What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
The scientist’s investigation covers issues in Algorithm, Mathematical optimization, Sensor fusion, Computational complexity theory and Artificial intelligence.
He interconnects Multiuser detection, Detector, Theoretical computer science, Detection theory and Probabilistic logic in the investigation of issues within Algorithm.
The concepts of his Mathematical optimization study are interwoven with issues in Decision theory and Job shop scheduling.
The study incorporates disciplines such as Kalman filter, Secondary surveillance radar and Assignment problem in addition to Sensor fusion.
His Computational complexity theory research incorporates themes from Fault and Graph.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Iterative method, Machine learning and Association.
His most cited work include:
- A generalized S-D assignment algorithm for multisensor-multitarget state estimation (323 citations)
- Ground target tracking with variable structure IMM estimator (259 citations)
- A new relaxation algorithm and passive sensor data association (231 citations)
What are the main themes of his work throughout his whole career to date?
Mathematical optimization, Algorithm, Artificial intelligence, Fault and Operations research are his primary areas of study.
His work carried out in the field of Mathematical optimization brings together such families of science as Computational complexity theory and Upper and lower bounds.
Krishna R. Pattipati combines subjects such as Multiuser detection, Detector, Assignment problem, Kalman filter and Sensor fusion with his study of Algorithm.
Kalman filter is the subject of his research, which falls under Control theory.
Krishna R. Pattipati has researched Artificial intelligence in several fields, including Machine learning, Task, Computer vision and Pattern recognition.
His Fault research is multidisciplinary, incorporating perspectives in Control engineering, Reliability engineering and Hidden Markov model.
He most often published in these fields:
- Mathematical optimization (20.70%)
- Algorithm (18.01%)
- Artificial intelligence (14.70%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (14.70%)
- Battery (4.35%)
- Decision support system (5.59%)
In recent papers he was focusing on the following fields of study:
Krishna R. Pattipati spends much of his time researching Artificial intelligence, Battery, Decision support system, Fault and Algorithm.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Computer vision and Pattern recognition.
His Battery research includes themes of Voltage, Ranging, Control theory and Fuel gauge.
His Decision support system research incorporates elements of Knowledge management, Operations research, Information integration, Dynamic priority scheduling and Process management.
His Fault research includes elements of Reliability engineering and Hidden Markov model.
His Algorithm study frequently draws connections to adjacent fields such as Kalman filter.
Between 2013 and 2021, his most popular works were:
- Open circuit voltage characterization of lithium-ion batteries (105 citations)
- Battery state of charge tracking, equivalent circuit selection and benchmarking (68 citations)
- A Markov Chain-Based Testability Growth Model With a Cost-Benefit Function (64 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Battery, Control theory, Fault, State of charge and Artificial intelligence.
His work on Fault detection and isolation and Fault indicator as part of his general Fault study is frequently connected to HVAC, thereby bridging the divide between different branches of science.
Krishna R. Pattipati has included themes like Estimation theory and Tracking in his State of charge study.
His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Classifier.
His biological study spans a wide range of topics, including Algorithm and Set.
His study in Algorithm is interdisciplinary in nature, drawing from both Node and Vehicle routing problem.
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