Workshop Schedule
December 19, 2022 (Monday)
Timings	Hall 1	Hall 2	Hall 3
9.00-13.00	Data Challenges in Assessing (Urban & Regional) Air Quality (DACAAQ 2022) Organizers: Prof. Girish Agrawal (O.P. Jindal Global University, Sonipat, India), Prof. Anirban Mondal (Ashoka University, Sonipat, India), Prof. P. Krishna Reddy (IIIT Hyderabad, Hyderabad, India)	Workshop on Big Data Analytics using HPCC Systems Organizers: Dr G. Shobha (RV College of Engineering, Bengaluru, India), Jyoti Shetty (RV College of Engineering, Bengaluru, India)	Workshop on Data Science for Justice Delivery in India (DSJDI-2022) Organizers: P. Krishna Reddy, IIIT Hyderabad, Telangana State, India, K.V.K. Santhy, NALSAR University of Law, Telangana State, India
13.00-14.00	Lunch break
14.00-18.00	DACAAQ 2022 (Afternoon session)	Workshop on Universal Acceptance and Email Address Internationalization Organizers: Shri Satish Babu (Technology Working Group Chair, UASG, www.uasg.tech), Harish Chowdhary (UA Ambassador, www.uasg.tech), Mr. K Mohan Raidu (President, ISoc India Hyderabad Chapter)	DSJDI-2022 (Afternoon session)

Main Conference Schedule (December 20-22, 2022)
December 20, 2022 (Tuesday)
Timings	Hall 1
08.30-09.00	Registration and networking
09.00-09.30	Inauguration (P.J.Narayanan (Directior,IIIT Hyderabad), B.Jagadeeshwar Rao (Vice-Chancellor, University of Hyderabad); Sanjay Kumar Madria (Missouri University of Science and Technology, USA); Y.Narahari (Indian Institute of Science, Bangalore), Raj Sharman (University at Buffalo), Venkata Ramana (Vice-chancellor, Rajiv Gandhi University of Knowledge Technologies (RGUKT) Basar), P. Krishna Reddy (IIIT Hyderabad); Chakravarthy Bhagvati (Dean, University of Hyderabad), A.Srinivasula Reddy (Principal, CMR Engineering College)
09.30-10.30	Keynote 1: “Data Driven Crop Portfolio Recommendation for Agricultural Farmers”, by Y. Narahari (Indian Institute of Science, Bangalore) Chair: Vikram Pudi (IIIT Hyderabad)
10.30-11.00	Tea break
11.00-11.30	Invited talk 1: “The role and use of big data in banking , in driving towards customer centric insights and the challenges in implementing effective and scalable solutions”, by Sridhar Viswanathan, Bank of America Chair: P. Radha Krishna (NIT Warangal, India)
11.30-12.30	Paper 1, Paper 2, Paper 3 Chair: P. Radha Krishna (NIT Warangal, India)
12.30-13.30	Lunch break
13.30-14.30	Keynote Talk2: “Machine Learning for Emotion Detection, Analysis and Visualization using COVID-19 Tweets”, Sanjay Madria (Missouri University of Science and Technology, USA) Chair: Satish Srirama (University of Hyderabad)
14.30-15.45	Paper 4, Paper 5, Paper 6, Paper 7 Chair: Satish Srirama (University of Hyderabad)
15.45-16.00	Tea break
16.00-17.30	Tutorial 1: “Malware Analysis and Detection” by Mohit Sewak (Microsoft, India), Hemant Rathore (BITS Pilani, Goa) Chair: Ashok Kumar Das (IIIT Hyderabad)

December 21, 2022 (Wednesday)
Timings	Hall 1
08.30-09.30	Registration and networking
09.30-10.30	Keynote 3: “Data Challenges and Societal Impacts – the case in favor of the Blueprint for an AI Bill of Rights”, by Raj Sharman (University at Buffalo) Chair: Praveen Paruchuri (IIIT Hyderabad)
10.30-11.00	Tea break
11.00-11.30	Invited Talk 2: “Big Data in Cognitive Neuroscience: Opportunities and Challenges”, by S. Bapi Raju (IIIT Hyderabad) Chair: Raj Sharman (University at Buffalo)
11.30-12.30	Paper 8, Paper 9, Paper 10 Chair: A.Mamatha (IIIT Hyderabad)
12.30-13.30	Lunch break
13.30-15.00	Tutorial 4: “Self-Supervised Learning to Process Labeled and Unlabeled Medical Image Data”, Mayuri Mehta (SCET, Surat) Chair: Avinash Sharma (IIIT Hyderabad)
15.00-15.30	Tea break
15.30-17.00	Panel (Title: Data Science for Sustainable Development Goals) Coordinators: Philippe Fournier-Viger (Shenzhen University), P. Krishna Reddy (IIIT Hyderabad)
17.00-18.00	Networking break/ Steering committee meeting (invited members only)
18.00-21.00	Cultural program and Banquet Dinner

December 22, 2022 (Thursday)
Timings	Hall 1
08.30-09.30	Registration and networking
09.30-10.30	Keynote 4: “Advances and challenges for the discovery of interesting patterns in data”, by Philippe Fournier-Viger (Shenzhen University, China) Chair: R Uday Kiran (The University of Aizu)
10.30-10.45	Tea break
10.45-11.15	Invited talk 3: “Advances in NLP Research for Automated Business Intelligence”, by Arvind Agarwal (IBM Research, India) Chair: Radhika Mamidi (IIIT Hyderabad)
11.15-12.30	Paper 11, Paper 12, Paper 13, Paper 14 Chair: Radhika Mamidi (IIIT Hyderabad)
12.30-13.30	Lunch break
13.30-15.00	Tutorial 3: “Federated Learning in the Real-World: From Theory to Practice” byTushar Semwal (Microsoft, India), Madhusudhanan Krishnamoorthy(Microsoft, India), Rajeev Gupta(Microsoft, India) Chair: Naresh Manwani (IIIT Hyderabad)
15.00-15.30	Tea break

Program Details

1. Keynote Speakers

Y Narahari

Professor,
Indian Institute of Science, Bangalore.

Title:

Data Driven Crop Portfolio Recommendation for Agricultural Farmers

Abstract:

Agriculture has a significant role to play in any emerging economy and provides the source of income and employment for a significant fraction of the population. A key challenge faced by small and marginal farmers is to determine which crops to grow to maximize their utililties. With a wrong choice of crops, farmers could end up with sub-optimal yields and possibly significant loss of revenue. In this talk, we describe a data driven system - ACRE (Agricultural Crop Recommendation Engine) - a novel tool designed by us, that provides a scientific method to choose a crop or a portfolio of crops, to maximize the utility to the farmer. ACRE uses available data such as soil characteristics, weather conditions, and historical yield data, and uses state-of-the-art machine learning/deep learning models to compute an estimated utility to the farmer. A technical novelty in ACRE is to harness the use of Sharpe Ratio, a popular risk metric in financial investments. Using the Sharpe ratio, we generate a ranking on candidate recommendations of portfolios of crops. We use publicly available data from the agmarknet portal in India to present several promising data driven thought experiments with ACRE.

Bio:

Narahari got his B.E. from Department of Electrical Communication Engineering in 1982, M.E. and Phd from Department of Computer Science and Automation in 1984 and 1987 respectively. In February 1988, he joined as the faculty of the Department of Computer Science and Automation and was Chair of the department during January 2010 – July 2014. He was the Dean of the Division of EECS (Electrical, Electronics, and Computer Sciences) from August 2014 to July 2021. He was also chairing the Office of DIGITS (Digital Campus and Informational Technology Services) from January 2016 to August 2020. During 1992, he was a Post-Doctoral Researcher at the Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, USA and during 1997, he was a Visiting Scientist on sabbatical at the National Institute of Standards and Technology, Gaithersberg, Maryland, USA.

The focus of Narahari’s current research is to apply game theory, mechanism design, and machine learning to research problems at the interface of computer science and economics. In particular, he is interested in algorithmic game theory, design of auctions and electronic markets, dynamic mechanisms with learning, crowdsourcing , online education, social network analysis, and blockchains.

Raj Sharman

Professor,
University at Buffalo.

Title:

Data Challenges and Societal Impacts – the case in favor of the Blueprint for an AI Bill of Rights

Abstract:

Artificial Intelligence (AI) technologies contribute tremendously to various areas of life and society. Therefore, we are witnessing massive investments in this area. Grand-view Research has calibrated the global AI market at 93.5 billion dollars as of 2021. Further, according to their report, it is expected to grow at a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030. It is believed that nations that adopt and use AI will have a competitive edge. AI, in some form, will be part of most products and services we use. I will talk about some of the challenges, tradeoffs, and remedies concerning BIG DATA in the age of Artificial Intelligence. I will also provide a brief overview of the maladies that plague the landscape of BIG DATA and some academic literature that provides solutions to the problems in the context of AI. The driving motivation for writing this article is to highlight our responsibility to create algorithms and automated systems that do not harm and are equitable and just. I also hope to create awareness that leads to businesses and Software laboratories that focus on testing software and data that alleviates our fear - modeling the work of the US Food and Drug Administration. The tenets echoed in this concord with the Blueprint for an AI Bill of Rights released by the United States (US) White House Office of Science and Technology Policy (OSTP) on October 4, 2022, and the AI Risk Management Framework by the US National Institute of Standards and Technology.

Bio:

Sharman's research is focused on extreme events from a decision-support system perspective and on health information technology-related issues. This includes factors influencing online health information search, meaningful use of ambulatory EMR, resilience in hospital information systems, health information exchanges, health care social networks as well as a simulation based study for managing the hospital's emergency room capacity in extreme events, active shooter incidents and mass casualty event management.

His expertise also includes information systems infrastructure management as it relates to information assurance, internet performance and distributed computing. Sharman's papers have been published in a number of national and international journals, and he is the recipient of several grants from the university as well as external agencies, including the National Science Foundation.

He serves as an associate editor for the following journals: Journal of Information Systems Security, Journal of Information Privacy and Security, and Springer Security Informatics Journal.

Philippe Fournier-Viger

Professor,
Shenzhen University, China.

Title:

Advances and challenges for the discovery of interesting patterns in data

Abstract:

Intelligent systems and tools play an important role in various domains such as for factory automation, e-business, and software engineering. To build intelligent systems and tools, high-quality data is generally required. Moreover, these systems need to process complex data and can yield large amounts of temporal data such usage logs, and data collected from sensors. Managing the data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in temporal data generated from intelligent systems or from other applications.
The talk will first briefly review early study on designing algorithms for identifying frequent temporal patterns in discrete sequences and time-interval data. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in complex data. Topics that will be discussed include high utility patterns, locally interesting patterns, trending patterns, time-interval patterns and periodic patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques for intelligent systems will be discussed.

Bio:

Philippe Fournier-Viger is distinguished professor at the Shenzhen University (China). He obtained his Ph.D at University of Quebec in Montreal (Canada) in 2010. After working as post-doctoral researcher at National Cheng Kung University, and being a faculty member at University of Moncton, he came to China in 2015 and became full professor at the Harbin Institute of Technology (Shenzhen). There, he received a title of national talent from the National Science Foundation of China. His interests are data mining, algorithm design, pattern mining, sequence mining, big data, and applications. He has published more than 340 research papers related to data mining, intelligent systems and applications, which have received more than 8500 citations (H-Index 46). He is associate editor-in-chief of the Applied Intelligence journal (SCI, Q1) and editor-in-chief of Data Science and Pattern Recognition. He is the founder of the popular SPMF data mining library, offering more than 200 algorithms for analyzing data, cited in more than 1,000 research papers. He is a co-founder of the UDML, MLiSE and PMDB series of workshops held at the ICDM, PKDD, KDD and DASFAA conferences.

Sanjay Madria

Professor,
Missouri University of Science and Technology, USA.

Title:

EMOCOV: Machine Learning for Emotion Detection, Analysis and Visualization using COVID-19 Tweets

Abstract:

The adversarial impact of the Covid-19 pandemic has created a health crisis globally all over the world. This unprecedented crisis forced people to lockdown and changed almost every aspect of the regular activities of the people. Thus, the pandemic is also impacting everyone physically, mentally, and economically, and it, therefore, is paramount to analyze and understand emotional responses during the crisis affecting mental health. Negative emotional responses at fine-grained labels like anger and fear during the crisis might also lead to irreversible socio-economic damages. In this talk, I will discuss a neural network model trained using manually labeled data to detect various emotions at fine-grained labels in the Covid-19 tweets automatically. I will discuss about a manually labeled tweets dataset on COVID-19 emotional responses along with regular tweets data. A custom Q&A roBERTa model to extract phrases from the tweets that are primarily responsible for the corresponding emotions has been designed. None of the existing datasets and work currently provide the selected words or phrases denoting the reason for the corresponding emotions. The classification model outperforms other systems and achieves a Jaccard score of 0.6475 with an accuracy of 0.8951. The custom RoBERTa Q&A model outperforms other models by achieving a Jaccard score of 0.7865. Further, I will present a historical emotion analysis using COVID-19 tweets over the USA including each state level analysis.

Bio:

Sanjay K Madria is a Curators’ Distinguished Professor in the Department of Computer Science at the Missouri University of Science and Technology (formerly, University of Missouri-Rolla, USA). He has published over 290 Journal and conference papers in the areas of mobile and sensor computing, Big data and cloud computing, data analytics and cyber security. He won five IEEE best papers awards in conferences such as IEEE MDM and IEEE SRDS. He is a co-author of a book (published with his two PhD graduates) on Secure Sensor Cloud published by Morgan and Claypool in Dec. 2018. He has graduated 20 PhDs and 33 MS thesis students, with 9 PhDs currently progressing. NSF, NIST, ARL, ARO, AFRL, DOE, Boeing, CDC-NIOSH, ORNL, Honeywell, etc. have funded his research projects of over $18M. He has been awarded JSPS (Japanese Society for Promotion of Science) invitational visiting scientist fellowship, and ASEE (American Society of Engineering Education) fellowship. In 2012 and in 2019, he was awarded NRC Fellowship by National Academies, US. He is ACM Distinguished Scientist, and served/serving as an ACM and IEEE Distinguished Speaker, and is an IEEE Senior Member as well as IEEE Golden Core Awardee.

2. Invited Talks

Bapi Raju S

Professor,
IIIT Hyderabad, India.

Title:

Big Data in Cognitive Neuroscience: Opportunities and Challenges

Abstract:

Cognitive brain mapping is enjoying its growth with the availability of large open data sharing efforts as well as the application of modern machine learning and deep learning methods. In this talk, about the current practices in cognitive neuroscience predominantly focusing on functional imaging and highlight the tremendous opportunities fostered by the unprecedented scale of datasets in cognitive neuroscience. I also discuss challenges and limitations to keep in mind while working with these datasets.

Bio:

Dr. S. Bapi Raju is a professor and head of the Cognitive Science Lab, IIIT Hyderabad. He was a former professor of School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India during 1999-2019. He worked as a Researcher at ATR Research Labs, Kyoto, Japan and as an EPSRC Research Fellow at University of Plymouth, UK before returning to India. He has over 20 years of teaching and research experience in AI, Machine Learning, Neural Networks and Cognitive Science. He has worked on a variety of inter-governmental collaborative projects such as Indo-French, Indo-Trento, in the areas of computational and cognitive neuroscience with multidisciplinary teams comprising computer scientists, linguists, neuroscientists, psychologists and clinicians. He is also currently heading the Healthcare vertical in the DST-funded National Mission for Cyberphysical Systems (NM-CPS) Technology Innovation Hub under at IIIT Hyderabad called IHub-Data.
He has degrees in BE (Electrical Engineering) from Osmania University, MS (Biomedical Engineering) and PhD (Computer Science) from University of Texas, Arlington, USA. He is a senior member of IEEE, a member of ACM, Society for Neuroscience, and Cognitive Science Society.

Arvind Agarwal

IBM Research,
India.

Title:

Advances in NLP Research for Automated Business Intelligence

Abstract:

Automated business intelligence derives insights from data to help businesses make right decisions for their business processes. These business processes can range from back end IT operations, to designing and executing a marketing campaign, to creating a business strategy, among many others. Automated business intelligence attempts to automate these processes by removing dependency on human, by providing them new ways to interact with data. Some of these interactions, which not so long ago seemed almost impossible, have now become possible due to the recent advances in NLP, and particularly, in deep learning and large language models. Specifying a SQL query in natural language, let the data speak for itself in human understandable text, being able to converse with data and get insights are few examples of such interactions. In this talk, we will cover some of these recent advances in NLP research, and how they are influencing the area of automated business intelligence. The talk shall cover both, an industrial view of the automated business intelligence in the form of available tools; and an academic view in the form of technical problems. We will cover a range of technical problems including data search and exploration through semantic technologies, data insights via natural language querying and free form interaction, and use of NLP for exploratory data analysis including for data insights and data stories. We will conclude the talk with some food for thought by discussing open research problems in this space.

Bio:

Arvind Agarwal is a Senior Technical Staff Member and Manager at IBM Research, India (Gurgaon) where he leads a team of research scientists and software developers to develop solutions in the space of AI-driven data processing and data analytics. Prior to joining IBM, he was a research scientist at Palo Alto Research Centre (PARC), Webster, New York. His research interests are in the areas of machine learning, natural language processing, deep learning, and text analytics . He is especially interested in conversational data analytics, and in machine learning sub-areas that deal with the problem of limited supervised data, such as self-learning, semi(un)-supervised learning, zero shot learning, domain adaptation, multitask learning etc. Arvind completed his PhD in Computer Science from University of Maryland, his M.S. in Computer Science from University of Utah and Bachelor’s from Birla Institute of Technology & Science, Pilani. He has about 20 patents, and more than 35 publications in top ML and NLP conference such as EMNLP, AAAI, KDD, NIPS, IJCAI, ATSTATS. He is also a recipient of Heidelberg Laureate Forum Young Researchers award, and ECML 2010 best student paper award.

Sridhar Viswanathan

Bank of America,
India.

Title:

Challenges of developing big data systems for customer specific insights in banking

Abstract:

Big data is bringing a drastic shift in the operations of modern banking by allowing them to access vast data volumes and extract valuable insights. Bank uses this data to make decisions daily and improve Consumer Banking clients through reporting, analytics and insights about the bank's financial relationship with them. Big Data is helping the bank to shift from a product centralized view to a client centralized view by obtaining data in batch and near real time format, analyze the data through different channels and prepare/present the data in graphical format. With more and more Banking products getting merged with the Big Data Platform, I will discuss about the Big data infrastructure that we are currently managing, the complex use cases that we solve day in and day out and the key challenges in handling large volume of data securely and enhancements that are being brought about in the Big data infrastructure.

Bio:

Sridhar Viswanathan, working as architect at BA Continuum India Pvt Ltd, Hyderabad India. I have over 17 years of experience in Big Data, Statistical and business analytics. Skilled in Java, Big Data systems, Hadoop, Spark, Kafka, Tableau and HBase. I have been with Bank for close to 11 years and involved in flagship projects by leading teams to achieve high performance and deliver complex technical solutions. I have also trained professionals on Data visualization. I have interests on engineering problems related to real time streaming and enjoy solving then and learn lessons from failures. I have completed executive masters in data science from IIT Hyderabad in 2017. Prior to that i have worked in Deloitte and Accenture in healthcare, health insurance and banking domains. I have bachelor degree from Coimbatore institute of technology in information technology.
I have filed a patent (Patent Reference Number: P12952US01) on "Generating and providing enhanced user interfaces by implementing data, ai , intents and personalization (DAIP) technology."

3. Research Papers

Paper ID	Paper Title
Paper 1	“Learning enhancement using Question-Answer generation for e-book using contrastive fine-tuned T5” [Abstract] Abstract The rise of E-learning systems offers a vast amount of free educational content for every inquisitive e-learner. However, reading only e-content does not makes their learning effective. Posing appropriate questions during the reading process can aid in the learner’s comprehension. We present a novel approach to create educational Question-Answers on eBook content. The model first summarizes key information from an input document, which is then used for creating relevant Question-Answers (QAs). We build our educational Question-Answers generation model by fine-tuning a pretrained LM (T5) using maximum likelihood estimation. We also present a contrastive fine-tuning method, in which the contrastive loss is added between the positive and negative training feature pairs during the fine-tuning process. The extensive evaluation methods on QA dataset (FairytaleQA and HotPotQA) and NCERT e-book, demonstrate the effectiveness and practicability of our eQA model. by Shobhan Kumar (IIIT Dharwad)*; Arun Chauhan (Indian Institute of Information Technology Dharwad); Pavan Kumar (IIIT Dharwad)
Paper 2	“A Deep Learning based Approach to Automate Clinical Coding of Electronic Health Records” [Abstract] Abstract The medical records in dierent electronic formats, such as handwritten notes, diagnosis summaries, lab reports, electronic pdfs, etc., contain valuable information that can be used for various medical purposes. These health records are currently coded manually or semiautomated to assign clinical codes (ICD-codes) for clinical research and analytics. This process is very timeconsuming, expensive, and errorprone. This paper presents a method for automated clinical coding of electronic health records (EHRs) given the patient diagnosis summary and other medical-related documents. The presented method uses natural language processing (NLP) techniques, which capture knowledge from the free-text diagnosis descriptions, do the text matching and semantic mapping, and translate diagnosis descriptions into clinical codes. We develop one baseline Word2vec and cosine similarity hybrid model, a transformer encoder model, and a BERT (Bidirectional Encoder Representations from Transformers) model for the automated clinical coding. The presented models are evaluated using a publicly available Medical Information Mart for Intensive Care III (MIMIC-III) dataset. The used dataset consists of various patient diagnosis descriptions and corresponding ICD-9 codes. The experimental results show that the presented BlueBERT based automated clinical coding model produced an AUC (area under ROC curve) value of 98.9% for the top10 ICD codes prediction. On the full MIMIC-III dataset, the transformer model produced an accuracy of 76.8%, a precision of 61.02%, a recall of 47.22%, a f1-score of 53.2%, and an AUC value of 92.1%. The hybrid baseline model and another used transformer encoder model also showed promising results. by Ashutosh Kumar (ABV-IIITM Gwalior); Santosh Singh Rathore (ABV-IIITM Gwalior)*
Paper 3	“Drugomics: Knowledge Graph & AI to Construct Physicians' Brain Digital Twin to Prevent Drug Side-effects and Patient Harm” [Abstract] Abstract Unintended toxic effects of a medication occur due to drug-drug interactions (DDI) and drug-disease interactions (DDSI). It is the fourth leading cause of death in the US. To overcome this crisis, we have constructed the Drugomics knowledge graphs comprising DDI and DDSI interactions mined from Drugs@FDA, FAERS (FDA Adverse Events Reporting System), PubMed, literature, DailyMed, drug ontology, and other biomedical data sources. We used Artificial Intelligence and Augmented Intelligence (AI&AI) to translocate these actionable DDI and DDSI knowledge into a network and stored it in a Neo4j property graph database in a cloud for anytime-anywhere access. For the first time, we present here an AIdriven Evidence-Based Clinical Decision Support (AIdEB-CDS) system that accepts human understandable plain text inputs and extracts knowledge from knowledge graphs to offer the right therapeutics for the right disease for the right person at the right time at any Point-ofCare. This functions like a physicians’ brain digital twin to reduce clinical errors, reduce medication errors, and increase general health equity at a reduced cost. This will eliminate the patient harm caused by drug interactions. by Asoke K Talukder (SRIT india)*; Erwin Selg (SRH Fernhochschule GmbH,); Ryan Fernandez (SJRI); Tony Raj (SJRI); Abijeet Waghmare (SJRI); Roland Haas (IIITB)
Paper 4	“A Novel Feature Selection Based Text Classification using Multi-layer ELM” [Abstract] Abstract Deep learning architectures used for text classification are becoming increasingly prevalent. However, the existing deep architectures have flaws such as slow speed, long training times, and the local minimum problem. Multi-layer Extreme Learning Machine has overcome these problems by avoiding backpropagation and thus saves a significant amount of training time, ensures global optimal, and can handle a vast quantity of data. The most important Characteristic of Multi-layer ELM is its feature space (FS), which allows the input features to be linearly separated without using any kernel techniques. The architecture of Multi-layer ELM and its technique of feature mapping are examined in this research with the help of a novel feature selection technique termed as Correlation based Feature Selection (CORFS). Empirical results of the proposed feature selection technique are compared with state-of-the-art techniques. Different classification algorithms are extensively tested on Multilayer ELM feature space and on TFIDF vector space to demonstrate the efficiency of the feature mapping technique. Results of the experiment revealed that the proposed feature selection technique is better than the conventional feature selection techniques, and the feature space of Multi-layer ELM outperforms TFIDF. by Rajendra Kumar Roul (Thapar Institute of Engineering and Technology, Patiala, Punjab)*; Gaurav Satyanath (Department of Electrical Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania)
Paper 5	“Determining the severity of Dementia using ensemble learning” [Abstract] Abstract According to WHO, 10 million individuals are subject to the risk of dementia annually. Geriatric care is expensive, restricted, and inaccessible for dementia care. The condition has no cure and medication is provided to inhibit the progress. Individuals exhibit a decline in cognition at severe stages impacting behaviors, hence detection of the early onset of dementia is critical. As an individual ages, the symptoms of dementia become harder to distinguish from symptoms of normal ageing. Behavioral patterns are identified using Activities of Daily Living (ADL) data. Traditional forms of dementia prediction widely use Magnetic Resonance Imaging (MRI). The detection of dementia and the severity utilizes both forms of data indvidually largely. A novel approach is proposed to combine motion sensor and brain scan data alternatively to detect the presence and eventually severity of dementia. Patterns of behavior are recorded, the interactions between the occupant and sensors placed in a smart environment. Daily activity recordings are used to detect the presence or absence of dementia in Phase 1. Phase 2 focuses on the utility of MRI data in longitudinal and crosssectional form to further assess the severity. Distinct features are extracted using feature selection method. The hyperparameters are tuned and stratified k-fold is applied as well. In both phases, Random Forest classifier performs effectively generating an accuracies of 95.74% and 90.29% in Phase 1 and Phase 2 respectively. Dementia prediction and severity prediction can be extended to provide direct support to members of the assistive care community. by Shruti Srivatsan (SVCE)*; Sumneet Kaur Bamrah (SVCE); Gayathri KS (SSN)
Paper 6	“A Machine and Deep Learning Framework to Retain Customers based on their Lifetime Value” [Abstract] Abstract Customer Lifetime Value (CLV) measures the average revenue generated by a customer over the course of their association with the firm. The Recency Frequency Monetary (RFM) Model is used to calculate the CLV. Recency is the latest item purchased. The number of times an item is purchased is the Frequency. Monetary is the price spent on the product by customers. CLV is measured using previous customer transactions of RFM factors. This research proposes a Deep Learning Customer Retention Framework to predict the Customer Lifetime Value in order to retain customers through an effective Customer Relationship Management strategy. The proposed framework combines clustering and regression models to analyze the significant variables for predicting the lifetime value of customers. Customers are categorized into levels such as high medium and low profitable customers based on their lifetime value. This research compares Deep Neural Network models, Machine Learning models and Probabilistic models. The Deep Neural Network is ANN. The machine learning models are Linear Regression, Random Forest, Gradient Boosting. The probabilistic models are Gamma-Gamma and Betageometric/negative binomial. The models are compared in order to predict the level of profitable customers. Results demonstrate that Deep Neural Network (DNN) model outperforms the other models with 71% accuracy. Improved prediction model for CLV and segmentation assists the firms to plan and decide relevant CRM strategies such as customer profitability analysis, cross-selling and one to one marketing for the future. by Kannan Kumaran (National College of Ireland, Dublin)*, Pramod Pathak (Technological University, Dublin), Rejwanul Haque (National College of Ireland, Dublin), Paul Stynes (National College of Ireland, Dublin)
Paper 7	“A distributed ensemble machine learning technique for emotion classification from vocal cues” [Abstract] Abstract Human-computer interaction and the creation of humanoid robots both depend heavily on emotions. By integrating the concept of emotion understanding, intelligent software systems become more effective and intuitive in resembling human-human interactions. Typically, we combine factors like intonation (speech), facial expression (visual modality), and word content (text). All possible multimodal combinations must be taken into consideration to process emotions appropriately. Among multimodal approaches, the use of human audio samples for emotion processing is given more weight than the use of facial expressions. To accomplish accurate categorization, analyzing massive volumes of realtime data has become more necessary. Machine Learning (ML) models that operate in a distributed fashion are crucial, given the size and complexity of the problem under study. In this respect, we propose a distributed ensemble model for vocal cue-based emotion classification. Three base ML models that work in a distributed manner were used. According to the findings, the ensemble model proposed differentiates between the seven fundamental emotions with reasonable accuracy. The proposed distributed ensemble model performed better than existing ML models on TESS, SAVEE, and RAVDESS, achieving 86% accuracy on the unified dataset. by Bineetha Vijayan (Cochin University of Science And Technology)*; Gayathri Soman (Cochin University of Science And Technology); Vivek M.V. (Cochin University of Science And Technology); M.V. Judy (Cochin University of Science And Technology)
Paper 8	“Hui2Vec: Learning Transaction Embedding Through High Utility Itemsets” [Abstract] Abstract Mining frequent itemsets (FIs) in transaction databases is a very popular task in data mining. It helps create meaningful and effective representations for customer transactions which is a key step in the process of transaction classification and clustering. To improve the quality of these representations, previous studies have adapted vector embedding methods to learn transaction embeddings from items and FIs. However, FIs are still a simple pattern type that ignores important information about transactions such as the purchase quantities of items and their unit profits. To address this issue, we propose to learn transaction embeddings from items and high-utility itemsets (HUIs), a more general pattern type. Since HUIs were shown to be more appropriate than FIs for a wide range of applications, we take for hypothesis that transaction embeddings learned from HUIs will be more representative and meaningful. We introduce an unsupervised method, named Hui2Vec, to learn transaction embeddings by combining both singleton items and HUIs. We demonstrate the superior quality of the embedding achieved with the proposed method compared to the embeddings learned from items and FIs on four datasets. by KHALED BELGHITH (NARD Intelligence)*; Philippe Fournier-Viger (Shenzhen University); Jassem Jawadi (ISTIC)
Paper 9	“Discovering Top-K Periodic Patterns in Temporal Databases” [Abstract] Abstract Discovering periodic-frequent patterns in temporal databases is a challenging data mining problem with abundant applications. It involves discovering all patterns in a database that satisfy the user-specified minimum support (minSup) and maximum periodicity (maxPer) constraints. MinSup controls the minimum number of transactions in which a pattern must appear in a database. MaxP er controls the maximum time interval within which a pattern must reappear in the database. Setting an appropriate minSup and maxPer values for any given database is an open research problem. This paper addresses this open problem by proposing a solution to discover top-k periodic-frequent patterns in a temporal database. Top-k periodic-frequent patterns represent a total of k periodic-frequent patterns with the lowest periodicity value in a database. An efficient depthfirst search algorithm, called Top-k Periodic Frequent Pattern Miner (k-PFPMiner), which takes only k threshold as an input was presented to find all desired patterns in a database. Experimental results on synthetic and real-world databases demonstrate that our algorithm is memory and runtime efficient and highly scalable. by Likhitha Palla (University of Aizu)*; Uday Kiran RAGE (University of Tokyo); Penugonda Ravikumar (The University of Aizu); Yutaka Watanobe (The University of Aizu)
Paper 10	“Extremely Randomized Tree based Sentiment Polarity Classification on Online Product Reviews” [Abstract] Abstract The sentiment analysis of user reviews on social networking is one among the fundamental process carried out by online business organizations to improve the quality of their products and retain the customers and thereby lift the monetary benefit. Although numerous analysis models exist, still there is space for improving the performance and accuracy of informal text data-based classification models. In this examination, we conduct a comparative assessment of the effectiveness of unigram feature set extricated utilizing n-gram technique with three ensemble methods namely Extremely Randomized Tree, Voting, and Bagging classifier based on the following five baseline classifiers Random Forest, Naïve Bayes, K-NearestNeighbor, Ridge Classifier, and Support Vector Machine to identify polarity from mobile product reviews. Among the three ensemble methods, the Extremely Randomized Tree technique has better outcomes with the accuracy of 98% for positive and 85% for negative cases, with an overall accuracy of 96.8%. The error rate of all the three ensemble classifiers is also under 0.5% which uncovers that ensemble classifiers performs better compared to individual classifiers. by Saranya R B R B (Narayana guru College of Engineering)*; K Ramesh (Anna University Regional Campus); NISHA K DEVI (Bannari Amman Institute of Technology)
Paper 11	“Analyze the Impact of Weather Parameters for Crop Yield Prediction using Deep Learning” [Abstract] Abstract Accurate crop yield prediction is one of the most important aspects for the agricultural policy decision for the policy makers and the farmers. However, prediction of crop yield depends on many parameters such as weather, soil, seed quality and farm practices. Importance of different parameters on crop is varying from crop to crop and region to region. With the availability of satellite images along with statistical data, deep leaning based model can capture growth of crop over temporal data. In this paper, we introduce informal methods to analyze and measure the impact of weather on the crop yield prediction using remote sensing images. Here, we applied Convolutional Neural Network (CNN) – Long Short-Term Memory (LSTM) based model over a large spatial and temporal data collected during crop growth season. We compared our model with two other models and found that it confers performance improvement over other models. From the experimental result, we dissect that inclusion of weather increase yield prediction and crop growth is highly correlated on the weather parameters. by Pragnesh Patel (Ahmedabad University)*; Sanjay Chaudhary (Ahmedabad University); Hasit Parmar (L. D. College of Engineering)
Paper 12	“Analysis of Weather Condition based Reuse among Agromet Advisory: A Validation Study” [Abstract] Abstract India Meteorological Department is delivering agromet advisories, i.e., weatherbased crop risk management advisories based on the medium range weather forecast (five days) across India. Based on the weather prediction, once in five days, agromet advisory is being provided for all crops by considering the district/block as a unit. In the literature, a framework was proposed to improve the process of advisory preparation by employing the notion of reuse. The idea is to reuse the advisory prepared for the given weather situation to prepare the advisory for similar weather situations in the future. A notion of category-based weather condition (CWC) was proposed to model a weather situation, and a given two CWCs are compared by comparing the corresponding categories. The experiments conducted based on CWC showed a significant improvement in reuse. In this paper, we presented a validation study by analyzing reuse by comparing the advisory text of the corresponding weather situations. The experiments on Rice crop agromet advisory text data delivered from 2016 to 2019 in Telangana State show that if the advisory texts are similar, there is a high probability that the corresponding CWCs are also similar. The results validate that the CWC-based reuse framework can be employed to exploit reuse among weather situations. by Mamatha Alugubelly (IIIT Hyderabad)*; Krishna Reddy P (International Institute of Information Technology, Hyderabad); Anirban Mondal (Ashoka University); Mahadevappa SG (P.J.T.S Agricultural University); Balaji Naik Banothu (P.J.T.S.Agricultural University); Sreenivas Gade (P.J.T.S. Agricultural Unversity)
Paper 13	“Community Detection in Large Directed Graphs” [Abstract] Abstract Many real-life systems are represented by directed networks but most of the existing community detection research is focused on undirected networks. We present here a new scalable Map-Reduce algorithm to discover PageRank-driven hierarchical community structures in directed graphs. Main features of our approach are that we seek to find a directed community centered around any selected core node of a directed graph, and a community is defined by two hierarchical structures connected to the core node. An upper structure, that points ”to” the core node, and a lower structure that is pointed to ”by” the core node. Nodes chosen for inclusion in our hierarchical community structure must display importance ratings (PageRank) very similar to the rating of the selected core node. We have demonstrated here that our algorithm finds meaningful hierarchical communities in real-world data sets. We have also evaluated the effects of the PageRank of the core node and the PageRank threshold parameter for the nodes included in its communities. by Siqi Chen (University of Cincinnati); Raj K Bhatnagar (University of Cincinnati)*
Paper 14	“ARCORE: Software Requirements Dataset for Service Identification” [Abstract] Abstract Service Oriented Software Engineering (SOSE) has been an integral process for developing medium to large scale software. However, identifying the needed services is largely dependent on expertise of software architects. We are interested in assisting architects by automating the process of service identification from requirements documents. One of the mechanisms to automate the service identification process is using Machine Learning Techniques and it requires working with large datasets. In this paper, we detail our work on creating a requirements dataset called ARCORE, that can be used for service identification. The motivation arises from the fact that no such dataset is available in public domain, and very minimal work has been done on automated service identification from requirements documents by Vijaya Peketi (Independant Consultant)*; Surekha satti (Independant Consultant)

4. Tutorial talks

Date and Time (Hall)	Tutorial talks
Tutorial 1 (December 20 (Tuesday), 2022, 16:00 to 17:30)	Title: Malware Analysis and Detection Speakers: Mohit Sewak (Microsoft, India) Hemant Rathore (BITS Pilani, Goa) Brief outline of the tutorial: Today computing devices like laptops, mobile phones, smart devices, etc., have penetrated very deep into our modern society and have become an integral part of our daily lives. Currently, more than half of the world's population uses computers/mobile devices for their professional/ personal needs. However, these computing devices are targeted by malware designers encouraged by profits/gains associated with the attack. According to a recent report, monetary losses due to cybercrime are expected to reach 10 trillion dollars annually by 2025. The primary role in providing defense against malware attacks is designed and developed by the anti-malware community (researchers and the anti-virus industry). Traditionally anti-viruses are based on the signature, heuristic, and behavior based detection engines. However, these engines are unable to detect next-generation polymorphic and metamorphic malware. Thus researchers have started developing malware detection engines based on machine learning to complement the existing anti-virus engines. However, there are many open research challenges in these models like adversarial robustness, explainability, fairness, etc., which we are going to discuss in detailduring the tutorial. Speakers Bio Mohit Sewak is an Artificial Intelligence and Cybersecurity researcher with over 15 years of experience in designing innovative AI software and solutions. Mohit holds more than a dozen patents across the US, India, and worldwide for innovative AI solutions that empower many international products. Mohit is the author of multiple AI book titles on topics including technologies like Deep Reinforcement Learning and Convolutional Neural Networks. Mohit's research is focused on designing AI-based malware and other advanced threat detection and protection systems. Currently, Mohit serves as a Principal Data Scientist for Security & Compliance Research at Microsoft R&D. Hemant Rathore is a cyber security expert with more than ten years of experience in industry and academia. His current work focuses on the topic of Adversarial Robustness and Explainability in Malware Detection Models. His research interests are in the area of Malware Analysis, Network Security, Machine Learning, and Operating Systems. He has guided several undergraduate and postgraduate students in their independent research projects and published many research papers in reputed journals/ conferences.
Tutorial 2 (December 21 (Wednesday), 2022, 13:30 to 15.00)	Title: Neuro-Symbolic Techniques for XAI and Logical Reasoning Speakers: Raghava Mutharaju (IIIT Delhi, Delhi) Brief outline of the tutorial: Neuro-Symbolic AI brings together the neural and symbolic aspects of AI. Symbolic techniques are transparent with provable guarantees for correctness. On the other hand, neural techniques are robust to noise and can easily pick up the patterns from the data. By combining the complementary strengths of these two approaches, it is possible to build AI systems that are robust and transparent. In this tutorial, we will discuss the use of neuro-symbolic techniques for explainable AI (XAI) and logical reasoning over Knowledge Graphs and ontologies. Speakers Bio Raghava Mutharaju is an Assistant Professor in the Computer Science and Engineering department of IIIT-Delhi, India and leads the Knowledgeable Computing and Reasoning (KRaCR; pronounced as cracker) Lab. He got his PhD in Computer Science and Engineering from Wright State University, USA, in 2016. He has worked in Industry research labs such as GE Research, IBM Research, Bell Labs, and Xerox Research. His research interest is in Semantic Web and in general in Knowledge Representation and Reasoning. This includes knowledge graphs, ontology modelling, reasoning, querying, and its applications. He has published at several venues such as ISWC, ESWC, ECAI, and WISE. He has co-organized workshops at ISWC 2020, WWW 2019, WebSci 2017, ISWC 2015 and tutorials at ISWC 2019, IJCAI 2016, AAAI 2015 and ISWC 2014. He is/has been on the Program Committee of several (Semantic) Web conferences such as AAAI, WWW, ISWC, ESWC, CIKM, K-CAP and SEMANTiCS. More information is available on his lab's homepage at https://kracr.iiitd.edu.in/.
Tutorial 3 (December 22 (Thursday), 2022, 13:30 to 15:00)	Title: Federated Learning in the Real-World: From Theory to Practice Speakers: Tushar Semwal (Microsoft, India) Madhusudhanan Krishnamoorthy(Microsoft, India) Rajeev Gupta(Microsoft, India) Brief outline of the tutorial: With the advent of the Internet of Things (IoT), there has been a huge surge in the volume of data collected by the devices at the edge of a network. This data is often collected and stored in the remote cloud servers to gain useful insights by training a model on this data. As an alternative, Federated Learning has been proposed where instead of learning a single global model centrally at the cloud server, each participating client device trains a model on its own local data and only share the weight gradients with the shared global model. Thus, in contrast to the sharing of raw data, the weights of the model are shared and distributed across the federation of client devices. One important use case could be an IoT in medical and health environments, Federated Learning (FL) can enable other organizations, which have similar data and have similar modelling requirements, to train in a single better global model which can then be distributed to each participating institute. In this tutorial, we will begin by providing a formal definition of FL, basic terminologies, architectures, and overview of challenges associated with centralized machine learning paradigms. We will then describe the federated learning framework through its various flavors such as horizontal federated learning, vertical federated learning, and federated transfer learning. In addition, the tutorial will also cover our recent published work on Federated Transfer learning. In this full day tutorial, after the first half of discussing the theoretical aspects of FL, the second half will begin with a hands-on introduction to python-based programming of a simple FL algorithm and testing on a benchmark dataset. In the tutorial, we will also introduce a fresh domain on Federated Graph Learning where the different components of FL are adapted for graph datasets. Speakers Bio Tushar Semwal is an Applied Scientist at Microsoft Search Assistant & Intelligence (MSAI), India. He got both master’s and PhD degrees in Computer Science and Engineering from IIT Guwahati. Before joining Microsoft, Tushar served as a Research Associate at the Soft Computing Labs in the University of Edinburgh, Scotland. He is a recipient of the prestigious 4-year research fellowship award from TCS India, for his industry applicable research. He has won several travel grants from Microsoft, SERB India, SIAM, and TCS. His research interests include privacy-aware ML, Graph Representation Learning, and large-scale distributed systems. Madhusudhanan Krishnamoorthy is a Senior Data Scientist at Microsoft Search Assistant & Intelligence (MSAI), India. He got his master's in Data science and Engineering from BITS Pilani. Before joining Microsoft, Madhu served as a Chief Data Scientist in Bank of America. He has more than 7 publications and 72 patents in the areas of cybersecurity, Mixed Reality, LiFi, Information extraction and cellular automata. His current work focuses on Graph representation learning and serving of embeddings at a larger scale. Rajeev Gupta is a Principal Applied Scientist at Microsoft Search Assistant & Intelligence (MSAI), India. He got his PhD from Indian Institute of Technology (IIT) Mumbai (Bombay) in the area of distributed data management. He has more than 30 publications and 20 patents in the areas of data management, information extraction, and distributed computing in reputed conferences and journals.
Tutorial 4 (December 22 (Thursday), 2022, 15:30 to 17:00)	Title: Self-Supervised Learning to Process Labeled and Unlabeled Medical Image Data Speakers: Mayuri Mehta (SCET, Surat) Brief outline of the tutorial: Medical imaging plays a significant role in developing automated clinical applications for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Deep learning is essential for in-depth and accurate analysis of medical images. Specifically, deep convolutional networks are most appropriate for extracting meaningful features from medical images. A huge amount of labeled data is required to train these deep convolutional networks. However, manually labeling medical images is time-consuming and expensive for medical experts. In addition, the major issue with the manual labeling of the huge dataset is the bias among human annotators. Therefore, applied deep learning is essential to process the dataset having a few labeled and largely unlabeled data. Applied deep learning includes semi-supervised learning, Self-Supervised Learning (SSL) and reinforcement learning. Among them, SSL has been widely used in recent years to process medical data to reduce the data labeling cost and leverage the unlabeled data pool. SSL attempts to learn the visual representations of the data using proxy tasks perceived as pretext tasks. Pretext tasks are responsible for learning the prominent visual representations of data to use the learned representations or model weights obtained in the process for the downstream task. The first half of this tutorial will comprise the emergence of AI in Healthcare, the significance of applied deep learning to process big healthcare data, various self-supervised learning frameworks, different types of pretext tasks, and how to design or select a suitable pretext task for processing medical images. In addition, medical image processing with labeled and unlabeled datasets will be discussed. In the second half of the tutorial, various SSL-based healthcare solutions (use cases) will be discussed. The discussion of each use case will include motivation, precise problem statement, the proposed solution, dataset, experimental results and challenges faced. Subsequently, the functioning of these healthcare solutions will be demonstrated. Finally, challenges and enormous future research opportunities will be discussed. Speakers Bio Dr. Mayuri Mehta is a passionate learner, teacher and researcher. She received a doctorate in Computer Engineering from the National Institute of Technology, Surat, India. Her areas of teaching and research include Data Science, Healthcare Informatics, Machine Learning/Deep Learning, Computer Algorithms and Python Programming. Her 22 years of professional experience includes several academic and research achievements along with administrative and organizational capabilities. She is awarded the "Researcher of the Year Award (Engineering, Female)" by the 3rd International Business and Academic Excellence Award (IBAE-2021) committee for her Exceptional Calibre and Outstanding Performance as an Academician, Researcher, Mentor, Advisor, and a Thought Leader. She has 11 patents and 1 copyright to her credit. She has published two books: (1) Tracking and Preventing Diseases with Artificial Intelligence and (2) Knowledge Modelling and Big Data Analytics in Healthcare with Springer and CRC Press, respectively. Her books on "Explainable AI: Foundations, Methodologies and Applications" and "Recent Advances in Data and Algorithms for e-Government" with Springer will be published this year. She is the author of 33 research papers and 3 book chapters. She has worked on several academic assignments in collaboration with professors of universities across the globe. She has visited Germany, France, Switzerland, Oman, Dubai, Hongkong, Macau and Thailand for professional and personal purposes. She is an adjunct professor at Gujarat's largest private university- Parul University. Her AI-powered Healthcare project was approved for funding by the Multidisciplinary Research Unit of Surat Municipal Institute of Medical Education and Research (SMIMER). She has also received funds several times from Gujarat Council on Science and Technology (GUJCOST). She has received funds from Student Start-Up & Innovation Policy (SSIP), Government of Gujarat, India, for filing 2 patents. She has served in several International Conferences in different positions. She has conducted 80+ sessions in International Conferences, Short Term Training Programs (STTPs), Faculty Development Programs (FDPs), etc. With the noble intention of applying her technical knowledge for societal impact, she is working on several AI-powered research projects in Healthcare in association with doctors doing private practice and doctors of Medical Colleges. She is a member of professional bodies such as IEEE, ISTE, CSI.

5. Workshops

Workshop Name	Link
Data Challenges in Assessing (Urban & Regional) Air Quality-DACAAQ 2022	https://dacaaq.github.io/dacaaq2022
Big Data Analytics on High Performance Computing Cluster (HPCC) System	https://sites.google.com/rvce.edu.in/bda-workshop-iiith/home
Workshop on Universal Acceptance and Email Address Internationalization	http://dns.int.in/bda/
Workshop on Data Science for Justice Delivery in India (DSJDI-2022)	https://bda-2022.github.io/DSJDI-2022/

6. Panel Session

Title: Data Science for sustainable development goals (SDGs)
December 21, 2022 (Wednesday), 15:30-17:00

Overview:

Seventeen sustainable development goals (SDGs) are listed by United Nations in the year 2015 (https://sdgs.un.org/goals). The scope of the panel is as follows:
"If data science researchers, select the problems related to SDGs, the societal growth can be accelerated."
The panel of eminent members share the perspectives on the above statement.

What are the typical research issues addressed by data science researchers?
List the potential research projects for SDGs, if any, in which data science has played a major role.
What are the challenges in carrying out research on Data Science for SDGs?
What is the framework for data science researchers to encourage research on Data Science for SDGs?

Moderators:

Philippe Fournier-Viger, Shenzhen University
P.Krishna Reddy, IIIT Hyderabad

Panelists:

(i) Masaru Kitsuregawa (The University of Tokyo)

Bio:Masaru Kitsuregawa is the Director General of National Institute of Informatics (NII) and University Professor at the University of Tokyo. Received Ph.D. degree from the University of Tokyo in 1983. Served in various positions such as President of Information Processing Society of Japan (2013–2015) and Chairman of Committee for Informatics, Science Council of Japan（2014-2016）. He has wide research interests, especially in database engineering.
For the Japanese Government, he served as a Steering Committee Chair of the Information Grand Voyage Project by the Ministry of Economy, Trade and Industry from 2007 to 2010, and as Science Advisor for the Ministry of Education, Culture, Sports, Science and Technology from 2008 to 2012. For the Science Council of Japan, he served as a Council Member from 2011 to present, and a Chair of the Informatics Committee from 2014 to 2017. In addition, he has been a Program Officer of JST CREST/PREST on Big Data from 2013 to the present, and was the President of the Information Processing Society of Japan from 2013 to 2015.
He received the ACM SIGMOD E. F. Codd Innovation Award in 2009 as the first recipient in Asia, as well as the IPSJ Contribution Award in 2011, the 21st Century Invention Award of National Commendation for Invention Japan, the IEEE Innovation in Societal Infrastructure Award in 2019, and in 2020, the Japan Academy Award. In addition, he was awarded the Medal with Purple Ribbon from the Japanese Government in 2013, and was made a Chevalier de la Légion d’Honneur by the French Government in 2016. He is an IEEE Life Fellow, an ACM Fellow, an IEICE Fellow, an IPSJ Fellow, and a China Computer Federation honorary member. Japan Academy Prize in 2020.

(ii) Jaideep Srivastava (University of Minnesota)

Bio: Jaideep Srivastava is a professor at the University of Minnesota, where he has established and led a research laboratory which conducts research in the information and knowledge aspects of computing. He has supervised 26 Ph.D. dissertations and 53 M.S. theses, and authored or co-authored over 220 papers in refereed journals and conferences. Dr. Srivastava has served on the editorial boards of various journals, including IEEE TPDS, IEEE TKDE, and the VLDB journal. He has also served as Program and Conference Chair for a number of prominent conferences, especially in the area of data mining, and is on the Steering Committee for the PAKDD series of conferences. He has delivered a number of keynote addresses, plenary talks, and invited tutorials at major conferences. Dr. Srivastava has a very active interaction with the industry, in both consulting and executive roles. Specifically, during a 2-year sabbatical during 1999-2001, he lead a corporate data mining team at Amazon.com (www.amazon.com) and built a data analytics department at Yodlee (www.yodlee.com) from the ground up. More recently, he spent two years as the Chief Technology Officer for Persistent Systems (http://en.wikipedia.org/wiki/Persistent_Systems), where he built an R&D; division and oversaw the redesign of the training and technical vitalization program for 2,200+ engineers. He has provided technology and technology strategy advice to a number of large corporations including Cargill, United Technologies, IBM, Honeywell, 3M, and Eaton. He has served in an advisory capacity to a number of small companies, including Lancet Software and Infobionics. Dr. Srivastava has also played an active advisory role in the government sector. Specifically, he has served as the US federal government's expert witness in a nationally significant tax case. He is presently serving as Senior Technology Advisor to the State of Minnesota, and is on the Technology Advisory Council to the Chief Minister of Maharashtra, India. He is a Fellow of the IEEE, and has been an IEEE Distinguished Visitor.

(iii) Longbing Cao (University of Technology Sydney)

Bio:Longbing Cao is a professor and an Australian Research Council Future Fellow (Professorial level) at the University of Technology Sydney (UTS), and the founding director of UTS Advanced Analytics Institute (now Data Science Institute). He received an Australian Eureka prize, serves as the Editor-in-Chiefs of IEEE Intelligent Systems and Springer-Nature’s Journal of Data Science and Analytics, and created several data science initiatives including the IEEE International Conference on Data Science and Advanced Analytics. His broad research interest covers AI, data science, machine learning, behavior informatics, complex intelligent systems, and their enterprise applications in public and private sectors.

(iv) Santanu Chaudhury (IIT Jodhpur)

Bio:Professor Santanu Chaudhury, Professor, Department of Electrical Engineering, IIT Delhi, has assumed charge as Director, IIT Jodhpur, on 10 December 2018. Professor Chaudhury holds B.Tech. (Electronics and Electrical Communication Engineering) and Ph.D. (Computer Science & Engineering) Degrees from IIT Kharagpur.
Professor Chaudhury joined as Faculty Member in the Department of Electrical Engineering, IIT Delhi, in 1992. He was Dean, Under-Graduate Studies at IIT Delhi. He has served as Director of CSIR-CEERI, Pilani, during 2016-18. Professor Chaudhury is a recipient of the Distinguished Alumnus award from IIT Kharagpur.
Professor Chaudhury is a Fellow of Indian National Academy of Engineers (INAE) and National Academy of Sciences (NAS). He is a Fellow of International Association Pattern Recognition (IAPR). He was awarded the INSA (Indian National Science Academy) Medal for Young Scientists in 1993. He received ACCS-CDAC award for his research contributions in 2012.
A keen researcher and a thorough academic, Professor Chaudhury has about 300 publications in peer reviewed journals and conference proceedings, 15 patents and 4 authored/edited books to his credit.

(v) Yun Sing Koh (University of Auckland)

Bio:Yun Sing Koh is an Associate Professor at the School of Computer Science, The University of Auckland, New Zealand. Her main research area is Artificial Intelligence (AI) and Machine Learning (ML). Specifically focusing on several research strands: continual learning and adaptation, transfer learning anomaly detection, and data stream mining. Yun Sing is passionate about using machine learning for social good, and her research has been applied to interdisciplinary applications in environmental and health domains. Yun Sing has published 100+ peer-reviewed publications in top conferences and journals, including IJCAI, IEEE ICDE, IEEE ICDM, Machine Learning Journal and Journal of Artificial Intelligence. She won the New Zealand Royal Society Fast-Start Marsden funding (2018) and the United States Office of Naval Research Grant (2019). Yun Sing has been active in the research community, including serving as the General Co-Chair at the IEEE International Conference on Data Mining 2021 and Australasian Data Mining Conference 2022, Workshop Co-Chair at the ECML/PKDD conference 2021, Program Co-Chair of the Australasian Data Mining Conference 2018 and as the Workshop Co-Chair for the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining. She leads the Advanced Machine Learning and Data Analytics Research (MARS) Lab.

Programme Schedule (Download the pdf)

Program Details

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5. Workshops

6. Panel Session

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