diff --git a/assets/css/main.css b/assets/css/main.css
index e79efbb..91dd9e4 100644
--- a/assets/css/main.css
+++ b/assets/css/main.css
@@ -12,6 +12,7 @@
   --accent: #86a7d8;
   --copper: #9e6339;
   --sage: #54724b;
+  --teal: #23746e;
   --shadow: 0 18px 42px rgba(13, 31, 60, 0.08);
   --shadow-soft: 0 10px 24px rgba(13, 31, 60, 0.06);
   --radius: 1.35rem;
@@ -1031,7 +1032,7 @@ a.chip {
 }
 
 .card-grid-wide {
-  grid-template-columns: repeat(3, minmax(0, 1fr));
+  grid-template-columns: repeat(2, minmax(0, 1fr));
 }
 
 .accent-copper {
@@ -1046,6 +1047,10 @@ a.chip {
   border-top: 4px solid var(--primary);
 }
 
+.accent-teal {
+  border-top: 4px solid var(--teal);
+}
+
 .person-card {
   display: flex;
   flex-direction: column;
diff --git a/content/_index.md b/content/_index.md
index 2e80603..11c4639 100644
--- a/content/_index.md
+++ b/content/_index.md
@@ -1,7 +1,7 @@
 +++
 title = "Home"
 hero_subtitle = "The Middleware Systems Research Group at the University of Toronto"
-hero_tagline = "Systems research that spans data, learning, and quantum computing"
+hero_tagline = "Systems research that spans data, learning, quantum computing, and reliability"
 +++
 
 Traditionally, the “M” in MSRG stood for “middleware”—the Middleware Systems Research Group at the University of Toronto. But don’t worry, “middleware is literally everywhere.” Over the years, that “M” has evolved to mean “mobile,” “microservices,” “machine learning,” and, last but not least, “matter,” as in entangled particles in space-time. What *more* could you ask for?
diff --git a/content/people/chenkai-zhang.md b/content/people/chenkai-zhang.md
index 17a2e48..ddb1d92 100644
--- a/content/people/chenkai-zhang.md
+++ b/content/people/chenkai-zhang.md
@@ -9,6 +9,6 @@ email = "zhangchenkai.zhang@mail.utoronto.ca"
 interests = ["AI Agent Reliability", "Fault Diagnosis"]
 author_names = ["Chenkai Zhang"]
 portrait = ""
-research = ["distributed-machine-learning"]
+research = ["distributed-machine-learning", "rca-aiops"]
 linkedin = "https://www.linkedin.com/in/chenkaizhang1/"
 +++
diff --git a/content/people/hans-arno-jacobsen.md b/content/people/hans-arno-jacobsen.md
index e7e6a45..b1015bf 100644
--- a/content/people/hans-arno-jacobsen.md
+++ b/content/people/hans-arno-jacobsen.md
@@ -5,7 +5,7 @@ role = "Professor"
 status = "current"
 start_date = "2000-01"
 location = "Toronto, Canada"
-research = ["quantum-computing-systems", "distributed-machine-learning", "data-management"]
+research = ["quantum-computing-systems", "distributed-machine-learning", "data-management", "rca-aiops"]
 email = "jacobsen@eecg.utoronto.ca"
 homepage = "https://www.eecg.toronto.edu/~jacobsen"
 orcid = "https://orcid.org/0000-0003-0813-0101"
diff --git a/content/people/mengli-duan.md b/content/people/mengli-duan.md
index d6757d0..66a1de8 100644
--- a/content/people/mengli-duan.md
+++ b/content/people/mengli-duan.md
@@ -5,7 +5,7 @@ role = "PhD Student"
 status = "current"
 start_date = "2026-09"
 location = "Toronto, Canada"
-research = ["distributed-machine-learning"]
+research = ["distributed-machine-learning", "rca-aiops"]
 email = "mengli.duan@mail.utoronto.ca"
 interests = ["Machine Learning Systems"]
 author_names = ["Mengli Duan", "Dawn (Mengli) Duan", "Dawn Duan"]
diff --git a/content/people/michalis-bachras.md b/content/people/michalis-bachras.md
index 689255b..25bac30 100644
--- a/content/people/michalis-bachras.md
+++ b/content/people/michalis-bachras.md
@@ -5,7 +5,7 @@ role = "PhD Student"
 status = "current"
 start_date = "2023-09"
 location = "Toronto, Canada"
-research = ["data-management"]
+research = ["data-management", "rca-aiops"]
 interests = ["Data Management", "Distributed Systems"]
 portrait = "/images/people/michalis-bachras.jpg"
 linkedin = "https://www.linkedin.com/in/michalis-bachras/"
diff --git a/content/people/rev-liu.md b/content/people/rev-liu.md
index 57dda5d..6e41bf0 100644
--- a/content/people/rev-liu.md
+++ b/content/people/rev-liu.md
@@ -1,12 +1,14 @@
 +++
-name = "Rev Liu"
+name = "Ruiwu Liu"
 slug = "rev-liu"
-role = "MSc Student"
+role = "PhD Student"
 status = "current"
-start_date = "2025-09"
+start_date = "2026-09"
 location = "Toronto, Canada"
+research = ["distributed-machine-learning", "rca-aiops"]
 email = "rev.liu@mail.utoronto.ca"
-interests = []
+interests = ["VLM", "RL", "Artificial intelligence in IT operations"]
 author_names = []
-portrait = ""
+portrait = "/images/people/ruiwu-liu.jpg"
+linkedin = "https://www.linkedin.com/in/ruiwu-liu-b1534827b/"
 +++
diff --git a/content/people/yifang-tian.md b/content/people/yifang-tian.md
index ecbcab4..e7c96a1 100644
--- a/content/people/yifang-tian.md
+++ b/content/people/yifang-tian.md
@@ -5,7 +5,7 @@ role = "PhD Student"
 status = "current"
 start_date = "2022-01"
 location = "Toronto, Canada"
-research = ["distributed-machine-learning"]
+research = ["distributed-machine-learning", "rca-aiops"]
 interests = ["Machine Learning Systems", "Distributed Systems"]
 portrait = "/images/people/yifang-tian.jpg"
 email = "yifang.tian@mail.utoronto.ca"
diff --git a/content/people/yifeng-he.md b/content/people/yifeng-he.md
index 57fa204..3d6a69e 100644
--- a/content/people/yifeng-he.md
+++ b/content/people/yifeng-he.md
@@ -5,6 +5,7 @@ role = "MSc Student"
 status = "current"
 start_date = "2025-09"
 location = "Toronto, Canada"
+research = ["rca-aiops"]
 email = "yifeng.he@mail.utoronto.ca"
 interests = []
 author_names = ["Yifeng He", "Yifeng (Jason) He"]
diff --git a/content/people/yiran-li.md b/content/people/yiran-li.md
index 50cb7af..912eaa0 100644
--- a/content/people/yiran-li.md
+++ b/content/people/yiran-li.md
@@ -5,7 +5,7 @@ role = "Post-Doc"
 status = "current"
 start_date = "2025-11"
 location = "Toronto, Canada"
-research = ["quantum-computing-systems", "distributed-machine-learning", "data-management"]
+research = ["quantum-computing-systems", "distributed-machine-learning", "data-management", "rca-aiops"]
 interests = ["Graph Analytics", "Graph Learning", "Quantum Data Management", "AI Agent Reliability"]
 email = ""
 homepage = "https://cyanidecentral.github.io/"
diff --git a/content/publications/fame-failure-aware-mixture-of-experts-for-message-level-log-anomaly-detection.md b/content/publications/fame-failure-aware-mixture-of-experts-for-message-level-log-anomaly-detection.md
index 0058553..1bb466b 100644
--- a/content/publications/fame-failure-aware-mixture-of-experts-for-message-level-log-anomaly-detection.md
+++ b/content/publications/fame-failure-aware-mixture-of-experts-for-message-level-log-anomaly-detection.md
@@ -4,7 +4,7 @@ year = 2026
 authors = ["Huanchi Wang", "Zihang Huang", "Yifang Tian", "Kristina Dzeparoska", "Hans-Arno Jacobsen", "Alberto Leon-Garcia"]
 venue = "arXiv"
 publication_type = "ArXiv Preprint"
-research = ["distributed-machine-learning"]
+research = ["distributed-machine-learning", "rca-aiops"]
 external_url = "https://arxiv.org/abs/2605.22779"
 abstract = "Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the specific message responsible. This coarse granularity forces operators to inspect many routine lines per alert. Message-level detection offers finer granularity, but remains challenging. A single event template may correspond to both normal and anomalous messages, failures arise from heterogeneous subsystems, and line-level labeling at scale is impractical. Although large language models (LLMs) can reason over log semantics, applying them to every line is too costly for continuous monitoring. We present FAME (Failure-Aware Mixture-of-Experts), a label-efficient message-level mixture-of-experts framework that uses an LLM only once offline. We annotate at most K labeled lines per template to derive binary normal/anomaly indicators and representative examples. The LLM proposes a partition of templates into failure domains, and a certification step validates the proposal before training. FAME trains a lightweight router and domain experts that run on-premise and output anomaly predictions and failure-domain labels. On BGL, FAME achieves F1 = 98.16 at K = 100 reducing annotation effort by 76x and detects 97.7% of anomalies from unseen EventIDs. On Thunderbird, FAME reaches F1 = 99.95 with perfect recall."
 +++
diff --git a/content/publications/gala-can-graph-augmented-large-language-model-agentic-workflows-elevate-root-cause-analysis.md b/content/publications/gala-can-graph-augmented-large-language-model-agentic-workflows-elevate-root-cause-analysis.md
index a643ed0..301fa0a 100644
--- a/content/publications/gala-can-graph-augmented-large-language-model-agentic-workflows-elevate-root-cause-analysis.md
+++ b/content/publications/gala-can-graph-augmented-large-language-model-agentic-workflows-elevate-root-cause-analysis.md
@@ -4,7 +4,7 @@ year = 2025
 authors = ["Yifang Tian", "Yaming Liu", "Zichun Chong", "Zihang Huang", "Hans-Arno Jacobsen"]
 venue = "arXiv"
 publication_type = "ArXiv Preprint"
-research = ["distributed-machine-learning", "data-management"]
+research = ["distributed-machine-learning", "data-management", "rca-aiops"]
 external_url = "https://arxiv.org/abs/2508.12472"
 abstract = "Root cause analysis (RCA) in microservice systems is challenging, requiring on-call engineers to rapidly diagnose failures across heterogeneous telemetry such as metrics, logs, and traces. Traditional RCA methods often focus on single modalities or merely rank suspect services, falling short of providing actionable diagnostic insights with remediation guidance. This paper introduces GALA, a novel multi-modal framework that combines statistical causal inference with LLM-driven iterative reasoning for enhanced RCA. Evaluated on an open-source benchmark, GALA achieves substantial improvements over state-of-the-art methods of up to 42.22% accuracy. Our novel human-guided LLM evaluation score shows GALA generates significantly more causally sound and actionable diagnostic outputs than existing methods. Through comprehensive experiments and a case study, we show that GALA bridges the gap between automated failure diagnosis and practical incident resolution by providing both accurate root cause identification and human-interpretable remediation guidance."
 +++
diff --git a/content/publications/gala-graph-augmented-llm-agents-for-root-cause-analysis-and-incident-response-in-microservices.md b/content/publications/gala-graph-augmented-llm-agents-for-root-cause-analysis-and-incident-response-in-microservices.md
index 93bedfc..45c5f22 100644
--- a/content/publications/gala-graph-augmented-llm-agents-for-root-cause-analysis-and-incident-response-in-microservices.md
+++ b/content/publications/gala-graph-augmented-llm-agents-for-root-cause-analysis-and-incident-response-in-microservices.md
@@ -4,7 +4,7 @@ year = 2026
 authors = ["Yifang Tian", "Yaming Liu", "Zichun Chong", "Zihang Huang", "Yiran Li", "Hans-Arno Jacobsen"]
 venue = "arXiv"
 publication_type = "ArXiv Preprint"
-research = ["distributed-machine-learning", "data-management"]
+research = ["distributed-machine-learning", "data-management", "rca-aiops"]
 external_url = "https://arxiv.org/abs/2608.08968"
 abstract = "Microservice root cause analysis (RCA) requires correlating failures across heterogeneous telemetry within complex service dependency graphs. Existing methods often rely on a single telemetry modality; recent LLM-based approaches can suffer from unconstrained exploration and hallucination; and most systems stop at fault ranking without producing actionable incident response. We present GALA+, a graph-augmented LLM agentic framework centered on graph-guided investigation, which uses service dependencies to bound exploration and refine diagnosis through localized multi-modal evidence. For initial hypothesis generation, GALA+ combines complementary telemetry signals with STRIX, a novel trace- and graph-structure-aware scoring module. GALA+ then produces ranked diagnoses, incident summaries, and stratified action recommendations. We further introduce SURE-Score, a human-guided evaluation framework co-developed with industry SRE experts for assessing RCA-specific output quality beyond conventional text similarity metrics. On two microservice benchmarks, GALA+ consistently achieves the strongest overall results, surpassing the best LLM-based baseline by more than 25 percentage points in AC@1, while also receiving the highest ratings from both SURE-Score and independent human SRE evaluation."
 +++
diff --git a/content/publications/orca-observability-grounded-program-repair-for-microservice-incidents.md b/content/publications/orca-observability-grounded-program-repair-for-microservice-incidents.md
index 6ad8762..b87d2a2 100644
--- a/content/publications/orca-observability-grounded-program-repair-for-microservice-incidents.md
+++ b/content/publications/orca-observability-grounded-program-repair-for-microservice-incidents.md
@@ -4,7 +4,7 @@ year = 2026
 authors = ["Yuanchen Gao", "Yifang Tian", "Yiran Li", "Charles Zhang", "Hans-Arno Jacobsen"]
 venue = "arXiv"
 publication_type = "ArXiv Preprint"
-research = ["data-management"]
+research = ["data-management", "rca-aiops"]
 external_url = "https://arxiv.org/abs/2608.17018"
 abstract = "Microservice failures are often diagnosed from operational telemetry. However, automated program repair systems usually start from issue reports, localized code context, or failing tests. This mismatch leaves a gap between telemetry-based diagnosis and patch generation. We present ORCA, an observability-grounded APR pipeline for microservice incidents. ORCA first distills the differences in paired failure and reference telemetry into a fault signature, then uses the signature to identify candidate code and deployment-configuration locations. Repair graph agents and an Exploration agent generate unified-diff patch candidates from these locations. ORCA evaluates generated patches with a Telemetry-Grounded Patch Verifier that separates patch validity, syntactic and semantic correctness, test-oracle integrity, and telemetry replay. On a 575-case benchmark, ORCA outperforms all evaluated baselines in terms of cost-effectiveness. Results show that operational telemetry can be transformed from diagnostic evidence into actionable repair context: paired telemetry supports repair-oriented localization, while repair graph agents convert localized code and configuration evidence into constrained patch-generation context for the LLM. Telemetry-grounded verification then exposes repair outcomes that issue- or test-only evaluation would miss."
 +++
diff --git a/content/publications/sregym-a-live-benchmark-for-ai-sre-agents-with-high-fidelity-failure-scenarios.md b/content/publications/sregym-a-live-benchmark-for-ai-sre-agents-with-high-fidelity-failure-scenarios.md
index e953352..66c447d 100644
--- a/content/publications/sregym-a-live-benchmark-for-ai-sre-agents-with-high-fidelity-failure-scenarios.md
+++ b/content/publications/sregym-a-live-benchmark-for-ai-sre-agents-with-high-fidelity-failure-scenarios.md
@@ -4,7 +4,7 @@ year = 2026
 authors = ["Jackson Clark", "Yiming Su", "Saad Mohammad Rafid Pial", "Yifang Tian", "Lily Gniedziejko", "Hans-Arno Jacobsen", "Yinfang Chen", "Tianyin Xu"]
 venue = "arXiv"
 publication_type = "ArXiv Preprint"
-research = ["distributed-machine-learning"]
+research = ["distributed-machine-learning", "rca-aiops"]
 external_url = "https://arxiv.org/abs/2605.07161"
 abstract = "AI agents are increasingly used to diagnose and mitigate failures in production systems, known as agentic Site Reliability Engineering (SRE). Current SRE benchmarks are limited to oversimplistic SRE tasks and are unfortunately hard to extend due to bespoke designs. We present SREGym, a high-fidelity benchmark for SRE agents. SREGym exposes a live system environment built atop real-world cloud-native system stacks, where high-fidelity failure scenarios are simulated through fault injectors. SREGym models the complexity of production environments by simulating (1) a wide range of faults at different layers, (2) various ambient noises, and (3) diverse failure modes such as metastable failures and correlated failures. SREGym is architected as a modular, extensible framework that orchestrates fault and noise injectors across stacks. SREGym currently includes 90 realistic, challenging SRE problems. We use SREGym to evaluate frontier agents and show that their capabilities varies significantly in addressing different kinds of failures, with up to 40% differences in end-to-end results. SREGym is actively maintained as an open-source project and has been used by researchers and practitioners."
 +++
diff --git a/content/publications/when-agentic-executions-fail-detecting-and-localizing-runtime-faults-from-telemetry.md b/content/publications/when-agentic-executions-fail-detecting-and-localizing-runtime-faults-from-telemetry.md
index 99e1ca3..dcc60d9 100644
--- a/content/publications/when-agentic-executions-fail-detecting-and-localizing-runtime-faults-from-telemetry.md
+++ b/content/publications/when-agentic-executions-fail-detecting-and-localizing-runtime-faults-from-telemetry.md
@@ -4,7 +4,7 @@ year = 2026
 authors = ["Chenkai Zhang", "Yiran Li", "Yifang Tian", "Michalis Bachras", "Hans-Arno Jacobsen"]
 venue = "arXiv"
 publication_type = "ArXiv Preprint"
-research = ["distributed-machine-learning"]
+research = ["distributed-machine-learning", "rca-aiops"]
 external_url = "https://arxiv.org/abs/2608.14680"
 abstract = "Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails. We present AGENTCHAOSBENCH, a benchmark for detecting and localizing runtime faults in agentic systems from their execution telemetry. We run five heterogeneous applications that coordinate agents over the Agent-to-Agent protocol and call tools through the Model Context Protocol, and inject ten types of operational fault (unavailable or slow tools, corrupted or oversized responses, and delayed, looped, or misrouted delegations and bypassed guardrails) at their tool, model, guardrail, and inter-agent boundaries, alongside a no-fault control. The resulting dataset contains 275 sanitized traces: 250 faulty executions spanning ten fault types and 25 no-fault controls. Each faulty trace is aligned with the no-fault execution of the same input; fault-type labels and, where applicable, location labels are held out from diagnosis. On structured single-trace inputs, a first set of zero-shot LLM baselines shows the task is far from solved: local detectors up to 14B parameters reach only 13.6-19.2% top-1 fault-type accuracy and the frontier DeepSeek-v4-pro only 24.8%, while jointly identifying the fault type and its location tops out at 22%; reference-dependent faults (above all a bypassed guardrail) stay near-unsolved from a single trace. An aligned reference improves selected relative faults but does not resolve guardrail bypass. The held-out labels and compact prediction format support reproducible comparison of LLM-based and non-LLM diagnosis methods."
 +++
diff --git a/content/research/_index.md b/content/research/_index.md
index 8c2aea3..b1a42e8 100644
--- a/content/research/_index.md
+++ b/content/research/_index.md
@@ -1,5 +1,5 @@
 +++
 title = "Research"
 layout = "overview"
-summary = "Three active research themes connect MSRG's work in systems, AI infrastructure, and quantum computing."
+summary = "Four active research themes connect MSRG's work in data management, distributed machine learning, quantum computing, and reliable systems."
 +++
diff --git a/content/research/data-management/_index.md b/content/research/data-management/_index.md
index 16b1d8d..88c67a9 100644
--- a/content/research/data-management/_index.md
+++ b/content/research/data-management/_index.md
@@ -20,6 +20,11 @@ can repeat computations and produce the same intermediate data. Our approach
 detects these overlaps online and coordinates execution so queries can share
 work and improve throughput.
 
+Our [Root Cause Analysis & AIOps](/research/rca-aiops/) research examines the
+operational reliability of cloud-native systems, using metrics, logs, traces,
+and service dependencies to support fault diagnosis, incident response, and
+automated repair.
+
 ## Blockchain, Distributed Ledger Technology & Consensus
 
 We develop consensus algorithms, fault-tolerance mechanisms, and consistency
diff --git a/content/research/distributed-machine-learning/_index.md b/content/research/distributed-machine-learning/_index.md
index 0047f4a..2e34070 100644
--- a/content/research/distributed-machine-learning/_index.md
+++ b/content/research/distributed-machine-learning/_index.md
@@ -33,6 +33,10 @@ Our distributed graph systems target graphs containing billions of vertices
 and edges. We also optimize the training and inference stages of graph neural
 networks.
 
+Our [Root Cause Analysis & AIOps](/research/rca-aiops/) research connects these
+interests with learning-based anomaly detection, LLM agents for failure
+diagnosis, and the reliability of agent executions.
+
 ## Current directions
 
 - Resource allocation and scheduling for distributed training and inference
diff --git a/content/research/rca-aiops/_index.md b/content/research/rca-aiops/_index.md
new file mode 100644
index 0000000..d032169
--- /dev/null
+++ b/content/research/rca-aiops/_index.md
@@ -0,0 +1,82 @@
++++
+title = "Root Cause Analysis & AIOps"
+slug = "rca-aiops"
+layout = "area"
+weight = 40
+accent = "teal"
+question = "How can we turn operational telemetry into reliable diagnosis and actionable incident response?"
+summary = "Root cause analysis, anomaly detection, and telemetry-grounded repair for reliable microservices and AI agents."
+blurb = """MSRG develops methods for **root cause analysis** and **AI for IT operations (AIOps)** in distributed systems. We combine **metrics, logs, and traces** with service dependency graphs and LLM agents to investigate failures and produce actionable diagnoses. Our work connects anomaly detection, incident response, automated repair, and realistic evaluation, with a focus on microservices and the reliability of AI agent executions."""
+# Research descriptions are based on the publication abstracts linked below.
++++
+
+## From telemetry to root causes
+
+A failure in one service can surface as errors, slowdowns, or unusual resource
+usage elsewhere. Root cause analysis (RCA) connects these symptoms to the
+underlying fault. Doing so requires reasoning across heterogeneous telemetry
+and the dependencies between services.
+
+We study how AI for IT operations (AIOps) can help engineers move from detecting
+an anomaly to understanding a failure and deciding what to do next. Our research
+brings together statistical analysis, graph structure, and LLM agents, with an
+emphasis on diagnoses and repair decisions grounded in operational evidence.
+
+## Graph-guided diagnosis and incident response
+
+[GALA](/publications/gala-can-graph-augmented-large-language-model-agentic-workflows-elevate-root-cause-analysis/)
+combines statistical causal inference with iterative LLM reasoning over metrics,
+logs, and traces. It investigates both root cause identification and
+human-readable remediation guidance.
+
+[GALA+](/publications/gala-graph-augmented-llm-agents-for-root-cause-analysis-and-incident-response-in-microservices/)
+uses service dependencies to guide investigation and focus agents on local,
+multimodal evidence. Its outputs include ranked diagnoses, incident summaries,
+and action recommendations. The work also introduces SURE-Score, developed
+with industry site reliability engineers to assess diagnostic quality beyond
+text similarity.
+
+## Message-level anomaly detection
+
+Effective investigation starts with finding useful signals in large volumes of
+telemetry.
+[FAME](/publications/fame-failure-aware-mixture-of-experts-for-message-level-log-anomaly-detection/)
+studies anomaly detection at the level of individual log messages. It uses an
+LLM during offline preparation to help organize failure domains, then trains
+a lightweight router and specialized experts for online detection with limited
+labeling effort.
+
+## Telemetry-grounded repair
+
+Diagnosis can also provide context for changing code and configuration.
+[ORCA](/publications/orca-observability-grounded-program-repair-for-microservice-incidents/)
+compares failure and reference telemetry to build a fault signature, locate
+candidate repair sites, and guide patch generation. Its verification process
+examines patch correctness, test integrity, and telemetry replay to connect
+automated repair with the incident that motivated it.
+
+## Reliable agents and realistic evaluation
+
+AI agents are both tools for incident response and systems whose own failures
+need diagnosis.
+[When Agentic Executions Fail](/publications/when-agentic-executions-fail-detecting-and-localizing-runtime-faults-from-telemetry/)
+introduces AGENTCHAOSBENCH to study fault detection and localization from agent
+execution telemetry, including faults at tool, model, guardrail, and inter-agent
+boundaries.
+
+[SREGym](/publications/sregym-a-live-benchmark-for-ai-sre-agents-with-high-fidelity-failure-scenarios/)
+evaluates SRE agents in live cloud-native environments with injected faults and
+ambient noise. These complementary settings help us examine whether an agent
+can identify a fault, locate its origin, and support incident resolution under
+realistic operating conditions.
+
+## Current directions
+
+- Correlating metrics, logs, and traces across service dependencies
+- Graph-guided and evidence-grounded LLM investigation
+- Connecting root cause diagnosis to remediation and automated repair
+- Evaluating diagnostic quality, agent reliability, and operational usefulness
+
+This research connects our work in
+[Data Management](/research/data-management/) and
+[Distributed Machine Learning](/research/distributed-machine-learning/).
diff --git a/hugo.yaml b/hugo.yaml
index f91bcd7..2ac0917 100644
--- a/hugo.yaml
+++ b/hugo.yaml
@@ -42,7 +42,7 @@ permalinks:
   data-sets: /data-sets/:slug/
 
 params:
-  description: Systems research at the University of Toronto spanning data management, distributed machine learning, and quantum computing systems.
+  description: Systems research at the University of Toronto spanning data management, distributed machine learning, quantum computing systems, and root cause analysis and AIOps.
   # Public contact: https://www.qscc.ca/open-positions/
   contact_email: jacobsen@eecg.utoronto.ca
   contact_phone: "+1 416 946 7586"
@@ -79,6 +79,10 @@ menu:
       parent: research
       pageRef: /research/data-management
       weight: 23
+    - name: Root Cause Analysis & AIOps
+      parent: research
+      pageRef: /research/rca-aiops
+      weight: 24
     - identifier: people
       name: People
       pageRef: /people
diff --git a/layouts/index.html b/layouts/index.html
index b7f343f..09ec66f 100644
--- a/layouts/index.html
+++ b/layouts/index.html
@@ -74,7 +74,7 @@ <h2>News</h2>
     <div class="site-shell">
       <div class="section-heading">
         <p class="eyebrow">Research Themes</p>
-        <h2>Three active tracks with room for overlap</h2>
+        <h2>Four active tracks with room for overlap</h2>
       </div>
       <div class="card-grid card-grid-wide">
         {{ range $researchSection.Pages.ByWeight }}
diff --git a/static/images/people/ruiwu-liu.jpg b/static/images/people/ruiwu-liu.jpg
new file mode 100644
index 0000000..7a83232
Binary files /dev/null and b/static/images/people/ruiwu-liu.jpg differ