ARCS 2021 held at PSG College of Technology, Coimbatore, India
Co-located with the ACM India Annual Event, ARCS (formerly IRISS) is a premier ACM India conference spanning two days. The program features:
The audience comprises students, faculty and industry leaders from across India coming together in an interactive environment, such as panel discussions, informal "Ask me Anything" sessions with the ARCS speakers as well as Annual Event invitees like Turing Award laureates.
Editions of ARCS
ARCS Keynote
Sunita Sarawagi (IIT Bombay)
Talks by Early Career Researchers
Experiences of young leaders from academia and industry.
Gayathri Ananthanarayanan (IIT Dharwad)
Praphul Chandra (KOINEARTH)
Kuldeep S. Meel (NUS, Singapore)
Rijurekha Sen (IIT Delhi)
Divy Thakkar
(Google Research)
Early Career Researcher Awardee Keynote
Keynote talks by the winner of ACM India Early Career Researcher Award for 2020.
Saket Saurabh (IMSc, Chennai)
Uday Reddy Bondhugula (IISc, Bangalore)
03DDA Awardee Talk
Talks by Doctoral Dissertation Awardee
Shikhar Vashishth, CMU, USA
Roohani Sharma, MPII, Germany
Contributed Talks
Talks spread out across different sessions.
05Lightning Talks
Short presentations across different sessions.
06Poster Presentations
Posters will be on display in the foyer area at all times.
07Panel Discussion
Discussion among eminent researchers and Q&A with the audience
08Institutional Awareness Session
Conversation with leaders from industry, including audience interaction
09Interesting competitions and prizes to be won
We also have planned interesting virtual competitions for participation and interactions. Register and participate for an active learning experience!
10
Event Speakers
Divy Thakkar
ResearcherGoogle Research ACM ARCS 2021
ECR keynote speaker
I like to believe I am a researcher + solutionist and I make sense of very abstract problems in the areas of Education and Access (broadly) by designing/implementing scalable high-impact programs. Since 2017, I am a Program Manager at Google Research, leading Education and Research Programs which aim to strengthen CS Research in universities, Computer Science and Primary Education at K-12 and. I work with Milind Tambe to lead Strategy and Operations for our AI for Social Good efforts in India.
Sessions Details
Gayathri Ananthanarayanan
Assistant ProfessorComputer Science and Engineering, IIT Dharwad ACM ARCS 2021
ECR keynote speaker
Her research interests lie in the broad areas of computer architecture and embedded systems, with specific focus on Power, Thermal and Performance modeling, characterization and management.
Sessions Details
Praphul Chandra
Founder and Chief ScientistKoineArth ACM ARCS 2021
ECR keynote speaker
Dr. Praphul Chandra is the Founder of KoineArth which works at the intersection of Blockchains & Supply Chains. He is also a professor of data science and machine learning at the International School of Engineering (Insofe). Prior to this, he was Principal Data Scientist at Hewlett Packard Enterprise. His other industry experience includes positions at HP Labs and Texas Instruments. He has an undergraduate degree in Electronics engineering from IIT BHU, a post graduate degree in Electrical Engineering from Columbia University, NY, a post graduate diploma in public policy from University of London and a PhD in Game Theory & Mechanism Design from the Indian Institute of Science.
Sessions Details
Kuldeep S. Meel
Assistant Professor,Department of Computer Science, School of Computing
National University of Singapore ACM ARCS 2021
ECR keynote speaker
His research interests are at the intersection of artificial intelligence and formal methods, which also lies at the intersection of theory and practice. His group strongly believes (and acts upon the beliefs) in developing mathematical frameworks whose faithful implementations can handle practical applications. He is also a receipient of 2019 NRF Fellowship for AI.
Sessions Details
Rijurekha Sen
Assistant ProfessorComputer Science and Engineering, IIT Delhi ACM ARCS 2021
ECR keynote speaker
I am currently an Assistant Professor in Computer science and Engineering, IIT Delhi. My research interests lie in building distributed, networked and privacy-aware systems, focused on problems at the boundary of information technology and society. I have built systems for road traffic monitoring, human mobility measurements, public policy audit and privacy enhancement in ubiquitous systems, among others. I like to work on problems where I can apply inter-disciplinary CS techniques and the solutions might potentially have societal impact, especially in developing regions. I like the tension created by methods with conflicting requirements, when incorporated in the same system. E.g. some sensing task might give best results with high-dimensional deep learning based features extracted from the data. But if those features need to be securely transmitted to a remote server for privacy reasons, cryptography will pose a conflicting requirement for the plaintext message to be small, to minimize network bandwidth.
Sessions Details
Saket Saurabh
ProfessorTheoretical Computer Science, IMSc Chennai ACM ARCS 2021
Winner of ACM India Early Career Researcher Award 2020
ECR Awardee Keynote Speaker
I am a Professor of Theoretical Computer Science at the Institute of Mathematical Sciences, Chennai, India. I am also affiliated to Department of Informatics, University of Bergen, Norway (as a Professor). My other affiliations include Adjunct Professor at Indian Statistical Institute (ISI) Kolkata (2019-2024) and a member of IRL 2000 ReLaX. I am interested in designing efficient algorithms (or prove it does not exist) for hard problems arising in every domain. In particular I design algorithms whose running time is analyzed in terms of different input parameters. In particular, I am interested in Multivariate Complexity or its two variable avatar Parameterized Complexity. My other interests include Graph Theory, Matroids, Matching Theory and Approximation Algorithms. In short my current research interests include: Parameterized Complexity, Moderately Exponential Time Algorithms, Graph Theory and Approximation Algorithms
Sessions Details
Uday Reddy Bondhugula
Associate ProfessorComputer Science and Automation, IISc Bangalore ACM ARCS 2021
Winner of ACM India Early Career Researcher Award 2020
ECR Awardee Keynote Speaker
My research interests are in the design of new programming and compiler technologies with an emphasis on high performance and automatic parallelization. Computational domains of particular interest to me include stencil computations, image processing pipelines, dense linear algebra, and deep learning. Before joining IISc, I was with the Advanced Compiler Technologies group at the IBM T.J. Watson Research Center, Yorktown Heights, New York. I obtained my Ph.D. in Computer Science and Engineering from the Ohio State University in 2008, and my Bachelors' (also in Computer Science and Engineering) from the Indian Institute of Technology, Madras in 2004. I am the primary author and maintainer of PLUTO. Other tools from my group include PolyMage. While on a sabbatical with the Google brain team, I was a founding team member of MLIR. More information on my research and publications can be found here. I am the founder of PolyMage Labs, a deep tech startup being incubated at the Indian Institute of Science since May 2019.
Sessions Details
Sunita Sarawagi
ProfessorComputer Science and Engineering, IIT Bombay ACM ARCS 2021
Keynote Speaker
My topics of interest span several fields including machine learning, data analytics, databases and statistics. My current research interests are sequence models for text and time-series, domain adaptation, effective human intervention in learning, graphical models and structured learning.
Sessions Details
Shikhar Vashishth
Carnegie Mellon University, Pittsburgh, USA ACM ARCS 2021Winner of ACM India Doctoral Dissertation Award 2020
DDA Keynote speaker
I am currently working as a Postdoctoral Researcher at Language Technologies Institute, Carnegie Mellon University under Carolyn Rose. Previously, I completed my PhD from Indian Insitute of Science under the guidance of Partha Pratim Talukdar, Chiranjib Bhattacharyya, and Manaal Faruqui. My thesis topic was on Neural Graph Embedding Methods for Natural Language Processing. I have been a recipient of the prestigious Google PhD Fellowship during my PhD and have got opportunity to intern at Google Research, NYC and Microsoft. I completed my graduation from BITS Pilani, Pilani in 2016.
Sessions Details
An Opportunity to Connect, Learn, Network
As a part of ARCS 2021, a large number of technical sessions is being organized on diverse topics where researchers from top-notch universities and scientists from R & D industries expound on various research domains.
Call for Papers
Paper Submission
ACM India announces the 15th Academic Research and Careers for Students
(ARCS) Symposium, Coimbatore, India. Earlier, the event was known as
IRISS. ARCS invites research scholars of computer science and allied areas
in India to showcase their recent work (either published in 2020 or accepted
in 2020 for publication) to a conclave of researchers and potential employers.
Apart from the contributed talks and poster presentations, ARCS 2021
will comprise talks by the ACM-India Doctoral Dissertation Award recipient (DDA), Early
Career
Research (ECR) Awardee and an invited Keynote
Speaker. Further to this there will be four talks by early career researchers
discussing the speaker’s transition from PhD to their career and job, their
expectations, disappointments, why they chose what they did etc. Finally,
we will also have several panel discussions.
Submissions will be in electronic form via EasyChair. Submissions must not exceed 2 pages
(including the title page, but excluding
bibliography). Submissions should begin with a title followed by the names
and affiliations of all co-authors. This should be followed by a detailed abstract and the
details of the venue where the work has either appeared or
been accepted for publication. Submissions should be no more than two
pages long. The usage of pdflatex and the ARCS style file are mandatory;
no changes to font size, page geometry, etc. are permitted. Because of ongoing
pandemic we will accommodate virtual presentations, if needed.
Click
<< here >> to download the ARCS style file.
Important Dates
Abstract Submission extended deadline: !!! Submission closed !!!
Notification to Authors: 1 st December, 2020
Accepted Papers
Full Paper
- Arpita Patra and Ajith Suresh. BLAZE: Blazing Fast Privacy-Preserving Machine Learning
- Arpita Patra, Thomas Schneider, Ajith Suresh and Hossein Yalame. ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation
- Utpal Bora, Santanu Das, Pankaj Kukreja, Saurabh Joshi, Ramakrishna Upadrasta and Sanjay Rajopadhye. LLOV: A Fast Static Data-Race Checker for OpenMP Programs
- Dileep Kumar Pattipati, Rupesh Nasre. and Sreenivasa Kumar P. Ontology-based Program Analysis Framework
- Rajesh Kedia, Shikha Goel, M. Balakrishnan, Kolin Paul and Rijurekha Sen. Design Space Exploration of FPGA Based System with Multiple DNN Accelerators
- Nikhil Gupta, Chandan Saha and Bhargav Thankey. A Super-Quadratic Lower Bound for Depth Four Arithmetic Circuits
- Lokesh Siddhu, Rajesh Kedia and Preeti Ranjan Panda. Leakage Aware Dynamic Thermal Management of 3D Memories
- Tirthankar Ghosal, Vignesh Edithal, Asif Ekbal, Pushpak Bhattacharyya, Sameer Chivukula and George Tsatsaronis. Is Your Document Novel? Let Attention Guide You. An Attention Based Model For Document Level Novelty Detection
- Aasheesh Dixit, Garima Shakya, Suresh Kumar Jakhar and Swaprava Nath. Egalitarian and Congestion Aware Truthful Airport Slot Allocation Mechanism
- Sriram Bhyravarapu and Subrahmanyam Kalyanasundaram. Combinatorial Bounds for Conflict-free Coloring on Open Neighborhoods
- Kartik Sharma, Iqra Altaf Gillani, Sourav Medya, Sayan Ranu and Amitabha Bagchi. Balance Maximization in Signed Networks via Edge Deletions
- Girija Limaye. Envy-freeness and Relaxed Stability : Hardness and Approximation Algorithms
- Supratim Shit, Rachit Chhaya, Jayesh Choudhari and Anirban Dasgupta. Streaming Coresets for Symmetric Tensor Factorization
- Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu and Ambuj Singh. GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs
Short Paper
- Varshini Subhash, Karran Pandey and Vijay Natarajan. GPU Parallel Computation of Morse-Smale Complexes
- Koundinya Koorapati, Prem Kumar Ramesh, Sairam Veeraswamy, Rubini P and Usha Narasappa. Cohesive Context-Aware Ontology for IoT Deployed with SDDC
- Charu Sharma and Manohar Kaul. Self-Supervised Few-Shot Learning on Point Clouds
- Neeldhara Misra and Aditi Sethia. Fair Division is Hard even for Amicable Agents
- Stanly Samuel, Kaushik Mallik, Anne-Kathrin Schmuck and Daniel Neider. Resilient Abstraction-Based Controller Design
- Arijit Nath, Sukarn Agarwal and Hemangee Kapoor. Reuse Distance based Victim Cache for Effective Utilisation of Hybrid Main Memory System
- Rachit Chhaya, Anirban Dasgupta and Supratim Shit. On Coresets For Regularized Regression
- Shivangi Singhal, Anubha Kabra, Mohit Sharma, Rajiv Ratn Shah and Ponnurangam Kumaraguru. SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning
- Parwat Singh Anjana, Hagit Attiya, Sweta Kumari, Sathya Peri and Archit Somani. Efficient Concurrent Execution of Smart Contracts in Blockchains using Object-based Transactional Memory
Poster/Speed Talk
- Rampriya R S and Suganya R. 3D Point Cloud: Supervoxel Based Segmentation
- Prakash Babu Yandrapati, Rajagopal Eswari and K Nimmi. Malayalam-English Code Mixed Sentiment Analysis Using Sentence BERT And Sentiment Features
- Elizabeth B Varghese, Sabu M. Thampi and Stefano Berretti. A Psychologically Inspired Fuzzy Cognitive Deep Learning Framework to Predict Crowd Behavior
- Saurabh Gupta, Anant Agarwal, Suryatej Reddy Vyalla, Arun Balaji Buduru and Ponnurangam Kumaraguru. #IVoted to #IGotPwned: Studying Voter Privacy Leaks in Indian Lok Sabha Elections on Twitter
- Saurabh Gupta, Siddhant Bhambri, Karan Dhingra, Arun Balaji Buduru and Ponnurangam Kumaraguru. Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments
- Saurabh Gupta, Arun Balaji Buduru and Ponnurangam Kumaraguru. imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks
- Aishwarya Venkatesh, Yamini Agarwal, Yash Patil, Vidhu Rojit and Viraj Kumar. Collaborative Classroom
- Palash Das and Hemangee K. Kapoor. CLU: A near-memory accelerator exploiting the parallelism in Convolutional Neural Networks
- Madhu Kumari, Ananya Misra, Sanjay Misra, Luis Fernandez Sanz, Robertas Damasevicius and V.B. Singh. Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug
- Ritwik Mishra, Aanshul Sadaria, Shashank Srikanth, Kanay Gupta, Himanshu Bhatia, Pratik Jain, Ponnurangam Kumaraguru and Rajiv Ratn Shah. Analyzing Traffic Violations through e-challan System in Metropolitan Cities (Workshop Paper)
- Karthika Veeramani and Suresh Jaganathan. Land Registration: Use-case of e-Governance using Blockchain Technology
- Jayant Vyas, Debasis Das and Sajal K Das. FedCom: Federated Learning Based Stress-Free Driver Recommendation System
- Vinita Verma, Sunil K. Muttoo and V. B. Singh. Multiclass malware classification via texture statistics
Schedule Details Information of Event Schedules
ARCS is committed to making participation in the event a meaningful experience for everyone, regardless of level of experience, in the Computing field.
Day 1 - 11th February 2021
Opening Remarks
Meenakshi D'Souza, IIIT Bangalore
(Chair, Steering Committee, ARCS)
Saket Saurabh, IMSc Chennai
(Chair, Program Committee, ARCS)
Prof. K. Prakasan
(Principal, PSG College of Technology)
Contributed Talks
Paper presentations of the accepted full papers
Chairperson: Mrinal Kumar (IIT, Bombay)
Break
ACM-India Early Career Researcher Awardee Presentations
Chairperson: Anand Deshpande (Persistent Systems)
Title: Invitation to Multivariate Algorithms
Saket Saurabh (IMSc, Chennai)
Title: Building High-Performance Compiler Infrastructure using the Polyhedral Framework
Uday Reddy Bondhugula (IISc, Bangalore)
Poster via Speed Talks & Lunch Break
Chairperson: Saket Saurabh (IMSc, Chennai)
Panel Discussion: Diversity and Inclusivity in Research
Chairperson: Meenakshi D'Souza (IIIT, Bangalore)
Neeldhara Misra (IIT, Gandhinagar) - Moderator
Ranjita Bhagwan (Miscrosoft Research)
R. Ramanujam (IMSc, Chennai)
Venkatakrishnan Ramaswamy (BITS, Pilani)
Contributed Talks
Paper presentations of the accepted full papers
Chairperson: Meenakshi D'Souza (IIIT, Bangalore)
Break
Early Career Researchers Talks
Chairperson: Venkatesh Raman (IMSc, Chennai)
Gayathri Ananthanarayanan (IIT Dharwad)
Praphul Chandra (KOINEARTH)
Kuldeep S. Meel (NUS, Singapore)
Rijurekha Sen (IIT Delhi)
Divy Thakkar (Google Research)
Day 2 - 12 th February 2021
ARCS Keynote Presentation
Chairperson: Saket Saurabh (IMSc, Chennai)
Machine Learning Models: The
Challenges of Real-World Deployment
Sunita
Sarawagi (IIT, Bombay)
Contributed Talks
Paper presentations of the accepted full papers
Chairperson: Swaprava Nath (IIT, Kanpur)
Break
Contributed Talks
Paper presentations of accepted full papers
Chairperson: Abir De (IIT, Bombay)
Short Talks
Paper presentations of the accepted short papers
Chairperson: Saket Saurabh (IMSc, Chennai)
Lunch break & Posters
Institutional Awareness Panel
Title: Industry Research in the Post Pandemic World
Chairperson: Hemant Pande (Director, ACM India)
Manish Gupta (Google Research) - Moderator
Gautam Shroff (TCS Research)
Pari Natarajan (Zinnov)
Shalini Kapoor (IBM AI Apps)
Sriram Rajamani (Microsoft Research India)
Suparna Bhattacharya (HP Enterprise)
ACM-India Doctoral Dissertation Awardee Presentations
Chairperson: Lipika Dey (Principal Scientist, TCS Research)
Title: Neural Graph Embedding Methods for Natural Language Processing
Shikhar Vashishth (IISc, Bangalore / CMU, USA)
Title: Advancing the Algorithmic Tool-kit for Parameterized Cut Problems
Roohani Sharma (IMsc, Chennai / MPII, Germany)
Group Photo and Break
"Ask Me Anything" Session
Committees
Programme Committee
Varsha Apte (IIT Bombay)
Sorav Bansal (IIT Delhi)
Arijit Bishnu (ISI Kolkata)
Sourav Chakraborty (ISI Kolkata)
Abir De (IIT Bombay)
Mrinal Kumar (IIT Bombay)
Neeldhara Misra
(IIT Gandhinagar)
Swaprava Nath (IIT Kanpur)
Biswabandan Panda (IIT Kanpur)
Arpita Patra (IISc Bangalore)
Krithika Ramaswamy
(IIT Palakkad)
Sayan Ranu (IIT Delhi)
Saket Saurabh
(IMSc Chennai), chair
Mayank Vatsa (IIT Jodhpur)
Steering Committee
Meenakshi D'Souza
(Chair, IIIT, Bangalore)
Jayant R. Haritsa
(IISc Bangalore)
Ponnurangam Kumaraguru
(IIIT, Delhi)
Hemant Pande
(Executive Director, ACM India)
Shekhar Sahasrabudhe
(COO, ACM India)
Organizing Committee
R. Nadarajan
(PSG TECH, CBE)
Ranga Rajagopal
(Director & CEO, ACENET Technologies, CBE)
Suresh Balusamy
(PSG TECH, CBE)
Frequently Asked Questions
Who helps us Platinum Sponsors
Silver Sponsors
Reach Us
PSG College of Technology, Coimbatore
Peelamedu, Avinashi Road
Coimbatore, Tamilnadu, India
Contact info
Name: Program Committee
Email: acmindia.arcs@gmail.com
Local Organising Committee
Name: Prof. R. Nadarajan
Email: rn.amcs@psgtech.ac.in
Invitation to Multivariate Algorithms
Abstract: In this talk we will give a brief introduction to the area of Multivariate algorithms. The talk will consist of challenges, excitement, thrills and road ahead of the area, from the perspective of speakers' work.
Machine Learning Models; The Challenges of Real-World Deployment
Abstract: Modern machine learning is characterized by two trends: First, the training data is huge, and often a mixture of several distributions, and model training incurs tremendous energy costs. Second, a model once trained is required to serve diverse real-world settings where the training distribution may not match the test distribution. In this talk, I will discuss current research on handling this distribution mismatch. The talk will span over topics like domain adaptation, domain generalization, out of distribution detection, and robustness.
Neural Graph Embedding Methods for Natural Language Processing
Abstract: Graphs are all around us, ranging from citation and social networks to Knowledge Graphs (KGs). They are one of the most expressive data structures which have been used to model a variety of problems. Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them. Recent research has resulted in the development of several large KGs; examples include DBpedia, YAGO, NELL, and Freebase. However, all of them tend to be sparse with very few facts per entity. For instance, NELL KG consists of only 1.34 facts per entity. In the first part of the thesis, we propose three solutions to alleviate this problem: (1) KG Canonicalization, i.e., identifying and merging duplicate entities in a KG, (2) Relation Extraction which involves automating the process of extracting semantic relationships between entities from unstructured text, and (3) Link prediction which includes inferring missing facts based on the known facts in a KG. For KG Canonicalization, we propose CESI (Canonicalization using Embeddings and Side Information), a novel approach that performs canonicalization over learned embeddings of Open KGs. The method extends recent advances in KG embedding by incorporating relevant NP and relation phrase side information in a principled manner. For relation extraction, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KGs for improved relation extraction. Finally, for link prediction, we propose InteractE which extends ConvE, a convolutional neural network-based link prediction method, by increasing the number of feature interactions through three key ideas -- feature permutation, a novel feature reshaping, and circular convolution. Through extensive experiments on multiple datasets, we demonstrate the effectiveness of our proposed methods.
Advancing the Algorithmic Tool-kit for Parameterized Cut Problems
Abstract: In this thesis we consider several (di)graph cut problems and study them from the perspective of parameterized complexity and kernelization. The goal of the study is three-fold: first to extend the otherwise limited understanding of parameterized cut problems on directed graphs; second to extend, and present novel applications of, the existing rich toolkit for undirected cut problems and; third to develop tools that allow the reuse of algorithms to solve the respective problems in the presence of an additional constraint. The concrete questions addressed in the thesis are inspired from some major open problems and concerns in the area. Some of these being the famously active open problem of the existence of a polynomial kernel for Directed Feedback Vertex/Arc Set, sub-exponentiality in FPT beyond tournaments, parameterized algorithms for partitioning problems beyond the classical partitioning problems, the existence of single exponential FPT algorithms for stable versions of classical cut problems and the parameterized complexity of Stable Multicut. We address the above questions either in full, or extend (possibly all) the results known in literature that take steps towards resolving the respective question.
Building High-Performance Compiler Infrastructure using the Polyhedral Framework
Abstract: This talk is on building compiler infrastructure for emerging programming models for the multicore and accelerator era. The advent of high-productivity programming models and high-performance accelerator chips (especially for machine learning and AI) brings in new challenges on how the next generation of compilers should be built for modularity and reusability. This talk will cover the role of the polyhedral compiler abstraction and techniques towards achieving these goals.