Alekh Jindal

Alekh Jindal
CEO and Co-founder, Tursio Inc.

3600 136th Pl SE # 300
Bellevue, WA 98006

e-mail: alekh@tursio.ai

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I am CEO and Co-founder at Tursio, an AI-based startup to help turn data into intelligence. Previusly, I was CTO and board member at Keebo, a Series-A startup that is reshaping enterprise analytics with data learning. I joined Keebo as its Founding Chief Architect in 2021. Before that, I managed the Redmond site of Gray Systems Lab (GSL), under Azure Data at Microsoft, that focused on research and development for databases, big-data, and cloud systems. My research interests revolve around improving the performance of large-scale data-intensive systems. Earlier, I was a postdoc associate in the Database Group at MIT CSAIL, working with Professors Sam Madden and Michael Stonebraker. I received my PhD from Saarland University, working with Prof. Jens Dittrich, where I worked on flexible and scalable data storage for traditional databases as well as for MapReduce. Prior to that I completed masters studies at Max Planck Institute for Informatics and received bachelor degree from IIT Kanpur.


News
  • 01/25: Tursio turns 2! Post
  • 10/24: Murali Mahalingam joins Tursio as Head of GTM. Announcement
  • 07/24: SmartApps is now Tursio! Blog Announcement
  • 07/24: SmartApps opens Bangalore office: Announcement
  • 10/23: Rony Chatterjee joins SmartApps as Founding Chief Product Officer. Announcement
  • 09/23: Demonstrated generative intelligence on Snowflake at VLDB'23. Paper
  • 08/23: SmartApps launches Generative AI for Enterprise Data, codename Rainier. Blog
  • 08/23: Invited to fireside chat in VLDB'23 Symposium on Data Markets.
  • 05/23: Invited to SIGMOD aiDM'23 panel on "Foundation Models and Databases: Challenges and Opportunities".
  • 05/23: SmartApps announces SanJuan for "Large Data Model" on private data.
  • 05/23: Keebo Warehouse Optimization paper accepted to SIGMOD industry track.
  • 04/23: SmartApps anounces PikePlace generative anaytics on Snowflake Data Marketplace.
  • 03/23: Introducing the notion of "Large Data Model" at SmartApps.
  • 01/23: Alekh and Shi start SmartApps for turning data into intelligence.
  • 01/23: Keebo gets patent on platform agnostic query acceleration.
  • 10/22: The thrills and perils of a startup: Part 1
  • 10/22: Keebo announces Series A funding. Venture Beats
  • 10/22: Alekh joins Keebo board.
  • 08/22: Alekh got promoted to CTO.
  • 06/22: Predictive price-perf optimization for Spark accepted to EDBT'23.
  • 05/22: Pipeline optimizer demo accepted to VLDB'22.
  • 03/22: War story for deployment steering optimizer at Microsoft accpeted to SIGMOD'22.
  • 03/22: Optimizer-as-a-service architecture accepted to SIGMOD Record.
  • 02/22: TASQ paper for optimal resource allocation accepted to EDBT'22.
  • 12/21: Alekh join Keebo as it Founding Chief Architect. Announcement
  • 12/21: PyScope goes into production!
  • 11/21: Steering query optimizer deployed in production for Cosmos!
  • 08/21: AutoExecutor wins the Best Demo Award at VLDB'21!
  • 08/21: PerfGuard paper for avoiding performance regression accepted to VLDB'22.
  • 08/21: Invited talk at LADSIOS and panel discussion in Poly panel at VLDB'21.
  • 07/21: Learning-based checkpoint optimizer paper accepted to VLDB'21.
  • 06/21: Steering query optimizer paper received Industry: Honorable Mention at SIGMOD'21!
  • 06/21: Paper on history and future of Cosmos big data platform accepted to VLDB'21 Ind.
  • 05/21: Tutorial on machine learning for cloud data systems accepted to VLDB'21.
  • 05/21: AutoExecutor demo accepted to VLDB'21.
  • 04/21: SparkCruise industry paper accepted to VLDB'21.
  • 01/21: Steering query optimizers paper accepted to SIGMOD'21 Ind.
  • 12/20: Learning optimizer paper accepted to ICDE'21 Ind.
  • 12/20: Experiences from shipping compute reuse accepted to EDBT'21 Ind. Teaser Talk
  • 10/20: Python at Cloud scale paper accepted to CIDR'21. Video Blog NWDS Talk
  • 10/20: Learned cardinality models deployed in Cosmos production!
  • 09/20: Seagull paper for load prediction accepted to PVLDB
  • 09/20: Applied research experiences appear in SIGMOD Record
  • 08/20: Dataset simulator from ~10K production pipelines released
  • 08/20: SparkCruise ships in Microsoft's own Apache distro!
  • 07/20: SparkCruise demoed on Synapse Spark @ Spark + AI Summit
  • 04/20: Plan-aware resource allocation paper accepted to HotCloud
  • 04/20: AutoToken paper for predicting resource allocation accepted to VLDB Ind. Blog
  • 03/20: CloudViews enabled for automatic reuse by default in Cosmos customers!
  • 01/20: Learned cost models paper accepted to SIGMOD

Research Interests
  • Machine Learning for Databases
  • Workload Optimization in Cloud Data Services
  • Large-scale Data-intensive Systems
  • Data Preparation and Design
  • Big Data Analytics

Current Projects
Past Projects
Publications
Google Scholar, DBLP
Patents
  • Generative Business Intelligence (US20240242154A1)
  • Query set optimization in a data analytics pipeline (US Patent 11,847,118)
  • System and method for scalable data processing operations (US Patent 11,829,359, US Patent 12,182,117)
  • Materialized view generation and provision based on queries having a semantically equivalent or containment relationship (US20230350892A1)
  • Managed tuning for data clouds (US Patent 11,693,857)
  • Query optimizer advisor (US20230177053A1)
  • Platform agnostic query acceleration (US Patent 11,567,936)
  • Resource optimization for serverless query processing (US Patent 11,455,192, US Patent 11,934,874)
  • Optimizing job runtimes via prediction-based token allocation (US Patent 12,189,629)
  • Data-driven checkpoint selector (US Patent 11,416,487)
  • System and method for machine learning for system deployments without performance regressions (US Patent 11,748,350, US Patent 12,093,255)
  • Cloud based query workload optimization (US Patent 12,013,853)
  • Learned resource consumption model for optimizing big data queries (US20200349161A1)
  • Computation Reuse in Analytics Job Service (US Patent 11,068,482).
  • Learning Optimizer for Shared Cloud (US Patent 11,074,256).
  • Selection of Subexpressions to Materialize for Datacenter Scale (US Patent 10,726,014).
  • Replicated data storage system and methods (WO2013139379).
  • A method for storing and accessing data in a database system (WO2012032184, US20130226959).

Professional Activities
Mentoring
  • 2025: Karan Hanswadkar, Intern, Clinical data search.
  • 2024: Yongye Su, Intern, Scalable vector indexing.
  • 2024: Xuye He, Intern, Small model tuning for data analytics.
  • 2023: Sathwik Reddy Madhula, Intern, Generative AI machine.
  • 2023: Kanupriya Raheja, Intern, Generative AI machine.
  • 2022: Isha Tarte, Intern, Automated warehouse optimization.
  • 2021: Parimarjan Negi, Intern, Deploying steered query optimizer.
  • 2020: Parimarjan Negi, Intern, Steering query optimizers.
  • 2019: Tarique Siddiqui, Intern, Forecasting query workloads.
  • 2018: Tarique Siddiqui, Intern, Cost models for big data query processing.
  • 2017: Chenggang Wu, Intern, Towards a learning optimizer for shared clouds.
  • 2016: Lalitha Viswanathan, Intern, Query and resource optimizations.
  • 2015: Anil Shanbag, 1st year PhD, Robust data partitioning.
  • 2014: Qui Nguyen, M.Eng. Thesis, Robust data partitioning for ad-hoc query processing.
  • 2013: Praynaa Rawlani, M.Eng. Thesis, Graphs anlaytics on relational databases.
  • 2012: Felix Martin Schuhknecht, 1st year PhD, Evaluating and improving database cracking algorithms.
  • 2012: Endre Palatinus, 1st year PhD, Evaluating vertical partitioning algorithms and their impact.
  • 2012: Karen Khachatryan, 1st year PhD, Techniques for emulating columns stores in row databases.
  • 2011: Stefan Chouteau, B.Sc Thesis, Implementing a log-structured main-memory database system.
  • 2011: Sebastian Wendland, M.Sc Thesis, Implementing column store access layer in PostgreSQL.
  • 2011: Marco Huester, M.Sc Thesis, Applying database cracking over two-dimensional data.
  • 2010: Felix Martin Schuhknecht, B.Sc Thesis, Compression schemes over hybrid data layouts.

Teaching
  • Tutorial, Machine Learning for Cloud Data Systems, Remote, VLDB 2021.
  • Teaching with Educational Technologies, MIT, USA, IAP 2015.
  • Teaching Certificate Program, MIT, USA, Summer 2014.
  • Lab Assistant, From ASCII to Answers, MIT, USA, Fall 2013.
  • TA, Advanced Information Systems Lab, Saarland University, Germany, Winter 2011.
  • TA, Advanced Information Systems Lab, Saarland University, Germany, Summer 2011.
  • TA, NOSQL: Managing Data (almost) without a Database System, Saarland University, Germany, Winter 2010.
  • TA, Advanced Information Systems Lab: OctopusDB, Saarland University, Germany, Summer 2010.
  • TA, Database Systems core lecture, Saarland University, Germany, Winter 2009.
  • Research Associate, National Program for Technology Enhanced Learning. Microcontrollers And Applications, IIT Kanpur, India, 2005-06.

Short CV
Experience Education