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Purdue University · ECE Department

Job Interview in the AI-Era

Coding · Systems · Agents — A Two-Week Intensive Bootcamp
📅 May 18 – 29, 2026
🕙 (W1) MWF, (W2) TWF · 10am–12pm EDT
🎓 Undergrad Seniors / MS Students
📧 @purdue.edu required
Course Overview
About This Bootcamp

This two-week program targets Undergraduate seniors and MS students preparing for coding interviews. Topics span the full structure of a typical coding interview process — data structures (trees, graphs, dictionaries), AI-assisted debugging, machine learning systems, and soft skills including communication strategies and problem breakdowns. The bootcamp features hands-on activities throughout.

Schedule
Lecture Syllabus
01
Foundation: Python & OOP
Monday, May 18, 2026 • 10:00 AM to 12:00 PM ET
Basics review · Complexity analysis · Object-oriented programming
⌄
Technical Skills
  • Analyze the time complexity of algorithms
  • Use Leetcode to practice coding problems.
    • Communication & Strategy
  • Hiring Pipelines
  • Interviewer vs Job Hunter Approach
  • The inverted Triangle Strategy for Job Hunting
    • WC
    Wei Chen
    chen2732@purdue.edu
    PC
    Prateek Chennuri
    pchennur@purdue.edu
    ▶ Technical Slides (PDF) ▶ Communication Slides (PDF) Python Docs Big-O Cheatsheet OOP Tutorial
    02
    Data & Scalability
    Wednesday, May 20, 2026 • 10:00 AM to 12:00 PM ET
    Core DSA — HashMap, stacks, queues, binary trees, binary search trees
    ⌄
    Technical Skills
  • Identify better code (time/space)
  • Understand the coding interview process.
    • Communication & Strategy
    How to talk to recruiters, professional email etiquette, and phone screens.
    WC
    Wei Chen
    chen2732@purdue.edu
    PC
    Prateek Chennuri
    pchennur@purdue.edu
    ▶ Slides (PDF) LeetCode DSA Guide Visualgo (Tree/Graph) Python Memory Model
    03
    Foundation: AI Agents
    Friday, May 22, 2026 • 10:00 AM to 12:00 PM ET
    AI agent. Elements of AI agents, usage of popular coding agents, and effective prompt drafting.
    ⌄
    Technical Skills
  • Learn basic concepts of an AI agent.
  • Use the coding agent to assist coding and prepare for interviews.
    • Communication & Strategy
    Ask for clarification effectively during coding interviews.
    DC
    Deming Chu
    chu292@purdue.edu
    ZP
    Zhaoying Pan
    pan433@purdue.edu
    ▶ Slides (PDF) Anthropic Docs
    04
    ML: Data Pipelines
    Tuesday, May 26, 2026 • 10:00 AM to 12:00 PM ET (Tuesday instead of Monday due to Memorial Day.)
    Preprocessing, ETL (Extract, Transform, Load), and handling data noise.
    ⌄
    Technical Skills
    Understand the basic idea of a data pipeline
    Communication & Strategy
  • Understand methodology for getting started on an ML repo
  • Understand principles of problem solving
    • DC
    Deming Chu
    chu292@purdue.edu
    ZP
    Zhaoying Pan
    pan433@purdue.edu
    ▶ Slides (PDF)
    05
    ML: Model Lifecycle
    Wednesday, May 27, 2026 • 10:00 AM to 12:00 PM ET
    Training loops · Loss functions · Evaluation metrics (Precision/Recall)
    ⌄
    Technical Skills
  • Understand the general ML pipelining
  • Understand general ML system design from ideation to deployment
    • Communication & Strategy
  • Tailor resumes for a given target role
  • Prepare yourself for the role.
    • GP
    Gaurav Patel
    pate1332@purdue.edu
    SD
    Suhas Dara
    daras@purdue.edu
    ▶ Slides (PDF)
    06
    AI-assisted coding & Panel Discussion
    Friday, May 29, 2026 • 10:00 AM to 12:00 PM ET
    Panel discussion with industry guests and students with recent interview success.
    ⌄
    Technical Skills
    Familiarize students with Coding interview platforms such as Coderpad.
    Communication & Strategy
    Panel Discussion.
    GP
    Gaurav Patel
    pate1332@purdue.edu
    SD
    Suhas Dara
    daras@purdue.edu
    ▶ Slides (PDF)
    Purdue University · 2026
    Course Details
    Dates
    May 18–22 & May 26, 27, 29, 2026
    Time
    Wk 1 MWF · 10:00am–12:00pm ET
    Wk 2 TWF · 10:00am–12:00pm ET
    In-Person
    Purdue Only
    WANG 2599
    Online
    Open to Anyone

    Zoom Link
    Registration
    Register Now ↗
    Registration Fee
    $99 general admission
    Free for Purdue students & recent grads
    Contact Wei Chen at chen2732@purdue.edu for the discount code.
    Instructors
    Lead Instructor
    Wei Chen
    chen2732@purdue.edu
    Faculty Advisor
    Stanley Chan
    stanchan@purdue.edu
    Eligibility & Prerequisites
    Who Can Apply
    Anyone.
    Prerequisites
    Basic Python ECE 20875 Python for Data Science
    The Team
    Meet the Instructors
    Graduate students and researchers teaching this bootcamp
    Wei Chen
    Wei Chen
    Lead Instructor
    I'm Wei Chen, final year PhD in ECE department. My research focus is representation learning and generative AI.
    Lecture 01 Lecture 02
    chen2732@purdue.edu
    Prof. James C Davis
    Prof. James C Davis
    Guest-Speaker/Lecturer
    Hi, I’m Jamie! I am an Assistant Professor at Purdue University, in the department of Electrical and Computer Engineering (ECE). I hold a PhD in Computer Science from Virginia Tech (2020) under Dr. Dongyoon Lee. I also interned at Microsoft Research (summer 2019 with Patrice Godefroid) and IBM Research Almaden (summer 2018 with Deepavali Bhagwat). Before that, I was a software engineer at IBM (2012-2015) working on the GPFS file system.
    davisjam@purdue.edu
    Prateek Chennuri
    Prateek Chennuri
    Student Instructor
    Prateek Chennuri is a Ph.D. candidate in Electrical and Computer Engineering at Purdue University, advised by Prof. Stanley H. Chan. He received his B.Tech–M.Tech dual degree from the Indian Institute of Technology (IIT) Gandhinagar in 2021. His research focuses on computational imaging and computer vision, with an emphasis on generative methods for inverse problems and perception. His work spans tasks such as denoising, super-resolution, depth estimation, and reflectivity recovery across conventional CMOS sensors as well as emerging modalities like single-photon detectors (SPADs) and event cameras. Alongside his research, he has recently gone through multiple industry interview processes at leading technology companies, gaining first-hand insight into modern hiring pipelines and evaluation strategies. He will be joining Apple’s Camera Algorithms team as a Computational Photography / Computer Vision Machine Learning Engineer in July 2026.
    Lecture 01 Lecture 02
    pchennur@purdue.edu
    DC
    Deming Chu
    Student Instructor
    He is a second-year Ph.D. student in the Department of Computer Science, specializing in theoretical computer science. His achievements include a Gold Medal at the ICPC Asia East Continent Final and the Microsoft Research Asia Star of Tomorrow Internship Award of Excellence. He also served as the Instructor of Record for CS 211: Competitive Programming I.
    Lecture 03 Lecture 04
    chu292@purdue.edu
    Zhaoying Pan
    Zhaoying Pan
    Student Instructor
    I am a third-year PhD student in Electrical and Computer Engineering at Purdue University, where my work focuses on building trustworthy and robust AI systems. My research centers on understanding and mitigating subpopulation shifts and spurious correlations in machine learning models.
    Lecture 03 Lecture 04
    pan433@purdue.edu
    Gaurav Patel
    Gaurav Patel
    Student Instructor
    Gaurav Patel is a PhD candidate in Electrical and Computer Engineering at Purdue University, where he works on responsible and efficient machine learning, with a focus on generative models, domain adaptation, and machine unlearning. His research spans topics such as representation learning, parameter-efficient adaptation, and post-training alignment for diffusion models, with publications at leading machine learning and computer vision venues like CVPR, ICLR, ICCV, and WACV. He has industry internship experience as an ML researcher at Amazon AGI, Apple AIML and MERL.
    Lecture 05 Lecture 06
    pate1332@purdue.edu
    Suhas Dara
    Suhas Dara
    Student Instructor
    I am a second-year master's student in Electrical and Computer Engineering at Purdue University, advised by Prof. Joseph Makin, with research focused on computational neuroscience, specifically brain-to-text decoding and brain-to-speech synthesis. I hold a BS in Computer Science from The University of Texas at Austin and spent several years as a software engineer at Two Sigma before pivoting toward research at the intersection of neuroscience and AI. Having been on both sides of the technical interview process, I have a good sense of what candidates tend to find most challenging.
    Lecture 05 Lecture 06
    daras@purdue.edu
    Evan Chen
    Evan Chen
    Website & Course Contributor
    Evan Chen is a Ph.D. candidate in the Elmore Family School of Electrical and Computer Engineering at Purdue University, advised by Prof. Christopher Brinton. His research focuses on machine learning systems, federated learning, distributed optimization, and large language models, with an emphasis on scalable AI in resource-constrained and distributed environments. He contributes to the bootcamp website and course materials and is currently a Research Intern at Microsoft Research.
    chen4388@purdue.edu