Текич Желько

The Skoltech1Million Entrepreneurial Challenge, (2020-2022) MIT - Skoltech Next Generation Program, budget: ~$500.000; Skoltech PL Zeljko Tekic, MIT PL Douglas Hart

Technological Domain Formation, MIT Skoltech Seed Fund; one-year project; MIT PI: Christopher L. Magee, Institute for Data, Systems and Society; Skoltech co-PI: Zeljko Tekic.

Extracting and Employing IP Constraints in the Space of Design Alternatives, MIT Skoltech Seed Fund; one-year project; MIT PI: Warren Seering, Department of Mechanical Engineering; Skoltech co-PI: Zeljko Tekic

Mastering innovation in Serbia through development and implementation of interdisciplinary post-graduate curricula in innovation management; Project No 544278-TEMPUS-1-2013-1-RS-TEMPUS-JPCR; European Commission; 2013-2016; TEMPUS; budget: 950.000 Euro; the author of the project and its coordinator (while at University of Novi Sad);

Fostering Students’ Entrepreneurship and Open Innovation in University-Industry Collaboration – Idea Lab; Project No 544373-TEMPUS-1-2013-1-RS-TEMPUS-JPHES; European Commission; 2013-2016; TEMPUS; budget: 900.000 Euro; the author of the project and project coordinator assistant (while at University of Novi Sad)

Friend to Understand, European Researchers' Night; Project No: 633396; European Commission; 2014-2015; Horizon 2020; budget: 90.000 Euro; the author of the project and its coordinator (while at University of Novi Sad).

В настоящее время:
Доцент департамента бизнес-информатики Высшей школы бизнеса НИУ ВШЭ

2014 – 2021  Доцент, Сколковский институт науки и технологий

2017 (Mar – June)  Visiting Researcher, Massachusetts Institute of Technology, Cambridge, US; Department of Mechanical Engineering (host: Professor Warren Seering)

2015 (Sep – Oct)  Visiting Assistant Professor Massachusetts Institute of Technology, Cambridge, US; Skoltech-MIT Initiative (host: Professor Douglas Hart); 

2013 – 2014   Assistant Professor, University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management; Novi Sad, Serbia

2008 – 2010 Junior Engineer, RT-RK Institute for Computer Based Systems, Novi Sad, Serbia

2008 – 2013 Teaching Assistant, University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management; Novi Sad, Serbia

Scope. I’m inviting students to write research-oriented theses (not applied company projects) on how artificial intelligence—especially machine learning (ML) and generative AI (genAI)—is reshaping how firms operate, innovate, and compete. Projects should advance knowledge in business and management.

Core Requirements

• Empirical basis (“hard data”) is mandatory. Possible sources:
  • Surveys or interviews with professionals, managers, or users
  • Web scraping or analysis of online platforms
  • Public datasets, search trends, or social-media data
  • Controlled experiments with genAI use
  • Company/industry secondary data (where available and shareable)
• Methods. Quantitative (statistics, experiments, text mining, network analysis, ML) or qualitative (semi-structured interviews, coding, comparative cases). Mixed methods welcome.
• Theoretical contribution. Frame clear research questions and connect to debates in strategy/innovation/IS/marketing/entrepreneurship.

Note: This track is not for implementation tasks inside a company (no consulting-style projects, no proprietary NDA-locked datasets that cannot be analyzed transparently).

Illustrative Topic Clusters

1. AI & Innovation Management

• How do firms integrate genAI into idea generation, screening, and testing?
• Do AI-assisted tools change idea originality and diversity?
• Rapid prototyping: how AI shifts product design/testing cycles.

1. AI & the Nature of Work

• Freelancers’ use of genAI: effects on income, efficiency, creativity.
• Consulting: impact on billable mix, cycle time, deliverable quality, and client satisfaction.
• Healthcare operations (non-diagnostic): LLM scribing/portal replies, turnaround, guideline adherence, patient satisfaction.
• Professional norms, governance, and ethics across knowledge-work settings.

1. AI & Business Strategy

• AI for resource optimization in operations/marketing (measurement & causal impact).
• AI-enabled competitive behavior (algorithmic pricing, content recommendation, platform governance).
• Startups vs. incumbents: strategic patterns and performance with AI.

1. AI Adoption & Diffusion

• Industry leaders vs. laggards in ML/AI adoption—drivers and outcomes.
• GenAI adoption among knowledge workers: speed, use cases, constraints.
• Cross-national comparisons of AI readiness using surveys or platform data.

1. LLMs in Software Development

• What drives or hinders LLM adoption (task type, seniority, team norms, tooling, governance)?
• Effects on PR throughput, bug density, review latency, and test pass rate.
• Security/compliance concerns and their impact on usage.

1. AI in Education

• What drives or inhibits genAI use by students and instructors?
• How does AI use change learning behaviors and outcomes?
• Which course/institutional policies enable responsible and effective use?

1. AI Agents in Business

• When do autonomous or multi-agent systems outperform conventional copilots (task structure, tool access, error tolerance)?
• Orchestration and governance: human-in-the-loop, escalation paths, auditability, safety/failure modes.
• Interaction with existing automation (RPA/BPM), role redesign, accountability, and KPI effects.

(You can also propose your own topic—if it’s empirical and theory-driven.)

What I’m Looking For

• Curiosity & clarity: sharp questions that matter.
• Feasibility: realistic data plan and method fit.
• Rigor: solid design, transparent analysis, reproducibility.
• Relevance: a clear link to literatures in management/innovation/strategy/IS.

What I Offer (Supervision & Support)

• Regular check-ins (typically bi-weekly) and fast feedback on design, instruments, and analyses.
• Method guidance (survey/interview design, scraping/experiments, causal ID/ML basics).
• Advice on positioning, structure, and (ambitious) pathways to conference/journal submission.
• Defense preparation

Interested? Email me a 1–2 page outline (title & questions, why it matters, data plan, method, timeline/risks, your tooling). If you’re unsure about the precise topic, share the area you’re most curious about and a tentative data source—we’ll shape it into a feasible thesis together.