Doctor of Philosophy in Computer Science

Case Western Reserve University, Cleveland, Ohio

Aug 2024 – Present

Key Courses:

• Large Language Models: Explored advanced techniques in machine learning, with a focus on practical applications of multitask learning models. Presented a paper titled "Measuring Multitask Language Understanding", which involved analyzing and discussing state-of-the-art models and evaluation metrics in natural language processing (NLP). Gained hands-on experience with advanced machine learning frameworks and methods to handle large-scale datasets. Outcomes: LLMs, Llama, GPT, BERT, Evaluation, Olmo, Reasoning in LLMs, High performance computing, Data Parallelization, Algorithm Parallelization etc.

Masters of Science in Computer Science (Specialisation in ML/AI)

Case Western Reserve University, Cleveland, Ohio

Aug 2023 – May 2025

Key Courses:

• Designing High performant systems for AI: Project: Incorporated SMM on OneAPI in SYCL | Python3 to enhance YOLOV4. Led development of YOLOV4 sycl-python integration, emphasizing advanced object detection and SYCL-Python optimization. Outcomes: GEMM, SMM, BMM, HPC, Data/Algorithm Parallelization.
• Analysis of Algorithms: Acquired comprehensive knowledge in Data Structures and Algorithms, encompassing Greedy Algorithms, Graph Theory, Dynamic Programming, and NP-Completeness.

Bachelors of Technology in Civil engineering with minor in AI and Computer science

Indian Institute of Technology Mandi, Mandi, Himachal Pradesh

June 2019 – May 2023

Key Courses:

• Deep Learning and its Applications: Contributed to a project focused on real-time sign language detection. Outcomes: LLM, CNN, AutoEncoders, GANs, VAEs, RNN, Perceptron etc.
• Pattern Recognition: Proficiency in Probability, Random Processes, Linear Algebra, Bayes Decision Theory, Parameter Estimation, Unsupervised Learning, Sequential Pattern Recognition, etc.

Summer Internship

MGenio (Machine Learning, Web and Mobile dev, IoT), OH, USA

June 2024 – August 2024

• Self Driven research on Machine learning models and their Integration on IoT platforms.
• Developed an efficient platform to manage data flow and monitoring Machine learning models training.
• Designed a pipeline flow and Automated data preprocessing system for machine learning models to feed directly in IoT Systems.
• Manager: Satish Ramade, CEO, MGenio

Teaching assistant (Computational Perception)

Case Western Reserve University (Python3, Graphical Methods, Probability), OH, USA

Jan 2024 – present

• Efficiently grade assignments, ensuring accuracy and providing constructive feedback.
• Deliver engaging lectures on specialized topics, fostering student understanding.
• Conduct effective office hours to address queries and offer additional guidance.
• Professor: Dr. Michael Lewicki, Assistant Professor, Department of Computer Science & Engineering, CWRU

Teaching assistant (Data Science I & II & III)

Indian Institute of Technology Mandi (Python3, Machine learning, Probability, Deep Learning), HP, India

Feb 2021 – Aug 2022

• Conducted engaging lectures and facilitated Python hands-on lab sessions.
• Assessed student understanding through various evaluation methods.
• Provided constructive feedback and assisted students during office hours.
• Professors: Dr. Deelip AD, Dr. Varun Dutt, Dr. Manoj Thakur, IIT Mandi

Research Internship

Hatch Marine Consultants (Startup) (Python3, Machine learning, Probability, Deep Learning), New Delhi, India

May 2021 – August 2021

• Predictive Modeling and Analysis: Utilized advanced machine learning models to predict the scour depth of a river in Taiwan.
• Model Optimization and Fine-Tuning: Fine-tuned models to meet project requirements, achieving 90% accuracy.
• Advisor: Dr. Karan Gupta, Sr Engineer, Hatch Marine Consul, IIT Mandi

Advancements in XAI with Specialization in Counterfactual Explanation Methods

Case Western Reserve University (Python3, DNNs, GNNs), OH, USA

Jan 2024 – July 2024

• Engaged in leading-edge research on Explainable AI, particularly specializing in Counterfactual Explanation methods.
• Advisor: Dr. Jing Ma, CWRU

Analyzation of Nano Particles in Environment using Deep Learning

IIT Mandi (Machine Learning, Python3, Probabilistic Models), Mandi, India

June 2020 – June 2021

• Developing a deep multi-modal architecture for accurately predicting behaviour of nano particles on different species using environmental data.
• Advisor: Dr. Tanushree Parsai, IIT Mandi

Deep Neural Network model for Early landslide warning system

IIT Mandi (Machine Learning, Python3, RNN), Mandi, India

July 2020 – Dec 2020

• Led a machine learning initiative analyzing hillside landslides, using datasets like weather, elevation, slope, and temperature.
• Advisor: Dr. Varun Dutt, IIT Mandi

VAEs for Satellite imagery dataset

DRDO, Ministry of Defence (Machine Learning, Python3, C++, VAEs, Matlab), Chandigarh, India

Jan 2022 – Sep 2022

• Developed an approach for satellite imagery analysis using segmentation, labeling, and training with VAEs, achieving 83% accuracy.
• Advisor: Dr. MK Kalra, DRDO