Take on a
focused technical project.

Position Overview

Develop a compact visual-recognition prototype and evaluate how reliably it performs beyond ideal sample images.

Key Responsibilities

• Prepare and audit image data; document classes, sources, and train/test separation.
• Build an interpretable computer-vision baseline and compare it with a compact learned model.
• Evaluate performance under lighting changes, blur, occlusion, and unfamiliar inputs.
• Measure CPU inference behavior and integrate the selected model into a usable demo.
• Document limitations, reproducibility, and engineering tradeoffs.

Preferred Background & Qualifications

Students in computer science, electrical or computer engineering, AI, or related fields. Basic Python programming; willingness to learn image processing and model evaluation. Careful experimentation, debugging, and clear technical communication.

Tools & Methods

Python, OpenCV, scikit-learn, a lightweight deep-learning framework, and Git.

Expected Project Deliverables

A reproducible visual-recognition demo (webcam or file input), evaluation report, and final presentation.

Professional Development

Practice scoped technical problem-solving, AI-assisted workflows, independent validation, documentation, weekly mentor feedback, and communicating a final project outcome.

Position Overview

Build and validate a sound-event recognition workflow that remains useful under realistic noise and uncertainty.

Key Responsibilities

• Explore audio waveforms, sampling, spectrograms, and signal quality.
• Extract MFCC and related features; develop classical ML baselines.
• Compare with a compact log-mel-based neural model using sound dataset folds.
• Analyze false alarms, silence, noise, and unknown sound conditions.
• Benchmark windowed CPU inference and build an audio-file or microphone demo.

Preferred Background & Qualifications

Students in EE, CS, signal processing, AI, or related disciplines. Basic Python; prior digital signal processing experience is helpful but not required. Interest in experimental evaluation and robust system behavior.

Tools & Methods

Python, NumPy, audio-processing libraries, scikit-learn, lightweight neural modeling, and Git.

Expected Project Deliverables

A sound-intelligence prototype, cross-fold and robustness evaluation, and documented demo.

Professional Development

Practice scoped technical problem-solving, AI-assisted workflows, independent validation, documentation, weekly mentor feedback, and communicating a final project outcome.

Position Overview

Develop an educational, non-diagnostic ECG analysis pipeline combining signal processing, interpretable features, and machine learning.

Key Responsibilities

• Work with public annotated ECG records and understand signal/label structure.
• Implement and compare filtering and QRS / R-peak detection methods.
• Engineer RR-interval and morphology features for baseline classification.
• Compare a compact learned model against an interpretable baseline.
• Apply patient/record-aware validation and investigate noise-related failure cases.
• Build a viewer that explains signal traces, detections, and model limitations.

Preferred Background & Qualifications

Students in biomedical engineering, EE, CS, data science, or related areas. Basic Python; interest in biosignals and quantitative validation. Ability to communicate research limitations responsibly.

Tools & Methods

Python, PhysioNet MIT-BIH data, WFDB, SciPy, scikit-learn, and optional 1-D CNN.

Expected Project Deliverables

An explainable ECG research viewer, validation evidence, technical report, and presentation. Not intended for clinical diagnosis.

Professional Development

Practice scoped technical problem-solving, AI-assisted workflows, independent validation, documentation, weekly mentor feedback, and communicating a final project outcome.

Position Overview

Investigate EEG motor-imagery classification and the challenges of generalizing from one subject to another.

Key Responsibilities

• Inspect EEG channels, event labels, artifacts, and trial quality.
• Build filtering, re-referencing, epoching, and feature-extraction workflows.
• Establish a CSP + LDA or equivalent interpretable classification baseline.
• Compare within-subject and cross-subject validation outcomes.
• Optionally evaluate a compact neural approach under controlled conditions.
• Create an interactive trial explorer and explain generalization failures.

Preferred Background & Qualifications

Students in EE, biomedical engineering, CS, ML, or related fields. Comfort with basic Python and quantitative reasoning; intermediate project complexity. Strong interest in signal processing and careful model evaluation.

Tools & Methods

Python, MNE, NumPy/SciPy, scikit-learn, and public PhysioNet EEG motor-imagery data.

Expected Project Deliverables

An EEG trial explorer, reproducible model comparisons, subject-generalization analysis, and final presentation.

Professional Development

Practice scoped technical problem-solving, AI-assisted workflows, independent validation, documentation, weekly mentor feedback, and communicating a final project outcome.

Position Overview

Translate a business question into a validated data workflow and an AI-enabled analytical solution.

Key Responsibilities

• Frame business decisions, stakeholders, KPIs, and domain terminology.
• Clean, profile, and document realistic structured data.
• Develop SQL queries, analytical metrics, and reproducible Python workflows.
• Use generative AI to assist exploration and coding while independently verifying outputs.
• Develop an interpretable predictive or risk-oriented analytical component.
• Build an interactive analytics application and test natural-language data interactions.

Preferred Background & Qualifications

Students in data science, business analytics, information systems, economics, CS, or related fields. Basic Python and/or SQL preferred; strong analytical curiosity. Ability to translate technical findings into clear business implications.

Tools & Methods

Python, SQL, data visualization, interactive Python web tools, and approved AI APIs/tools.

Expected Project Deliverables

An AI-enabled analytics prototype, validated metrics, business-focused report, and presentation.

Professional Development

Practice scoped technical problem-solving, AI-assisted workflows, independent validation, documentation, weekly mentor feedback, and communicating a final project outcome.

Position Overview

Combine financial-domain reasoning, structured data, and responsible AI assistance to evaluate performance and risk.

Key Responsibilities

• Study financial statements, business models, and relevant financial terminology.
• Analyze profitability, balance-sheet trends, capital, liquidity, and credit quality.
• Prepare and validate structured financial or regulatory datasets.
• Benchmark institutions or peer groups and interpret differences.
• Identify risk signals, concentrations, and potential deterioration.
• Use AI-assisted research and reporting with explicit source and claim verification.
• Develop a financial-intelligence dashboard or analytical prototype.

Preferred Background & Qualifications

Students in finance, accounting, economics, business analytics, data science, or related fields. Financial statement literacy and spreadsheet skills; Python/SQL helpful. Sound judgment, attention to data quality, and clear written communication.

Tools & Methods

Public financial/regulatory data, spreadsheets, Python/SQL, visualization, and approved AI tools.

Expected Project Deliverables

A financial intelligence prototype, peer/risk analysis memo, validation log, and presentation.

Professional Development

Practice scoped technical problem-solving, AI-assisted workflows, independent validation, documentation, weekly mentor feedback, and communicating a final project outcome.

Position Overview

Turn a business question into a structured analysis and an AI-assisted decision-support workflow, with clear requirements and independently validated findings.

Key Responsibilities

• Frame the business question, stakeholders, objectives, constraints, and decision criteria.
• Gather and organize business needs through structured research, interviews, or approved project materials.
• Map current and proposed workflows; translate needs into user stories, functional requirements, and acceptance criteria.
• Profile relevant business data and define KPIs, assumptions, and analytical questions for the project.
• Use AI tools to assist with research, synthesis, or drafting, then verify sources, calculations, and requirements before use.
• Develop or support an AI-enabled analysis or decision-support prototype; document limitations and validation needs.
• Review outputs against stakeholder needs, record feedback and follow-up items, and present evidence-based recommendations.

Preferred Background & Qualifications

Students in business analytics, information systems, business administration, economics, operations, or related fields. Strong analytical writing and structured problem-solving. Spreadsheet skills required; basic SQL, process mapping, or data visualization experience is helpful. Interest in applying AI tools with careful verification.

Tools & Methods

Spreadsheets, Python and/or SQL, process-mapping and visualization tools, approved AI tools, and project documentation.

Expected Project Deliverables

A business-analysis brief with documented requirements and workflows, validated KPI or data findings, an AI-assisted decision-support prototype or workflow, and a final presentation.

Professional Development

Practice requirements analysis, AI-assisted research and synthesis, independent validation, stakeholder communication, weekly mentor feedback, and presenting business recommendations clearly.

Position Overview

Design and implement a human-centered AI application prototype while practicing reliable LLM integration and product evaluation.

Key Responsibilities

• Define a target user scenario, product boundaries, and success criteria.
• Implement a basic app flow and connect an approved model/API.
• Design prompts, structured responses, and repeatable evaluation cases.
• Implement general context/state management and document architecture decisions.
• Test quality, consistency, hallucinations, privacy, and edge cases.
• Improve UI/UX, error handling, latency behavior, and end-to-end reliability.
• Prepare a working beta demo and product/technical presentation.

Preferred Background & Qualifications

Students in CS, software engineering, information systems, AI, HCI, or related fields. Basic programming skills; experience with app frameworks or APIs is helpful. Interest in product thinking, testing, and responsible AI engineering.

Tools & Methods

A suitable app framework, version control, approved LLM APIs, and evaluation/test tooling.

Expected Project Deliverables

A generic AI companion prototype, test and safety evidence, code repository, and final demo. No access to proprietary product features is implied.

Professional Development

Practice scoped technical problem-solving, AI-assisted workflows, independent validation, documentation, weekly mentor feedback, and communicating a final project outcome.