X Topic Abstract

How we are leveraging user and customer first principles and building out self-serve data products and data foundations to deploy effective data analytics for business decisions.

Speaker Profile

Anand leads the data engineering, data science & BI capabilities for customer facing functions of TVS motors like sales, marketing, distribution network, Digital products, parts, services and international business.

In his current role, his goal is to help end-customers get a world class TVSM experience and help business stakeholders drive key business KPIs, by enabling faster and better decision making with actionable AI and world-class data management.

X Topic Abstract

ML is rapidly getting adopted to solve critical Technical & Business Problems in the Industry. For example in the Semi-Conductor Industry availability & cost of Computing resources are key issues for Design Simulation & Verification using EDA tools. This is becoming more pronounced as Design Complexity increases with 3nm, 5nm & 7nm design Techniques. Also, Semi Companies do multiple chip designs simultaneously & which adds to the crunch of computing resources & can add to several weeks of delay. ML-based Orchestration Engine has helped to solve this problem. Similarly in the Manufacturing Industry using intelligent ML software, supply chain managers can optimize inventory and find the most suited suppliers to keep their business running efficiently.

X Topic Abstract

My talk will focus on my work in niche microinsurance based product offerings which are tailor made for a focussed audience group, such as: - Easy-to-purchase flight delay insurance using blockchain architecture (ref. fizzy.axa) focussing on millenials and frequent flyer groups - Usage based motor insurance as per the driving behaviour & risk score of the user - Parametric event interruption insurance (such as rains during cricket match etc.) I shall specifically stress on how are we building active machine learning workflows in designing the experience these products cater to their users while also touching upon the architecture which makes it feasible for implementation and monitoring production in the long run.

Speaker Profile

Amitanshu heads the New Products and Business Initiatives team at Bharti Axa General Insurance and has a diverse experience in speaking at various data science keynotes for corporates and student groups alike.

After completing his bachelors in Mathematics and Scientific Computing from Indian Institute of Technology, Kanpur (IITK), Amitanshu has extensive experience in both academia and corporates – where he worked across verticals such as Telecommunications, Retail, E-Commerce and Insurance.

In his current role, Amitanshu leads the development of innovative micro-insurance product portfolios encompassing delays (flight delay insurance, cab trip delay insurance etc.), consumer durables & electronics such as gadget insurance, bicycle insurance etc. offering seamless customer claims experience with minimal customer involvement or paperwork.

Prior to this, Amitanshu has lead the end-to-end development & deployment of various Data Products leveraging sophisticated Machine Learning Algorithms on various fronts viz. Chatbots, Voicebots, Recommender Systems etc.

On a typical work day, you can find Amitanshu busy in strategizing optimal insurance cross-sell/up-sell strategies along with hands-on problem solving with Data Science & Product teams while implementing robust data architectures for optimum digital-first insurance product offerings. Apart from work, Amitanshu is a professional guitarist, with a passion for classic rock bands such as Pink Floyd, Led Zeppelin, Radiohead, etc. and an avid adventure sports enthusiast.

X Topic Abstract

I will be picking up use cases for ML application in Credit Risk Management ( Trade matching automation), Data Mining for third party data vendor files, Data matching using Elastic search for Third party data, Use cases of Fraud Risk Management and potential opportunities for bankers to upscale from near time detection to real time detection

30 minutes Presentation with real life examples - I will be discussing both real life examples and other use cases of AI/ML in banking.

X Topic Abstract

Key takeaways from this session:

This talk would cover American Express’ exciting journey to explore AI/deep learning technique to generate next set of data innovations by deriving intelligence from the data within its global, integrated network. Learn how using credit card data has helped improve fraud decisions elevate the payment experience of millions of Card Members across the globe.

Speaker Profile

Featured among Top 10 Data Scientists in India, Manish Gupta is Vice President in American Express, he leads machine learning and data science team that builds state-of-the-art machine learning solutions and leverages them in risk and analytics decisions across the globe. He is also center head of Credit & Fraud Risk CoE Bangalore.

Manish Gupta has extensive experience in successfully leading enterprise wide AI, machine learning and data science practices across diverse industries such as Internet/E-commerce, Banking, BPO and Defence. He has also served the country as a Scientist at DRDO and holds PhD degree from IIT Delhi in the area of Machine Learning with 15+ research papers and 1 US Patent which have 400+ citations.

X Topic Abstract

NLP and Image processing using Deep Learning has achieved leaps and bound. But can we trust them? How about breaking Deep Learning models? How do you know the model you trained is not subject to adversaries in data or in practice? In this talk, I will cover different techniques to test your model robust ness to the extreme and simple methods to overcome problem of training highly ovedfitted models which might lead to very erroneous output.

Speaker Profile

I am a deep learning researcher, after graduating from Indian Statistical Institute with Master's in Computer Science, with specialization in NLP and IR, I have worked with Bing Ads, Microsoft R&D and HSBC Bank as decision scientist to build risk models for bank. I am currently working in Amazon as a data scientist to build models to cater to amazon internal network.

X Topic Abstract

Interpreting Machine Learning models is no longer a luxury but a necessity. In this session, we will explore practical techniques to interpret ML models using real time datasets across domains. Explainable AI is a developing field and many of the ideas presented here are pretty new

Speaker Profile

Sayan is a Data Science and Analytics Professional with around a decade's worth of rich experience across the analytics technology stack. He has worked across a multitude of roles spanning corporate trainer, individual contributor, developer, consultant, project manager, scrum master and client engagement manager. His USP is having a unique blend of extensive production experience on cutting edge AI problems and excellent training experience through his association with several of the world’s top training vendors both in the online and offline formats. He has successfully trained tens of thousands of IT professionals spanning 10000+ hours across experience levels ranging from 0 to 30+ years including directors and founders. He is a regular visiting faculty for some of the best institutes like Great Lakes and IIIT Bangalore catering to the AI/ML technology stack to name a few. He is extremely passionate in this domain and besides consulting with organisations like Walmart, he is also actively working with a few startups on high end projects in Computer Vision and Natural Language Processing.

X Topic Abstract

How AI can be used to Save Food Waste.
ML Strategy for ML Model
Data Strategy for Various Food
ML data Strategy to detect, track, and update food status
help the consumer by AI-powered informationn

Speaker Profile

Prashant Bhat is a Thought Leader in AI/ML Data Science Digital Transformation. As a Global COE Head of Data Science, Machine Learning and Artificial Intelligence instrumenting combine strategies, collaboration, analytics, and technical proficiency to launch successful AI/ML models. working with technologies that deliver results in the disciplines of Data Science, Data wrangling, Machine Learning, software engineering, data visualization, ML in Production. Along with AI/ML, he is leading AR/VR/MR or XR technology too. His Strategic Data Science Road Map has been apricated a lot. He has worked for esteem organizations like Whirlpool, Ulatrons (founder), MSC, HoneyWell, GE. Carrying two decades in software and product development, working on the landing prediction, F1 Turbo Analysis, AI-Powered Smart Appliance, tools for object tracking and count, End to End ML Pipelines, etc.

X Topic Abstract

Today, the brands have customers that connect with them at different touchpoints. Customer behaviours have been shifting and the digital pure players are setting new benchmarks in customer experience. What are the key evolving engagement trends ? Would you like to know how brands/enterprises (with examples) deal with these customer behaviour trends to provide the best in class. customer service to keep their customers delighted and connected to them. Are you intrigued, how conversational AI can play a key role in helping brands/enterprises achieve great Customer Engagement/loyalty scores ?

Speaker Profile

Product Leader with 20+ years of professional experience spanning product strategy, product management, P&L Ownership & product engineering with expertise in Customer Engagement Modernization, Digital Experience & Digital Asset

Monetization. In my current role, I develop product strategies to build and sustain the organisation’s product leadership position and market adoption. I focus on where we need to invest in the near and long term and translate it to platform’s success. Have deep expertise in Conversational AI with 3 patents granted at US patent office.

X Speaker Profile

Dr. V. Prasanna Shrinivas, is currently the VP – Cognitive Machine Learning, leading a team of smart data scientists that powers the product suite of AntWorks, specialising in digitizing and extracting unstructured data from document images. He is a seasoned analytics leader specializing in the area of financial services risk management. He has managed the development of custom fraud detection models using machine learning techniques for BFSI clients across several geographies. He also led analytics consulting teams catering to development/validation/audit of credit risk models catering to regulations like CCAR/IFRS 9. He has worked for EY, IBM, FICO and GE in his previous roles. He received his PhD and MSc (Engineering) degrees from IISc, Bangalore in the area of network economics which is in the cusp of game theory and machine learning. He also has a MSc in Mathematical Economics from MKU, Madurai. He is polyglot and enjoys reading, playing cricket, music, cooking and traveling with family.

X Topic Abstract

Natural Language Processing is one of the most challenging area of Artificial Intelligence. NlP application spans across a very broad spectrum of business problems right from customer sentiment analysis , document categorization, information retrieval, spam detection, translation, document summarization to conversational AI. The talk will be about covering unique challenges if NLP with real-life examples and explaining how these unique problems have led the research in this field to continue to evolve. How it begun and where we are currently with the state of art models of BERT. What kind of problems BERT has solved , what are the challenges in adopting and implementing BERT for a real-life problem and what kind of problems remain unsolved with workarounds done if any.

Speaker Profile

Shalini heads the Data Science and analytics architecture team at Mobileum, a Cupertino headquarter company providing Telcos analytics solutions in the area of fraud, roaming, security and Testing. Prior to this, she was Executive Director - Credit Risk Technology at JP Morgan. Her Data Science journey started in Oracle where she started the deeper insights initiatives for CRM Vertical. Her product development expertise combined with the data science knowledge helped her specialize in the area of productizing ML solutions. She believes experiments and statistical analysis done by data-scientists should be fed back into the ML pipeline to make it data-driven ML pipelines. Her core expertise is around Packaged Analytics, Machine Learning, NLP, Statistical Data Analysis, Customer Relationship Management (CRM), Data Engineering, Business Analytics and Data Warehousing. An IIT Kanpur alumnus, Shalini has also been associated with Numerify, Oracle, Wipro & Infosys.

X Topic Abstract

With the rapid advances in AI techniques in recent years, especially in the area of deep learning, numerous ML frameworks have emerged. Some of these are backed by technology stalwarts like Microsoft, Google, Facebook etc., while some of the others are academia based like MIT, IISc libraries for NLP. One area of concern with numerous frameworks is interoperability among these frameworks. ONNX (Open Neural Network Exchange) is a community project aimed at building a universally interchangeable format for representing ML models and tools to support the same.

This talk focusses on ONNX and Interoperability of AI frameworks.

Emergence of various deep learning networks
Challenge of interoperability
ONNX framework Overview
Build, Deploy and Export cycle with ONNX
ONNX Model Zoo
Interoperability example with Tensorflow and Caffe2
Conclusion and future state

Speaker Profile

Srikanth is a technologist with experience in middleware technologies, web technologies, IoT, AI/ML, RPA and Analytics. He's worked on both services and product organisations for more than 18 years, designing and building solutions for complex problems in finance, manufacturing, travel and healthcare domains. He also serves as a guest faculty at BITS, Pilani.

X Topic Abstract

Ever wondered & got stuck on how to do enterprise planning by having ML & AI. Then this session is for you.One will get to know on:
What is ML & AI based Enterprise Planning?
Planning Goals
AI Maturity model
Discovery maps
AI Accelerators
AI Based Enterprise roadmap
AI Application Use cases

The use cases will cover broad range including:
Predictive maintenance
Sales forecasting
Conversational AI with NLP
Churn prediction

Speaker Profile

“Bharathi (Principal Engineer, Unisys India) is best identified as tech enabler in 4G and 5G space who is also a pioneer in AI field and also in RPA.
He is a PYTHOnator by heart who like to transform the industry for greater good. Under his mentorship, hundreds of people (from students community and also from the industry) got into the bandwagon of Python and automation. He is holder of THREE nanodegrees (Two in AI and one in Front end web development). He loves to develop Alexa based skills as his hobby and ready to embrace new challenges every day.

X Topic Abstract

Improving the efficiency of the supply chain plays a crucial role for any enterprise. Operating their businesses within tough profit margins, any kind of process improvements can have a great impact on the bottom line profit. Most accurate Demand forecasting is especially important for all industries because it leads to lower inventory costs, faster cash turnover cycles, quicker response to trends, and better margins

Automated AI & ML driven demand planning and optimization solutions can forecast future demand and generate the stock replenishment requirement dynamically and add value to the business (for example Dynamic stock replenishment).

X Topic Abstract

The need for custom chatbot (offers more flexibility to tune to your specific industry needs, compared to the standard ones provided by Amazon Lex / Google Dialog Flow / IBM Watson etc) and how to build them using Open source libraies

Speaker Profile

Expert in setting up COEs / Innovation Hubs / Practices to nurture Newer Technologies like AI And develop Solutions & Accelerators.
Industry Experience: 20 years at Infosys + 6 years of AI Experience in start-ups & mid-sized companies
Deep understanding of Machine Learning & Deep Learning Algorithms, NLP, and experience of solving business problems.
Conceptualized & Implemented Data Science projects in multiple industry verticals.
Conducted Webinars, Guest lectures in Machine Learning and Big data space.
Handled portfolio of USD 30M, 450+ people
Unique blend of Technical expertise and Business acumen.
Defined AI strategy for Products as well as Enterprises.
Mentor Engineering students on Data Science / AI / ML projects.

X Topic Abstract

In this session we are going to discuss how Natural Language Processing enables machines to understand human language.

We also discuss the different Natural Language Processing Services and how it helps us to build applications to find insights and relationships hidden inside the language.

We will also go through the rapid innovations happening in the Machine Learning and Deep Learning world to perform Natural Language Processing more efficiently.

Speaker Profile

Suman Banerjee is a Global Enterprise Solution Architect at Amazon Internet Services Pvt Ltd. A Builder at heart with more than 20 years of experience in partnering with client leadership team, framing Cloud Architecture Strategies, Enterprise Architecture Strategies, Digital Strategies, Integration Strategies and Technology roadmaps and in delivering IT enabled business transformation projects.

X Topic Abstract

Enterprises are now seeing value and investing in Data Science and Machine Learning projects. While many are able to successfully build models that seem to solve their business problems, very few model deployments are making it to production and able to sustain the model performance where the business applications can continuously leverage the insights. Why are the success metrics low for model deployment and operationalisation in production? What are the common challenges?

Are your Data scientists spending more time on operationalising their models and related life cycle activities in in production than really building and tuning models? What can you do to improve this ratio? Learn in this session

I plan to share those and practical insights and solutions especially around ML and DL models in enterprise production. Also the various trends in this field and what practitioners would have to be cognizant of.

Speaker Profile

Vinod Khader has around 20 years experience in Software Development and is an Associate Director at IBM Software Labs in the Data and AI division. In the current role, he leads the development of Watson Machine Learning Platform on IBM's public and private cloud platforms with teams across the globe. Watson Machine Learning platform help Data Scientists, Data Engineers and App Developers in managing the end to end Machine Learning and Model management life cycle starting from Training, Evaluation, Deployment and Scoring (prediction).

X Topic Abstract

PayPal has a huge Credit portfolio, offering revolving credit and instalment options to consumers across the world – US, UK, France, Germany, etc. The decision to offer credit to a customer is made in a few milliseconds in real-time. This is powered by PayPal’s state-of-the-art deep learning algorithms and the rich data we have about consumer preferences. This presentation will provide insights in to the these algorithms and data with real examples.

Speaker Profile

Venkat Subramanian heads the Credit Data Science team in India as part of the Global Data Science group within PayPal. His team focuses on supporting PayPal’s credit business across markets with predictive machine learning models.

Venkat has overall 13 years of experience spanning Data Science, Corporate Strategy and Financial Technology. His prior stints include leading the Pricing Data Science team in Chennai for Maersk (global shipping conglomerate), and being part of the quantitative trading technology team for D E Shaw (hedge fund based in New York).

He graduated with MBA from IIM Bangalore and BE (Comp Sci) from College of Engineering Guindy, Anna University, Chennai. He also holds a CFA certification from the Global CFA Institute in the US.

X Topic Abstract

I will cover the concept of Reinforcement Learning, show 1 video of an application and will like to take inputs from participants on potential use cases in their own areas of work

X Topic Abstract

Automated vehicles of the near future will need more than cameras and radar to operate effectively and safely – they will need a brain. Just as humans rely on thought processes to coordinate and direct their arms, legs, eyes and ears, autonomous vehicles need to be able to think – not just indiscriminately respond to sensor stimuli. Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it also will be critical for the automobile to interpret content from those images and react accordingly. Object detection no longer has to be a hand-crafted coding exercise. Deep neural networks, such as CNNs, allow features to be learned automatically from training examples. A two-step process involving RoI (Region of Interest) followed by CNN-based object detection can be used for efficient & accurate prediction of vehicle course

Speaker Profile

Aashish Bhatia is the President of visteon India. He is responsible for strategizing and leading a profitable growth for Visteon India. In this role, he is also responsible to drive productivity and capability of product development team of Visteon Corporation. Aashish has over 25 year of experience in product engineering, automative electronic design, program management, sales and business development, and enterprise-wide leadership. He has a Bachelors degree in Electronics Engineering, a Masters degree in Design Engineering from IIT Delhi, and has successfully completed an executive management program form IIM Bangalore. Soon after his masters, he joined GE Aircraft Engines in research and development. He embarked upon his automative electronics career at Delhi and later held various senior management position at Hella Automative and Inteva products, Before joining Visteon in 2019. Aashish has published 9 technical papers; He is a life member of various professional organizations and has secured various honors and awards during his professional career.

X Topic Abstract

Efficient Police resource allocation is critical to the readiness of Law Enforcement Officers for a timely and an optimal response to crime. This allocation process relies on the ability of police departments to predict over time, hotspots of crime where policing is most needed. In this talk, we will share our experience in developing an end-to-end machine learning (ML) based dynamic crime hotspot predictive system for Conduent Public Safety offerings.

Speaker Profile

Saikat Saha is currently a senior manager & senior research scientist in Conduent Labs, India. He is an experienced technology leader and hands-on research scientist with proven delivery & innovation experience in variety of industrial domains. He has spoken at various AI forums, was Technical Programme Committee member for & chaired sessions at international conferences. With 15+ years of experience in AI domain (both corporate research labs & academic faculty member), he is still a learner and loves to mentor his younger colleagues. Before Conduent, Saikat has worked in GE Global Research & Linkoping University, Sweden (as a faculty member). Saikat holds a PhD in applied statistics from University of Twente (NL), masters & bachelors in engineering from IISc Bangalore & IIEST, Shibpur.

X Topic Abstract

1. What is network data and how to read syslog.
2. Interpretation of syslog data
3. Text Analytics/ NLP on syslog data
4. Identification of devices which has caused the outage
5. Reduction in TAT (turn around time) for resolution

Speaker Profile

Vivek is currently a data scientist @Cisco. He has 7+ years of work experience across E-commerce, mortgage, Retails and CPG domains. He is an extensive NLP researcher, he has a pragmatic approach in text mining, NLP, Machine learning and social media analytics. Prior to Cisco, he has also worked across organizations like Xerox, BRIDGEi2i and Altisource.

X Topic Abstract

What makes explainability difficult in AI/ML
How to go about adding explainability and interpreting it
Adding MLI (Machine learning interpretability) to Deep Learning models across text and vision projects
Pros and Cons of adding MLI in AI/ML models

X Topic Abstract

In business areas where there are decisions involved that have social implications and we have seen biases in human decisions or data skewed undesirably towards a particular category, it is important that our ML/AI algorithms are tuned to eliminate any such bias that are bound to creep in automatically due to historical data. How to we identify those scenarios and eliminate biases effectively from our Machine Learning algorithms.

X Topic Abstract

Reinventing Digital Marketing with Machine Learning
The innovative use cases in the Banking digital marketing will be explained in the session to show case how machine learning is used to personalize the products and services of a bank and how it is communicated to each customer on a real-time basis by providing the personalized recommendations and messages through the preferred channel for each customer based on his preferred time. Different ML models are used for leads optimization, channel optimization and personalization.
The customers will get real-time recommendations for all his financial needs on a daily basis like purchase recommendations, dining, movie, entertainment, home, car purchase etc. Different ML models are built to personalize the products and services for each customer and collaboration with merchants enables the customers to get real-time offers from the bank for all the purchases he will make. Different use cases to show how machine learning is helping the bank to redefine their business horizons, drive business strategies, gain business insights and understand customers to give real-time personalized products and services

Speaker Profile

Mathew Joseph is the Vice President of Apar Technologies Pvt. Ltd. He is the Head of Artificial Intelligence Labs for CIMB bank. An alumnus of the University of Chicago and a keynote speaker, he is known for his thought leadership in the area of Artificial Intelligence and Machine Learning. Mathew is the Head of Artificial Intelligence Labs leading the ideation, design, development and implementation of Artificial Intelligence solutions for the CIMB bank in seven countries in Asia.
Developing Artificial Intelligence solutions to drive the business strategy, manage business decisions and provide personalized products and services to the bank customers spread across seven countries in Asia. CIMB is the fifth largest banking group in the ASEAN region having operations in 16 countries across Asia and having total assets USD 134 billion.
He is a mentor for many start-up companies in the Artificial Intelligence domain. He is the official mentor of the Kerala start up Mission. Kerala Start up Mission is the largest start up business incubator in India promoted by the Government of Kerala. He is also a keynote speaker in many International conferences and universities including different Indian Institute of Managements (IIM) and Indian Institute of Technologies (IIT).
Mathew has completed his post-graduation in Data Science and Machine Learning from the University of Chicago and Bachelor of Technology (B.Tech.) from College of Engineering Trivandrum. He has over 28 years of experience of innovation, software development and project management. Earlier he worked with IBM, Siemens and Tata Consultancy Services (TCS). He is a certified Project Management Professional (PMP) and a certified Industry Consultant
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X Topic Abstract

Explanation of multiple data exchange across different sectors and type of data are critical to stitch large volume and variety of data for effective predictions and successful AI project implementation.

Speaker Profile

As a leader in IT industry Senthil constantly look forward to delivering value to customers by engaging and developing global teams by leveraging the 25+ years of experience in the software industry.

Currently Senthil is solution lead for 890 by Capgemini – Flagship platform one stop solution for analytics. He nurtures talents from various Data and Analytics Technology streams to build AI/ML, Advanced analytics solutions across global industry verticals – focused on B2C and B2B segments. Currently manages 120 solutions and multiple data exchange ecosystem.

Prior joining Capgemini, he was associated with Aricent, Hewlett Packard and Wipro Technologies. He has performed multiple roles like Delivery manager, Product Support Leader and Center of Excellence leader. He is a certified PMP and mentored young architects and managers. He was Director of Project Management India Bangalore Chapter.

He has travelled to more than 15 countries for business and project delivery related assignments. He has participated in multiple though leadership workshops and network events.