About Workshop
This intensive 3-day workshop bridges theory and real-world application in advanced data science. Day 1 focuses on mathematical and statistical foundations for AI, including Bayesian modeling, SVD, and custom optimizer development. Day 2 covers programming, data engineering, and multi-language AI workflows, teaching participants to handle massive datasets efficiently. Day 3 explores machine learning, deep learning, and visualization, including AutoML pipelines, high-dimensional clustering, and deployment of AI-powered dashboards. Participants will gain hands-on skills using Python, R, PyTorch, and distributed computing tools.
Aim
This workshop aims to equip participants with advanced data science skills integrating AI, automation, and large-scale data engineering. Attendees will gain hands-on experience in modeling, multi-language data workflows, and cutting-edge machine learning techniques. The program emphasizes end-to-end pipeline design, from statistical foundations to deployment-ready AI systems. Participants will develop practical expertise in handling high-dimensional, multi-terabyte datasets and building predictive models that scale.
What Participants Will Learn
- Understand and apply advanced statistical and mathematical foundations for AI modeling.
- Build high-performance AI workflows integrating Python and R for large-scale data.
- Design and implement predictive and recommendation systems using ML and DL techniques.
- Develop end-to-end AI pipelines with deployment-ready visualization and dashboards.
- Apply best practices in optimization, high-dimensional analysis, and distributed computing.
Structure
📅 Day 1: The Convergence: AI as the Engine for Synthetic Biology
- Core Objective: Understand the foundations of synthetic biology and explore how artificial intelligence accelerates every step of the Design–Build–Test–Learn (DBTL) cycle.
- Foundations of synthetic biology and the role of AI in modern biological design
- AI-driven acceleration of the Design–Build–Test–Learn (DBTL) cycle
- Conjugate priors, likelihood estimation, and Markov Chain Monte Carlo (MCMC) sampling
- Optimization strategies for AI-enabled synthetic biology workflows
- Custom adaptive optimizer design using Adam and hardware acceleration
- Applications of AI in gene design, protein prediction, cellular engineering, and SynBio workflows
🛠️ Hands-on:
- Hands-on Lab: Visualize a 3D loss surface optimization and write a custom adaptive optimizer using hardware acceleration.
- Hands-on Lab: Explore AI-assisted synthetic biology workflows using gene, protein, and cell-design tools.
🧰 Tools Covered: Matplotlib, AlphaFold, GeneFormer, Cell-Pioneer, SynBioCAD
📅 Day 2: AI-Enabled Protein, Enzyme & Smart Cell Design
- Core Objective: Learn how AI enables the creation of novel proteins, enzymes, genetic circuits, and smart cells for therapeutics, sustainable chemistry, and advanced materials.
- Programming life through AI-driven genetic circuits and smart cell engineering
- Real-world case studies in enzyme design, protein engineering, and programmable cells
- Leveraging R’s statistical depth alongside Python’s production capabilities
- Query optimization, partitioning, clustering, and execution plans in large biological datasets
- Designing new therapeutics using AI-assisted protein and enzyme design approaches
- Role of green chemistry in sustainable biological and chemical innovation
- Applications of nanomaterials and nanochemistry in synthetic biology and bioengineering
🛠️ Hands-on:
- Hands-on Lab: Explore AI-assisted protein structure prediction using AlphaFold Viewer and ESMFold.
- Hands-on Lab: Review AI-designed enzymes that degrade plastics or serve as biosensors.
- Hands-on Lab: Understand AI-driven protein design workflows using ProteinMPNN and EvoDesign.
🧰 Tools Covered: ROSETTA Fold, ESM-3, DiffDock, AlphaFold Viewer, ESMFold, ProteinMPNN, EvoDesign
📅 Day 3: Automation, Self-Driving Labs, Deep Learning & Visualization
- Core Objective: Understand how automation, self-driving laboratories, machine learning, deep learning, and visualization tools are shaping the future of synthetic biology, healthcare, and bioengineering careers.
- Core concepts from molecular biology, biochemistry, and systems biology
- Machine learning models for predicting cell behavior and optimizing genetic circuits
- Biosensors for pathogen detection and programmable probiotic systems for healthcare
- Automation and self-driving labs for faster biological experimentation
- Deep learning methods including ANN and CNN for biomarker analysis and medical image interpretation
- Future-ready skills, tools, and global career roadmap for bio-engineers
🛠️ Hands-on:
- Hands-on Lab: Simulate genetic circuits using Cello and understand AI-supported circuit design workflows.
- Hands-on Lab: Explore synthetic biology platforms such as Benchling, Geneious, and SynBioCAD.
- Hands-on Lab: Use deep learning concepts to analyze biomarkers and medical image data for disease diagnosis and prognosis.
🧰 Tools Covered: Cello, Benchling, Geneious, SynBioCAD, Protein Design AI Tools, GeneNet, Deep Circuit, ANN, CNN
Important Dates
Registration Ends
7:00 PM IST
Workshop Dates
2026-07-13
8:00 PM
8:00 PM
What You Will Gain
Outcomes
- Master advanced mathematical/statistical methods for AI applications.
- Gain practical experience in building large-scale AI pipelines using Python and R.
- Develop predictive models and recommendation systems for real-world datasets.
- Handle multi-terabyte datasets efficiently using distributed and parallel processing.
- Build deployment-ready dashboards and visualization tools for enterprise use.
Who Should Attend
- Undergraduate/postgraduate degree in Computer Science, Data Science, Statistics, AI/ML, Bioinformatics, or related fields.
- Professionals in research, analytics, data engineering, AI development, or IT sectors.
- Data scientists and ML engineers looking to upskill in advanced AI, automation, and predictive modeling.
- Individuals with an interest in end-to-end AI pipelines and high-performance computing.
Prof. Kumud Malhotra
Department of AI
Speciality: AI, ML, DL, Automation, Statistics, Bayesian, Optimization, Programming, Visualization