Time: 4:45pm–5:30pm on Sunday, December 14, 2025
Location: Blackwattle 1
Event description: Following the (now) long-standing tradition initiated at ITCS, graduating PhD students and postdoctoral researchers will give short 2-minute presentations introducing themselves, their research interests and plans. Many of these junior researchers will be looking for positions in the upcoming year.
My work is largely in algorithmic economics and mechanism design. My dissertation focuses on incorporating the Pandora’s Box model of hidden information into algorithmic and mechanism design problems. My work includes auction design and matching problems, and my most recent work has been peer prediction, value of information, and some social choice applications.
My research interests are in combinatorial optimization, approximation algorithms, and beyond-worst-case analysis of algorithms. For my PhD research, I studied problems related to maximizing submodular functions and unique coverage, and for these problems I designed approximation algorithms with performance bounds depending on parameters that capture beyond-worst-case instances. One of the highlights of my PhD was visiting Keio University in Japan to study the problem of Submodular Maximization with a Connectivity Constraint.
What are the fundamental capabilities and limitations of quantum computers? I am broadly interested in quantum computation and complexity theory, as it connects to cryptography, learning, simulation, and verification.
I am a PhD student at Georgetown university, advised by Alexander Golovnev. My main focus are questions lying in the intersection of complexity theory and algorithm design. Specifically, I like to prove lower bounds by designing non-trivial algorithms for problems like SAT and Range Avoidance.
My research lies at the intersection of cryptography, complexity theory, and quantum information. I study how classical and quantum computational models govern the feasibility and fundamental limitations of computation. My recent works include the first obfuscation scheme for quantum unitary programs, as well as communication-complexity results for cryptographic and streaming models. Broadly, my goal is to develop a rigorous theoretical framework that characterizes both the possibilities and inherent barriers of computation in classical and quantum settings.
I am a final-year PhD researcher at the University of Sydney specialising in computational geometry, with a focus on similarity measures between curves. I work on designing efficient algorithms with strong theoretical guarantees, collaborating closely with internationally recognised researchers. My research combines deep mathematical reasoning with practical problem-solving, making it transferable to data-driven and algorithm-heavy industry roles. I am now seeking industry research or applied R&D positions where rigorous thinking and real-world impact intersect.
I am a postdoc at the Institute for Advanced Study working on a mix of complexity theory, cryptography, and proof complexity. I recently finished my PhD at MIT advised by Ryan Williams. I especially like to use ideas from one area of theory to prove results in other areas. My work has won five best student paper awards and resolved questions in a number of areas, including metacomplexity, cryptographic proof systems, program obfuscation, differential privacy, circuit lower bounds, and algorithms.
I am a PhD student at UIUC advised by Chandra Chekuri, interested in algorithm design and combinatorial optimization. My research focus has been on approximation algorithms for network design problems, where the goal is to construct fault-tolerant and low-cost networks that meet a variety of routing or connectivity demands. Broadly, I am excited about structural results on graphs and related combinatorial objects, and look forward to pursuing a broad research agenda during a post-doc.
I am a fifth-year Ph.D. candidate in New York University, advised by Prof. Christopher Musco and Prof. Chinmay Hegde, with expected graduation in May 2026. My work lies at the intersection of algorithms, computational complexity, the theoretical foundations of modern machine learning, and numerical linear algebra. It centers on two main directions: using fine-grained complexity to characterize the computational limits of deep learning architectures, and developing a query-complexity framework for structured matrix recovery, inspired by problems in operator learning and scientific machine learning. I also enjoy working in combinatorics; recently, I played a central role in resolving a long-standing open problem in stochastic Boolean function evaluation with an exact algorithm for the Unanimous Vote problem.
I am a fifth-year PhD student in Computer Science at Georgia Tech, advised by Santosh Vempala. My research focuses on algorithms for high-dimensional sampling and Markov chain Monte Carlo, particularly on understanding the complexity of sampling. More broadly, I am interested in foundational problems in machine learning and data science.
Shyamal is a final year PhD student at Columbia University advised by Xi Chen and Cliff Stein. He is broadly interested in theoretical computer science, particularly in sublinear algorithms, property testing, and computational learning theory. Currently, he is excited about designing efficient learning and testing algorithms in settings with noise and under arbitrary distributions.
I am a fifth year PhD student at the University of Michigan, Ann Arbor. My interests are in coding theory, pseudorandomness, and complexity theory. My research focuses on understanding the behavior of linear codes with respect to local properties, such as list-decoding and list-recovery. My recent work gives the first explicit constructions of capacity achieving list-recoverable codes with optimal output list sizes.
I am a fifth-year PhD student at the University of Waterloo advised by Prof. Sepehr Assadi. My research is mainly on analyzing space requirements to process large datasets in various settings that model different forms of accessing the input like streaming, CONGEST model in distributed computing and random sampling. I have worked predominantly on graph problems like shortest path and matching in streaming, and triangle detection in CONGEST model so far. I am looking for postdoctoral positions starting in Fall 2026.
Hi! I am Lasse Wulf. I am currently a postdoctoral researcher at IT University Copenhagen. I am working in problems at the second level of the polynomial hierarchy, robust optimization, and min-max optimization problems in TCS or Operations Research. My research interests are very broad, I have also worked on derandomizing the Exact Matching problem, and on some geometric problems. In general I love all aspects of reductions in TCS. I am looking for a postdoc (or permanent) position either in Copenhagen or some place that is convenient to reach Copenhagen by train or plane, due to personal relationship.
Sorrachai Yingchareonthawornchai is a computer scientist and theoretical researcher specializing in fast graph algorithms, combinatorial optimization, and data structures. He earned his PhD in Computer Science from Aalto University in Finland in 2023, completing a thesis that made significant advances in the algorithmic study of vertex connectivity—a fundamental measure of network robustness—and received the university’s Doctoral Thesis Award. His academic work has been published at top theory conferences such as STOC and FOCS, and he has collaborated on breakthroughs in efficient algorithms for graph problems that had resisted faster solutions for decades. Prior to his current role, he held postdoctoral positions including at the Simons Institute for the Theory of Computing, UC Berkeley, and the Hebrew University of Jerusalem
I am a final-year Ph.D. student at Nanjing University, advised by Yitong Yin. I have a broad interest in randomized algorithms and probability. Specifically, my current research focuses on sampling and counting algorithms based on MCMC method.
I am a 5th year PhD student at MIT working with Dor Minzer. My primary research interests are in probabilistic proofs (PCPs, IOPs) and their applications in theoretical computer science and cryptography.