About ARCS ARCS 2022

Co-located with the ACM India Annual Event, ARCS (formerly IRISS) is a premier ACM India conference spanning two days. The program features:

  • Research scholars including the awardee(s) of ACM India best doctoral dissertation award talking about their published work,
  • Eminent early career researchers from industry and academia sharing their experiences, perceptions and ground realities of PhD, and the professional life thereafter,
  • 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

    2021: PSG College of Technology
    2020: IIT Gandhi Nagar
    2019: Rajagiri School of Engineering and Technology - Kochi
    2018: VNIT/Persistent Nagpur
    2017: Calcutta University/Amity University - Kolkata
    2016: Techno Park - Trivandrum
    2015: BITS Pilani - Goa
    2014: IIT Delhi
    2013: IIT Madras
    2012: Pune
    2011: Hyderabad
    2010: Bangalore
    2009: IIT Guwahati
    2007: IIIT Hyderabad
    2006: IIT Madras
    2005: IIT Kanpur
    2004: IIT Bombay
    2003: IIT Delhi
    2002: IISc

    Early Career Researcher Awardee Keynote

    Prateek Jain

    Google Research, India

    Speaker Bio

    Prateek Jain is a research scientist at Google Research India and an adjunct faculty member at IIT Kanpur. Earlier, he was a Senior Principal Researcher at Microsoft Research India. He obtained his PhD degree from the Computer Science department at UT Austin and his BTech degree from IIT Kanpur. He works in the areas of large-scale and non-convex optimization, high-dimensional statistics, and ML for resource-constrained devices. He wrote a monograph on Non-convex Optimization in Machine Learning summarizing many of his results in non-convex optimization. Prateek regularly serves on the senior program committee of top ML conferences and is an action editor for JMLR, and an associate editor for SIMODS. He has also won ICML-2007, CVPR-2008 best student paper award and more recently his work on alternating minimization has been selected as the 2020 Best Paper by the IEEE Signal Processing Society. Prateek also received the prestigious Young Alumnus Award 2021 from IIT Kanpur. He is the recipient of ACM India Early Career Researcher Award for 2021.

    Title of the talk: Pitfalls of Deep Learning


    While deep neural networks have achieved large gains in performance on benchmark datasets, their performance often degrades drastically with changes in data distribution encountered during real-world deployment. In this work, through systematic experiments and theoretical analysis, we attempt to understand the key reasons behind such brittleness of neural networks in real-world settings and why fixing these issues is exciting but challenging.

    We first hypothesize, and through empirical+theoretical studies demonstrate, that (i) neural network training exhibits "simplicity bias" (SB), where the models learn only the simplest discriminative features and (ii) SB is one of the key reasons behind non-robustness of neural networks. A natural way to fix SB in trained models is by identifying the discriminative features used by the model and learning new features "orthogonal" to the learned feature.

    Post-hoc gradient-based attribution methods are regularly used to identify the key discriminative features for a model. But, due to lack of ground truth, a thorough evaluation of even the most basic input gradient attribution method is still missing in literature. Our second contribution is to overcome this challenge through experiments and theory on real and designed datasets. Our results demonstrate that (i) input gradient attribution does NOT highlight correct features on standard models (i.e., trained on original data) but surprisingly, it does highlight correct features on adversarially trained models (i.e., trained using adversarial training) and (ii) "feature leakage", which refers to the phenomenon wherein, given an instance, its input gradients highlight the location of discriminative features in the given instance as well as in other instances that are present in the dataset, is the reason behind why input gradient attribution fails for standard models.

    Our work raises more questions than it answers, so we will end with interesting directions for future work.

    Talks by DDA Awardees

    Vrunda Dave


    Suhail Sherif

    Vector Institute, Toronto

    Suprovat Ghoshal

    University of Michigan

    Talks by Early Career Researchers

    Shweta Jain

    Indian Institute of Techonology, Ropar

    Chaya Ganesh


    Dr. Parul Ganju

    Ahammune Biosciences Private Limited

    Karthik Ramachandra

    Microsoft Azure Data SQL R&D, India

    ARCS 2022 Keynote

    Amit Kumar

    IIT Delhi

    Speaker Bio

    "Amit Kumar is a Professor in the Department of Computer Science and Engineering at IIT Delhi. He holds a B.Tech. degree from IIT Kanpur and Ph.D. from Cornell University. He was a member of technical staff at Bell Laboratories, Murray Hill during 2002-03, and has been a faculty member at IIT Delhi since 2003. His research lies in the area of combinatorial optimization, with emphasis on problems arising in scheduling, graph theory and clustering. He has received IBM Faculty Award (2005), INAE (Indian National Academy of Engineering) Young Engineer Award (2006) and INSA (Indian National Science Academy) Medal for Young Scientists (2011). He is a Fellow of Indian Academy of Sciences, and has received the Shanti Swarup Bhatnagar Award for Mathematical Sciences (2018)."

    Title of the talk: Online Algorithms with Recourse


    Online algorithms model situations where the input data arrives over time, and the algorithm needs to take decisions without knowing the future input. They are typically analyzed in the framework of competitive analysis -- the performance of such an algorithm is compared against an adversary which knows the entire future beforehand. Although this has been a very fruitful approach, it often leads to pessimistic results because the adversary is much more powerful than the online algorithm. Recently, there have been attempts to evolve alternate ways of analyzing online algorithms which give more power to the online algorithm (as compared to the offline adversary). I will discuss some recent work on models which allow the algorithm to change a small number of decisions taken in the past. Drawing from examples in scheduling, graph algorithms, and recent work on discrepancy minimization, I will show that one can get interesting results in this ''online algorithms with recourse'' model.

    Panel Discussion

    Topic Fairness, Accountability and Transparency


    Abhijnan Chakraborty

    IIT Delhi

    Anupam Guha

    IIT Bombay

    Balaji Parthasarathy

    International Institute of Information Technology Banglaore

    Diptikalyan Saha

    IBM Research, India

    Manjira Sinha

    IIT Kharagpur

    Industry Panel Discussion

    Topic Relevance of Computer Science Theory in Industry


    Shweta Agrawal

    IIT Madras

    Dr Mukta Paliwal

    Persistent Systems

    Nishanth Chandran

    Microsoft Research India

    Prahlad Sampath


    R. Venkatesh


    • Shweta Agrawal, IIT Madras
    • Nishanth Chandran as Moderator, Microsoft Research India
    • Mukta Paliwal, AI Research Lab CTO, Persistent Systems
    • Prahlad Sampath, Mathworks
    • R. Venkatesh, TCS Research

    Oral Presentations

    Paper Session 1: Graphs and Spatio-temporal processes
    Sr. No.AuthorsPaper titlePublication Venue
    1Jayant Jain, Vrittika Bagadia, Sahil Manchanda and Sayan RanuNeuroMLR: Robust & Reliable Route Recommendation on Road NetworksNeurIPS 21
    2Soumi Das, Harikrishna Patibandla, Suparna Bhattacharya, Kshounis Bera, Niloy Ganguly and Sourangshu BhattacharyaTMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous DrivingICCV 21
    3Ismi Abidi, Ishan Nangia, Paarijaat Aditya and Rijurekha SenPrivacy in Urban Sensing with Instrumented Fleets, Using Air Pollution Monitoring As A UsecaseNDSS 22
    4Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya and Abir DeA Neural Approach for Modeling Continuous Time Sequences with Intermittent ObservationsAISTATS 21
    5Sunil Nishad, Shubhangi Agarwal, Arnab Bhattacharya and Sayan RanuGraphReach: Position-Aware Graph Neural Network using Reachability EstimationsIJCAI 21
    Paper Session 2: Classification
    Sr. No.AuthorsPaper titlePublication Venue
    1Arindam Bhattacharya, Sumanth Varambally, Amitabha Bagchi and Srikanta BedathurFast One-class Classification using Class Boundary-preserving Random ProjectionsKDD 21
    2Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar and Manik VarmaECLARE: Extreme Classification with Label Graph CorrelationsWWW 21
    3Kunal Dahiya, Ananye Agarwal, Deepak Saini, Gururaj K, Jian Jiao, Amit Singh, Sumeet Agarwal, Purushottam Kar and Manik VarmaSiameseXML: Siamese Networks meet Extreme Classifiers with 100M LabelsICML 21
    4Nilesh Gupta, Sakina Bohra, Yashoteja Prabhu, Saurabh Purohit and Manik VarmaGeneralized Zero-Shot Extreme Classification KDD 21
    Paper Session 3: Theory
    Sr. No.AuthorsPaper titlePublication Venue
    1Sruthi Gorantla, Amit Deshpande and Anand LouisOn the Problem of Underranking in Group-Fair RankingICML 21
    2Yash Khanna, Anand Louis and Rameesh PaulIndependent Sets in Semi-random Hypergraphs17th Algorithms and Data Structures Symposium
    3Siddharth Barman, K Ramakrishnan and Saladi RahulOptimal Algorithms for Range Searching over Multi-Armed Bandits IJCAI 2021
    4Bhaskar Mukhoty, Subhajit Dutta and Purushottam KarRobust non-Parametric Regression via Incoherent Subspace Projections ECML 2021
    Paper Session 4: ML and NLP
    Sr. No.AuthorsPaper titlePublication Venue
    1Debanjan Konar, Siddhartha Bhattacharyya, Bijaya K. Panigrahi and Elizabeth C BehrmanQutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Image Segmentation IEEE Transaction on Neural Networks and Learning Systems
    2Nishat Koti, Mahak Pancholi, Arpita Patra and Ajith SureshSWIFT: Super-fast and Robust Privacy-Preserving Machine LearningUsenix Security Symposium
    3Yatin Nandwani, Deepanshu Jindal, Mausam Mausam and Parag SinglaNeural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output SpacesICLR 21
    4Punyajoy Saha, Binny Mathew, Kiran Garimella and Animesh Mukherjee"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp GroupsWWW 21
    5Rajdeep Mukherjee, Uppada Vishnu, Chandana Peruri, Sourangshu Bhattacharya, Niloy Ganguly, Pawan Goyal and Koustav RudraMTLVS: A Multi-Task Framework to Verify and Summarize Crisis-Related MicroblogsWSDM 2022
    6Rajdeep Mukherjee, Tapas Nayak, Yash Butala, Sourangshu Bhattacharya and Pawan GoyalPASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet ExtractionEMNLP 2021

    Poster Presentations

    Poster Session 1: Theory
    Sr. No.AuthorsPaper title
    1Pranjal Duttatau-Conjecture for sum-of-squares: A unified approach to lower bound and derandomization
    2Debojyoti Dey, Bhaskar Mukhoty and Purushottam KarAGGLIO: Global Optimization for Locally Convex Functions
    3Ashwin Jacob, Diptapriyo Majumdar and Venkatesh RamanFaster FPT Algorithms for Deletion to Pairs of Graph Classes
    4Raghavendra SridharamurthyComparative Analysis of Merge Trees using Local Tree Edit Distance
    5Sriram Bhyravarapu, Tim A. Hartmann, Subrahmanyam Kalyanasundaram and Vinod ReddyConflict-Free Coloring: Graphs of Bounded Clique Width and Intersection Graphs
    6Meghana Nasre, Prajakta Nimbhorkar, Keshav Ranjan and Ankita SarkarPopular Matchings in the Hospital-Residents Problem with Two-sided Lower Quotas
    7Debajyoti Bera and Tharrmashastha SapvQuantum and Randomized Algorithms for Non-linearity Estimation
    Poster Session 2
    Sr. No.AuthorsPaper title
    1Prachi Kashikar and Sharad SinhaModel Compression for Edge AI
    2Jayant Vyas, Debasis Das and Santanu ChaudhurySAFER: Safe and Fuel Efficient Driving Recommendation
    3Pavitra Prakash Bhade and Dr. Sharad SinhaDetection of Microarchitectural Side-Channel Attacks using Thread Level Performance Counters
    4Mohit Chandra, Manvith Reddy, Shradha Sehgal, Saurabh Gupta, Arun Balaji Buduru and Ponnurangam Kumaraguru“A Virus Has No Religion”: Analyzing Islamophobia on Twitter During the COVID-19 Outbreak
    5Asmit Kumar Singh, Chirag Jain, Jivitesh Jain, Rishi Raj Jain, Shradha Sehgal, Tanisha Pandey and Ponnurangam KumaraguruWhat's Kooking? Characterizing India's Emerging Social Network, Koo
    6Shivangi Singhal, Mudit Dhawan, Rajiv Ratn Shah and Ponnurangam KumaraguruInter-modality Discordance for Multimodal Fake News Detection
    7Indu Joshi, Ayush Utkarsh, Riya Kothari, Vinod Kurmi, Antitza Dantcheva, Sumantra Dutta Roy and Prem KalraData Uncertainty Guided Noise-Aware Fingerprint Preprocessing
    8Deepankar NankaniCardiac Abnormality Detection from Multichannel ECG using Feature Fused Parallel CNN-GAP


    The Symposium is open to all research scholars, researchers and academicians pursuing research in Computer Science and related areas. Registration is free and the page will open in the month of January 2022. Registration is mandatory to attend the event.

    Register Now!

    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 - 10th February 2022

    09.00 - 09.15 AM

    Opening comments

    Speakers : Sayan Ranu, Meenakshi D’Souza, R Nadarajan

    Speakers : Meenakshi D'Souza, Sayan Ranu, Principal, PSG College of Technology.

    9.15 - 10.30 AM

    Paper Session 1

    5 Papers

    Session Chair : Amit Awekar

    10.30 - 11.00 AM


    11.00 AM - 12.00 PM

    ECR keynote talk

    Pitfalls of Deep Learning

    Speaker : Prateek Jain

    Session Chair : Saket Saurabh

    12.00 - 01.00 PM

    Paper Session 2

    4 Papers

    Session Chair : Swaprava Nath

    01.00 - 02.00 PM

    Lunch break

    02.00 - 04.00 PM

    ECR Talks

    Session Chair : Meenakshi D'Souza

    04.00 - 4.30 PM


    04.30 - 05.30 PM

    Panel Discussion

    Fairness, Accountability and Transparency

    Session Chair : R. Nadarajan

    05.30 - 6.30 PM

    Poster Session 1

    7 Posters

    Session Chair : Neeldhara Misra

    Day 2 - 11 th February 2022

    09.00 - 10.30 AM

    DDA talks

    3 talks by DDA Awardees

    Session Chair : Supratik Chakraborty

    10.30 - 11.00 AM


    11.00AM - 12.00 PM

    Paper Session 3

    4 Papers

    Session Chair : Syamantak Das

    12.00 - 01.00 PM

    ARCS 2022 Keynote

    Speaker : Amit kumar

    Session Chair : Sayan Ranu

    01.00 - 02.00 PM

    Lunch break

    02.00 - 03.30 PM

    Paper Session 4

    6 Papers

    Session Chair : Rohan Paul

    03.30 - 04.00 PM


    04.00 - 05.00 PM

    Poster Session 2

    8 Posters

    Session Chair : Sayan Ranu

    05.00 - 06.00 PM

    Industry Panel Discussion

    Role of Computer Science Theory in the Industry

    Session Chair : Hemant Pande

    06.00 - 06.15 PM

    Closing Comments

    ARCS 2022 Career Fair

    The organizers of ARCS are happy to announce an exclusive career fair for the research scholars attending ARCS 2022! Internships and job openings in R&D in some of the Sponsoring firms will be announced and advertised throughout the ARCS sessions on 10 and 11 February 2022. This is an exclusive opportunity only for the research scholars attending ARCS, so be ready and attend all the sessions!

    Call for Papers Paper Submission

    ACM India announces the 16th 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 2021 or accepted in 2021 for publication) to a conclave of researchers and potential employers.

    Apart from the contributed talks and poster presentations, ARCS 2022 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 deadline extended: 22nd October, 2021 30th October, 2021
    Notification to Authors: 1st December, 2021 11th December, 2021

    Paper submission and notification process is complete for ARCS 2022.

    Download Call for Papers

    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


    Programme Committee

    Sayan Ranu (IIT Delhi), Chair

    Names of rest of the committee members will be announced soon Ankit Anand (DeepMind)
    Amit Awekar (IIT Guwahati)
    Nipun Batra (IIT Gandhinagar)
    Abhijnan Chakraborty (IIT Delhi)
    Ayon Chakraborty (IIT Madras)
    Syamantak Das (IIIT Delhi)
    Manoj Gupta (IIT Gandhinagar)
    Neelima Gupta (University of Delhi)
    Aritra Hazra (IIT Kharagpur)
    Neeldhara Misra (IIT Gandhinagar)
    Adway Mitra (IIT Kharagpur)
    Meghna Nasre (IIT Madras)
    Swaprava Nath (IIT Bombay)
    Biswabandan Panda (IIT Bombay)
    Rohan Paul (IIT Delhi)
    Krithika Ramaswamy (IIT Palakkad)
    Aishwarya Thiruvengadam (IIT Madras)
    Rohit Vaish (TIFR)
    Hamim Zafar (IIT Kanpur)

    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
    Ranga Rajagopal
    (Director & CEO, ACENET Technologies, CBE)
    Suresh Balusamy
    Rajnish Sharma
    Dean (Research & Academics), Chitkara University
    A Kaja Mohideen
    PSG College of Technology
    Neeldhara Misra
    IIT Gandhinagar

    Who helps us Platinum Sponsors

    Gold Sponsors

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    Research Scholars Experience

    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