Machine Learning, AI Development

Akash Singh (pronounced uh-KAHSH)

*****.*.*****@*******.*** Mobile: +1-716-***-**** LinkedIn: sdeakash github.com/imfoobar42 Buffalo, NY Education

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University at Buffalo, The State University of New York (UB) Buffalo, USA Masters in Computer Science & Engineering Aug 2024 - Expected Dec 2025 Courses: Machine Learning, Reinforcement Learning, Data Intensive Computing, Analysis Of Algorithms

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University of Mumbai - Thakur College of Engineering and Technology Mumbai, India Bachelor of Engineering - Information Technology Aug 2014 - June 2018 Courses: Data Structures, Algorithms, Operating Systems, Cloud Computing, DBMS, TOC Experience

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University at Buffalo Buffalo, USA

AI Researcher at XLabs-UB Jan 2025 - Present

Built and deployed a scalable Multimodal LLM(Large Language Model) handling user input and chat platform with React, Node.js, and MongoDB, leveraging AWS for high availability and performance.

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Infosys Pune, India

Senior Associate Consultant (SDE-2) Nov 2021 - Aug 2024

Created and deployed a Hybrid recommendation system leveraging collaborative and content-based filtering techniques to predict user dress preferences. This initiative accelerated user engagement with a 45% improvement in click-through rates (CTR) through personalized notifications, ultimately increasing sales.

Implemented multithreading in a RESTful application to enhance API request handling and optimize data interactions, resulting in a 35% reduction in response time and effective processing of concurrent requests.

Championed CI/CD implementation within an Agile framework, using Jenkins for automation, Docker for containerization, Maven for Java, and npm for builds, achieving a 25% increase in release frequency.

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Capgemini Mumbai, India

Associate Consultant (SDE-1) Feb 2019 - Nov 2021

Implemented advanced SQL indexing strategies, optimized 20+ views, and refactored 15+ stored procedures to enhance query performance, achieving up to 50% faster data retrieval and significantly improving DB efficiency.

Designed a responsive frontend and backend with Angular and SpringBoot respectively, implementing API routes and asynchronous calls to meet client needs, reducing user-facing latency by 20%.

Incorporated robust authentication and authorization using JWT and OAuth2, enhancing application security and achieving a 35% reduction in unauthorized access attempts.

Completed 3 months of professional training in Full-stack development with Angular, Spring Boot, MongoDB, and Git, focusing on backend functionality and API integration. Projects

• Guest Tagging System Design for Surveillance using IoT: Developed an RFID-based host detection and crowd management system for GEV –UNESCO –awarded site –using Raspberry Pi, securing a funding of INR 60k from MU.

• AI Hackathon Project: Created a tool with React, Firebase, and the OpenAI API to analyze 100+ resumes and recommend skill upgrades, integrating Griptape AI for course suggestions and upskilling duration estimates for professionals in Buffalo.

• Crime Trend Analysis and Prediction in Buffalo: Applied machine learning algorithms (Random Forest, MapReduce, SARIMA, KNN, Logistic Regression) to analyze crime patterns in Buffalo, achieving 82% accuracy in crime classification and forecasting future crime trends over the next 10 years, providing actionable insights for law enforcement resource allocation. Skills

• Languages: Python, SQL, C++, JAVA

• Machine Learning Libraries: Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch, Keras, XGBoost, LightGBM

• Computer Vision: OpenCV, YOLO, Image Processing, Object Detection, Segmentation

• Natural Language Processing (NLP): NLTK, SpaCy, Hugging Face Transformers, Gensim, TextBlob, Word2Vec, BERT.

• Machine Learning Techniques: Supervised and Unsupervised Learning, Deep Learning, Reinforcement Learning

• Model Evaluation: Cross-validation, Hyperparameter Tuning, Grid Search, Random Search, Precision, F1-Score, ROC-AU

• Data Handling: NumPy, Pandas, SciPy, Matplotlib, Seaborn, Plotly, Data Preprocessing (Cleaning, Scaling, Encoding)

• Cloud and Deployment: AWS Sagemaker, GCP AI Platform, Docker, Kubernetes, Model Deployment (Flask, FastAPI)

• Other Tools: MLflow, Apache Kafka, Apache Spark, Jupyter Notebooks, TensorBoard, Kubernetes Achievements & Certification

• Vice-President, CSE-GSA: Spearheaded initiatives that enhanced communication and collaboration among 500+ CSE members, resulting in increased engagement and representation of student interests in departmental meetings.

• Speaker for System Design Workshop, CSE UB: Conducted a session on designing scalable systems for millions of users.

• Speaker for Large Language Models - NLP, CSE UB: Presented on RNN, Transformer architecture & training of LLM.

• Certified in Deep Learning by DataCamp: Gained hands-on experience with neural networks, convolutional neural networks (CNNs), RNN, LSTM, dropout regularization, multi modal NN and fine-tuning using real-world datasets.

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