Main Image

Dr Riddhi Mandal

PhDGITGCP certified

7+

years of experience in geophysics & seismic data science

I am a Geophysicist and Data Scientist with expertise in numerical modeling, machine learning, and high-performance computing, specializing in seismic analysis, fluid-fault interactions, and AI-driven geophysical solutions. I have developed large-scale simulations for fault slip prediction, engineered deep learning models for real-time seismic event detection, and optimized AI frameworks for geophysical applications. My experience includes consulting for AI-driven geophysics projects, enhancing model accuracy through feature engineering, data processing, and performance optimization. Proficient in Python, MATLAB, C++, and cloud-based AI tools, I bring a strong analytical approach to solving complex industry challenges, from resource exploration to seismic risk mitigation.

View Resume Get in Touch

Experience

                                  • Earth Science Researcher
                                  • OutlierAI
                                    • Consulted as a geophysics expert, training AI models to solve complex geophysical problems by integrating domain knowledge into machine learning frameworks by analyzing real-world geophysical challenges in magnetism, seismology, and gravity.
                                    • Developed solutions, reviewed AI-generated results, and advised on feature engineering, data processing, and model architecture to improve accuracy and optimized code to accelerate training speed.
                                    • Enhanced AI-driven geophysics applications, boosting model precision and enabling adoption in seismic analysis, resource exploration, finishing the project one month early due to faster model training.
                                  • PhD Researcher – Real-time event precursor detection
                                  • University of Toronto
                                    • Developing a deep learning-based detection system using lab acoustic data to identify aseismic transients that may precede seismic events.
                                    • Engineering a pipeline combining feature extraction, machine learning models, and real-time analysis, optimizing it for high sensitivity and accuracy.
                                    • Potential for early detection of seismicity in EOR, wastewater injection, and geothermal scenarios.
                                  • PhD Researcher – 2016 Pawnee Earthquake
                                  • University of Toronto
                                    • The 2016 Pawnee Earthquake (M5.8), the largest induced earthquake in the central U.S., required detailed analysis to determine the role of fluid injection and stress interactions.
                                    • Investigated the earthquake’s triggering mechanisms by conducting hydrogeological modeling, numerical simulations, and Coulomb Failure Stress analysis.
                                    • Modeled poroelastic stress changes, foreshock/aftershock sequences, and kinematic fault slip to assess the impact of fluid injection on fault stability.
                                    • Identified key stress transfer mechanisms and fluid-induced instability factors, providing new insights into induced seismicity hazard assessment.
                                  • PhD Researcher – Contextual Peak Detection Algorithm
                                  • University of Toronto
                                    • Developed a machine learning-based peak detection algorithm for time-series signal processing with 2000% frequency variance, surpassing traditional methods.
                                    • Achieved 96% accuracy and 92% recall, enabling robust, noise-resistant detection across seismic analysis, financial time series, and biomedical signals.
                                    • Outperformed nearly all established peak detection techniques, being almost two orders of magnitude faster than most other techniques.
                                  • PhD Researcher – Effects of fluid injection on fault slip
                                  • University of Toronto
                                    • Developed a large-scale numerical model with over 5,000 parameters to analyze how injection rate and volume influence fault slip and induced seismicity.
                                    • Simulated complex fluid-fault interactions, testing multiple stress regimes and pore pressure conditions to identify safe operational thresholds.
                                    • Provided insights for reducing induced seismic risk, helping define safer injection strategies for energy and extraction operations.
                                  • Geophysicist
                                  • Taiso Alloys
                                    • Conducted geophysical surveys and data analysis, integrating diverse datasets to assess subsurface structures and mineral potential.
                                    • Developed and implemented exploration plans, optimizing survey methodologies for resource identification.
                                    • Interpreted geophysical data using specialized software and advanced modeling techniques, providing actionable insights for exploration teams, collaborating with multidisciplinary teams to refine exploration strategies and improve decision-making.
                                  • Masters Research
                                  • Indian Institute of Science Education and Research, Kolkata
                                    • Conducted seismic surveys, deployed long-term and short-term seismometers, performing focal mechanism inversions, CFS analysis, receiver function analysis, and rupture propagation modeling.
                                    • Investigated the region’s stress regime, fault interactions, and rupture propagation through seismic surveys and numerical modeling.
                                    • Provided critical insights into stress transfer and fault behavior, improving regional seismic hazard assessment and advancing tectonic deformation models.

                                  • Earth Science Researcher
                                  • OutlierAI
                                    • Consulted as a geophysics expert, training AI models to solve complex geophysical problems by integrating domain knowledge into machine learning frameworks by analyzing real-world geophysical challenges in magnetism, seismology, and gravity.
                                    • Developed solutions, reviewed AI-generated results, and advised on feature engineering, data processing, and model architecture to improve accuracy and optimized code to accelerate training speed.
                                    • Enhanced AI-driven geophysics applications, boosting model precision and enabling adoption in seismic analysis, resource exploration, finishing the project one month early due to faster model training.
                                  • AI Data Analyst
                                  • DATech
                                    • Reviewed models that were designed for complex problem-solving but needed in-depth performance evaluation to assess accuracy, user experience, and comparison with competitors.
                                    • Analyzed model responses, user scores, and competitor benchmarks to identify strengths, weaknesses, and areas for optimization using data mining, statistical models, and machine learning techniques to evaluate model effectiveness, uncover performance trends, and diagnose pain points.
                                    • Provided data-driven insights that improved AI model performance, enhanced user experience increasing the Net Promoter Score by more than one point.
                                  • PhD Researcher – Real-time event precursor detection
                                  • University of Toronto
                                    • Developing a deep learning-based detection system using lab acoustic data to identify aseismic transients that may precede seismic events.
                                    • Engineering a pipeline combining feature extraction, machine learning models, and real-time analysis, optimizing it for high sensitivity and accuracy.
                                    • Potential for early detection of seismicity in EOR, wastewater injection, and geothermal scenarios.
                                  • PhD Researcher – Contextual Peak Detection Algorithm
                                  • University of Toronto
                                    • Developed a machine learning-based peak detection algorithm for time-series signal processing with 2000% frequency variance, surpassing traditional methods.
                                    • Achieved 96% accuracy and 92% recall, enabling robust, noise-resistant detection across seismic analysis, financial time series, and biomedical signals.
                                    • Outperformed nearly all established peak detection techniques, being almost two orders of magnitude faster than most other techniques.

                                  • AI Data Analyst
                                  • DATech
                                    • Reviewed models that were designed for complex problem-solving but needed in-depth performance evaluation to assess accuracy, user experience, and comparison with competitors.
                                    • Analyzed model responses, user scores, and competitor benchmarks to identify strengths, weaknesses, and areas for optimization using data mining, statistical models, and machine learning techniques to evaluate model effectiveness, uncover performance trends, and diagnose pain points.
                                    • Provided data-driven insights that improved AI model performance, enhanced user experience, increasing the Net Promoter Score by more than one point.
                                  • Market Analyst – Green Fashion Niche
                                  • Purepicks
                                    • Identified target audience for a sustainable fashion service by analyzing 10k+ surveys.
                                    • Applied sentiment clustering, segmentation metrics, Random Forests, and PCA for feature emphasis, achieving a 96% potential customer conversion rate.
                                  • Researcher – Fluid Injection Parameter Space Analysis
                                  • University of Toronto
                                    • Analyzed a 3000-point fluid injection parameter space, each containing 200GB of data, to identify safe operational boundaries.
                                    • Utilized statistical anomaly detection and hypothesis testing to classify data and uncover previously undetectable category boundaries, improving the understanding of fluid dynamics.
                                  • Data Analyst
                                  • Sarah’s Tees
                                    • Analyzed customer signup and sales data to uncover trends, customer behavior patterns, and key drivers of revenue.
                                    • Identified actionable insights by segmenting customer demographics, purchase frequency, and retention rates, leading to data-driven decision-making.
                                    • Recommended strategic changes based on data trends, optimizing marketing efforts and improving customer conversion rates.

                                  Geophysics
                                  Data Science
                                  Data Analytics

                                  Education

                                  PhD

                                  University of Toronto

                                  2019-2025

                                  MS

                                  Indian Institute of Science Education and Research

                                  2017-2019

                                  BS

                                  Indian Institute of Science Education and Research

                                  2014-2017

                                  Cert.

                                  Google Data Analyst Certification

                                  2023

                                  Cert.

                                  Google Cloud Platform Certification

                                  2024

                                  Skills

                                  Programming Languages

                                  Data Analysis

                                  Geophysics

                                  High Performance Computing

                                  Awards

                                  Publications

                                  Contact Me

                                  Business Card

                                  Send Me A Message