Alekh Jindal

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

• 06/25: Microsoft Alumni Network features Tursio co-founders' story. Coverage
• 05/25: New paper on searching clinical data using generative AI. paper
• 04/25: Geekwire features Tursio in Startup Radar. Coverage
• 01/25: Tursio turns 2! Post
• 10/24: Murali Mahalingam joins Tursio as Head of GTM. Announcement

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

• Tursio

Past Projects

• Keebo
• Magpie
• Peregrine
• Cartilage
• Vertexica
• OctopusDB
• Hadoop++/HAIL

Publications

• Sulbha Jain, Shivani Tripathi, Shi Qiao, Alekh Jindal
  Tursio Database Search: How far are we from ChatGPT?
  arXiv:2603.18835 [cs.DB], Mar 2026
• Shivani Tripathi, Ravi Shetye, Shi Qiao, Alekh Jindal
  Tursio for Credit Unions: Powering Structured Data Search with Automated Context Graph
  arXiv:2603.07304 [cs.DB], Mar 2026
• Shivani Tripathi, Ravi Shetye, Shi Qiao, Alekh Jindal
  Scalable Join Inference for Large Context Graphs
  arXiv:2603.04176 [cs.DB], Mar 2026
• Alekh Jindal, Shi Qiao, Shivani Tripathi, Niloy Debnath, Kunal Singh, Pushpanjali Nema, Sharath Prakash, Aditya Halder, Ronith PR, Sadiq Mohammed, Abdul Hameed, Karan Hanswadkar, Ayush Kshitij, Sarthak Bhatt, Rony Chatterjee, Jyoti Pandey, Christina Pavlopoulou, Ravi Shetye
  Making Databases Searchable with Deep Context
  arXiv:2602.08320 [cs.DB], Feb 2026
• Shivani Tripathi, Pushpanjali Nema, Aditya Halder, Shi Qiao, Alekh Jindal
  Migration: Stabilizing GenAI Applications with Evolving Large Language Models
  arXiv:2507.05573 [cs.DB], July 2025
• Karan Hanswadkar, Anika Kanchi, Shivani Tripathi, Shi Qiao, Rony Chatterjee, Alekh Jindal
  Searching Clinical Data Using Generative AI
  arXiv:2505.24090 [cs.DB], May 2025
• Hanxian Huang, Tarique Siddiqui, Rana Alotaibi, Carlo Curino, Jyoti Leeka, Alekh Jindal, Jishen Zhao, Jesus Camacho-Rodriguez, Yuanyuan Tian
  Sibyl: Forecasting Time-Evolving Query Workloads
  SIGMOD 2024, Santiago, Chile.
• Brandon Haynes, Rana Alotaibi, Anna Pavlenko, Jyoti Leeka, Alekh Jindal, Yuanyuan Tian
  GEqO: ML-Accelerated Semantic Equivalence Detection
  SIGMOD 2024, Santiago, Chile.
• Alekh Jindal, Shi Qiao, Sathwik Reddy Madhula, Kanupriya Raheja, Sandhya Jain
  Turning Databases Into Generative AI Machines
  CIDR 2024, Chaminade, USA.
• Shi Qiao, Alekh Jindal
  PikePlace: Generating Intelligence for Marketplace Datasets
  VLDB 2023, Vancouver, Canada. (Demo paper)
• Barzan Mozafari, Radu Alexandru Burcuta, Alan Cabrera, Andrei Constantin, Derek Francis, David Groemling, Alekh Jindal, Maciej Konkolowicz, Valentin Marian Spac, Yongjoo Park, Russell Razo
  Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning
  SIGMOD 2023, Seattle, USA.
• Rathijit Sen, Abhishek Roy, Alekh Jindal
  Predictive Price-Performance Optimization for Serverless Query Processing
  EDBT 2023, Ioannina, Greece.
• Alekh Jindal, Jyoti Leeka
  Query Optimizer as a Service: An Idea Whose Time Has Come
  SIGMOD Record, September 2022
• Sunny Gakhar, Joyce Cahoon, Wangchao Le, Xiangnan Li, Kaushik Ravichandran, Hiren Patel, Marc Friedman, Brandon Haynes, Shi Qiao, Alekh Jindal, Jyoti Leeka
  Pipemizer: An Optimizer for Analytics Data Pipelines
  VLDB 2022, Sydney, Australia. (Demo paper)
• Sihem Amer-Yahia, Yael Amsterdamer, Sourav S Bhowmick, Angela Bonifati, Philippe Bonnet, Renata Borovica-Gajic, Barbara Catania, Tania Cerquitelli, Silvia Chiusano, Panos K Chrysanthis, Carlo Curino, Jérôme Darmont, Amr El Abbadi, Avrilia Floratou, Juliana Freire, Alekh Jindal, Vana Kalogeraki, Georgia Koutrika, Arun Kumar, Sujaya Maiyya, Alexandra Meliou, Madhulika Mohanty, Felix Naumann, Nele Sina Noack, Fatma Özcan, Liat Peterfreund, Wenny Rahayu, Wang-Chiew Tan, Yuanyuan Tian, Pinar Tözün, Genoveva Vargas-Solar, Neeraja Yadwadkar, Meihui Zhang
  Diversity and Inclusion Activities in Database Conferences: A 2021 Report
  SIGMOD Record, June 2022
• Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari, Karlen Lie, Marc Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal
  Deploying a Steered Query Optimizer in Production at Microsoft
  SIGMOD 2022 (Industry), Philadelphia, USA.
• Anish Pimpley, Shuo Li, Rathijit Sen, Soundararajan Srinivasan, Alekh Jindal
  Towards Optimal Resource Allocation for Serverless Queries
  EDBT 2022, Edinburgh, UK.
• Rathijit Sen, Abhishek Roy, Alekh Jindal
  Predictive Price-Performance Optimization for Serverless Query Processing
  arXiv:2112.08572 [cs.DB], Dec 2021
• Remmelt Ammerlaan, Gilbert Antonius, Marc Friedman, H M Sajjad Hossain, Alekh Jindal, Peter Orenberg, Hiren Patel, Shi Qiao, Vijay Ramani, Lucas Rosenblatt, Abhishek Roy, Irene Shaffer, Soundarajan Srinivasan, Markus Weimer
  PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!
  VLDB 2022, Sydney, Australia.
• Anish Pimpley, Shuo Li, Anubha Srivastava, Vishal Rohra, Yi Zhu, Soundararajan Srinivasan, Alekh Jindal, Hiren Patel, Shi Qiao, Rathijit Sen
  Optimal Resource Allocation for Serverless Queries
  arXiv:2107.08594 [cs.DB], July 2021
• Yiwen Zhu, Matteo Interlandi, Abhishek Roy, Krishnadhan Das, Hiren Patel, Malay Bag, Hitesh Sharma, Alekh Jindal
  Phoebe: A Learning-based Checkpoint Optimizer
  VLDB 2021, Copenhagen, Denmark.
• Conor Power*, Hiren Patel*, Alekh Jindal*, Jyoti Leeka, Bob Jenkins, Michael Rys, Ed Triou, Dexin Zhu, Lucky Katahanas, Chakrapani Bhat Talapady, Joshua Rowe, Fan Zhang, Rich Draves, Marc Friedman, Ivan Santa Maria Filho, Amrish Kumar
  The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward
  VLDB 2021 (Industry), Copenhagen, Denmark.
• Alekh Jindal, Matteo Interlandi
  Machine Learning for Cloud Data Systems: the Promise, the Progress, and the Path Forward
  VLDB 2021 (Tutorial), Copenhagen, Denmark.
• Rathijit Sen, Abhishek Roy, Alekh Jindal, Rui Fang, Jeff Zheng, Xiaolei Liu, Ruiping Li
  AutoExecutor: Predictive Parallelism for Spark SQL Queries
  VLDB 2021, Copenhagen, Denmark. (Demo paper)
  Best Demo Award (VLDB Announcement)
• Abhishek Roy, Alekh Jindal, Priyanka Gomatam, Xiating Ouyang, Ashit Gosalia, Nishkam Ravi, Swinky Mann, Prakhar Jain
  SparkCruise: Workload Optimization in Managed Spark Clusters at Microsoft
  VLDB 2021 (Industry), Copenhagen, Denmark.
• Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc Friedman, Alekh Jindal
  Steering Query Optimizers: A Practical Take on Big Data Workloads
  SIGMOD 2021 (Industry)
  Industry Honorable Mention (SIGMOD Announcement)
• Alekh Jindal, Shi Qiao, Rathijit Sen, Hiren Patel
  Microlearner: A fine-grained Learning Optimizer for Big Data Workloads at Microsoft
  ICDE 2021 (Industry)
• Alekh Jindal, Shi Qiao, Hiren Patel, Abhishek Roy, Jyoti Leeka, Brandon Haynes
  Production Experiences from Computation Reuse at Microsoft
  EDBT 2021 (Industry)
• Alekh Jindal, Venkatesh Emani, Maureen Daum, Olga Poppe, Brandon Haynes, Anna Pavlenko, Ayushi Gupta, Karthik Ramachandra, Carlo Curino, Andreas Mueller, Wentao Wu, Hiren Patel
  Magpie: Python at Speed and Scale using Cloud Backends
  CIDR 2021
• Olga Poppe, Tayo Amuneke, Dalitso Banda, Aritra De, Ari Green, Manon Knoertzer, Ehi Nosakhare, Karthik Rajendran, Deepak Shankargouda, Meina Wang, Alan Au, Carlo Curino, Qun Guo, Alekh Jindal, Ajay Kalhan, Morgan Oslake, Sonia Parchani, Vijay Ramani, Raj Sellappan, Saikat Sen, Sheetal Shrotri, Soundararajan Srinivasan, Ping Xia, Shize Xu, Alicia Yang, Yiwen Zhu
  Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation
  VLDB 2021, Copenhagen, Denmark. arXiv
• Alekh Jindal
  Applied Research Lessons from CloudViews Project
  SIGMOD Record, September 2020
• Rathijit Sen, Alekh Jindal, Hiren Patel, Shi Qiao
  AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft
  VLDB 2020, Tokyo, Japan.
• Malay Bag, Alekh Jindal, Hiren Patel
  Towards Plan-aware Resource Allocation in Serverless Query Processing
  HotCloud 2020, Boston, USA.
• Tarique Siddiqui, Alekh Jindal, Shi Qiao, Hiren Patel, Wangchao Le
  Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings
  SIGMOD 2020, Portland, USA. arXiv
• H M Sajjad Hossain, Lucas Rosenblatt, Gilbert Antonius, Irene Shaffer, Remmelt Ammerlaan, Abhishek Roy, Markus Weimer, Hiren Patel, Marc Friedman, Shi Qiao, Peter Orenberg, Soundarajan Srinivasan, Vijay Ramani, Alekh Jindal
  PerfGuard: Deploying ML-for-Systems without Performance Regressions
  MLOps Systems 2020, Austin, USA.
• Ashvin Agrawal, Rony Chatterjee, Carlo Curino, Avrilia Floratou, Neha Gowdal, Matteo Interlandi, Alekh Jindal, Kostantinos Karanasos, Subru Krishnan, Brian Kroth, Jyoti Leeka, Kwanghyun Park, Hiren Patel, Olga Poppe, Fotis Psallidas, Raghu Ramakrishnan, Abhishek Roy, Karla Saur, Rathijit Sen, Markus Weimer, Travis Wright, Yiwen Zhu
  Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
  CIDR 2020, Amsterdam, Netherlands.
  
• Alekh Jindal, Hiren Patel, Abhishek Roy, Shi Qiao, Jarod Yin, Rathijit Sen, Subru Krishnan
  Peregrine: Workload Optimization for Cloud Query Engines
  SOCC 2019, Santa Cruz, California.
• Hiren Patel, Alekh Jindal, Clemens Szyperski
  Big Data Processing at Microsoft: Hyper Scale, Massive Complexity, and Minimal Cost
  SOCC 2019, Santa Cruz, California. (poster)
• Abhishek Roy, Alekh Jindal, Hiren Patel, Ashit Gosalia, Subru Krishnan, Carlo Curino
  SparkCruise: Handsfree Computation Reuse in Spark
  VLDB 2019/PVLDB, Los Angeles, USA. (Demo paper)
• Chenggang Wu, Alekh Jindal, Saeed Amizadeh, Hiren Patel, Wangchao Le, Shi Qiao, Sriram Rao
  Towards a Learning Optimizer for Shared Clouds
  VLDB 2019/PVLDB, Los Angeles, USA.
• Alekh Jindal, Lalitha Viswanathan, Konstantinos Karanasos
  Query and Resource Optimizations: A Case for Breaking the Wall in Big Data Systems
  arXiv:1906.06590 [cs.DB], June 2019
• Alekh Jindal, Anil Shanbhag, Yi Lu
  Robust Data Partitioning
  Encyclopedia of Big Data Technologies, 2019, Springer.
  Invited Chapter
• Alekh Jindal, Konstantinos Karanasos, Sriram Rao, Hiren Patel
  Selecting Subexpressions to Materialize at Datacenter Scale
  VLDB 2018/PVLDB, Rio de Janeiro, Brazil.
• Alekh Jindal, Shi Qiao, Hiren Patel, Jarod Yin, Jieming Di, Malay Bag, Marc Friedman, Yifung Lin, Konstantinos Karanasos, Sriram Rao
  Computation Reuse in Analytics Job Service at Microsoft
  SIGMOD 2018, Houston, USA.
• Lalitha Viswanathan, Alekh Jindal, Konstantinos Karanasos
  Query and Resource Optimization: Bridging the Gap
  ICDE 2018, Paris, France (Short paper).
• Kristin Tufte, Kushal Datta, Alekh Jindal, David Maier, Robert L Bertini
  Challenges and Opportunities in Transportation Data
  Symposium on Smart Cities and Communities 2018, Portland, USA.
• Anil Shanbhag, Alekh Jindal, Samuel Madden, Jorge Quiane, Aaron Elmore
  A Robust Partitioning Scheme for Ad-Hoc Query Workloads
  SOCC 2017, Santa Clara, USA.
• Yi Lu, Anil Shanbhag, Alekh Jindal, Samuel Madden
  AdaptDB: Adaptive Partitioning for Distributed Joins
  VLDB 2017, Munich, Germany.
• Alekh Jindal, Jorge-Arnulfo Quiane-Ruiz, Samuel Madden
  IngestBase: A Declarative Data Ingestion System
  arXiv:1701.06093 [cs.DB], Jan 2017
• Anil Shanbhag, Alekh Jindal, Yi Lu, Samuel Madden
  Amoeba: A Shape changing Storage System for Big Data
  VLDB 2016, New Delhi, India. (Demo paper)
• Ankur Dave, Alekh Jindal, Li Erran Li, Reynold Xin, Joseph Gonzalez, Matei Zaharia
  GraphFrames: An Integrated API for Mixing Graph and Relational Queries
  GRADES 2016, California, USA.
• Alekh Jindal, Samuel Madden, Malú Castellanos, Meichun Hsu
  Graph Analytics using Vertica Relational Database
  IEEE BigData 2015, Santa Clara, USA.
• Felix Martin Schuhknecht, Alekh Jindal, Jens Dittrich
  An Experimental Evaluation and Analysis of Database Cracking
  The VLDB Journal, August 2015
  Special Issue on best papers of VLDB 2014
• Zuhair Khayyat, Ihab F. Ilyas, Alekh Jindal, Samuel Madden, Mourad Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiane-Ruiz, Nan Tang, Si Yin
  BigDansing: A System for Big Data Cleansing
  SIGMOD 2015, Melbourne, Australia.
• Alekh Jindal
  Robust Data Transformations
  CIDR 2015, Asilomar, USA. (Abstract)
• Alekh Jindal, Samuel Madden, Malu Castellanos, Meichun Hsu
  Graph Analytics using the Vertica Relational Database
  arXiv:1412.5263 [cs.DB], Dec 17, 2014
• Alekh Jindal, Samuel Madden
  GraphiQL: A Graph Intuitive Query Language for Relational Databases
  IEEE BigData 2014, Washington DC, USA. (Acceptance rate: 18.5%) [slides]
• Alekh Jindal, Praynaa Rawlani, Eugene Wu, Samuel Madden, Amol Deshpande, Mike Stonebraker
  Vertexica: Your Relational Friend for Graph Analytics!
  VLDB 2014, Hangzhou, China. (Demo paper)
• Felix Martin Schuhknecht, Alekh Jindal, Jens Dittrich
  The Uncracked Pieces in Database Cracking
  VLDB 2014/PVLDB, Hangzhou, China. [Source Code]
  Best Paper Award (VLDB Announcement)
• Alekh Jindal, Endre Palatinus, Vladimir Pavlov, Jens Dittrich
  A Comparison of Knives for Bread Slicing
  VLDB 2013/PVLDB, Riva, Italy.
• Alekh Jindal, Jorge-Arnulfo Quiane-Ruiz, Samuel Madden
  Cartilage: Adding Flexibility to the Hadoop Skeleton
  SIGMOD 2013, New York, USA. (Demo paper) [poster]
• Barzan Mozafari, Carlo Curino, Alekh Jindal, Samuel Madden
  Performance and Resource Modeling in Highly-Concurrent OLTP Workloads
  SIGMOD 2013, New York, USA.
• Alekh Jindal, Jorge-Arnulfo Quiane-Ruiz, Jens Dittrich
  WWHow! Freeing Data Storage from Cages
  CIDR 2013, Asilomar, USA.
• Alekh Jindal, Felix Martin Schuhknecht, Jens Dittrich, Karen Khachatryan, Alexander Bunte
  How Achaeans Would Construct Columns in Troy
  CIDR 2013, Asilomar, USA. [slides]
• Jens Dittrich, Jorge-Arnulfo Quiané-Ruiz, Stefan Richter, Stefan Schuh, Alekh Jindal, Jörg Schad
  Only Aggressive Elephants are Fast Elephants
  VLDB 2012/PVLDB, Istanbul, Turkey.
• Alekh Jindal, Jorge-Arnulfo Quiane-Ruiz, Jens Dittrich
  Trojan Data Layouts: Right Shoes for a Running Elephant
  ACM SOCC 2011, Cascais, Portugal. [slides] [poster]
• Alekh Jindal, Jens Dittrich
  Relax and Let the Database do the Partitioning Online
  VLDB BIRTE 2011, Seattle, USA. TR [slides]
• Jens Dittrich, Alekh Jindal
  Towards a one-size-fits-all Database Architecture
  CIDR 2011, Outrageous Ideas and Vision Track, Asilomar, USA.
  Best Outrageous Ideas and Vision Paper Award (CCC Blog)
• Jens Dittrich, Jorge-Arnulfo Quiane-Ruiz, Alekh Jindal, Yagiz Kargin, Vinay Setty, and Jörg Schad.
  Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing)
  VLDB 2010, Singapore.
• Alekh Jindal
  The Mimicking Octopus: Towards a one-size-fits-all Database Architecture
  VLDB 2010 PhD Workshop, Singapore. [slides] [poster]

Patents

• Generative Business Intelligence (US Patent 12,608,670)
• 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)
• Machine learning accelerated semantic equivalence detection (US Patent 12,436,950)
• Materialized view generation and provision based on queries having a semantically equivalent or containment relationship (US Patent 12,561,321, US20260072913A1)
• 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 (US Patent 12,572,537)
• 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

• 2026: PC Member, ICDE(Industry)
• 2025: PC Member, ICDE ICDE(Industry) PVLDB
• 2024: PhD Symposium Co-Chair, ICDE
• 2023: Demo Co-Chair VLDB
            PC Member, SIGMOD ICDE(Industry)
• 2022: D&I Co-Chair CIDR
            PC Member, ICDE ICDE(Industry) AIDM
• 2021: PC Member, PVLDB SIGMOD SoCC AI-ML-Systems DEEM BiDEDE
• 2020: PC Member, ICDE VLDB(Demo)
• 2019: PC Member, SIGMOD ICDE EDBT SOCC CIKM VLDB(Demo)
• 2018: PC Member, CIKM DEEM DASFAA TKDE Poster
• 2017: PC Member, PVLDB SIGMOD ICDE EDBT VLDB(Demo) SIGMOD(SRC)
            Reviewer, DAPD
• 2016: Proceedings Chair, SIGMOD
            PC Member, SIGMOD SIGMOD(Demo) VLDB(Demo) ICDE(Demo) EDBT(Vision)
• 2015: PC Member, PVLDB SIGMOD EDBT(Demo)
            Reviewer, SIGMOD Record TON TKDE TODS
• 2014: PC Member, PVLDB
            Referee, SIGMOD Record
• 2013: Reviewer, PVLDB SIGMOD ICDE CIKM DAPD
• 2012: Reviewer, PVLDB ICDE
• 2011: Reviewer, EDBT
• 2010: Reviewer, VLDB

Mentoring

• 2026: Md Ashraful Islam, Intern, University of Massachusetts, Amherst, Context graph.
• 2025: Karan Hanswadkar, Intern, University of Washington, Clinical data search.
• 2024: Yongye Su, Intern, Purdue University, Scalable vector indexing.
• 2024: Xuye He, Intern, Cornell Unversity, Small model tuning for data analytics.
• 2023: Sathwik Reddy Madhula, Intern, UCLA, Generative AI machine.
• 2023: Kanupriya Raheja, Intern, Columbia University, Generative AI machine.
• 2022: Isha Tarte, Intern, UT Austin, Automated warehouse optimization.
• 2021: Parimarjan Negi, Intern, MIT, Deploying steered query optimizer.
• 2020: Parimarjan Negi, Intern, MIT, Steering query optimizers.
• 2019: Tarique Siddiqui, Intern, UIUC, Forecasting query workloads.
• 2018: Tarique Siddiqui, Intern, UIUC, Cost models for big data query processing.
• 2017: Chenggang Wu, Intern, UCB, Towards a learning optimizer for shared clouds.
• 2016: Lalitha Viswanathan, Intern, UW-Madison, Query and resource optimizations.
• 2015: Anil Shanbag, 1st year PhD, MIT, Robust data partitioning.
• 2014: Qui Nguyen, M.Eng. Thesis, MIT, Robust data partitioning for ad-hoc query processing.
• 2013: Praynaa Rawlani, M.Eng. Thesis, MIT, Graphs anlaytics on relational databases.
• 2012: Felix Martin Schuhknecht, 1st year PhD, UdS, Evaluating and improving database cracking algorithms.
• 2012: Endre Palatinus, 1st year PhD, UdS, Evaluating vertical partitioning algorithms and their impact.
• 2012: Karen Khachatryan, 1st year PhD, UdS, Techniques for emulating columns stores in row databases.
• 2011: Stefan Chouteau, B.Sc Thesis, UdS, Implementing a log-structured main-memory database system.
• 2011: Sebastian Wendland, M.Sc Thesis, UdS, Implementing column store access layer in PostgreSQL.
• 2011: Marco Huester, M.Sc Thesis, UdS, Applying database cracking over two-dimensional data.
• 2010: Felix Martin Schuhknecht, B.Sc Thesis, UdS, 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

• 2023-Present: CEO and Co-founder, Tursio Inc., Bellevue, USA.
• 2022-2022: CTO and Board Member, Keebo Inc., Bellevue, USA.
• 2021-2022: Founding Chief Architect, Keebo Inc., Bellevue, USA.
• 2020-2021: Principal Scientist Manager, Gray Systems Labs, Microsoft, Redmond, USA.
• 2019-2020: Principal Scientist, Gray Systems Labs, Microsoft, Redmond, USA.
• 2015-2019: Senior Scientist, Gray Systems Labs, Microsoft, Redmond, USA.
• 2013-2015: Postdoctoral Associate, MIT CSAIL, Cambridge, USA.
• 2012-2013: Postdoctoral Research Associate, Saarland University, Saarbruecken, Germany.
• 2010-2012: Research Assistant, Saarland University, Saarbruecken, Germany.
• 2008-2010: IMPRS Scholar, Max Planck Insitute for Informatics, Saarbruecken, Germany.
• 2007-2008: Senior Software Engineer, Ibibo Web, Gurgaon, India.
• 2006-2007: Associate Consultant, British Telecom, Bangalore, India.
• 2005-2006: Project Research Associate, NPTEL, IIT Kanpur, India.
• 2005: Intern, IBM Software Labs, Pune, India.

Education

• April 2013 - August 2015:
  Postdoctoral Associate, Big Data Analytics
  CSAIL, Massachusetts Institute of Technology, USA.
  Research Statement Teaching Statement
  Mentor: Prof. Samuel Madden
• February 2010 - August 2012:
  Ph.D. (Summa Cum Laude), Computer Science
  Saarland University, Germany.
  Thesis: OctopusDB: Flexible and Scalable Storage Management for Arbitrary Database Engines
  Supervisor: Prof. Jens Dittrich
• October 2008 - January 2010:
  Master of Science (honors), Computer Science
  Saarland University & Max Plank Institute for Informatics, Germany.
  Thesis: Quality in Phrase Mining
  Supervisors: Prof. Jens Dittrich, Prof. Gerhard Weikum
• July 2002 - June 2006:
  Bachelor of Technology, Electrical Engineering
  Indian Institute of Technology, Kanpur, India.
  Thesis: Microcontroller Based Power Distribution Monitoring & Control
  Supervisor: Prof. S. P. Das