Arijit Pramanik

I am a first year graduate student in the Department of Computer Sciences at University of Wisconsin-Madison (UW-Madison), where I work on Networked Systems and Machine Learning (for Systems). I'm currently working under Prof. Aditya Akella.

Prior to that, I spent four amazing years at Indian Institute of Technology Bombay (IIT Bombay) with a B.Tech (Hons) in Computer Science and Engineering (CSE) and a minor in Applied Statistics and Informatics. I was a recipient of the Institute Academic Excellence Award for securing Dept. Rank 1 in 2017-18. I also spent a wonderful semester on exchange at the National University of Singapore (NUS) in the School of Computing.

I'm also a national level swimmer and won 4 gold, 5 silver and 11 bronze medals across 4 years at the Inter-IIT Aquatics Meet for which I was awarded the Institute Sports Color in 2016 and Hostel Color in 2017. I led my team in 2018-19 and was awarded the Institute Sports Citation in 2019 for being the best outgoing swimmer.

Email  /  Resume  /  Github  /  LinkedIn  /  Other links

Updates
  • [Jan 2020] Continuing with Teaching Assistantship at University of Wisconsin-Madison for CS 559 (Computer Graphics) in Spring'20
  • [Aug 2019] Got Teaching Assistantship at University of Wisconsin-Madison for CS 559 (Computer Graphics) in Fall'19
  • [Apr 2019] Declined admission acceptance offers from Cornell, UCSD and Georgia Tech
  • [Feb 2019] Accepted admit offer from University of Wisconsin-Madison
Publications
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Generating summaries tailored to target characteristics
20th International Conference on Computational Linguistics and Intelligent Text Processing (CiCLing 2019)

Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries. While there have been attempts to incorporate a few handpicked characteristics such as length or entities, a holistic view around these preferences is missing and crucial insights on why certain characteristics should be incorporated in a specific manner are absent. With this objective, we provide a categorization around these characteristics relevant to the task of text summarization: one, focusing on what content needs to be generated and second, focusing on the stylistic aspects of the output summaries. We use our insights to provide guidelines on appropriate methods to incorporate various classes characteristics in sequence-to-sequence summarization framework. Our experiments with incorporating topics, readability and simplicity indicate the viability of the proposed prescriptions.

pdf version

Research Projects
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Dataplane-Only Policy-Compliant Routing under Failures
Supervisor: Prof. Aditya Akella, University of Wisconsin-Madison

On failures, routers typically have inconsistent state, which leads to high convergence times. In such cases, the central software controller could be a bottleneck and finding policy-compliant paths is hard. We propose for computation of such paths in the data plane with a central policy plane across end-host interfaces

  • Used search algorithms to compute routes in the data plane using P4 stacks with recirculation along with software emulation on mininet for the RocketFuel set of toplogies
  • Provided support for Weighted Cost Multipath load balancing with dynamic weights and per-flow and per-session consistency guarantees
  • Handled failures through register updates utilizing Failure Carrying Packets in Tofino
  • Added support for policies like middlebox chaining and hierarchical routing to avoid excessive recirculations


    Slide Deck 1 | Slide Deck 2

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Imaging Techniques with Raman Spectroscopic Imaging
Supervisor: Prof. Ajit Rajwade, Indian Institute of Technology Bombay

Typical Raman spectroscopy takes a very long acquisition time and is used for diagnosing critical diseases like cancer. The aim of this project is to reduce the acquisition time without compromising on quality

  • Learned a compact representation of paraffin subspace for spectral separation of biopsy sample using Nonnegative Sparse Coding, employing Blind Dictionary Learning with PCA for signal and noise separation
  • Performed inpainting to enable compressed sensing of Raman spectral images, to speedup image acquisition
  • Extended the same to the super-resolution use case with significant improvements over simple bicubic interpolation
  • Achieved better results with Gaussian Mixture Models trained on a smaller representative set using the Expectation-Maximization (EM) algorithm


    Report | Presentation | ICIP 2019 Paper Draft

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State Replication and Fault Tolerance in P4
Supervisor: Prof. Mythili Vutukuru, Indian Institute of Technology Bombay

The project aims at replicating locally stored states in the primary switch to the secondary switch in real-time to avoid loss of state information in case of failures. Locally stored states aid in packet processing at line rate

  • Constructed a synchronous cum asynchronous write-consistent bmv2 model to store "hard" network states (which can't be recovered from flow statistics) on the switch with consistent migration across backup switches in the data plane
  • Achieved faster flow switchover compared to root controller-mediated state updates (where the controller stores and syncs such states across all switches)
  • Proposed an anannotation-based API for a generalized fault-tolerant primitive to be incorporated in p4c


    Report | Presentation

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Benchmarking of Software Switches
Supervisor: Prof. Mythili Vutukuru, Indian Institute of Technology Bombay

VPP and Open vSwitch are currently the fastest DPDK-based software switches out there. The aim was to determine the minimal resources required for optimal performance of a switch for different use cases

  • Tested latency, throughput, efficiency in terms of cycles per packet with increasing cores, routing table entries and hierarchical cache sizes using uniform and skewed Gaussian traffic loads of 10 Gbps generated with DPDK-based packet generator MoonGen
  • Analyzed VPP's batch packet processing paradigm and tested batch size as a function of different parameters
  • Studied Cisco Express Forwarding implemented using multiway prefix trees in VPP, patented by Cisco


    Report | Presentation

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Optimizing Performance of Model-Counting Algorithms
Supervisor: Prof. Kuldeep Meel, National University of Singapore

The task involved a study of different model-counting algorithms, which enumerate solutions to a boolean formula. The aim was to identify performance bottlenecks in the implemented model for optimization

  • Studied the SPARSE-COUNT algorithm and extended the same using GMP & MPFR libraries to support arbitrarily large number of variables and multi-precision computations
  • Implemented the above in the ApproxMC framework which is another similar framework for model-counting, using θ(logn) low-density parity constraints with tolerance guarantees for results within a specified confidence interval
  • Results were validated using the IJCAI'16-CMV benchmarks


    Report

Internships
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University of Washington, Seattle [May'19-August'19]
Supervisor: Prof. Arvind Krishnamurthy

Hardware Acceleration of Proxies

  • The aim was to decrease the latency incurred at the level of proxies which mostly run as host-based processes
  • Worked on Layer 4 and Layer 7 load balancing of different proxies like Envoy, Nginx & HAproxy to demarcate functionalities for host and SmartNIC offloading
  • Performed benchmarking experiments using wrk2 to determine feasibility of SSL certificate verification offloading and scalability, with a detailed study of Envoy worker threads

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Adobe Systems (Research), Bengaluru [May'18-July'18]
Supervisor: Dr. Balaji Vasan Srinivasan, Senior Research Scientist, BigData Experience Labs

Characteristics-Tailored Summary Generation

  • Unlike typical abstractive text summarisation, the aim was to tune our summaries to characteristics like being more formal as required by news agencies or focus on financial aspects as desired by corporate organisations
  • Adapted Facebook AI Research's Convolutional seq2seq model for translation to characteristic-driven text generation with modified attention weights to focus on specific input embeddings for topic-tuned summaries
  • Altered beam search paradigm for tweaking decoder state probability distributions, thus enhancing word-level features like descriptiveness with token-based learning for length based summarisation
  • Incorporated a Reinforcement Learning term in loss function and achieved a 6.4% increase in ROUGE scores
  • Implemented the above insights on pointer-generator framework and submitted a patent application (P8322-US) for the same at United States Patent and Trademark Office

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Focus Analytics, Mumbai [Nov'17-Dec'17]
Supervisor: Sudin Kadam, Head of Research

Contextual Marketing for Retail Analytics

  • Leveraged topic-modeling and word2vec similarity scores for customer segmentation and retail-affinity estimation using gensim and SpaCy
  • Implemented a probabilistic graphical model based recommendation engine, contributing to pgmpy github repo
  • Created a new query language with pyparsing for internal database system on neo4j, utilizing EBNF grammar rules

    Brief Report

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Philips Innovation Campus, Bengaluru [May'17-July'17]
Supervisor: Dr. Rajendra Sisodia, Principal Scientist, Philips Research

Domain-specific Customer Care Chatbot

  • Modern chatbots perform well in conversations comprising simple question-answer pairs. The aim was to develop a semantic control algorithm to track context switches to predict favourable next steps in the conversation
  • Designed a chatbot leveraging word2vec, Latent Semantic Indexing and Latent Dirichlet Allocation for topics relevant to user query with tf-idf weighted word n-grams for improving accuracy
  • Incorporated probabilistic finite automata to model conversation state changes guided by sentiment scores
  • Built an emotion classifier SVM, and ontologies for knowledge representation from RDF sources with SPARQL queries for fetching data

    Brief Report

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OliveSync, Zone Startups India, Mumbai [Dec'16]
Supervisor: Ketan Ghatode, CTO, OliveSync Pvt. Ltd.

Automated Timetable Generation

  • Designed a scheduling algorithm leveraging genetic algorithms to generate the best fit optimal timetable for institutions
  • Added live sync to MySQL database on PHP to track occurrence of classes, and course adjustments
  • Employed Gale-Shapley algorithm for alloting time slot priorities to students and professors

    Brief Report

Key Projects
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A Detailed Study and Comparison of General-Purpose Fuzzers
Supervisor: Prof. Barton Miller, University of Wisconsin-Madison

We made a comparison of general-purpose mutation-based grey-box fuzzers like libFuzzer, American Fuzzy Lop (AFL) and honggfuzz and evaluated their performance on the Google fuzzer-test-suite across 24 applications on metrics like code coverage (basic blocks and edges) and bug-finding capabilities. We found a new unreported bug in pcre2-10.0 with the key finding that only libFuzzer can find memory leaks with the help of LeakSanitizer. Also proposed a new framework for ensemble fuzzing which uses different base fuzzers in tandem

Brief Report | Poster

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Stereo Image Reconstruction using Energy Minimization
Supervisor: Prof. Cheong Loong Fah & Prof. Feng Jiashi, National University of Singapore

I implemented normalized graphcuts with α-expansion for image segmentation and denoising using multilabel 8-connected Markov Random Fields, and compared the same with mean-shift algorithm. I employed the PatchMatch algorithm to establish patch correspondences, for better alignment for homography. The other component involved obtaining dense correspondences from two images belonging to different viewpoints using manual methods and KLT tracker, to estimate the Fundamental Matrix using the 8-point algorithm

Component 1 Report | Component 2 Report

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Tetrisbot
Supervisor: Prof. Zick Yair, National University of Singapore

We designed a utility-based agent based on genetic algorithms, using a set of 10 state-dependent features like numer of holes, height differences between adjacent columns, max height of a column, etc. We used the single-point crossing over heuristic and implemented a multithreaded training approach random independent block sequences in parallel. Particle swarm optimization was also employed along with this for optimal convergence of weights to add an exploratory component. We achieved a maximum of over 856,000 cleared rows. Additionally, we implemented an auto-encoder approach with Q-learning for a low dimensional game state representation. Though not quite successful with Tetris, we demonstrated a simple game "Catch the Ball" with the above approach to demonstrate its effectiveness

Report | Github repo | A study of genetic algorithms

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A Java-like Compiler for OCaml
Supervisor: Prof. Razvan Voicu & Prof. Chin Wei Ngan, National University of Singapore

I designed an abstract syntax tree comprising unary and binary operations, conditionals, functions, recursive functions, applications and let constructs on boolean and integer data types for the compiler, utilizing the Gram parser for parsing the instructions. I implemented a virtual machine instruction interpreter utilizing an operand stack with associated instructions to act upon it. I used the type-checking system with Hindley Milner type inference system with support for optional data types. The compiler was further optimized to leverage tail recursion and contiguous stack frames

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Legal Case Retrieval System
Supervisor: Prof. Zhao Jin, National University of Singapore

We designed a freetext search engine supporting both phrasal and boolean queries, leveraging NLTK to retrieve and rank legal case judgments. We finished 2nd out of 33 teams on the leaderboard based on the assignment given by the Singapore-based legal intelligence firm, Intellex. Positional indices were implemented to aid proximity search with additional zone and field indices like court hierarchy, legal case dates to aid in retrieval. We were able to get a high F1 score using various query expansion techniques like pseudo relevance feedback using the Rochhio algorithm, WordNet synonyms and co-occurence thesaurus generated from the corpus dictionary. We used the LNC model of tf-idf for freetext search

Github repo

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Figaro : A Probabilistic Programming Language
Supervisor: Prof. Razvan Voicu & Prof. Chin Wei Ngan, National University of Singapore

Explored the Probabilistic Programming Monad in Figaro, which combines the object-oriented paradigm with the functional programming paradigm in Scala. Modeled real-life problems using Bayesian Networks with inference algorithms like Variable Elimination, Belief Propagation and Dynamic Reasoning algorithms like Factored Frontier. Simulated a simple market model using Decision Models to calculate the optimal policy. Extended the language by implementing a new Element class to model the distribution of the maximum value of a random variable, sampled from 0 to a given upper bound

Report | Presentation | Github repo

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Generation of Nintendo Entertainment System Game layouts
Supervisor: Prof. Ganesh Ramakrishnan, Indian Institute of Technology Bombay

We built a Deep Convolutional GAN model on pytorch for generating new game levels, i.e. tile sheets from previous game layouts. We used Leaky ReLU as the activation function for both the discriminator and generator with the Adam Optimizer for stochastic gradient descent

Brief Presentation

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Image Quilting for Texture Synthesis and Transfer
Supervisor: Prof. Suyash Awate & Prof. Ajit Rajwade, Indian Institute of Technology Bombay

We employed the Efros & Leung algorithm to synthesize larger textures, and used the same algorithm with a modified cost function for iterative texture transfer to target images using correspondence maps. We also implemented the minimal error boundary cut using dynamic programming to avoid block-seam artifacts

Report

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OpenGL based 3D Animation Film
Supervisor: Prof. Cheng Ho-lun Alan, National University of Singapore

Dynamic rendering techniques were used to create this animation film, based on OpenGL's various timer functions. Different camera transformations were used like dolly zoom to add artistic effects. I used motion simulation along Bezier curves, adding soft shadows and transparency effects using Ray tracing. For object modeling, Phong illumination and Phong shading were used with texture mapping and bump mapping to mimic real-life surfaces

Teaching Assistantships
  • CS 559 (Computer Graphics) at UW-Madison [Fall '19]
  • CS 101 (Computer Programming and Utilization) at IIT Bombay [Spring '19]
  • CS 341 (Computer Architecture) at IIT Bombay [Fall '18]
  • BB 101 (Introduction to Biology) at IIT Bombay [Spring '17]
Relevant Coursework
  • Machine Learning: Computer Vision, Advanced & Digital Image Processing, Machine Learning, Artificial Intelligence, Information Retrieval, Optimization
  • Systems: Advanced Operating Systems, Computer Networks, Computer Architecture, Compilers, Databases and Information Systems
  • Statistics: Regression Analysis, Statistical Inference, Probability Theory, Derivatives Pricing, Numerical Analysis, Data Analysis & Interpretation
  • Others: Computer Graphics, Automata Theory, Logic in CS, Discrete Structures, Data Structures & Algorithms, Digital Logic Design, Abstractions and Paradigms in Programming, Electrical and Electronic Circuits, Economics
Other Projects

PokeDB : A Pokemon RPG Game
Supervisor: Prof. S Sudarshan, Indian Institute of Technology, Bombay

We built a multiplayer Pokemon game on PostgreSQL backend with JDBC API from pokeAPI JSON data with over 14,000 tuples. Online gym battles, navigable maps with probability models for capturing wild pokemon and evolution of pokemon with battle experience were also added

Report

Feed'er : An All-purpose Academic App
Supervisor: Prof. Sharat Chandran, Indian Institute of Technology, Bombay

Developed an integrated Android and Django based web app for displaying submission deadlines, exam dates and other important reminders via push-notifications. Implemented automatic sync and signup with social logins, with security measures against XSS, CSRF etc

User Manual | Presentation

Sudoku GamePlay Software
Supervisor: Prof. Amitabha Sanyal, Indian Institute of Technology, Bombay

Built a GUI based solver on MIT Scheme with features like Undo, Auto-solve, and filters for seeding games of varying difficulty levels. Employed backtracking algorithm to solve any given initial configuration

Report

Ldap Authenticated Chat Application
Supervisor: Prof. Varsha Apte, Indian Institute of Technology, Bombay

A server-client model with X11 based GUI was developed using Socket programming, with LDAP Authentication using openLDAP. Additional functionality for group chats, offline inbox via hashmaps and multimedia message exchanges were also supported

Ethernet-enabled ATM Controller
Supervisor: Prof. Supratik Chakraborty, Indian Institute of Technology, Bombay

Developed an ethernet-enabled FPGA module on VHDL to dispense cash leveraging greedy algorithm in Xilinx ISE, with Tiny Encryption algorithm to provide secure exchange of user data. Enforced insufficient balance, incorrect pin using LED displays, and frontend caching to protect against server crashes

Text Processor
Supervisor: Prof. Varsha Apte, Indian Institute of Technology, Bombay

Built a class for enumeration of characters, words with support for Find and Replace using Knuth Morris Pratt algorithm, regular expressions, LZW compression and encryption and decryption via Caesar cipher

Movie Recommendation Engine
Supervisor: Prof. Sharat Chandran, Indian Institute of Technology, Bombay

Designed a python program for generating correlation between the user and critic rating based on Euclidean distances. Using the critic ratings, generated a list of recommended movies for the user sorted according to ratings weighted by Pearson correlation coefficient calculated using similarity between user and critic's rating

Simulation of Rube Goldberg Model
Supervisor: Prof. Sharat Chandran, Indian Institute of Technology, Bombay

Designed and simulated a Rube Goldberg Machine using Box2D, a physics simulation engine in C++, which involves compilation and linking to libraries like GLUI (GLUT based C++ user interface library). Designed a Star Wars arena by rendering attraction, repulsion among magnetic objects

Body Fat Estimation
Supervisor: Prof. Chan Yiu Man, National University of Singapore

Estimated body fat mass using stepwise regression with statistical tests to check for multicollinearity, lack of fit, outliers and influential points derived from cook's distance, dffits, dfbetas, studentised residuals implemented in R. The same was validated with partial F-test for the significance of model and Durbin-Watson test with Kolmogorov-Smirnov test for testing the independence and normality of residuals

Report

A Study of Statistical Tests and Sampling Algorithms
Supervisor: Prof. Radhendushka Srivastava, Indian Institute of Technology, Bombay

Performed a critical study and simulation of the Random Excursions test and the famous sampling algorithm, Metropolis Hastings Algorithm

Random Excursions Report | Metropolis Hastings Algorithm Report


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