Swastik Bhattacharya
PhD Student, University College Dublin
Swastik Bhattacharya is a PhD Student in Statistics and Machine Learning at University College Dublin, Ireland. He holds an MSc in Statistics from the University of Manchester, where he was a Global Future Scholar, and is now an Ad-Astra Research Scholar at University College Dublin, with doctoral work on ranking algorithms, Bayesian modelling and recommender systems.
His wider interests include Machine Learning, Physics-Informed AI and Uncertainty Quantification, Theoretical aspects of Neural Networks, Neuroscience and Directional Statistics.
In 2026, he was selected as part of the cohort for the ELLIS Summer School on Probabilistic Machine Learning at the University of Cambridge.
Kushagra Agrawal
MSc Student, Åbo Akademi University
Kushagra Agrawal is a young researcher working at the intersection of artificial intelligence, machine learning, data-intensive systems, and intelligent software. He is currently an Erasmus Mundus Scholar in the EDISS (Engineering of Data-intensive Intelligent Software Systems) Joint Master's Programme at Åbo Akademi University, Finland.
His research interests include Agentic AI, Multi-Agent Systems, Large Language Models, Computer Vision, Edge AI, and Efficient Intelligent Systems. His recent work focuses on understanding the efficiency, cost, and reliability of AI systems. His work has been presented at ICML 2026 and accepted at the REALM Workshop at EMNLP 2026.
Akshay Prashad
PhD Student, University College Dublin
Akshay Prashad is a PhD researcher at University College Dublin, Ireland working on AI for Meteorology. His work sits at the point where AI meets mathematics, and he is specially interested in exploring different AI architectures and understanding what makes each one effective for weather and climate prediction. He is also fascinated by space and the atmosphere, and loves how data-driven models can help us better understand and forecast the world around us.
Outside of research, he loves reading, whether it's a thought-provoking book or a gripping manga series. He's always eager to learn, collaborate, and connect with people who share similar interests.
Akshita Srivastava
BSc Actuarial Science and Mathematics, University of Manchester
Akshita is in her second year of Actuarial Science and Mathematics at the University of Manchester, United Kingdom. Probability is her favourite part, especially combinatorial probability and stochastic processes. So much of life is uncertain, and she likes having a way to actually reason about it.
Outside of Uni, she works as a WorldQuant BRAIN Research Consultant, where she has the opportunity to explore quantitative research and apply mathematical and statistical ideas to financial problems. She also enjoys taking part in hackathons whenever she can. She finds that working under a deadline pushes her to think creatively, learn quickly, and turn ideas into practical solutions.
Looking ahead, her goal is to pursue a PhD after completing the degree.
Kanishk K. Sachan
MSc Artificial Intelligence, University of Manchester
Kanishk K. Sachan is an MSc AI student at the University of Manchester, United Kingdom and a consultant on WorldQuant BRAIN, where he researches alphas. Most of what he builds is agentic: systems that have to act without being told what to do. Somewhere along the way, his work began circling one question: "How do you make a good decision when you can't see the whole picture?" His dissertation asks it of LLM trading agents, and the markets ask it to him every day, usually with a reminder that timing counts as much as being right. He likes hackathons for a similar reason, since a clock forces you to find out what you actually believe. He does his best work when things get difficult, which mostly means waiting for the right angle and then committing to it fully. A PhD is the next step, because he is nowhere near done with that question.
Sourit Mitra
MSc Data Science, Chennai Mathematical Institute
Sourit is an MSc Data Science student at the Chennai Mathematical Institute, India with a strong interest in Machine Learning, Statistics, and their applications to financial markets. His interests span areas such as derivative pricing, quantitative finance, reinforcement learning, statistical modelling, and emerging applications of AI. He enjoys exploring ideas at the intersection of mathematical theory and practical machine learning.
Recently, Sourit completed a research internship at Algolabs, where he worked on Federated Learning and explored approaches to developing machine learning models in privacy-preserving and distributed environments. He is particularly interested in learning how modern ML techniques can be combined with rigorous statistical and mathematical frameworks to solve challenging real-world problems.
