Siddhartha Roy

About

Siddhartha Roy is a researcher at Rutgers University whose work focuses on environmental science and engineering, particularly related to water quality, corrosion, and public health issues associated with water systems. His research includes studying lead leaching from various materials used in water delivery systems, evaluating the efficacy of corrosion control measures, and addressing waterborne health risks such as those highlighted during the Flint Water Crisis. Roy's work also encompasses citizen science approaches to water safety, ethical considerations in engineering, and the impact of water chemistry on infrastructure integrity. He has contributed to understanding the mechanisms of erosion corrosion in potable water systems, assessing the safety of drinking water, and developing strategies to prevent lead contamination. His research has been recognized for its scientific rigor and societal relevance, with some of his publications being highly cited and featured in prominent science outlets. Roy's academic background includes a PhD dissertation on water chemistry and corrosion, and he actively engages in research that aims to improve water safety and infrastructure resilience, emphasizing ethical practices and public health implications.

Research topics

• Computer Science
• Computer Security
• Data Mining
• Artificial Intelligence
• Information Retrieval
• Machine Learning
• Materials science
• Chemistry
• Computer network
• World Wide Web

Selected publications

• Botnet traffic identification using neural networks

  Multimedia Tools and Applications · 2021 · 31 citations

  Senior authorCorresponding
  DOI

• Foresight 2035: a perspective on the next decade of research on the management of <i>Legionella</i> spp. in engineered aquatic environments

  FEMS Microbiology Reviews · 2025-01-01 · 7 citations

  reviewOpen access

  The disease burden from Legionella spp. infections has been increasing in many industrialized countries and, despite decades of scientific advances, ranks amongst the highest for waterborne diseases. We review here several key research areas from a multidisciplinary perspective and list critical research needs to address some of the challenges of Legionella spp. management in engineered environments. These include: (i) a consideration of Legionella species diversity and cooccurrence, beyond Legi…

  PublisherDOI

• A Citizen Science Approach to Evaluating Consumer Water Safety Concerns (2011–2022)

  ACS ES&T Water · 2024-04-02 · 7 citations

  articleOpen access

  Citizen science is utilized by the water industry to collect samples under the provisions of the U.S. Environmental Protection Agency Lead and Copper Rule. Citizen science was also used in Washington, DC; Flint, MI; St. Joseph, LA; and elsewhere to contradict official messaging regarding water safety, leading to increased media coverage, declared water emergencies, and financial relief. Herein, we share our team’s process for engaging citizens, including a retrospective analysis of outcomes and…

  PublisherOA PDFDOI

• Competition for engineering tenure-track faculty positions in the United States

  PNAS Nexus · 2024-04-30 · 4 citations

  articleOpen access1st authorCorresponding

  Abstract How likely are engineering PhD graduates to get a tenure-track faculty position in the United States? To answer this question, we analyzed aggregated yearly data on PhD graduates and tenure-track/tenured faculty members across all engineering disciplines from 2006 to 2021, obtained from the American Society of Engineering Education. The average likelihood for securing a tenure-track faculty position for engineering overall during this 16-year period was 12.4% (range = 10.9–18.5%), imply…

  PublisherOA PDFDOI

• Combing for Credentials: Active Pattern Extraction from Smart Reply

  2022 IEEE Symposium on Security and Privacy (SP) · 2024 · 4 citations

  Pre-trained large language models, such as GPT-2 and BERT, are often fine-tuned to achieve state-of-the-art performance on a downstream task. One natural example is the "Smart Reply" application where a pre-trained model is tuned to provide suggested responses for a given query message. Since the tuning data is often sensitive data such as emails or chat transcripts, it is important to understand and mitigate the risk that the model leaks its tuning data. We investigate potential information lea…

  DOI

Frequent coauthors

• Venkatesan T. Chakaravarthy

  IBM Research - India

  30 shared

• Yogish Sabharwal

  IBM Research - India

  20 shared

• Eric Allender

  Rutgers, The State University of New Jersey

  10 shared

• Anamitra R. Choudhury

  IBM Research - India

  9 shared

• Amit Kumar

  Intel (India)

  8 shared

• Vinayaka Pandit

  IBM Research - India

  6 shared

• Samir Datta

  6 shared

• Suman K. Bera

  Apple (United States)

  5 shared

Education

• PhD, Civil Engineering

  Virginia Tech

  2018

• MS, Environmental Engineering

  Virginia Tech

  2015

• BTech, Chemical Engineering

  Nirma University

  2010

Similar researchers at Rutgers University

• John Reinfelderh-51
• Anthony Broccolih-47
• Enrique Curchitserh-44