area content, including ML / DL, Data Visualization, Quant Finance, and Open Data Science
ODSC WEST 2023 - November 1st-3rd
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