Emre Kiciman: Leading AI for Industry at Microsoft Research

Emre Kiciman: Leading AI Innovation at Microsoft Research

Emre Kiciman is a Senior Principal Research Manager at Microsoft Research Redmond, where he leads the AI for Industry team within Research for Industries. His work focuses on the large-scale applications of Artificial Intelligence (AI), its societal impacts, and the development of causal machine learning algorithms and generative AI systems.

Research Focus Areas:

Kiciman's research interests are broadly categorized into three main areas:

1. Causal Machine Learning and Generative AI: He is dedicated to expanding the use of causal methods for decision-making across various application domains. His work aims to enhance current applications of correlational machine learning by leveraging causal insights. This involves scaling causal inference techniques to handle large and high-dimensional datasets, adapting them to new settings, and improving the robustness and bias of prediction and classification algorithms through causal or causal-inspired approaches.
2. AI's Societal Impact: Kiciman is committed to promoting the positive applications of AI while mitigating its negative implications. His projects delve into the intersection of security and machine learning, exploring new attacks and defenses for security-critical AI systems. He also investigates data biases, their consequences, and the development of infrastructure and methods for privacy-preserving AI systems, misinformation, and other related topics.
3. Computational Social Science and Social Media Analysis: With a strong interest in computational social science, Kiciman focuses on social media analyses that require a causal understanding of phenomena in health, mental health, data bias, and how new technologies influence our perception of the world and enable new forms of information discovery and retrieval.

Past Research and Expertise:

Kiciman's prior research experience includes the reliability, architecture, and operations of distributed systems. He was among the first to apply machine learning methods to challenges in fault detection and diagnosis in large-scale systems. His work also encompassed monitoring and optimization of web applications, and various information retrieval tasks such as entity linking and using social context to improve document ranking.

He holds a Ph.D. and M.S. from Stanford University and a B.S. in Electrical Engineering and Computer Science from U.C. Berkeley.

Highlights and Publications:

Kiciman's recent work and contributions are highlighted through several key publications and projects:

• AI Controller Interface: Generative AI with a lightweight, LLM-integrated VM: This work introduces the Artificial Intelligence Controller Interface (AICI), which simplifies the creation and experimentation of new strategies for improving LLM generations. AICI utilizes AI Controllers integrated with LLM inference engines, enabling constrained decoding, dynamic prompt editing, and coordinated execution across multiple generations.
• Causal Reasoning and Large Language Models: Opening a New Frontier for Causality: This publication explores the causal capabilities of Large Language Models (LLMs), discussing their implications for critical domains like medicine, science, law, and policy. It distinguishes between different types of causal reasoning tasks and addresses construct and measurement validity threats.
• DoWhy evolves to independent PyWhy model to help causal inference grow: This blog post announces the evolution of DoWhy into the independent PyWhy model, aiming to foster the growth of causal inference. It emphasizes the importance of identifying causal effects in scientific inquiry across various fields.
• Foundations of causal inference and its impacts on machine learning: This video discusses the foundational aspects of causal inference and its influence on machine learning, highlighting its role in decision-making and how ML models can be enhanced with causal insights.
• DoWhy: Causal Reasoning for Designing and Evaluating Interventions: This project focuses on building methods for causal reasoning, viewing computing systems as interventions and exploring how to tune them for desired outcomes.
• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries: This publication addresses the challenges and ethical considerations associated with social data in digital applications, emphasizing the need for careful analysis and responsible use.

Research Areas and Groups:

Kiciman's work spans across several key research areas at Microsoft Research, including:

• Artificial Intelligence
• Human-Computer Interaction
• Search and Information Retrieval
• Social Sciences
• Systems and Networking

He is also affiliated with the following research groups:

• Causality and Machine Learning
• Research for Industry

Contact Information:

• Email: emrek@microsoft.com
• Websites: http://x.com/emrek, http://kiciman.org/
• Google Scholar: https://scholar.google.com/citations?user=QZCU3NkAAAAJ&hl=en&oi=ao

Microsoft Research Lab – Redmond:

Emre Kiciman is based at the Microsoft Research Lab in Redmond, Washington. The lab's address is:

Microsoft Building 99, 14820 NE 36th Street, Redmond, Washington, 98052 USA

Learn more about the Redmond Lab

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