Full Machine Learning Week 7-Track Agenda 2021 – Detailed Session Descriptions

Healthcare risk adjustment is the most impactful financial mechanism to account for the differences in risk in members that enroll with different carriers. In the ACA program alone, billions of dollars are moved each year between health plan carriers. The calculations rely on risk assessment programs which are typically based on linear regression. Simplicity, understandability, and transparency are paramount considerations in the modeling due to the zero-sum nature of risk adjustment. This brings us to an important question: if healthcare expenditures by member are non-linear (which in fact they are), is the additional complexity of machine learning worth it to model these more accurately?

This session provides an overview of this mathematics that is critical to healthcare financials in each line of business (commercial, medicare, medicaid) and also compares and contrasts traditional methods such as linear regression against machine learning variants that involve use of neural networks. We will also discuss the considerations involved in picking the right tools for a couple of applications related to risk assessment.

A single doctor, overworked and overwhelmed, will make a less-informed decision than a team of doctors. Now, imagine the potential knowledge we could gain if we combined insight from doctors all over the world. In this case study, we will explain how Innovative Precision Health (IPH) uses large amounts of aggregated anonymous population data to learn about the trajectory of a disease and predict the most effective treatment for a patient.  By exponentially increasing the amount of information available, IPH is able to provide better predictions for doctors, better care for patients, and profitable outcomes for insurance and pharmaceutical companies alike.

I4.0 has created a plethora of applications and vendors competing for attention in the manufacturing space. Many vendors are pushing platforms and enablement yet what manufacturers really need are hard core scalable use cases that deliver measurable impact and value. This cannot be solved by an individual vendor or platform. I4.0 success is dependent on cooperation and partnership between different players in the I4.0 landscape and a new approach to continuous improvement in order to massively reduce the time it takes to derive action, impact and value from data. 

Accurate, timely estimation of carbon emissions is critical for businesses and governments to take action on climate change. The Climate TRACE coalition aims to furnish these estimates for all major sources of emissions globally, on a near real-time basis. In this session we’ll give an overview of our work on estimating emissions from one of the most important sources of greenhouse gases, power generation from coal. The approach focuses upon detecting plumes using a variety of approaches, including multi instance deep learning.

Hardly a week goes by without news of money laundering affecting this or that financial institution. As gatekeepers of the financial system, banks play a crucial role in finding and reporting instances of financial crime. In this talk, we describe the challenges of detecting money laundering and outline why employing machine learning techniques is critically important. We will discuss some of the challenges that we faced at ABN AMRO as well as the solutions we came up with, including a method to deal with changes in feature space while running models in production and an in-house-developed model explainability technique.

This session details for big data collected on over 1.4 million people has been used to develop AI-led, personalised interventions that are clinically-proven to reverse type 2 diabetes, which has redefined the use of digital therapeutics in the USA, Canada, United Kingdom, Germany and India. 

Scanning Electron Microscope (SEM) images are the primary means used by expert engineers to identify and diagnose defects in semiconductor variable shape beam (VSB) mask writers. Deep learning (DL) offers an attractive alternative to this tedious process. However, extremely robust mask writers preclude collecting a large variety of SEM images to train DL models. Using digital twins that can mimic SEM images provides an exceptional way to synthesize ample DL training data. This talk will take a deep dive into synthesizing SEM images and leveraging them to build DL models for VSB mask writer defects analysis.

The energy landscape is going through a drastic transformation. We are moving away from centralized power plants to more distributed energy resources such as solar, electric vehicles. This transformation makes it very difficult for the grid to handle and manage. Through this session we would explore how using data science and artificial intelligence, industries can optimize their energy usage while aiming to reduce costs and meet their sustainability goals. We'd be delving deeper into some of the forecasting and predictive analytics techniques we have created and measuring their impact. Also, we would share insights from real-life use cases of our customers and how AI has helped them monitor, control, and optimize their energy assets.

Engineering design simulation generates enormous amounts of structure data, the bulk of which is never studied or mined for insights. Several case studies will be presented for advancing the belief that simulation data presents an opportunity to allow innovation, hidden insights and guidance to emerge thru the use of simple ML techniques. 

Medicine is an ever-unfolding quest to ensure patients receive life improving therapies to return to full life. Advances in intelligent data, AI, data automation, machine learning, and computational capability allow an efficient pursuit of better outcomes for patients while reducing health care costs. To help understand new approaches to healthcare and new forms of innovation, Maneesh Shrivastav PhD, Director of Market Development, Science and Analytics at Medtronic, will run through innovation at the medical technology company and provide examples of how the company is leveraging data science to improve patients’ lives.

With the need to cater to a global audience, there is a growing demand for applications to support speech identification/translation/transliteration from one language to another. This talk starts off with a brief introduction to the topic of Machine Translation (MT), the evolution/application of Machine Translation. The focus will be on Neural Machine Translation (NMT). Then it moves on to introduce some of the typical customer cases and finally ends with how to embed such functionality in your application. The talk will showcase how maintenance and company records across various subsidiaries which might be in their local language can be standardized into a common language so that all the global data can be used for NLP models.

Anyone can make a pretty bar graph, but can you make sound decisions based on that graph? Is it actionable, or is it only fluff? How do you turn flashy concepts into actionable visualizations? Can you see the end result of those concepts; will they ever become reality? Do you have the vision to combine beauty with brains, thereby driving decisions with data? Or do you settle for destroying direction with disaster? American mathematician John Tukey once said, "The greatest value of a picture is when it forces us to notice what we never expected to see." What value do you see in your data? And what ideas do you have when you see it? Learn how you can capitalize on your ideas by blending internal with external, leveraging them into a cohesive strategy for both the short term AND the long term. See the five "Stages of the Spectrum" in action while discovering the difference between impact and influence, and how that difference plays into making data actionable. Catch the right blend of art and science, or beauty and brains, as you go from concept to reality. 

VIPR is a predictive reliability framework focusing on a boosted payload application. The project addresses and decreases high costs and flight vehicle maintenance downtimes due to reactive onboard sensing. By developing a proof of concept corresponding virtual sensor that matches an onboard sensor, predictive sensing and modeling can occur to address any number of fleet aging issues. This new capability will use enhanced machine learning and AI algorithmic framework, anchored with aero data, to develop a modular virtual sensing system which can be trained and applied to any vehicle with sufficient heritage onboard sensor data. 

"Can I drive more efficiently?" was the question to be answered at the beginning of all data science efforts at Tracks. How can we accurately measure efficient driving of the thousands of truck drivers on the roads? This is a typical real-life question that is difficult to solve with ML methods. The answer to this question has sparked a number of follow up business questions that we are tackling with Tracks' complex AI system. In this session you will learn how Tracks' solution looks like, which ML methods are employed and which business questions are being answered. In a nutshell, how to turn ML into saved CO2. 

Thomson Reuters produces timely financial news and alerts that is used by many of our corporate and institutional customers. This is extracted from multiple sources, including corporate disclosures, and presented as an ongoing series of alerts and bulletins. One of the major challenges in this kind of financial work is the lack of accurate or weak training data which required us to employ various innovative data cleaning techniques. We also discuss our NLP-based production system that makes strong use of a BERT pre-trained model in combination with an unsupervised strategy, as well as some of the engineering challenges around making the system operate adequately in real-time.

Hospitals make lots of efforts to improve their capacity in emergency departments by adding boxes and beds, organizing shifts, processes and protocols. And, by also implementing systems to record, control and visualize better what's the situation in real time. But decisions, the ones that ultimately drive the output of the process, are entirely left to human capacity. What if an algorithm could help selecting the sequence for attending emergency patients so that life threatening situations are prioritized and overall queueing time is shortened, with no change in the physical resources? Benjamin Arias Gálvez shares the experience at one of the largest public hospitals in Chile, where an algorithm helps sequencing the waiting queue of patients at in the emergency room in real time, with no investment in the physical resources.    

Elder Research, a data science consultancy, and Sira-Kvina kraftselkap, a large Norwegian power producer, have deployed deep learning models as part of a predictive maintenance solution to identify likely component failures within hydroelectric generator and turbine systems. In this session, we discuss the design concept, modeling approach, solution architecture, and how it is implemented as part of the maintenance team workflow. We will also explain what makes machine learning so challenging in the utlities industry where labeled cases are in short supply. 

As demand for carbon credits accelerates, there is an immense challenge in scaling the supply of carbon offsets. It’s hard to create credits that are additional, non-leaky, and durable, and it’s impossible for all but the largest landowners to participate in carbon programs. Over the last 10 years, SilviaTerra has built technology that generates comprehensive forest inventories of unprecedented resolution and scale, enabling measurement and payment for a comprehensive set of beneficial outcomes across the landscape. This new market is making carbon and other types of natural capital work for all landowners - for every acre, every value, every year.

The shock of a pandemic can be mitigated by developing a plan for being proactive with modeling that can inform decision making. At Paychex we developed a comprehensive COVID-19 insights package released in March 13 that was then transformed into a real time indicator site that included predictive modeling for client losses.

The health care industry is changing rapidly, therefore, it’s necessary to improve efficiency to production with appropriate and targeted automation. CDPHP, a mid-size payer in New York’s Capital Region, is implementing an Analytics Factory to achieve this end. It does this using a CI/CD/CT framework. In this session, you will learn about our strategy, as well as practical lessons learned on scaling and operationalizing data products. Three use cases will be discussed to illustrate the streamlined process to production: (1) NLP-driven quality measure detection, (2) prioritizing member voice responses for action, and (3) deploying and hosting a readmission model. 

Unplanned downtime is one of the largest sources of lost production in the manufacturing world, leading directly to a loss of possible revenue. The rise of the Internet of Things has led to the development of advanced predictive maintenance solutions aiming to help manufacturers achieve the ultimate goal: zero unplanned downtime. While advances in technology have led to broader adoption of these IoT solutions, many of them are still not accurate enough to rely on. Markus Larsson from Xerox’s Palo Alto Research Center (PARC) talks about what innovators can do to bring IoT predictive maintenance to the next level. 

Climate change is increasing the frequency and severity of natural disasters. Natural catastrophes impact all critical infrastructures, and their resilience is essential for businesses and cities to operate effectively and safely. At One Concern, we combine machine learning and hazard modeling along with ML operational tools to better model the impacts of natural disasters on these critical infrastructures. By taking advantage of modeling, we can understand these potential impacts sooner to plan for and mitigate them. This helps to make our communities more resilient. This session will cover how One Concern applies Machine Learning algorithms to Natural Disaster Modeling. 

Algorithmic trading revolves around optimal scheduling, when and how to trade each asset of a portfolio. The schedules produced, however, should not be static; being able to optimally adapt to changing intraday market conditions and analysing effectively other exogenous information is of paramount importance. In this talk, we focus on how machine learning methods like RNNs, LSTMs and decision trees, used in conjunction with classical dynamical system techniques, such as kalman filters, can enrich the toolbox available for real-time algorithmic trading.

The speaker will review case studies from real-world projects that built AI systems using Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization. He will also cover why and how NLP was used, what deep learning models and libraries were used, and what was achieved. Key takeaways for attendees will include important considerations for NLP projects including how to build domain-specific healthcare models and using NLP as part of larger and scalable machine learning and deep learning pipelines in distributed environment. 

Machine teaching is a complementary approach to machine learning. It helps those without AI expertise break a complex problem into simpler tasks and give the AI model important clues about how to find a solution quickly. In this session, we talk about the current limitations with control systems and how AI is bridging that gap by training intelligent controllers .

We give an overview of recent developments in physics-informed AI and big data that are transforming the prediction of climate and weather in applications ranging from climate risk modeling for insurance to real-time forecasting for energy. Traditional climate and weather models require computationally expensive simulation of physical laws on supercomputers with hours to days of processing time and have limited capacity to incorporate ground-truth data sources. The development of cloud-based AI workflows based on deep neural networks provides an alternative approach to develop physical emulators of climate and weather processes that are highly scalable and natively tuned to utilize the petabytes of remote-sensing, ground-based and numerical simulation data from Earth observation that are generated daily. We present work that we are doing at Terrafuse AI, a startup out of Berkeley National Lab, to develop an AI-native climate risk and forecasting platform for problems ranging from high-resolution mapping of wildfire risk in California to real-time wind forecasting for aviation and renewable energy. 

Analytics adoption in healthcare has come a long way over the last two decades. In the early years, advanced machine learning was limited to a handful of experts working on narrowly-defined subjects such as hospital readmissions. Among most healthcare professionals, there was limited understanding of what machine learning is, and even less eagerness to integrate analytic models into medical practice or organizational operations in any meaningful way. Today, any healthcare organization that is not using advanced analytics is an anomaly. Analytic methods are being employed almost everywhere in healthcare from insurance to precision medicine with tailored treatment plans guided by artificial intelligence. And, analytics are embedded in the systems used by hospitals, such as the HIS or the laboratory technology. Slowly, advanced analytics is being integrated throughout the healthcare system even though many professionals are not aware of its influence. Join our expert panel as we discuss the current state of analytics/machine learning, the work that remains, and opportunities to better use this incredible technology. Attendees are invited to join in the conversation and contribute questions and comments to the discussion.

The Safety National Casualty Corporation is the leader in Excess Workers' Compensation. Predictive Analytics is used internally to identify those claims that have potential of exceeding high-cost levels in years. Structured claim data, such as claimant age, nature of injury and body parts, provide fundamental features for predicting claim severity. However, in many cases, they are not informative and distinguishing enough to identify extremely sever claims which are rare in the claim data pool. To address this issue, we explore the unstructured claim data – claim notes, and investigate how different text mining techniques can be used to extract predictive insights from claim notes.

Seagate is a multinational data storage solution company with over 40,000 employees from around the world. We have embarked on the advanced analytics journey 6 years ago. In the last 6 years, teams worked on many initiatives to help the company and its staff to transform digitally and get ready for industry 4.0. In this talk, we will share our business and technical journey and talk about some of the challenges we encountered in the last 6 years. We will explain the solutions we came up with and the initiatives we established to help with the digital transformation journey at Seagate. We will also share practices and approaches that work well for us and share examples of our data science solutions. We hope our experience will serve as a good industrial reference for companies that are interested in starting their own digital transformation and analytics journey. 

Methane, the primary component of natural gas, is responsible for 15% of global warming. Our mission of finding and stopping greenhouse gas emissions at huge scale is a critical step towards controlling climate change, but it also presents unique challenges. And, as a small startup, navigating the trade-offs between speed, accuracy, and cost in our data pipeline can often be the difference between survival and failure. In this talk, we will examine the difficulties ML pipeline design in cases where information, time, and money are constrained, and how to do so while hiding the sausage-making from our customers, who just want to know where their equipment is leaking, and want to know fast. By using a lean, iterative approach that involves input from every department, including engineering, operations, and business development, we stay focused on creating analytics that maximize value while reducing risk to the company. 

Predictive Analytics is transforming commercial insurance underwriting. It delivers more accurate and real-time risk assessment which helps underwriters efficiently acquire information, prepare more accurate quotes, and speed up and even automate the underwriting process. This talk presents one practical application of predictive analytics on building the triage process to help underwriters prioritize their attention on the most valuable submissions.

Counterfeited, adulterated, and stolen pharmaceuticals are threats to US citizens. The Drug supply Chain Security Act (DSCSA) was established to secure the supply chain through various means including the establishment of an interoperable traceability system. The current industry stakeholder design is a distributed structure with exchanges between established trading partners. While this is flexible, it creates challenges to detecting nefarious activity and performing supply-demand management. We have implemented an interoperable, traceable prototype system which solves some of the problems and fulfills DSCSA requirements that must be met by 2023 .

Enhanced accessibility and availability to data has resulted in the increasing demand for data scientists within the logistical and supply end of industry. Yet, with this great reservoir of information, data scientists are faced with key challenges in the development of their solutions. In this session, we explore both the traditional as well as new challenges of the data scientist in this environment. The alignment of the right data infrastructure in solving the right business problem is just one example. New data sources and new tools exacerbate this issue but the disciplined process approach towards the data science process is the constant. By looking at a variety of industries such as insurance, travel , and health, we examine how to develop the best practices within this process. A number of examples and case studies will be presented to explore the increased shift and emphasis towards data science skills that focus less on the technical and more on identifying the business problem. 

The global recycling industry has been in economic crisis over the last few years as China and other international importers of recyclables enacted stricter requirements on the purity of recycled materials. COVID-19 exacerbated the recycling crisis, forcing many businesses to suspend operations due to concerns for worker safety. At the same time, the pandemic increased demand for high-quality recycled feedstock to overcome supply chain interruptions and shifts in raw material availability. AMP’s technology applies computer vision and deep learning to identify and differentiate recyclables found in the waste stream by color, size, shape, opacity, consumer brand, and more, storing data about each item it perceives. AMP’s AI can recognize and recover material as small as a bottlecap and as unique as a Keurig coffee pod from complex, mixed material streams of plastics, cardboard, paper, cans, cartons, and many other container and packaging types reclaimed for raw material processing for the global supply chain. Learn how AMP is helping the industry overcome the crisis by modernizing recycling—keeping recycling businesses open, ensuring worker safety, increasing productivity, improving bale purity, overcoming labor shortages, lowering the costs to recycle, diverting materials from landfill, and increasing overall rates of recycling. 

Reducing unplanned downtime requires the proactive maintenance of assets to keep operation running smoothly. Unfortunately, many organizations rely on time-based or usage-based maintenance rather than actual need to determine when to best schedule a maintenance activity. Predictive maintenance instead uses data from operations to determine the health of the asset and can be used to determine when it is best to perform maintenance. We developed intelligent digital twin technology that utilizes sensor data to assess the health and prognostics of a system based on predictive models built with physics-based simulation data. A digital twin is a digital representation of a physical asset that acts as a “single source of truth” for the asset. By incorporating predictive analytics into a digital twin, we can calculate maintenance-related parameters that would allow for appropriate intervention. In this session we will discuss some of the technology available to implement digital twins. We will also discuss how to augment the basic capabilities of digital twins with predictive algorithms for condition-based maintenance. In particular we will discuss developing a digital twin of a naval vessel that assesses the efficiency of various propulsion system components to determine the state of decay of the components.

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Our Next Best Action journey commenced with the simple aspiration of delivering smarter personalised experiences at the moment of need for our customers. While the aspiration was simple, orchestrating NBA is a long-distance race often fraught with challenges, twists in the road, and steep hills to climb. There are many moving parts to orchestrate the right experiences specially when customers adapt their own non-linear paths, as they move through the different aspects of life events. But how does one set in motion a truly effective execution? In this session, Harphajan would share the art & science of his journey in executing Next Best Action strategy within retail financial services especially where customer relationships evolve over long periods of time

While the counts of covid cases and deaths and policies are imperfect, the efforts to make these data available have been immense. So, despite the inaccuracy of the measurements, we can see some of the truths of what the genuine correlations are. Now is a good time to look at them through robust modeling techniques.

Webex Contact Center is a multi-tenanted Cloud Contact Center solution from Cisco, that scales to thousands of concurrent interactions. Improving Agent Performance directly impacts the efficiency and effectiveness of Contact Centers. We are building the next generation AI enabled interaction experiences to create super agents. With the help of AI, agents can now focus on the customer, understand their sentiment and answer questions quickly. AI capabilities that were built include Speech-To-Text transcriptions, sentiment analysis, Question-Answering and Topic Modeling. AI Models by themselves don't become a scalable solution. Serving these models at scale, specifically for a multi-tenanted cloud service, takes additional planning, architectures, capabilities and operations. This session discusses the unique challenges, solutions and takeaways in building and deploying these models and solutions. Key topics addressed are- Multi-tenanted Cloud Services: unique considerations- Model Scaling Challenges for real time- Architecture options for scaling Models- Adapting Models to serve at scale- Tuning Models for Resource & Cost scaling

Financial institutions are playing an increasing role in the low-carbon transition by taking steps to accurately estimate, price, and disclose future climate risk. By quantifying their exposure to climate risks, financial institutions can more effectively allocate investments, avoid ‘stranded’ assets, and track adherence to Paris Agreement goals and shareholder commitments. However, it remains difficult for these institutions to assess climate related risks across a portfolio of assets and across different benchmark warming scenarios.I will cover large scale data transformation approaches as part of an end-to-end framework for quantifying annual, asset-level climate risk over multiple climate hazards including wildfires, inland flooding, and heat waves using simulations from global climate models participating in the Coupled Model Inter-comparison Project Phase 6 (CMIP6).We will be discussing techniques to quantify forward looking climate risk from 2020 to 2050 under multiple climate scenarios such as high-emissions (SSP5-8.5) and medium-emissions (SSP2-4.5) warming scenarios. I will also showcase intermediate steps to make the climate simulations and spatiotemporal data interpretable and actionable. We will cover ways to harmonize near real time observations from ground measurements and satellite derived data with forward looking climate risk projections for acute physical hazards for high accuracy predictive modeling.