Each track will focus on a specific area with invited talks and a panel discussion.
Proliferation of deep learning techniques and availability of efficient computing infrastructure has allowed research community to make great strides in the areas like NLP, NLU, recommender systems to name a few. The various advancements achieved in the niche areas of
Increase in the consumer devices and IOT devices with new sensors, multimedia processing intelligently has become an important aspect of consumer domain.
Modern computer systems have enabled the AI/ML systems that are compute and data intensive. However the rising demand also brings new challenges and opportunities, some of which are:
Day 1, Wednesday, 27 October 2021 08:00 - 16:00 (GMT+05:30 Indian Standard Time)
Latest research and Industry challenges/cases sharing on areas of low resource language NLP, NLU, ranking, KG, recommender systems.
Advancements in large scale multilingual models, deployments challenges, intelligent operations and site reliability engineering
| Topic | Time (GMT+05:30) | Speakers | Video |
|---|---|---|---|
| Opening Remarks & Context Setting from Huawei | 08:10 - 08:20 | Mr. Zeng Ronghua Vice president of the Consumer Cloud Service Advertising BU and Director of the R&D Department of the Advertising BU Huawei Technologies, China |
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| Opening Remarks & Context Setting from ACM | 08:20 - 08:30 | Dr. Hemant Pande Executive Director, ACM India Council |
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| Keynote: Towards relation inference and causal reasoning in video | 08:30 - 09:10 | Prof. Tat Seng Chua KITHCT Chair Professor, Director at NUS-Tsinghua Extreme Search Center (NExT), National University of Singapore |
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| Human Inspired Artificial Intelligence | 09:10 - 09:50 | Dr. Manish Gupta Director at Google Research India | Infosys Foundation Chair Professor, IIIT Bangalore |
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| Neural Models for Monolingual & Multilingual Information Extraction | 09:50 - 10:30 | Prof. Mausam Professor, Jai Gupta Chair, Department of CSE, IIT Delhi |
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| Scaling Natural Language Processing for the Next Billion Users | 10:30 - 11:10 | Prof. Partha Pratim Talukdar Associate Professor, Departments of CDS & CSA, IISC Bangalore | Staff Research Scientist at Google Research, Bangalore |
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| Unfolding biases in e-commerce platforms: sponsored recommendations, buybox winners, and beyond | 11:10 - 11:50 | Prof. Animesh Mukherjee Associate Professor (A K Singh Chair) Department of CSE, IIT Kharagpur |
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| Panel Discussion on biases and ethical challenges in AI | 11:50 - 12:40 | Mr. Prabhat Kumar Saraswat (Huawei Technologies, India), Mr. Debdoot Mukherjee (Sharechat), Prof. Animesh Mukherjee (IIT Kharagpur) Prof. Abhijnan Chakraborty (IIT Delhi) |
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| Multilingual content security and it's challenges | 12:40 - 13:20 | Mr. Kuan Eeik Tan Head of Europe Cloud Competence Center CBG Cloud Service/Helsinki Research Center at Huawei Technologies, Finland |
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| AIOps Practice For PB-level Data And Hundreds of Millions Users | 13:20 - 14:00 | Mr. Hu Peng AIOPS expert at Huawei Technologies, Ireland |
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| Performing physics-based AI for ground truth hard sub-cellular organelle segmentation using simulated expert | 14:00 - 14:40 | Prof. Dilip K. Prasad Associate professor, Department of Computer Science, UiT The Arctic University of Norway |
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| Machine learning modeling best practices | 14:40 - 15:20 | Dr. Rajeev Rastogi Vice President, Machine Learning at Amazon | ACM Fellow |
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| Low resource adaptation of language and speech models | 15:20 - 16:00 | Prof. Sunita Sarawagi Institute Chair Professor, Department of CSE, IIT Bombay |
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Day 2, Thursday, 28 October 2021 08:30 - 16:10 (GMT+05:30 Indian Standard Time)
Advancement in multimedia frameworks/pipelines for consumer devices
| Topic | Time (GMT+05:30) | Speakers | Video |
|---|---|---|---|
| Introduction to PipeWire | 08:30 - 09:10 | Mr. Olivier Crête Multimedia Lead at Collabora, Canada |
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| OpenVINO™ Toolkit DL Streamer: Developing and deploying optimized video analytics | 09:10 - 09:50 | Mr. Neelay Shah, Principal Software Engineer and Solutions Architect, Intel Ms. Neena Maldikar, Product Manager, Intel® Edge Software Hub |
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| — Coffee Break : 10 min. — | |||
| Data in the Context of IoT: Practices and Recommendations | 10:00 - 10:40 | Prof. Sudip Misra Professor, Department of CSE IIT Kharagpur |
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| What does it take to build an autonomous vehicle? | 10:40 - 11:20 | Prof. Arjun Jain Adjunct Faculty Member, IISC |
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| Low Latency Video Capture: Challenges and Solution | 11:20 - 12:00 | Mr. Binu Evamma Rolden Technologist, Computer Vision & Machine Learning at Ittiam Systems Pvt Ltd |
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| Network Streaming & Multimedia Frameworks | 12:00 - 12:40 | Mr. Keshav Karunakar Co-Founder, VP Engineering at Pathpartner Technology Mr. Adish Kumar Senior Technical Manager at Pathpartner Technology |
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| — Break : 40 min. — | |||
How the modern computer systems and systems-AI are answering the grand challenge problems of data store
| TransJIT: Data Driven Specialization of Compiled Queries | 13:20 - 14:00 | Mr. Ken Zhang Chief architect of Huawei Kunpeng Big Data Technology, Director of the Data Application Lab at Huawei Technologies, Canada |
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| Hidden Query Extraction | 14:00 - 14:40 | Prof. Jayant R. Haritsa Senior Professor, Department of CDS & CSA, IISC Bangalore | President of ACM India Council |
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| Parallel Graph Processing Systems on Heterogeneous Architectures | 14:40 - 15:20 | Prof. Bingsheng He Dean’s Chair Associate Professor, National University of Singapore |
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| Data, Systems and AI: An Industrial Perspective | 15:20 - 16:00 | Dr. Eliezer Levy CTO Data Systems & AI at Tel Aviv Research Center, Huawei Technologies, Israel |
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| Closing notes | 16:00 - 16:10 | Prof. Rajeev Shorey Chief Executive Officer, University of Queensland - IIT Delhi Academy of Research (UQIDAR) Adjunct Faculty at Department of CSE, IIT Delhi | ACM Distinguished Scientist |
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Technical Team
KITHCT Chair Professor, Director at NUS-Tsinghua Extreme Search Center (NExT), National University of Singapore
Prof. Tat Seng Chua is the KITHCT Chair Professor at the School of Computing, National University of Singapore. He is also the distinguish Visiting Professor of Tsinghua. Dr. Chua was the Founding Dean of the School from 1998-2000. He is also the Director of a joint research Center between NUS and Tsinghua (NExT) to research into big unstructured multi-source multimodal data analytics. His research focuses on the extraction, retrieval and question-answering of text, video and live media arising from the Web and social networks. He is the recipient of ACM SIGMM Technical Achievements Award 2015. He is the chair of steering committee of ICMR (International Conference on Multimedia Retrieval) and Multimedia Modeling conference series. Dr. Chua co-founded of two technology startup companies based on research in NExT.
In this research, we focus on extending the research from visual object recognition to that of relation inference. It permits the system to extract basic knowledge relation triplets in the form of which elevates the extractable semantic of video to that of text, and supports a wide range of higher level video-text fusion tasks such as the multi-perspective video captioning, video QA, and multimodal knowledge graph. It also supports causal reasoning, such as the counterfactual inference. In this talk, he will present the background on object recognition, and present his recent works in transforming the research from object recognition to video relation inference and causal reasoning. He will conclude the talk by outlining several future research directions and applications. As part of this research, we have constructed and released a large-scale video dataset to facilitate relevant research in this emerging field. The dataset has been used for a grand challenge in the ACM Multimedia 2019 conference.
Vice president of the Consumer Cloud Service Advertising BU and
Director of the R&D Department of the Advertising BU
Huawei Technologies, China
Zeng Ronghua is vice president of the Consumer Cloud Service Advertising BU and director of
the R&D Department of the Advertising BU. Plans Huawei's advertising products, nurtures
advertising product capabilities, and is responsible for the competitiveness and business
success of Huawei's advertising products.
He has worked at Huawei for over 16 years and is a senior business and technical expert.
Executive Director, ACM India Council
Dr. Hemant Pande is the current Executive Director of the ACM India Council, where along with the Council’s elected members and Executive Committee, he leads the efforts to increase the level and visibility of ACM activities across India. Prior to joining the ACM India Council, Pande held a number of executive leadership roles in business and research organizations. For many years, he worked at Persistent Systems, a technology services company based in Pune, where he held various roles including Chief Planning Officer, Executive Vice President of Platform Solutions and Global Head of Development Centers. Pande received a Bachelor of Technology degree in Computer Science and Engineering from the Indian Institute of Technology, Bombay, and MS, MPhil and PhD degrees in Computer Science from Rutgers University.
Director at Google Research India | Infosys Foundation Chair Professor, IIIT Bangalore
Dr. Manish Gupta is the Director of Google Research India. He holds an additional appointment as Infosys Foundation Chair Professor at IIIT Bangalore. Previously, Manish has led VideoKen, a video technology startup, and the research centers for Xerox and IBM in India. As a Senior Manager at the IBM T.J. Watson Research Center in Yorktown Heights, New York, Manish led the team developing system software for the Blue Gene/L supercomputer. IBM was awarded a National Medal of Technology and Innovation for Blue Gene by US President Barack Obama in 2009. Manish holds a Ph.D. in Computer Science from the University of Illinois at Urbana Champaign. He has co-authored about 75 papers, with more than 7,000 citations in Google Scholar, and has been granted 19 US patents. While at IBM, Manish received two Outstanding Technical Achievement Awards, an Outstanding Innovation Award and the Lou Gerstner Team Award for Client Excellence. Manish is a Fellow of ACM and the Indian National Academy of Engineering, and a recipient of a Distinguished Alumnus Award from IIT Delhi.
As artificial intelligence (AI) begins to impact various aspects of our lives, there is a need to combine it with a better understanding of drivers of human behavior. We describe our ongoing work in diverse areas, including health and information access where combining AI with human factors is key to developing an effective solution. We present an AI-based approach to modeling the diligence of health workers in a public health scenario, which is needed to ensure that data being fed to downstream algorithms is of high quality. We describe additional scenarios of "data corruption" in the context of information access where the "corruption" of data is not a bug, but a feature based on natural human behavior. We then present a wellness-based approach to improve health outcomes for people, which requires a combination of mobile sensing, machine learning and influencing human behavior. Finally, we describe a deep learning based modeling of human behavior in a specific game-based setting, which has very interesting implications if we are able to generalize that approach to broader settings.
Professor, Jai Gupta Chair, Department of CSE, IIT Delhi
Prof. Mausam is a Professor and Jai Gupta Chair in the Department of Computer Science and Engineering, Indian Institute of Technology Delhi. He is also the founding head of the School of Artificial Intelligence, at IIT Delhi. He is also the affiliate Faculty in the Department of Computer Science and Engineering at the Washington University. His research involves exploring neuro-symbolic machine learning, computer vision for radiology, NLP for robotics, multilingual NLP, and several threads in intelligent information systems that include information extraction, knowledge base completion, question answering, summarization and dialogue systems. He was ranked the 56th most influential NLP scholar and 64th most influential AI scholar by ArnetMiner AI2000 Ranking. He is a NitiAyog AI member India, and served as Program co-chair of AAAI 2021.
Information Extraction is the task of identifying argument-relation-argument tuples from sentences. Traditional IE approaches use a pre-defined relation vocabulary, whereas Open IE outputs textual relations without a given ontology. In this talk, I describe our recent results on neural models for both Open IE, as well as distantly supervised relation extraction. We also describe automatic data collection approaches to extend monolingual (English) IE approaches to multilingual IE.
Associate Professor, Departments of CDS & CSA, IISC Bangalore | Staff Research Scientist at Google Research, Bangalore
Prof. Partha Talukdar is currently a Staff Research Scientist at Google Research, Bangalore where he leads a group focused on Natural Language Understanding. He is also an Associate Professor (on leave) in Departments of CDS & CSA, at IISc Bangalore. Partha founded KENOME, an enterprise Knowledge graph company with the mission to help enterprises make sense of unstructured data. His recent research focuses on graph-based learning algorithms for large-scale information extraction and data integration, temporal information processing, automatic knowledge harvesting from large data, and neuro-semantics. Partha is a recipient of several awards, including an Outstanding Paper Award at ACL 2019. He is a co-author of a book on Graph-based Semi-Supervised Learning. He has presented more than 100 papers in international conferences.
Even though there are more than 7000 languages in the world, language technologies are available only for a handful of these languages. Lack of training data poses a significant challenge in developing language technologies for these languages. Recent advances in Multilingual Representation Learning presents an opportunity to transfer knowledge and supervision from high web-resource languages to languages with lower web-resources. In this talk, I shall present an overview of my group's research in this exciting and emerging area.
Associate Professor (A K Singh Chair) Department of CSE, IIT Kharagpur
Dr. Animesh Mukherjee is an Associate Professor and A K Singh Chair in the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur. My main research interests center around (a) investigation of hate and abusive content on social media platforms, (b) fairness and bias in information retrieval systems, (c) media bias, and (d) quality monitoring of Wikipedia articles. Some of the notable awards that I have received INAE Young Engineering Award, INSA Medal for Young Scientist, IBM Faculty Award, Facebook AI and Ethics Research Award, Google Tensorflow award, GYTI Award, Humboldt Fellowship for Experienced Researchers.
Head of Data Science - Recommendations at Huawei Technologies, India
Mr. Prabhat is currently leading the Recommendation efforts on Huawei Video and Music apps. He has 13 years of research and industry experience, primarily creating ML/AI solutions for various unicorns like MakeMyTrip, Paytm, Wynk Music and Airtel Xstream. His primary research interests lie in Recommender Systems, User Modeling, Information retrieval and extraction, Counterfactual evaluations.
Vice President- AI, ShareChat
Mr. Debdoot Mukherjee is currently the Vice President of AI at Sharechat, where he builds, tests, deploys, and drives the data science pipeline at one of the largest Indic language social media network. He previously held positions such as the Head of Data Science at Hike Messenger and Lead Data Scientist at Myntra. He is an IIT-Delhi Gold Medalist with over 15 patents to his name.
Assistant Professor and TBO Group Faculty Fellow at the Department of CSE, IIT Delhi
Prof. Abhijnan Chakraborty is an Assistant Professor at Indian Institute of Technology (IIT) Delhi. His research interests fall under the broad theme of Computing and Society, covering the research areas of Social Computing, Information Retrieval and Fairness in Machine Learning. Prior to joining IIT Delhi, he spent two and half years at the Max Planck Institute for Software Systems (MPI-SWS), Germany as a post-doctoral researcher. He obtained his PhD from Indian Institute of Technology (IIT) Kharagpur, where he was awarded the Google India PhD Fellowship and the Prime Minister's Fellowship for Doctoral Research. Prior to joining PhD, he spent two years at Microsoft Research, working in the area of mobile systems. He has authored several papers in top-tier computer science conferences including WWW, KDD, AAAI, AAMAS, CSCW, ICWSM and MobiCom. His research works have won the best paper award at ASONAM'16 and best poster award at ECIR'19. He is one of the recipients of an internationally competitive research grant from the Data Transparency Lab to advance his research on fairness and transparency in algorithmic systems.
Head of Europe Cloud Competence Center CBG Cloud Service/Helsinki Research Center at Huawei Technologies, Finland
Kuan Eeik Tan joined Huawei as the Head of Europe Cloud Competence Center in 2016. He has
been building up the R&D center for Consumer Cloud Service in Helsinki. He and his teams
focus on creating AI-driven technology for online safety, privacy preserving solution,
visual search and voice search in Petal Search, Petal Maps and Huawei Mobile Services.
During this time, he has also successfully established collaborations with universities
including joint labs in Europe.
Kuan Eeik Tan is a technology leader with over 20 years of professional experience
specializing on cybersecurity, cloud computing, big data and AI. Prior to joining Huawei,
he has long history with F-Secure where he had hold multiple key roles and led many key
initiatives, delivered secure and scalable content cloud solution to operators globally.
In the era of connected world where massive content can be easily created and accessible by
anyone, online abusive content continuous to emerge in various forms increasingly and harm
the online safety of the users and the communities.
In this session, we will discuss the challenges in detecting abusive text such as hate
speech in multilingual settings and our journey in addressing that to create a safe and
healthy HMS ecosystem.
We will cover the complexity in various aspects such as but not limited to low resource,
curse of multilinguality and cultural challenges and the future direction.
AIOPS expert at Huawei Technologies, Ireland
Hu Peng is engaged in the R&D and planning of intelligent network unified communications solutions since 2005. He has rich experience in designing and maintaining complex and highly reliable solutions. In 2015, he took the charge of the design and R&D of some cloud platform services. In 2017, he started to engage in the R&D of AIOPS-related services. In 2018, as a part of Huawei Ireland Research Center he researched AIOPS-related capabilities and build data-driven intelligent O&M capabilities. Currently he is in charge of AIOPS product planning and R&D
With the rapid development of business, how to support real-time storage and analysis of
massive O&M data, support analysis of O&M problem scenarios, and improve the processing
efficiency of the DevOps team in quality assurance abnormal detection, root cause analysis
and etc.
This topic will introduce the AIOps technology practice of Huawei Consumer BG Cloud O&M
Dept. The data platform supports real-time data import and analysis of hundreds of millions
of data per second, and supports digital data governance and real-time monitoring. In
addition, the data intelligent capability for perception, analysis and decision-making is
built to support adaptive abnormal detection of millions of indicators, root cause analysis
of the full process, and decision-making from diagnosis to self-healing. The intelligent
knowledge graph based chatops which connect all these up capabilities and also provide the
data analysis and decision making orchestration capability enables the transformation to
digital SRE, which we think is the final goal of our AIOPS.
Associate professor, Department of Computer Science, UiT The Arctic University of Norway
Dr. Dilip K. Prasad is currently an associate professor at Department of Computer Science, UiT The Arctic University of Norway. He completed PhD from Nanyang Technological University (NTU) in Singapore in 2013 and B.Tech in Computer Science from Indian Institute of Technology(ISM), Dhanbad in 2003. Before joining UiT, he was a senior research fellow at Nanyang Technological University (2015-2019) and a research fellow at National University of Singapore (2012-2015). Before his PhD studies (2003-2008), he worked as a IT professional on smart technology with different globally recognized companies such as Philips, Panasonic, IBM, Mediatek, and Infosys.
Computer vision for performing object segmentation in images and videos is relatively routine. Therefore, when a biologist asks an AI scientists if we can use AI for segmenting sub-cellular structures from optical microscopy images of living cells, one wonders what has been stopping this. The answer lies in the fact that for certain sub-cellular structures, the size of the structures and resolution of the microscopes are comparable. Therefore, the images contain an inaccurate depiction of the reality. Manual segmentation by experts who understand the morphologies of the sub-cellular structures as well as how microscopes distort the morphologies is the only way out. However, manual annotation for quantitative analysis in microscopy videos is a big bottleneck since it takes a few days to segment all mitochondria. We solve this problem by creating a simulated expert. The simulated expert simulates the diverse morphologies of the structures, the distortions introduced by an optical microscope, the target segmentation, as well as the AI model for segmentation. Through this simulation-supervised approach, we contribute a physic-based AI solution, which then reduces the large data annotation bottleneck from several man-months to a few seconds.
Vice President, Machine Learning at Amazon | ACM Fellow
Dr. Rajeev Rastogi, is currently the Vice President of Machine Learning at Amazon India, where he is developing ML platforms and applications for the e-commerce domain. Previously, he was Vice President of Yahoo! Labs Bangalore and the founding Director of the Bell Labs Research Center in Bangalore, India. Rajeev is an ACM Fellow and a Bell Labs Fellow. He is active in the fields of databases, data mining, and networking, and has served on the program committees of several conferences in these areas. He currently serves on the editorial board of the CACM, and has been an Associate editor for IEEE Transactions on Knowledge and Data Engineering in the past. He has published over 125 papers, and holds over 50 patents. Rajeev received his B. Tech degree from IIT Bombay, and a PhD degree in Computer Science from the University of Texas, Austin.
Best practices when developing Machine Learning models such as generating uncertainty estimates for predictions, balancing exploitation with exploration, generating explanations for predictions, handling noise in training data, etc. How we apply these modeling best practices at Amazon using real-world examples from our India e-commerce business.
Institute Chair Professor, Department of CSE, IIT Bombay
Prof. Sunita Sarawagi is currently a Professor in Computer Science and Engineering Department at IIT Bombay, she is also the Professor In Charge of the Center of Machine Intelligence and Data Science at IIT-B. Her current research focuses current research interests are sequence models for text and time-series, domain adaptation, effective human intervention in learning, graphical models and structured learning. She is the recipient of the Infosys Science Foundation Award in Computer Science and Engineering, the Distinguished Alumni Award, IIT Kharagpur, and the H.H. Mathur Award for Excellence in Applied Sciences all in 2019. She was awarded the PAKDD Most Influential Paper in 2014. She received IBM Faculty award twice; in 2003 & 2008.
For various NLP tasks, multilingual language models (mLM) were designed to provide shared representations to enable transfer of task-specific supervision from high resource languages to Low web-resource languages (LRLs). However, incorporating a new language in an LM still remains a challenge, particularly for languages with limited corpora and in unseen scripts. We show how to learn representations for LRLs by bridging the gap between related languages using transliteration, dictionaries, and special alignment loss.
Another challenging low-resource setting is automatic speech recognition for accented speech. We show how to adapt blackbox ASR services for mainstream accents using a fine-grained splicing of the outputs of two models. We address the issue of collecting labeled data for new accents using an efficient data selection algorithm.
Multimedia Lead at Collabora, Canada
Mr. Olivier Crête has been involved in Open Source Software since 2000. He has been involved in GNOME since 2003 and in Gentoo from 2003 to 2012. He currently works for Collabora, where he leads the multimedia team. He's been an active GStreamer developer since 2007, first working on VoIP and video calls, but lately he's been working on all kinds of multimedia projects, particularly around embedded platforms.
PipeWire is the new multimedia stream sharing system. It enables applications running on a device to share access to hardware devices, both audio and camera, but also to share streams with each other. It replaces PulseAudio and JACK on the audio side to provide low latency, low power audio service. It also enables screen sharing for Wayland compositors. PipeWire also provides a security barrier where the policy control is in a separate programmable demon.
In this talk, I'll introduce PipeWire and how it works. I'll also go over WirePlumber, the programmable policy service that makes it easy to create advanced policies specific to the device you're build.
Principal Software Engineer and Solutions Architect at Intel Corporation,
United States
Mr. Neelay Shah is a Principal Software Engineer and Solutions Architect at Intel focused on simplifying the development and deployment of media analytics pipelines. He received his bachelor’s degree in Computer Science from Williams College and his Master’s Degree from the University of Illinois at Urbana Champaign.
DL Streamer and Video Analytics Serving enable developers to quickly develop and deploy optimized video analytics pipelines in the cloud and at the edge. Developers can build pipelines to analyze video and audio streams, capture results and send them to the cloud, and create actionable insights within minutes all while leveraging the efficiency and computational power of Intel hardware. Join us to learn how to incorporate DL Streamer and Intel hardware into your media analytics solutions and leverage Intel’s developer resources to accelerate your time to market today.
Product Manager at Intel Corporation, United States
Ms. Neena Maldikar is a Product Manager for Intel® Edge Software Hub. Prior to this role, she was a product manager for Intel’s OpenVINO Toolkit – DL Streamer, Video Analytics Serving and Intel® User Awareness Service. She has also worked as a Software engineer at Intel and developed libraries and use cases for contextual awareness. She has a master’s degree in Computer Science from Portland State University.
DL Streamer and Video Analytics Serving enable developers to quickly develop and deploy optimized video analytics pipelines in the cloud and at the edge. Developers can build pipelines to analyze video and audio streams, capture results and send them to the cloud, and create actionable insights within minutes all while leveraging the efficiency and computational power of Intel hardware. Join us to learn how to incorporate DL Streamer and Intel hardware into your media analytics solutions and leverage Intel’s developer resources to accelerate your time to market today.
Professor, Department of CSE IIT Kharagpur
Prof. Sudip Misra is a Professor and Abdul Kalam Technology Innovation National Fellow in the Department of Computer Science and Engineering at the Indian Institute of Technology Kharagpur. He received his Ph.D. degree in Computer Science from Carleton University, in Ottawa, Canada. His current research interests include Wireless Sensor Networks and Internet of Things. Professor Misra has published over 350 scholarly research papers and 12 books. He has won nine research paper awards in different conferences. He was awarded the IEEE ComSoc Asia Pacific Outstanding Young Researcher Award at IEEE GLOBECOM 2012, California, USA. He was also the recipient of several academic awards and fellowships such as the Faculty Excellence Award (IIT Kharagpur), Young Scientist Award (National Academy of Sciences, India), Young Systems Scientist Award (Systems Society of India), Young Engineers Award (Institution of Engineers, India), (Canadian) Governor General’s Academic Gold Medal at Carleton University, the University Outstanding Graduate Student Award in the Doctoral level at Carleton University and the National Academy of Sciences, India – Swarna Jayanti Puraskar (Golden Jubilee Award), Samsung Innovation Awards-2014 at IIT Kharagpur, IETE-Biman Behari Sen Memorial Award-2014, and the Careers360 Outstanding Faculty Award in Computer Science for the year 2018 from the Honourable Minister for Human Resource Development (MHRD) of India.
Internet of Things (IoT) allows users to connect diverse kinds of devices to accumulate real-time data from the exclusive domain. The vast amount of complex data collected from the IoT domain is hard to process and analyze by utilizing regular tools. Further, the data in IoT are polymorphous, heterogeneous, large in quantity, and time-limited. There are also some difficulties in data transmitting as well as maintaining interoperability which necessitates the need for programming and statistical approaches for analyzing and managing the data. This talk will explore characteristics of data in IoT, various properties/ requirements for processing data in IoT driven technologies, and the intent to ascertain the emphases and difficulties of data processing in IoT. This talk will discuss our various existing works, which utilize virtual sensing, fog computing, edge intelligence, blockchain, and others to process and analyze gigantic data of IoT. The talk will also discuss various types of databases used to handle IoT data and their suitability for the same. Lastly, this talk will also address the security issues and the methods used to solve these.
Adjunct Faculty Member, IISC | Co-Founder and Chief Scientist, NameUAVIO Labs | Fastcode AI | Perceptive Code LLC
Dr. Arjun Jain is an Adjunct Faculty at the Center for Data Sciences (CDS dept.) at Indian Institute of Science (IISc Bangalore). He received his Ph.D. in Computer Science from the Max-Planck Institute for Informatics in Germany and worked with 2020 Turing award winner Yann LeCun during his postdoc at NYU. He is part of the Theano development team and a contributor to Torch - both of which are widely used libraries for Deep Learning. Dr. Arjun Jain is also Founder at UAVIO Labs and Fast Code AI. Previously, he co-founded Perceptive Code LLC, a Silicon Valley startup that builds intelligence into automobiles. Arjun has worked for several companies, including Yahoo! in Bangalore, Weta Digital in New Zealand and Apple in Cupertino. He has been especially active in the human sensing related industries. Arjun served as a developer for Weta Digital’s motion capture system. This system has been used in many feature films, and Arjun was credited for his work in Steven Spielberg’s, The Adventures of Tintin (2011). Arjun’s work has resulted in several academic publications with currently over 5000 citations, patents, and has been featured by mainstream media, including in the magazines: New Scientist, Discovery, BCC, Vogue, Wired, India Today, and The Hollywood Reporter, among other outlets.
In this talk, we will take a closer look at Autonomous Vehicles and their various subsystems. We start with a bit of history and then dig into the various modules such as perception, localization, prediction, planning and control that enable Autonomous Driving. We will then address the enabling technologies that make a vehicle autonomous and how these technologies are integrated together to allow a vehicle to navigate streets autonomously. Next, we will discuss their current challenges and brainstorm how to overcome them. Finally, I will talk about my current research and collaboration with industry where we are working on the gesture and intent of pedestrians to infer a more detailed model of their behavior so that the vehicle is able to cope with more complex scenarios in urban environments. While a human driver makes constant judgments and driving decisions based on perceived VRU context cues, such as intention (e.g. inferred by head and body orientation, gait cycle) or explicit gestures (e.g. cyclist hand gestures), this also motivates the estimation and use of such cues for future driver assistance or automated driving systems.
Technologist, Computer Vision & Machine Learning at Ittiam Systems Pvt Ltd
Binu E R has over two decades of experience building software solutions for Telecom,
Automotive, Media Streaming and IoT market space. He has architected, designed and
implemented components spanning from device drivers to cloud systems to realize products
involving multiple operating systems (including Linux, Android, GHS Integrity, VxWorks and
pSOS).
Currently as Technologist, Binu is anchoring a computer vision, machine learning and AI
based system for retail analytics as well as video systems
Prior to Ittiam Binu was working at Wipro Technologies Bangalore. He holds a Master's
Degree in Computer Science from Cochin University of Science and Technology as well as a
Master's Degree in Physics from IIT Madras.
Video4Linux was the first major Linux video capture framework for accessing video from cameras, TV tuners and other video devices. With more complex video processing systems, the complete image processing pipeline from application developer's perspective became very unwieldy. Pipewire and libcamera tries to address these challenges with a promise of near zero latency for video capture.
Co-Founder, VP Engineering at Pathpartner Technology
Keshav is co-founder, VP - Engineering in Pathpartner technology pvt. Pathpartner is a
focused engineering services company, working on enabling intelligent systems with products
and solutions.
Keshav has more than 2 decades of experience working from Video switcher systems to mobile
MM systems. He was instrumental in developing MM framework on very early days of Mobile
systems. He led the team which developed the MM framework, which was contender to be
selected in one of the very popular open source Mobile OS. He has worked extensively in
Microsoft DSHOW frameworks , Gstremer framework. He led a team which developed complex
multiparty video conferencing and video sharing system across different platforms using
Gstremer frameworks.
Over the time the strain on the networks for streaming multimedia data has grown exponentially along with new devices & use cases coming into the market. There are multimedia framework systems which have undergone enormous improvements over the time and will need further enhancements to cater this need. The important challenges these frameworks are facing repeatedly are new codecs, streaming protocols, bandwidth adaptation, and latency. We will be covering these challenges over the presentation.
Senior Technical Manager at Pathpartner Technology
Senior Professor, Department of CDS & CSA, IISC Bangalore | President of ACM India Council
Prof. Jayant R. Haritsa is a Senior Professor in the Department of Computational and Data Sciences and the Department of Computer Science and Automation at the Indian Institute of Science (IISc) in Bangalore. His core research interests are the design, implementation and testing of database systems. He is known for pioneering contributions to the design and optimization of database engines that form the core of modern enterprise information systems. He has also produced software tools for SQL query optimization and relational database testing. Prof. Haritsa is the recipient of the Shanti Swarup Bhatnagar Prize for Science and Technology in 2009, as well as the 2014 Infosys Prize in Engineering and Computer Science. A current member of the ACM India Council, he was named an ACM Fellow (2015) for contributions to the theory and practice of data management systems. He received his Ph.D. Computer Science from University of Wisconsin (Madison).
Chief architect of Huawei Kunpeng Big Data Technology, Director of the Data Application Lab at Huawei Technologies, Canada
Mr. Ken Zhang is the Chief architect of Huawei Kunpeng big data technology, director of the data application lab, and PMC chairman of the openLooKeng community. He has 20 years of experience in big data development, and has worked on global big data projects at Thomson Reuters and IBM. Joined Huawei in 2018 and took charge of Huawei's big data technology planning and technical competitiveness building. He envisions a simplified big data platform in the hope of higher system and human efficiency. He leads the overall development of analytics engine to support both AI and BI use cases requiring 100+ms response time through standard interfaces. In his current role, he provides leadership and guidance to R&D members in Toronto, Shenzhen, Chengdu, Bangalore and Dublin.
CTO Data Systems & AI at Tel Aviv Research Center, Huawei Technologies, Israel
Dr. Eliezer Levy is currently the head of Pnueli Lab for Data Systems and AI in the Tel Aviv
Research Center of Huawei. He heads a team of Database, Systems and AI experts that provide
technical leadership and innovation to Huawei’s Software products, such as openGauss and
Mindspore.
Before joining Huawei in 2012, Eliezer Levy established and managed the SAP Research group
in Israel from 2008. Prior to that, he was the Chief Architect of the Small Businesses
business unit in SAP responsible for the popular Business One product. Eliezer has over 30
years of experience in various research, development and management roles in Huawei, SAP,
Intel, Tandem Computers (Non-Stop SQL product line) and an Israeli startup. Eliezer has a
Ph.D. in Computer Sciences from the University of Texas at Austin in the area of databases,
and a B.Sc. in Computer Science from the Technion. Eliezer has numerous academic
publications and patents.
Pnueli Lab is a Research and Development group in the areas of Data Systems and AI in the Tel Aviv Research Center of Huawei. In the Lab, we are promoting innovation by integrating techniques from multiple domains: Machine Learning, Databases, Datacenter Networking and Operating Systems. In this talk I will share some of our experiences in working on synergies between the methodically different disciplines of AI and Systems. I will review a spectrum of examples: In-Network Tensor Manipulation, Vector-DB and large-scale KNN systems, and finally using Semantics and AI for the vision of Data-Driven Systems.
Dean’s Chair Associate Professor, National University of Singapore
Dr. Bingsheng He is currently a Dean’s Chair Associate Professor and Vice-Dean (Research) at School of Computing, National University of Singapore. Before that, he was a faculty member in Nanyang Technological University, Singapore (2010-2016), and held a research position in the System Research group of Microsoft Research Asia (2008-2010), where his major research was building high performance cloud computing systems for Microsoft. He got the Bachelor degree in Shanghai Jiao Tong University (1999-2003), and the Ph.D. degree in Hong Kong University of Science & Technology (2003-2008). His current research interests include cloud computing, database systems and high performance computing. He has served in editor board of international journals, including IEEE Transactions on Cloud Computing (IEEE TCC), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), Springer Journal of Distributed and Parallel Databases (DAPD) and ACM Computing Surveys (CSUR). He is an ACM Distinguished member (class of 2020).
Graphs are de facto data structures for many data processing applications, and their volume is ever growing. Many graph processing tasks are computation intensive and/or memory intensive. Therefore, we have witnessed a significant amount of effort in accelerating graph processing tasks with heterogeneous architectures like GPUs, FPGAs and even ASIC. In this talk, we will first review the literatures of large graph processing systems on heterogeneous architectures. Next, we present our research efforts, and demonstrate the significant performance impact of hardware-software co-design on designing high performance graph computation systems and applications. Finally, we outline the research agenda on challenges and opportunities in the system and application development of future graph processing.
Chief Executive Officer, University of Queensland - IIT Delhi Academy of Research (UQIDAR) Adjunct Faculty at Department of CSE, IIT Delhi | ACM Distinguished Scientist
Prof. Rajeev Shorey is the CEO of The University of Queensland – IIT Delhi Academy of Research (UQIDAR). Rajeev also serves as an adjunct faculty in the Computer Science & Engineering department at IIT Delhi. His areas of interest are Wireless Networks including 5G Networks, Telematics, IoT, Industrial IoT, IoT Security and Automotive Networks, including Automotive Cybersecurity. He received his Ph.D. and M.S. (Engg) in Electrical Communication Engineering from the Indian Institute of Science (IISc), Bangalore, India. His career spans several reputed research labs – TCS Research & Innovation, General Motors (GM) India Science Laboratory (ISL), IBM India Research Laboratory and SASKEN Technologies. Dr. Shorey’s work has resulted in more than 70 publications in international journals and conferences and several US patents. For his contributions in the area of Communication Networks, Dr. Shorey was elected a Fellow of the Indian National Academy of Engineering in 2007. Dr. Shorey was recognized by ACM as a Distinguished Scientist in December 2014. He is a Fellow of the Institution of Electronics and Telecommunication Engineers, India and a Senior Member of IEEE. He serves on the editorial boards of two of the top journals in the area – IEEE Internet of Things Journal and Springer’s Journal of Wireless Networks.
