k-Stanley conjecture

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Project Year: 2024
Student(s):   Sofiia Kotsiubynska | Charles University (Prague, Czech Republic)   ,   Volodymyr Kuznietsov | Charles University (Prague, Czech Republic)  
Mentor(s): Swee Hong Chan - Mathematics
In this project we studied properties of linear extensions of a poset and worked on proving/disproving conjectures related to them.

Mathematical Modeling of Wrinkles in Thinly Curved Sheets

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Project Year: 2024
Student(s):   Shreya Sinha | Princeton University NJ  
Mentor(s): Ian Tobasco - Mathematics
What rules dictate the wrinkle-patterns in general, simply-connected curved sheets? This project aimed to expand on results in the case of simply-connected sheets to non-simply connected sheets. The overall goal was to find a relation between the upper and lower convex envelopes of admissible convex functions (ones that satisfied certain requirements in order to successfully model wrinkle patterns), which we achieved and wish to further understand and simplify. We found concrete functions that solved the optimization problem in the case of an annulus, and found leads into the cases of a shifted annulus (where the hole is not centered on the disk) as well as have some idea as to how to solve the case of a disk with concentric regions of differently-signed curvature.

NP-hardness of Finding Optimal Learning Rate in a Sequential Social Network

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Project Year: 2024
Student(s):   Filip Uradnik | Charles University (Prague, Czech Republic)   ,   Amanda Wang | Princeton University NJ  
Mentor(s): Jie Gao - Computer Science
Sequential learning models situations where agents predict a ground truth in sequence, having access to their private, noisy measurements, and the predictions of agents who came earlier in the sequence. We study a generalization of this model to networks, where agents only see a subset of the previous agents' actions - those in their own neighborhood. We consider a setting where agents' rationality is bounded, only basing their decisions on a simple majority rule. The fraction of agents who predict the ground truth correctly depends heavily on the ordering in which the predictions are made. An important question in this area is whether there exists an ordering, under which the agents predict the ground truth correctly with high probability. In our project, we show that it is in fact NP-hard to answer this question for a general network for agents with bounded rationality.

On Approximating Diameter with Outliers

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Project Year: 2024
Student(s):   Todor Antic | Charles University (Prague, Czech Republic)   ,   Guillermo Gamboa | Charles University (Prague, Czech Republic)   ,   Jelena Glisic | Charles University (Prague, Czech Republic)   ,   Patrik Zavoral | Charles University (Prague, Czech Republic)  
Mentor(s): Karthik Srikanta - Computer Science

We study the furthest pair with outliers problem, where the goal is to identify a given number of outlier points such that the diameter of the remaining pointset is minimized. This is closely related to various clustering problems such as the Max-k-diameter with outliers. We show that the furthest pair with outliers is NP-complete by a reduction from the independent set. Further, we share found limits of (in)approximability for the furthest pair with outliers for the usual ell-p metrics. We report that 4 + ε approximation is efficiently computable and that no PTAS exists for the problem.

Privately Estimating visualization using correlation

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Project Year: 2024
Student(s):   Nigel Seymour | University of Maryland-Baltimore County MD  
Mentor(s): Anand Sarwate - Electrical and Computer Engineering
Researchers are interested in using data to demonstrate the results of health and human behavior of individuals. In sharing data, researchers have begun to shift to utilizing more data visualization techniques, and while these charts and graphs are visually more aesthetically pleasing, it is important to not violate the privacy of individuals who have reported their information. These data points must be protected using different techniques. While many focus on both qualitative support and quantitative results, the focus on the outputs must use algorithms to ensure that the data privacy remains secure and protected. I experimented using a linear correlation formula to understand how correlation works with different pieces of data from the US Census. Our goal for this project is to create differentially private correlation estimates.

Protein-DNA Binding Site Prediction with Sequence-Dependent Force Field Energy Minimization

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Project Year: 2024
Student(s):   Ryan Ding | University of California-San Diego CA  
Mentor(s): Wilma Olson - Chemistry and Chemical Biology
The conformational preferences of B-DNA sequences hold significant research value, particularly in the context of gene expression and DNA replication processes. Equally important are the interactions between protein structures that bind to these sequences and impact the conformational preferences of the complex. However, identifying the ideal binding site to which the protein should be bound on a sequence can be challenging, as differing locations may lead to less favorable conformations that impact DNA processes. We present a method that utilizes sequence-dependent force field models derived from high-resolution structures to predict the ideal binding site for a protein structure on a DNA sequence. We enhance existing energy minimization protocols within the emDNA software and conduct a case study using the DNA gyrase enzyme and a 601 base pair DNA minicircle. Through iteratively energy minimization procedure across a sequence with varying protein binding sites, we are able to calculate and derive low-energy states of the protein-DNA complex which may reveal ideal binding locations and thus make educated inferences that may assist in solving crystal structures.

Secure and Efficient Digital Signatures

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Project Year: 2024
Student(s):   Justin Kim | Rutgers University-New Brunswick NJ  
Mentor(s): Periklis Papakonstantinou - Management Science and Information Systems
Essentially all of cryptography requires the use of unproven assumptions, and much work is dedicated to relaxing these assumptions as much as possible. Digital signatures were thought to require public-key cryptographic primitives until 1989, when Naor and Yung proposed the class of Universal One-Way Hash Function families, which can be constructed from injective one-way functions, a private-key primitive. This assumption was later relaxed again to arbitrary one-way functions. Naor and Yung showed these hash functions are sufficient to construct efficient cryptographically secure digital signature schemes, however, in their original paper, many details and proofs are omitted for this claim. Most of this project is dedicated to analyzing and replicating Naor and Yung's result with full exposition, and we worked towards improving their scheme in different settings.

Testing a start in a graph

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Project Year: 2024
Student(s):   Ben Bencik | Charles University (Prague, Czech Republic)  
Mentor(s): Sumegha Garg - Computer Science
Property testing is a notion of approximation for decision problems, where given a property, the task is to distinguish whether a given instance has this property or is "far" from any instance having the property. A key complexity measure for property testing algorithms is its query complexity, which is the maximum number of input elements queried to determine whether the graph satisfies a given property. While there are established bounds for the query complexity of various graph properties, there are hardly any results on the impact of memory constraints. This project investigates property testing on graphs, studying how memory constraints affect the complexity of property testing in both deterministic models and scenarios involving random queries.

The Effects of Adversarial Agents on Social Networks

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Project Year: 2024
Student(s):   Rhett Olson | University of Minnesota-Twin Cities MN  
Mentor(s): Jie Gao - Computer Science
To make better decisions, agents often try to aggregate information from others. This phenomenon has been studied extensively through the lens of social networks. However, in real-world settings, one cannot always assume that others intend to make the best decision, or share their information honestly. Such agents act as adversaries for the task of social learning. This research investigates whether social networks can be used to aggregate information in a way that is robust against such adversaries. We present results on the robustness of two specific social networks: the Celebrity network and the Butterfly network. These results improve our understanding of how information can be aggregated effectively in social networks, both in networks with high-degree nodes and networks where all nodes have a constant degree.

Toward Lower Bounds on Memory-Sample Tradeoffs for Learning Parity with Noise

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Project Year: 2024
Student(s):   Katarina Cheng | Massachusetts Institute of Technology MA  
Mentor(s): Sumegha Garg - Computer Science
Proving the amount of storage necessary to learn under memory-constraints has important applications to machine learning and bounded-storage cryptography. In this project, we study memory-sample tradeoffs on the learning parity with noise problem (LPN) in the streaming model. The longterm goal is to prove a lower bound on memory-sample tradeoffs for LPN of Omega(n^2/eps^2) or an exponential number of samples. n parameterizes the size of the LPN secret, while epsilon parameterizes the sample noise. In this report, we describe intermediate observations, obstacles, and results toward proving the tradeoff within our modified branching program model, similar to prior work. Finally, we provide a conjecture and some intuition toward a weaker tradeoff, which instead tolerates a polynomial number of samples.

Triple Product L-function

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Project Year: 2024
Student(s):   Dmitriy Shvydkoy | University of Illinois at Urbana-Champaign IL  
Mentor(s): Michael Woodbury - Mathematics
Triple product L-functions are of great interest in number theory and quantum physics. Trilinear forms have deep connections with these L-functions, and these forms are the main objects of study in this work. This project explored the relation between two separate trilinear forms of the group GL(2, K), for some finite field K. We first used Sage to gather a large amount of experimental computations of these trilinear forms for small values of q = |K|. Then using the computational results as insight, we proved these results for general q.

Understanding a platform's strategic entry into the marketplace

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Project Year: 2024
Student(s):   Robert Jaworski | Charles University (Prague, Czech Republic)  
Mentor(s): Xintong Wang - Computer Science
Recent years have witnessed the rise of online marketplaces (e.g., Amazon, App Store) that create great convenience to facilitate matchings and improve economic efficiency. While these platforms use third-party sellers data to facilitate better matchings, they have also leveraged those sales data to inform the decisions of entering the marketplace with their own products. We analyze historical data of Amazon products provided by Keepa.com in order to understand Amazon's entry strategy and analyze the impact on third party sellers.

Venn diagrams and related structures

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Project Year: 2024
Student(s):   Adam Dzavoronok | Charles University (Prague, Czech Republic)   ,   Tymofii Reizin | Charles University (Prague, Czech Republic)   ,   Jakub Sosovicka | Charles University (Prague, Czech Republic)  
Mentor(s): Bhargav Narayanan - Mathematics

A hypergraph ℋ on {1 ... n} is just a collection of subsets of {1 ... n}. We say that there is a "k-Venn diagram" in ℋ if there are k sets ( A1, ... , Ak ) in ℋ such that all 2k intersections ( B1 ∩ ... ∩ Bk ) are non-empty where each Bi can either be Ai or the complement of Ai (equivalently, if we draw the Venn diagram associated with (A1, ... , Ak), all the regions of this picture are nonempty). Our goal is to try to understand the following question: how many sets can our hypergraph ℋ have if it fails to contain a k-Venn diagram?

When Fourier analysis meets ergodic theory and number theory

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Project Year: 2024
Student(s):   Abbas Dohadwala | Purdue University-Main Campus IN   ,   Ish Shah | Rutgers University-New Brunswick NJ  
Mentor(s): Mariusz Mirek - Mathematics
Ergodic theory is an area of mathematics which studies the statistical properties of dynamical systems. The Birkhoff ergodic theorem is a classical result of ergodic theory, which establishes pointwise convergence almost everywhere for a specific average under certain conditions. In this project, we worked towards proving an ergodic theorem with some similarities, but involving fractional powers of primes. With techniques from analytic number theory and Fourier analysis, we studied exponential sums involving these fractional powers of primes to obtain certain bounds. Using these bounds, we use harmonic analysis tools to prove a sequence of theorems, eventually leading to our desired result.

Adjustments for Kurtosis and Continuity on the Prentice Test

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Project Year: 2023
Student(s):   Lily Gebhart | Occidental College CA  
Mentor(s): John Kolassa - Statistics and Biostatistics
Nonparametric statistics is a sub-field of statistics involving minimal assumptions about the distribution of data, making it applicable to the analysis of real-world phenomena. The test of Prentice is a non-parametric statistical test for the two-way analysis of variance using ranks. The null distribution of this test is approximated using the Chi-square distribution. However, the exact null distribution deviates from the Chi-square approximation in certain cases commonly found in applications, motivating adjustments to the distribution. This summer, we presented adjustments to this null distribution, and that of related tests with non-polynomial scoring systems, correcting for continuity, skewness, and kurtosis in the multivariate case.

Algorithms for Streaming Tournaments

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Project Year: 2023
Student(s):   Sahil Kuchlous | Harvard University MA  
Mentor(s): Prantar Ghosh - DIMACS
We present the first (non-trivial) Ω(n2) lower bound on streaming algorithms for tournaments on n vertices, demonstrating that (exactly) solving the minimum feedback arc set (FAS) problem on tournaments is hard. This complements recent work on upper bounds for the (approximate) FAS problem on tournaments, and shows that even though acyclicity testing can be solved in near-linear memory on tournaments, some important related questions remain hard. We also investigate a number of fundamental graph problems on tournaments: we prove new upper and lower bounds for s-t distance and reachability, and settle the streaming complexity of acyclicity testing. Finally, we settle the streaming complexity of sink finding in general directed graphs.

Characterizing LEF and Sofic Groups

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Project Year: 2023
Student(s):   Russell Stetson | Rutgers University-New Brunswick NJ  
Mentor(s): Simon Thomas - Mathematics

For each positive integer n, let Sn be the symmetric group on { 1,2, ..., n }. Then a group G is said to be locally embeddable into finite groups if for every finite subset F ⊂ G, there exists an injection φ : F → Sn for some n ≥ 1 such that whenever g, h, gh ∈ F, then φ(gh) = φ(g)φ(h). In this case, we say that G is an LEF group.

In the group theoretic literature, LEF groups are usually characterized in terms of embeddings into ultraproducts of finite symmetric groups. It is natural to ask whether there is a characterization in terms of the more concrete notion of a reduced product of finite symmetric groups. In more detail, let P = ∏n ≥ 1 Sn be the full direct product and let N be the normal subgroup of elements (πn) ∈ P such that πn = 1 for all but finitely many positive integers n. Then the reduced product is the quotient P0 = P/N.

We have shown that it is neither provable nor disprovable using the classical ZFC axioms of set theory that if G is a group such that |G| ≤ 2ℵ0, then G is an LEF group if and only if G embeds into P0. We have obtained results concerning characterizations in terms of nonprincipal ultrafilters. Certain subtle questions about the nature of the independence remain. We have obtained analogous results for sofic groups.

Concordance Invariants of Satellite Knots

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Project Year: 2023
Student(s):   Jay Patwardhan | Rutgers University-New Brunswick NJ   ,   Zheheng Xiao | Columbia University in the City of New York NY  
Mentor(s): Kristen Hendricks - Mathematics
Satellite knots are a method of producing new knots from existing ones, utilizing a pattern knot P inside a solid torus and a companion knot K. Two knots are said to be concordant if they form the boundary of an embedded annulus in S3 x [0,1]. P. Ozsvath and Z. Szabo defined an invariant of the concordance class of a knot, called the tau-invariant, and in 2014, A. Levine computed tau for satellite knots with Mazur patterns, which loop twice clockwise around the torus before turning around and looping once counterclockwise and joining back. We determined a formula for tau for generalized Mazur patterns with m clockwise turns and n counterclockwise turns by computing the box tensor product of the CFA- and CFD modules associated to the pattern and exterior of the companion knot, respectively.

Developing a Data Driven Method to Predict Overheating in Powder Bed Fusion

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Project Year: 2023
Student(s):   Ethan Regal | Gannon University PA  
Mentor(s): Weihong 'Grace' Guo - Industrial and Systems Engineering
Powder Bed Fusion (PBF) is an extremely lucrative additive manufacturing technique that allows for highly customizable parts, increased resource efficiency, and reduced cost. However, PBF is prone to overheating which results in part defects. One possible solution to this deficiency is to employ a machine learning based, data driven method to predict overheating before it occurs. Such a method must predict process behaviors with high accuracy, provide explanations and clarification for the prediction, and obey physics principles. In this paper, methods of meeting these three conditions are explored, and a resulting technique is proposed.

Division Game

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Project Year: 2023
Student(s):   Ondrej Chwiedziuk | Charles University (Prague, Czech Republic)   ,   Ondrej Sladky | Charles University (Prague, Czech Republic)  
Mentor(s): Bhargav Narayanan - Mathematics

We examined a combinatorial game called Division game. There are 2n coins and two players, Alice and Bob, who want to split the coins so that each player has exactly n coins. In the first turn, Alice picks a coin and Bob decides who gets it. Then, the roles swap and the game continues until one of the players gets n coins. The other player then obtains the remaining coins and the player with the larger sum of coins wins the game. We conjecture that Bob always has a non-losing strategy which we proved for a small number of coins and for special classes of games.

Exact planar emulators

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Project Year: 2023
Student(s):   George Li | University of Maryland-College Park MD  
Mentor(s): Zihan Tan - DIMACS
We study vertex sparsification for distances in planar graphs: given a weighted planar graph G and a subset of k terminal vertices, the goal is to construct an emulator, which is a smaller (weighted) planar graph G' that contains the terminals and exactly preserves pairwise distances between the terminals. We construct exact planar emulators of size O(k^2) in the special where all terminals lie on exactly 2 faces.

Existence of the MLE in High-Dimensional Multinomial Logistic Regression

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Project Year: 2023
Student(s):   Sumi Vora | Pomona College CA  
Mentor(s): Pierre Bellec - Statistics
Logistic regression is a supervised classification algorithm that predicts the probability of an event given a set of features. This technique relies on maximum likelihood estimation (MLE) to estimate the regression parameters for the log-odds of the observed data. The MLE, however, does not always exist. In particular, if there exists a linear decision boundary separating the classes, then the MLE will not converge. In 2018, Candes and Sur proved a theoretical phase transition curve for the existence of the MLE in binary logistic regression parametrized by the ratio of features to number of observations p/n and the norm of the regression coefficients. In our project, we are interested in finding an empirical and theoretical phase transition curve for the existence of the MLE in unregularized multinomial regression for K classes (K>2).

Genomic Data-Guided Computational Modeling of Cancer

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Project Year: 2023
Student(s):   Elm Markert | Smith College MA  
Mentor(s): Subhajyoti De - Rutgers Cancer Institute of New Jersey
Tumors grow from somatic cells inside the human body, often undetected without any major symptoms. But tumors shed cell-free tumor DNA (cfDNA), as well as proteins, intact tumor cells, and other molecules into the blood and other bodily fluids. As cfDNA travels through the bloodstream, it is degraded by various nucleases, salt, etc. such that when blood is drawn from patients for liquid biopsy for diagnostic purposes, the cfDNA has been fragmented into smaller pieces. The methylation, length, and molecular signatures of these cfDNA fragments have the ability to provide information about the location and type of cancer. However, the methods through which different types of cfDNA are degraded remain a mystery, and there is currently no broad, all-encompassing cancer diagnostic tool that utilizes this data. We utilize a mathematical technique called non-negative matrix factorization (NMF) to identify predominant characteristics of end-sequences of cfDNA from cancer patients with multiple types of cancer.

Grid Multi-Robot Path Planning with the Corner-Following Constraint: Intractability and Algorithms

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Project Year: 2023
Student(s):   Marcus Gozon | University of Michigan-Ann Arbor MI  
Mentor(s): Jingjin Yu - Computer Science
Grid Multi-Robot Path Planning (MRPP) has been extensively studied, but much less is known about the corner-following constraint (CFC) variant, which has applications ranging from automated garages to grocery packing warehouses. On an m1 x m2 grid, the problem has a tight upper and lower bound for the makespan objective when there are Θ(m1m2) escorts, but there is a gap when there are only Θ(m1+m2) or Θ(1) escorts. In addition, nothing is known about the intractability of this problem or its related variants. In this work, we close the gap for an arbitrary number of escorts, finding an expected constant factor algorithm when there are at most min(m1,m2) escorts and a high probability constant factor algorithm when there are more. We also show that the problem is NP-hard under the makespan objective, and by the same reduction, standard grid MRPP is NP-hard also, unifying the two grid MRPP makespan hardness results. In addition, we show that when there is a single escort, the two-colored variant and partial variant are both NP-hard, which significantly impacts the design of efficient systems using the MRPP with CFC paradigm. These results contribute to our understanding of the design of efficient algorithms from both an upper and lower bound perspective.

Inapproximability of Minimum Diameter Clustering for Few Clusters

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Project Year: 2023
Student(s):   Kyrylo Karlov | Charles University (Prague, Czech Republic)   ,   Ashwin Padaki | Columbia University in the City of New York NY   ,   Stepan Zharkov | Stanford University CA  
Mentor(s): Karthik Srikanta - Computer Science

We consider the problem of approximate clustering with the diameter objective function. When the number of clusters is allowed to be unbounded, the inapproximability ratio is fairly well understood. However, this problem has not been well-studied when the number of clusters is constant. In this work, we show improved hardness bounds when there are a fixed number of clusters. Specifically, we show that the problem is hard to approximate within a factor of 1.5 in Hamming space, and within 1.304 in Euclidean space. Additionally, we prove several barrier results to known methods of proving hardness, and we provide a polynomial time algorithm for the problem when the number of dimensions is constant.

KT Orientation in Graphs

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Project Year: 2023
Student(s):   Barbora Dohnalova | Charles University (Prague, Czech Republic)   ,   Gaurav Kucheriya | Charles University (Prague, Czech Republic)  
Mentor(s): Sophie Spirkl - Department of Combinatorics and Optimization, University of Waterloo

In this article, we study the problem of classifying graphs that admit `KT orientations'. A~directed graph $G$ is said to have a KT orientation if there is at most one directed path between any pair of vertices of $G$. We extend the known examples of classes of graphs that admit a KT orientation and those graph families which do not. We show that the problem of determining whether a given graph admits a KT orientation is NP-complete, in particular this also applies if we restrict ourselves to planar graphs. Moreover we provide an algorithm to decide if a digraph with max-degree at most 3 has a KT orientation, while for graphs with max-degree $4$, this remains NP-complete. Finally we construct a graph family with small independence number (sub-linear in the number of vertices), and thus has unbounded fractional chromatic number, that admits a KT orientation.

Lagrangian Fillings of (CP1)n

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Project Year: 2023
Student(s):   Jemma Schroder | Massachusetts Institute of Technology MA  
Mentor(s): Christopher Woodward - Mathematics
Given a Legendrian submanifold Λ of a circle-fibred contact manifold Z over a symplectic base, one can ask whether Λ admits a Lagrangian filling L. In this paper, we disprove the existence of such a filling for the Legendrian given by the n-fold power of the Riemman Sphere CP1. Wehrheim and Woodward proved a dimension requirement of the image of H(Λ) in H(L). We use this obstruction and a combinatorial count of relations between elements in H1(Λ) and Hn-1(Λ) induced by the filling to show an obstruction for fillings of (CP1) n for n>2.

On Steiner Trees of the Regular Simplex

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Project Year: 2023
Student(s):   Guillermo Gamboa | Charles University (Prague, Czech Republic)   ,   Josef Matejka | Charles University (Prague, Czech Republic)   ,   Jakub Petr | Charles University (Prague, Czech Republic)  
Mentor(s): Karthik Srikanta - Computer Science

In this project, we studied Steiner minimal trees for the points given by the corners of the regular simplex. We provide an explicit formula for the coordinates of the Steiner points of a conjectured Steiner minimal tree for simplexes of dimension 2k-1. Also, we explore the function APTC(T), a notion used to characterize trees with respect to the sum of all distances between the terminals, and proved that the conjectured topology for Steiner minimal trees for the regular simplex minimizes this function over all tree with n terminals and n-2 interior points.

Predicting Correlation of Bagging Estimators in Logistic Regression

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Project Year: 2023
Student(s):   Iris Chang | Columbia University in the City of New York NY  
Mentor(s): Pierre Bellec - Statistics
Logistic regression is used to make a prediction between two different outcomes based on a data set. Typically, when the sample size is much larger compared to a fixed dimension, maximum likelihood estimation is used to estimate the parameters of the model, in which case the estimate has many convenient properties such as being unbiased. It was shown by Sur and Candes (2018) as well as Salehi et al. (2019) that these assumptions do not hold in the high dimensional regime where the sample size and dimension are proportional. While these two groups were able to find adequate methods of characterizing model performance in high dimensions, there is an absence of work on the performance and impact of bagging on high dimensional logistic regression models. In our case, bagging refers to the method of dividing the larger data set into two or more overlapping subsets and fitting a logistic regression model on each subset before aggregating the parts together. This work aims to show a single scalar that would be able to predict the correlation between the two unaggregated estimates. Drawing from previous results in linear regression and the unbagged setting, we were able to successfully infer this result.

Prevalence and Associations of ARID2 Mutations in Melanoma

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Project Year: 2023
Student(s):   Daniella Shomshonov | SUNY at Binghamton NY  
Mentor(s): Jian Cao - Rutgers Cancer Institute of New Jersey
In this study we focused on the prevalence of ARID2 mutations in publicly available datasets and their association with known melanoma genetic features. Leveraging publicly available data from cBioPortal, we conducted comprehensive mutation analysis and statistical tests to explore the associations between ARID2 mutations and other melanoma-associated mutations, including BRAF, NF1, NRAS, KRAS, and HRAS. Our findings shed light on the potential functional significance of ARID2 mutations in melanoma biology

Simplifying explicit equations of fake projective planes

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Project Year: 2023
Student(s):   Mattie Ji | Brown University RI  
Mentor(s): Lev Borisov - Mathematics
A fake projective plane is a complex surface with the same Betti numbers as CP2 but not biholomorphic to it. We studied the fake projective plane P2fake = (a = 7, p = 2, ∅, D327) in the Cartwright-Steger classification. In this summer, we exploit the large symmetries given by Aut(P2fake) = C3xC7 to construct a simpler embedding of this surface into CP5 as a system of 56 sextics with coefficients in Q(\sqrt{-7}). For each torsion line bundle T ∈ Pic(P2fake), we also compute and study the linear systems |nH + T| with small n, where H is an ample generator of the Neron-Severi group. Finally, we constructed explicit equations of the fake projective plane $(a = 7, p = 2, ∅, D3X7), a closely related fake projective plane to P2fake.

Some studies on topics relevant to cohomology rings of flag varieties

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Project Year: 2023
Student(s):   Yakov Burton | Rutgers University-New Brunswick NJ  
Mentor(s): Anders Buch - Mathematics
This project was focused on flag varieties and computations in cohomology rings. I studied symmetric polynomials and covered different basis for this family of functions, as well as computations in one of these basis, the set of Schur functions. I also studied root systems, Weyl Groups and the classification of irreducible root systems. Finally, I worked on some actual cohomology rings and how they connect to root systems and multiplication of Schur functions.

Sorting Probability for Linear Extensions

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Project Year: 2023
Student(s):   Molly MacDonald | University of Notre Dame IN  
Mentor(s): Swee Hong Chan - Mathematics
The 1/3−2/3 Conjecture states that in every finite partially ordered set that is not totally ordered, there exists a pair of elements x and y with the property that at least 1/3 and at most 2/3 of the linear extensions of the partial order place x earlier than y. This conjecture has become one of the more prominent conjectures in set theory, and while no one has succeeded to prove such a result for all partially ordered sets, there are partial results for posets with certain properties. Our goal this summer was to prove that this conjecture holds for a specific set P3,n. which consists of {x1, y1, z1, x2, y2, z2, ..., xn, yn, zn} where xi≤xi+1, yi≤yi+1, zi≤zi+1, and xi≤yi≤zi, for all i.

Tight Bounds for Approximate Minimum Cuts in Insertion-Only Streaming Model

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Project Year: 2023
Student(s):   Alexandro Garces | Massachusetts Institute of Technology MA  
Mentor(s): Sepehr Assadi - Computer Science
Finding the minimum cut of a graph has been studied extensively on its own as a fundamental graph problem and as a tool for understanding other important problems such as connectivity and network flow. We studied the minimum-cut problem in the insertion-only semi-streaming model, where edges of G are received one at a time in an input stream and space usage is confined to Õ(n) = O(n poly log n) bits. As such, we can't afford to store the entire graph at once, so we are forced to compute an approximation to the minimum cut. We present improved upper bound and lower bound results for the (1+ε)- approximation to the minimum cut problem in the insertion-only semi-streaming model. We achieve new results that are optimal in their dependence on ε.

Truth Learning in a Social Setting

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Project Year: 2023
Student(s):   Jordan Chong | New York University NY   ,   Matt Lu | Washington University in St Louis MO  
Mentor(s): Jie Gao - Computer Science
We are interested in designing social networks which support the spreading of true information while limiting the ability for disinformation to spread. Having access more information doesn't always lead to discovering the truth as phenomena known as "information cascades" can lead to herd mentality propagating false information. Representing social networks with graphs, we studied two areas: high degree and low degree graphs. High degree graphs have the ability to quickly aggregate information and decipher truth into a few high value nodes with many neighbors. One example we studied was the preferential attachment model by Albert-Barabasi. In low or constant degree graphs, a more nuanced approach is needed to allow truth learning to succeed. We studied two examples of these: an adjusted Connected Caveman graph and a grid structure. Moreover, in large-scale networks such as news portals or content-sharing platforms, having a solid grasp of the underlying network structure and ordering helps optimize the process of dissemination and improve the accuracy of information spreading. In modern society, misinformation is a pervasive issue that can have large societal consequences. As we strive to combat the spread of false information, studying the ways information spreads in a variety of networks becomes crucial.

Uncovering the process of ribosome formation

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Project Year: 2023
Student(s):   Colton Fitzjarrald | University of Missouri-St Louis MO  
Mentor(s): Wilma Olson - Chemistry and Chemical Biology
The ribosome is responsible for creating proteins. Understanding how the ribosome itself folds into its final 3-dimensional structure is critical to deepening our understanding of the mechanisms behind proteins, and thus life itself. There is much research that has been done in understanding the 3-dimensional structure of ribosomes, however many of these studies have fallen short in giving an intuitive understanding of the 3D structure. In this project, we used computational models to simulate large subunit ribosomal RNA of Escherichia Coli.

Visual Utility of Differentially Private Scatterplots under US Census data

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Project Year: 2023
Student(s):   Martha-Victoria Parizot | Harvey Mudd College CA  
Mentor(s): Anand Sarwate - Electrical and Computer Engineering
In data-driven research fields like healthcare and technology, access to sensitive individual information is crucial for generating valuable insights. However, privacy regulations pose challenges for publishing such data, hindering researchers' access. Differential privacy has emerged as a promising solution, allowing researchers to learn from sensitive data while protecting individual privacy. This research focuses on generating differentially private scatterplots, a common data visualization tool, while retaining visual utility. The approach combines strategies of partitioning data, adding calibrated noise, and post-processing to suppress noise. The study explores factors like ε (privacy level), algorithms, bin size, data distribution, and sample size to understand their impact on visual utility. Two small datasets are used, derived from the US Census and World Health Organization, with direct application for Differential Privacy. The results highlight the trade-offs between privacy and visualization integrity, aiding researchers in making informed decisions to balance privacy and data visualization accuracy. Future directions include exploring more sophisticated point regeneration techniques and integrating differentially private heatmaps techniques to improve visual accuracy

A Bohmian Analysis of Energy and Momentum in 1-D Scattering of Relativistic Particles

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Project Year: 2022
Student(s):   Kabir Narayanan | Brown University RI   ,   Abigail Perryman | The University of Texas at Austin TX  
Mentor(s): Shadi Tahvildar-Zadeh - Mathematics
In this paper, we lay the groundwork for a formal justification of the plane wave assumptions underlying Arthur Compton's scattering calculation. Using a recent relativistic formulation of Bohmian mechanics, we start by examining the behavior of a single free photon and a single free electron. We offer significant evidence that the electron wave function becomes asymptotically plane wave-like, meaning the particle's momentum and energy approach fixed values as assumed in Compton's calculations. We examine the effects the wave function's initial parameters have on the behavior of particle trajectories, and we explore different ways to visualize trajectories and this asymptotic behavior prior to interaction.

A reanalysis of the US Census Bureau's Disclosure Avoidance System

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Project Year: 2022
Student(s):   Leah Ghazali | University of Richmond VA  
Mentor(s): Ruobin Gong - Statistics
Differential privacy is a promising mechanism for privacy protection, providing a way to calculate the maximum amount of privacy risk when an individual's data is included in a study. The US Census Bureau recently incorporated differential privacy to protect census data using the Disclosure Avoidance System (DAS). The DAS essentially consists of 2 steps: noise injection and post-processing. Researchers have conducted analyses on the DAS to examine its effectiveness and found numerous biases, which they specifically credited to the post-processing step. However, the US Census Bureau did not release the census data following the noise injection but before the post-processing, so there is no way to differentiate the effects of each step. Through the production of noisy data and replication of research by Kenny et al. (2021, Science Advances), we found that the biases found were due to post-processing. More importantly, we found that the analyses we replicated presented misleading results. When analyzing the error of the DAS, the researchers used a fitted error, which they calculated using a generalized additive model (GAM). The model's predictor variables included parameters plotted on the x-axis of their figures, essentially creating the illusion of stronger trends.We found that using the fitted model resulted in deceptive images that exaggerate the effect of the biases. By replicating their study, we found that the biases are still present, though less intense. We hope that our findings will provide a more accurate depiction of the biases of the DAS.

Approximation Algorithms for token swapping on Graphs

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Project Year: 2022
Student(s):   Samuel Hiken | Carleton College MN  
Mentor(s): Nicole Wein - DIMACS
Consider the following problem: we are given a graph with $n$ vertices and $n$ distinct tokens. Given two arrangements of the tokens on the vertices, we wish to know the length of the shortest possible sequence of "edge-swaps" needed to get from one arrangement to the other. Here, an edge-swap refers to the act of swapping tokens that lie on adjacent vertices. This problem, known as token swapping, is APX-hard, and a constant-factor approximation has been known since 2016. This summer, I studied the approximability of token swapping on general graphs.

Curvature Motifs in Connectome Data: Pitfalls with Path Motifs

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Project Year: 2022
Student(s):   Liron Karpati | University of Maryland-College Park MD  
Mentor(s): Jie Gao - Computer Science
Nervous systems are organized for efficient integration of information. It has been shown that, in neuronal connectomes of the C. Elegans worm, high degree nodes are highly connected to form what is called a "rich-club" structure. This rich-club structure allows different neurons to reach one another in relatively few edge hops. The rich-club organization is a global architectural feature of the C. Elegans connectome. It has yet to be explored how the local organization of the connectome is supporting integration integration. By using the notion of course Ricci curvature (a generalization of Ricci curvature) and a path motif analysis, we found that the C. Elegans connectome exhibits local weak-bridge structures. More importantly, our analysis highlights some of the critical pitfalls in hypothesis testing graph properties. These pitfalls motivate the need for a principled investigation into statistical methods for determining the significance of graph properties.

Data-Driven Security Measurements to improve Safety in NYC and NJ Mass Transit

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Project Year: 2022
Student(s):   Michael Bsales | University of Notre Dame IN   ,   Nithya Nalluri | The College of New Jersey NJ  
Mentor(s): Christie Nelson - Masters of Business and Science in Analytics and CCICADA
Public transit in America in recent years has suffered from attacks and crimes since they are very vulnerable to terrorist/mass casualty attacks. These vulnerabilities are due to the lack of strict screening and content policing, unlike security at airports. Although current public transit is designed to efficiently allow a way that it allows for passengers to quickly travel as needed, there is not a strong security system in place. Utilizing metro station security check systems (SCS) can achieve great scrutiny by transit authorities and the public due to their high throughput, risk factors, and a demand for safety. Modern SCS achieves safety through many layers of active and passive security checks. In order to implement strong security around public transit around America, it is important to understand the different types of transit stations and how they are operated, and the types of passengers that utilize them. This paper aims to develop an understanding of the current state of security check systems as applicable to high-traffic subway stations and what must change in the near future to establish effective security check systems in metro systems. By working toward creating a proof-of-concept risk analysis model using crime and other types of publicly available data on the NYC and NJ regions was used to make predictions on how transit stations are more at risk than others. With these predictions, it would be up to local governments and stations to implement more appropriate security measures.

Dimensionality Reduction Using Error-Correcting Codes

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Project Year: 2022
Student(s):   Lakshay Patel | University of California-Berkeley CA  
Mentor(s): Karthik Srikanta - Computer Science
The Johnson-Lindenstrauss lemma shows that for any n points in Rd and ε>0, there is a map into O(\log(n)ε-2)-dimensional Euclidean space that distorts the pair-wise ℓ2 distances of S by a multiplicative factor of at most (1+ε). This result is known to be optimal for ℓ2, whereas our understanding of dimensionality reduction in Hamming space is far from complete. In this project, we looked for dimensionality reduction methods in Hamming space, with a focus on an approach using the 7-4-3 Hamming code.

Equivalence between Restricted Non-interactive Statistical Zero-knowledge Classes

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Project Year: 2022
Student(s):   Jacob Gray | University of Massachusetts-Amherst MA   ,   Pengxiang Wang | University of Michigan-Ann Arbor MI   ,   Saachi Mutreja | University of California-Berkeley CA  
Mentor(s): Eric Allender - Computer Science

This project primarily focused on the class NISZK_L, introduced in a paper from 2020 done with REU students and Eric Allender. Little was known about this class beyond the results in that paper and results from REU students the following summer, mainly comprising of two complete problems, hardness results relating to MKTP, and equivalence to a weaker subclass of NISZK, NISKZ_AC^0. This summer, we expanded these results to primarily show equivalences between NISZK_AC^0, NISZK_L, and NISZK_PM, among an array of other related results.

Exploring Fine-Grained Buy-Many Mechanisms

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Project Year: 2022
Student(s):   Vikram Kher | University of Southern California CA   ,   George Li | University of Maryland-College Park MD  
Mentor(s): Ariel Schvartzman - DIMACS
Multi-item revenue-optimal mechanisms are known to be extremely complex, often offering buyers randomized lotteries of goods. In the standard buy-one model it is known that optimal mechanisms can yield revenue infinitely higher than that of any "simple" mechanism, even for the case of just two items and a single buyer.We build off of the manuscript of Assadi and Schvartzman, who first defined the notion buy-$k$ mechanisms, which smoothly interpolate between the classical buy-one mechanisms and the recently studied buy-many mechanisms. Buy-$k$ mechanisms allow the buyer to buy up to $k$ many menu options. Our main results are that the revenue gap with respect to bundling, an extremely simple mechanism, is bounded by O(2n·n2) for any arbitrarily correlated distribution D over $n$ items for the case of buyer's with arbitrary monotone valuations. This is a generalization of the known O(n2) bound for additive valuations. In addition to this, we also conjecture that there exists a distribution D over two items such that Buy(k)Rev(D) > Buy(k+1)Rev(D). We make partial progress towards proving this conjecture by providing a candidate distribution D and a candidate optimal mechanism Mk which we believe witnesses such a gap.

Exploring Trade-offs Between Compression and Accuracy in Neural Networks

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Project Year: 2022
Student(s):   Derek Montanez | Texas State University TX  
Mentor(s): Waheed Bajwa - Electrical and Computer Engineering
Machine learning, specifically neural networks, are at the heart of many important everyday jobs such as image classification, and natural language processing tasks. Advances in today's neural networks, have led to very impressive results in varying tasks, with the impressive results comes the disadvantage of very large neural network models, with parameters in the range of hundred millions to billions. There has been a lot of ongoing research to be able to provide solutions to the problem of compressing large neural network models, to perform as well as the original model or even better. The goal currently is to be able to compress deep neural networks to be able to deploy and use on edge devices, failure to deploy large models is inevitable due to memory and computational constraints on edge devices. We explores the trade-offs between compression and accuracy in neural networks using a neural network compression method 'Pruning Filters for Efficient ConvNets'. Neural network compression methods are used to reduce the memory space and computational costs of neural networks, and these methods include but are not limited to pruning, quantization, and knowledge distillation. The tests were conducted using the MNIST Fashion data-set, as well as the CIFAR10 data-set, both of which are image classification data-sets. The model SCNNB is a convolutional neural network that was used for conducting tests.

Extension of the FKG Inequality

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Project Year: 2022
Student(s):   Mihir Dhanakshirur | Indian Institute of Science  
Mentor(s): Siddhartha Sahi - Mathematics
The 1971 Fortuin-Kasteleyn-Ginibre (FKG) inequality for two monotone functions on a distributive lattice is well studied and has shown to have many applications in statistical mechanics and other fields of mathematics. In 2008 Sahi conjectured an extended version of this inequality, called (E_n) for all n>2 monotone functions on a distributive lattice. We considered a special version, namely (F_n) and examined its properties on the space on n monotone Boolean functions. We showed that (F_n) does not satisfy the quasi-concave property by formulating a counter-example. We proved that (F_3), over one-dimensional monotone Boolean functions, does not possess non-zero minima and we examined a method to prove a more general version.

Formalizing Elementary Analytic Number Theory in Lean

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Project Year: 2022
Student(s):   Emma Hasson | Bard College at Simon's Rock MA  
Mentor(s): Alex Kontorovich - Mathematics
We formalized essential theorems in Elementary Analytic Number Theory into the Lean 3 Theorem Prover, with the eventual goal of contributing the work to the already vast library or formalized math in Lean called Mathlib. We focused on Dirichlet's Hyperbola Method as applied to the Divisor function beginning with a mathematical description of the theorem.

Generating Meta-DAGs

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Project Year: 2022
Student(s):   Ishaan Ivaturi | Rutgers University-New Brunswick NJ  
Mentor(s): James Abello - Computer Science
Graph Cities are a new framework for visualizing, exploring, and interpreting graphs that have the order of billions of edges. Graphs of this size are simply too much data to be meaningfully processed by humans, from the macro scale down to the actual semantics. Graph cities break such "huge" graphs into metaphorical buildings and meta-nodes that can be individually explored. In this manner, large graphs can be explored at different levels of detail to extract patterns at both the macro and micro scales. The focus of this paper is on speeding up the computation of the meta-graphs within each building, allowing the graph city to be explored interactively rather than needing to be precomputed.

Hardness Amplification of One Way Functions

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Project Year: 2022
Student(s):   Saachi Mutreja | University of California-Berkeley CA  
Mentor(s): Periklis Papakonstantinou - Management Science and Information Systems
In this project, I mainly explored how the hardness of one way functions is preserved. In particular, given a certain circuit C that computes a one way function that has certain hardness, what can one say about the existence of circuits that compute one way functions that are harder? We investigated a possible approach to prove lower bounds on the hardness of one way functions whose existence is implied by the existence of the circuit C.