Soham Palande

Hi, I’m Soham. I’m currently a research engineer in J.P. Morgan’s AI Research group.

Research: I study how machine and human models of cognition exhibit strategic reasoning in structured, multi-step environments—and their emergence through behavioral evaluation, learned representational structures (from language and fine-tuning), and mechanistic insight in stylized decision-making scenarios involving uncertainty, adaptation, and long-term planning—such as bidding, bluffing, or probabilistic inference—where optimal strategies must be inductively discovered rather than precomputed.

My goal is to understand when and how models demonstrate intelligent, goal-directed behaviors, and precisely which internal structures—learned, emergent, or structurally implicit—support these capabilities. This involves behavioral analysis, algorithmic investigation, and mechanistic insight, drawing from game theory, cognitive modeling, and computational geometry. I’m particularly interested in how adaptive reasoning naturally develops in response to incentives, reinforced feedback signals, inductive contexts, and the geometric structure of internal language representations.

Ultimately, through empirical evaluations, my work addresses deeper theoretical questions—such as how models inductively simulate, generalize, or abstract analogously to human cognition in scientific and mathematical reasoning. By studying reasoning within computational systems and natural cognition through a computational lens, I aim to uncover generalizable principles of intelligent behavior, enabling the design of more powerful and adaptive systems.

Previously: I graduated from Rutgers University-NB majoring in Computer Science and Mathematics. I was an R&D intern at L3Harris where I worked on building anomaly detection models.

news

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May 10, 2022	I was awarded the Chancellor-Provost’s Research Excellence Award
May 5, 2022	I was awarded the Nicholas Novielli Prize for academic excellence by the Rutgers CS Dept.

selected research

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Aresty

Understanding Correlated Error Events in Quantum Computers

Arpan Gupta, Michael Schleppy, Soham Palande, and 1 more author

Aresty Research Symposium, Apr 2022

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Quantum Computing is at a critical juncture. Prototype quantum computers are now at a level of reliability and scale where researchers can run small scale algorithms. However, modeling errors in near-term Noisy Intermediate-Scale Quantum (NISQ) devices is necessary to harness their full potential. Existing frameworks such as IBM Qiskit are limited in their capacity to model and simulate complex noise events. We study the use of Probabilistic Graphical Models (PGMs) as a natural abstraction to efficiently simulate and model quantum circuits with no noise, uncorrelated noise (bit-flip and amplitude damping) and correlated noise using complex-valued Bayesian networks. By definition, Bayesian networks are a type of PGM that articulate conditional dependencies between events through the use of Directed Acyclic Graphs (DAGs). We study well-known quantum algorithms such as Deutsch- Jozsa and Simon’s algorithms, and transform their circuit representations into Bayesian network models by extending Python’s pgmpy library to allow for complex- valued networks. By using exact inference algorithms like Variable Elimination, we are able to create valuable inferences that can be converted into density matrices using our extension of pgmpy. We validate that our models produce correct density matrices for noisy Quantum circuits using IBM Qiskit, showing that Bayesian networks are a valid abstraction for Quantum correlated and uncorrelated noise events. The correctness of our results point to new languages and methods for representing Quantum Computations
Aresty

Solar Irradiance Nowcasting Using All-Sky Imager Data

Soham Palande, and Ahmed Aziz

Aresty Research Symposium, Apr 2021

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As we continue to look for ways to accelerate our transition to renewable energy, one of the fundamental obstacles to achieving this remains the unpredictable nature of wind and solar energy. Clouds moving in front of the sun and rapid changes in weather conditions cause fluctuations in the measured solar irradiance and cause serious challenges in power grid operation. In this study, we address the unpredictable nature of solar irradiance by using past irradiance measurements and leverage images captured by an All-Sky imager using a Deep Learning Framework to forecast solar irradiance over the short term. We train the model on datasets of varying sizes and show that the models significantly outperform the smart persistence model and state of the art statistical approaches using optical flow techniques.