About

Rahul Chatterjee is an Assistant Professor in the Computer Sciences department at the University of Wisconsin—Madison. His research focuses on designing secure systems to make digital technologies safe and secure for everyone. He employs a research methodology that combines empiricism with analytical techniques. His recent work includes designing secure and usable authentication systems, securing private data in trigger-action platforms, mitigating abuse of smart home devices by abusive intimate partners, and building a security mindset in computer science undergraduate students. He leads the Madison Tech Clinic (MTC), which supports survivors of domestic and intimate partner violence who experience technology-facilitated abuse. Professor Chatterjee is actively involved in mentoring motivated students interested in real-world digital security and privacy problems and encourages collaboration on relevant research ideas.

Research topics

• Genetics
• Medicine
• Biology
• Immunology
• Computer Science
• Microbiology
• Virology
• Bioinformatics
• Computational biology
• Engineering

Selected publications

• Towards Sustainable Material Process Technologies: Examining Polymer Property‐Processing Relationships in the Material Extrusion of Plastic Waste‐Blended <scp>HDPE</scp> for Bike Fairing Design and Simulation

  Journal of Applied Polymer Science · 2025-06-27 · 3 citations

  articleOpen access1st author

  ABSTRACT Plastic pollution, driven by the overuse of plastics and inadequate waste management, poses severe environmental and health risks. Despite the potential benefits of recycling, rates remain low due to the high costs of centralized recycling systems and the associated greenhouse gas emissions. Motivated by this challenge, this study explores the reuse of plastic waste for 3D printing as a viable and sustainable alternative. A Brabender melt mixer was used to combine discarded plastic bags and high‐density polyethylene. The resulting composite was processed into 3D printing filaments using a single‐screw extruder. Dumbbell‐shaped specimens were then created by melt extrusion following ASTM D412 specifications. Mechanical properties were tested using a Universal Testing Machine, and morphological features were examined via Scanning Electron Microscopy. The goal is to reduce plastic pollution and encourage innovative recycling practices. Our approach integrates 3D printing with Finite Element Analysis to accelerate product development and minimize material waste, enabling rapid prototyping and design optimization. Optimized prototypes were fabricated through 3D printing for efficient material use. A 5 wt% waste plastic blend demonstrated superior tensile strength and thermal stability compared to pure HDPE. This blend was successfully used to design a bike fairing, validated through AutoCAD and Finite Element Simulation.

  PublisherOA PDFDOI

• Influence of simulated ambience on melt crystallization of isotactic polypropylene towards developing warp-free 3D printing

  Progress in Additive Manufacturing · 2025-02-03 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Carboxymethyl Guar Gum Designed with Hyperbranched Grafts of Poly(2-Methacryloyloxyethyl Trimethylammonium Chloride) for Enhanced Selective Flocculation of Kaolin-Hematite Mixture

  Journal of Polymers and the Environment · 2025-02-18 · 3 citations

  article
  PublisherDOI

• Construct validity and learning curve of six basic skill simulator exercises utilised by the Society of European Robotic Gynaecological Surgery (SERGS)

  Journal of Robotic Surgery · 2025-11-06

  article
  PublisherDOI

• Can Social Media Privacy and Safety Features Protect Targets of Interpersonal Attacks? A Systematic Analysis

  Proceedings on Privacy Enhancing Technologies · 2025-03-07 · 1 citations

  articleOpen accessSenior author

  Social media applications have benefited users in several ways, including ease of communication and quick access to information. However, they have also introduced several privacy and safety risks. These risks are particularly concerning in the context of interpersonal attacks, which are carried out by abusive friends, family members, intimate partners, co-workers, or even strangers. Evidence shows interpersonal attackers regularly exploit social media platforms to harass and spy on their targets. To help protect targets from such attacks, social media platforms have introduced several privacy and safety features. However, it is unclear how effective they are against interpersonal threats. In this work, we analyzed ten popular social media applications, identifying 100 unique privacy and safety features that provide controls across eight categories: discoverability, visibility, saving and sharing, interaction, self-censorship, content moderation, transparency, and reporting. We simulated 59 different attack actions by a persistent attacker — aimed at account discovery, information gathering, non-consensual sharing, and harassment — and found many were successful. Based on our findings, we proposed improvements to mitigate these risks.

  PublisherOA PDFDOI

• SF025/#1065  Utilizing indocyanine green (ICG) for ureter identification in gynaecological oncology surgery

  International Journal of Gynecological Cancer · 2024-10-01 · 1 citations

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  PublisherDOI

• Thermoplastic Polymers and Composites Explored: Evaluating Fused Deposition Modeling and Investigating Structure-Property Processing Interdependencies

  Elsevier eBooks · 2024-06-19 · 3 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Beyond Traditional Stimuli: Exploring Salt-Responsive Bottlebrush Polymers─Trends, Applications, and Perspectives

  ACS Omega · 2024-07-26 · 3 citations

  reviewOpen access

  Bottlebrush polymers represent an important class of high-density side-chain-grafted polymers traditionally with high molecular weights, in which one or more polymeric side chains are tethered to each repeating unit of a linear polymer backbone, such that these macromolecules look like "bottlebrushes". The arrangement of molecular brushes is determined by side chains located at a distance considerably smaller than their unperturbed dimensions, leading to substantial monomer congestion and entropically unfavorable extension of both the backbone and the side chains. Traditionally, the conformation and physical properties of polymers are influenced by external stimuli such as solvent, temperature, pH, and light. However, a unique stimulus, salt, has recently gained attention as a means to induce shape changes in these molecular brushes. While the stimulus has been less researched to date, we see that these systems, when stimulated with salts, have the potential to be used in various engineering applications. This potential stems from the unique properties and behaviors these systems show when exposed to different salts, which could lead to new solutions and improvements in engineering processes, thus serving as the primary motivation for this narrative, as we aim to explore and highlight the various ways these systems can be utilized and the benefits they could bring to the field of engineering. This Review aims to introduce the concept of stimuli-responsive bottlebrush polymers, explore the evolutionary trajectory, delve into current trends in salt-responsive bottlebrush polymers, and elucidate how these polymers are addressing a variety of engineering challenges.

  PublisherOA PDFDOI

• Material extrusion of SEEPS blended isotactic PP for possible application as automotive bumper: Performance analysis through Finite Element Simulation

  Journal of Manufacturing Processes · 2024-01-17 · 7 citations

  article1st author
  PublisherDOI

• Compact: Approximating Complex Activation Functions for Secure Computation

  Proceedings on Privacy Enhancing Technologies · 2024-06-25 · 6 citations

  articleOpen access

  Secure multi-party computation (MPC) techniques can be used to provide data privacy when users query deep neural network (DNN) models hosted on a public cloud. State-of-the-art MPC techniques can be directly leveraged for DNN models that use simple activation functions (AFs) such as ReLU. However, these techniques are ineffective and/or inefficient for the complex and highly non-linear AFs used in cutting-edge DNN models. We present Compact, which produces piece-wise polynomial approximations of complex AFs to enable their efficient use with state-of-the-art MPC techniques. Compact neither requires nor imposes any restriction on model training and results in near-identical model accuracy. To achieve this, we design Compact with input density awareness, and use an application specific simulated annealing type optimization to generate computationally more efficient approximations of complex AFs. We extensively evaluate Compact on four different machine-learning tasks with DNN architectures that use popular complex AFs silu, gelu, and mish. Our experimental results show that Compact incurs negligible accuracy loss while being 2x-5x computationally more efficient than state-of-the-art approaches for DNN models with large number of hidden layers. Our work accelerates easy adoption of MPC techniques to provide user data privacy even when the queried DNN models consist of a number of hidden layers, and trained over complex AFs.

  PublisherOA PDFDOI

Frequent coauthors

• Abhijit Bandyopadhyay

  University of Calcutta

  15 shared

• Majed Almansoori

  University of Wisconsin–Madison

  14 shared

• Andrea Gallardo

  Carnegie Mellon University

  9 shared

• Julio Poveda

  9 shared

• Adil Ahmed

  University of Wisconsin–Madison

  9 shared

• Thomas Ristenpart

  Cornell University

  8 shared

• Namrata Misra

  KIIT University

  6 shared

• Mrutyunjay Suar

  KIIT University

  6 shared

Labs

• Rahul Chatterjee's LabPI

  Designs secure systems to make digital technologies safe and secure for everyone.

Education

• Ph.D.

  University of Wisconsin—Madison