I am a 5th year CS PhD candidate advised by Mohit Iyyer. After years of working as a lead/AI developer in video games and then as an engineering manager, I reassessed my career path so I could pursue my lifelong desire to create engaging interactive storytelling experiences. My research focuses on fundamental advances in NLP and creative use of language.

I'm a third year CS PhD student at UMass Amherst, working with Don Towsley in Quantum Networks. My research interests are in Distributed Quantum Computing, Quantum Network Tomography and Entanglement in Graph States. I'm part of the Center for Quantum Networks, a multi-university research center funded by NSF. I've been serving as Chair for CQN's Student Leadership Council since January 2021. I'm currently in the organizing committee for CQN's Summer and Winter School. I graduated from the Federal University of Rio de Janeiro (UFRJ) in Computer and Information Engineering in 2018. I obtained a Master's degree in Computer and Systems Engineering from COPPE UFRJ in 2020, where I was co-advised by Daniel Ratton Figueiredo and Franklin de Lima Marquezino.When I'm not doing research, I enjoy playing the guitar and working out.

Akanksha Atrey is a Ph.D. student in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. She is a member of the Laboratory of Advanced Software Systems, where she is advised by Prof. Prashant Shenoy. Her research interests lie at the intersection of machine learning and distributed systems with a focus on applications in IoT and mobile computing. Her work seeks to make machine learning more trustworthy and privacy-preserving for applications in edge systems. Prior to joining UMass, she was a software engineer at IBM where she worked on the IBM z/OS Mainframe. In 2016, she received her Bachelor of Science degree in Mathematics and Computer Science from the State University of New York at Albany.

I am a 4th Year PhD student working in Artificial Intelligence with Prof. Shlomo Zilberstein. I am interested in building general-purpose autonomous agents that can operate efficiently in the real world, where time and resources are limited. At present, my research efforts lie at the intersection of deep reinforcement learning, planning, and meta-reasoning.

Rajarshi Bhattacharjee: I am a third-year Ph.D. student advised by Prof. Cameron Musco. My research interests include sublinear algorithms, randomized numerical linear algebra, and online learning. More broadly, I'm interested in developing theoretically sound algorithms for problems involving large-scale data.

Thuy Trang Nguyen: I am a third year PhD student working with professor Cameron Musco in the Theory lab. I am currently working on sketching and caching algorithms.

Known to some as The Count of Monte Carlo, Cyrus Cousins studies all manner of problems involving sampling, randomization, and learning in data science and beyond, with a particular interest in uniform convergence theory, and the rigorous treatment of fair machine learning. He is currently working with Yair Zick as a postdoctoral researcher under the CDS Postdoctoral Fellowship Program, and formerly served as a visiting assistant professor at Brown University under the Dean’s Faculty Fellowship, where he also completed his doctoral studies under the tutelage of the great Eli Upfal.

Li Ding: Li Ding is a 3rd-year Ph.D. student at UMass Amherst CICS, advised by Prof. Lee Spector. His research interests are evolutionary machine learning, computer vision, human-centered AI, and quantum computing. Before joining UMass, he was a research engineer at MIT. More information can be found at http://ld-ing.github.io/

Ryan Bahlous-Boldi: Ryan is an Undergraduate Compute Science student at UMass Amherst's CICS. He has been working in the sub-field of Artificial Intelligence known as Evolutionary Computation with Professor Lee Spector. More specifically, his research interests include Neuroevolution, Genetic Programming, Artificial Life, Reinforcement Learning, and Robotics. His current research projects are exploring selection strategies such as Lexicase selection for the evolution of computer programs and neural networks.

Sylee Dandekar: I am currently an MS/PhD student at the University of Massachusetts Amherst. I am a part of the BINDS lab where I work under Dr Hava Siegelmann. I am interested in biologically inspired continual learning research. Prior to starting graduate school, I worked as an Associate Scientist at Raytheon BBN Technologies in Cambridge, MA. I completed my undergraduate studies in Computational Mathematics with minors in Computer Science and Economics at the University of Massachusetts Amherst.

Sidharth Anupkrishnan: I am a 2nd year MS/PhD student in Computer Science. My research interests lie broadly in Computer Vision and Neuro-Symbolic approaches to AI. I wish to build intelligent and robust visual systems that can operate under minimal supervision.

I am a fourth-year PhD student in the College of Information and Computer Sciences working with Prof. James Allan in the Center for Intelligent Information Retrieval lab at the University of Massachusetts Amherst.

Fadhil is a 3rd year MS/PhD student who is mainly working on distributed systems that are practical, provide better performance, have good reliability, and preserve users' privacy. His current focus is on distributed consensus and systems for machine learning. He is the recipient of the Jim Gray Scholarship from CICS in 2021.

Elita is a 3rd year PhD student at UMass Amherst advised by Prof. Yair Zick. Her research interests are in the field of Reinforcement Learning and Explainable AI. Her current research focus is on developing robust and practical solutions for explaining ML models and RL agents. Previously, she has worked on developing novel Batch RL algorithms that provide robustness against epistemic uncertainties and adversarial attacks on Batch RL.

I’m a fifth-year PhD student advised by Prof. Shlomo Zilberstein. My research interests include search algorithms, reinforcement learning, and human-computer interactions. I’m particulary interested in autonomous decision-making in the presense of complex constraints and objectives.

I am a 5th year PhD student working with Prof. Alexandra Meliou in the DREAM Lab. My research interests lie in the broad area of ethical, equitable, and responsible systems. I focus on issues of diversity, data and algorithmic fairness, while I am particularly fascinated by the areas of Fair & Explainable AI, Machine Learning, and Recommendation Systems.

Samer Nashed is a graduate student working with the Laboratory for Perceptual Robotics, where he works on using sensors like lasers and cameras to build models of the world so robots can complete their tasks safely and efficiently, and the Resource Bounded Reasoning Laboratory, where he works on explainable, ethical, and efficient planning under uncertainty. Before coming to UMass, he attended Swarthmore College where he majored in Physics and Computer Science.

I am a second year PhD student working with Prof. Hui Guan on Federated Learning, which is a paradigm focused on privacy-preserving machine learning. I am particularly working in Personalized Federated Learning which deals with incentivizing a statistically heterogenous client to participate in a federated training.

I am a fourth-year MS/PhD student working with Prof. Evangelos Kalogerakis on 3D Computer Vision. My main research focus is controllable 3D shape generation and reconstruction that allows for high-level edits of resulting shape structure and geometry. I received my BS and MS degrees from the Higher School of Economics in Moscow, Russia. In the past, I have also interned at Adobe (2021, 2022) working on 3D shape generation and reconstruction.

I am fifth year Ph.D. candidate in Knowledge Discovery Lab (KDL), advised by Prof. David Jensen. My research interests are in causal inference, reinforcement learning, and probabilistic approaches to machine learning. My research focuses on designing decision-making agents which can generalize to novel environments using principles of causal inference. During my Ph.D., I spent 9 months as AI resident at Google X, The Moonshot Factory where I worked on applying causal inference techniques in solving the most challenging real-world problems. I also worked with Amazon Research in Cambridge, UK in 2019 where I worked on designing transfer learning methods for reinforcement learning. Before joining Ph.D. program at UMass I worked as a quantitative analyst at Goldman Sachs for three years. I earned my Undergraduate Degree in Computer Science and Engineering from the Indian Institute of Technology Roorkee in 2015.

I’m a second year PhD student at the Center for Intelligent Information Retrieval (CIIR), supervised by Hamed Zamani. My recent work is on conversational search, representation learning and ranking for information retrieval. In the past, I have solved challenging problems at 3 AI start-ups (adtech, fintech, biotech) and in academia (Research Assistant at the National University of Singapore). My long-term goal is to help overcome the limitations of current machine learning models (efficiency, robustness, limited training resources), and improve multimodal content organization & discovery.

Jade is a 3rd year PhD student in the SPIN research group. Her research interests are censorship circumvention and traffic obfuscation.

Simeng is a Ph.D. candidate advised by professor Mohit Iyyer. Her research is focused on language modeling and machine translation. She’s particularly interested in understanding the role of context in neural autoregressive language models and efficient methods for dealing with long input sequences.

My name is Xi Wang and I am a 2nd year PhD student supervised by Prof Justin Domke. My research interests lie in the intersection between Bayesian methods and deep learning. I am currently working with Prof Domke on developing a reduced-variance gradient estimator for Black Box Variational Inference. In addition, we are developing a benchmark that evaluates the performance of various approximate inference algorithms on a selection of representative models. Beside approximate inference, I am also interested in deep generative model (in particular VAE) as well as Bayesian neural networks.

5th-year PhD student from Youngstown, Ohio researching data visualization accessibility, advised by Ali Sarvghad in the HCI-VIS Lab. In my free time I enjoy playing the euphonium, board games, and disc golf.