Gabriela Jarzębowska-Lipińska is an Assistant Professor at the School of Liberal Arts, University of Warsaw, specializing in Critical Animal Studies and Environmental Humanities. Her work examines interspecies relations through historical, cultural, and sociological lenses. Education: PhD in Cultural and Religious Studies (2020, University of Warsaw) International Experience: Fulbright Junior Fellow (2018–2019), visiting scholar at Linköping University (2016) and UC Santa Cruz (2019) Research focuses on animal representation in culture, environmental conflict rhetoric, and modernity's impact on human-animal relations. She explores topics like rat extermination as cultural practice, interspecies ethics in state socialism, and posthumanist collectives. Her recent publications address factory farming as capitalist imagination failure, animal biographies, and urban rodent media narratives. Key scientific contributions include the concept of "species cleansing" and critical analysis of sanitary discourse in totalitarian regimes. She advocates for animal welfare frameworks that challenge anthropocentric policies.
Arya Mazumdar is a tenured Professor of Data Science and Computer Science at the Halıcıoğlu Data Science Institute (HDSI) , part of the School of Computing, Information and Data Sciences at the University of California San Diego (UCSD). He also holds affiliations with the Computer Science and Engineering and Electrical and Computer Engineering departments at UCSD. Previously, he was an Assistant and Associate Professor at the University of Massachusetts Amherst (2015–2021), and held a postdoctoral position at MIT (2011–2012). He is an IEEE Distinguished Lecturer (2023–2024) and recipient of the NSF CAREER Award (2015–2020). Education : PhD in 2011 from the University of Maryland, College Park (advisor: Alexander Barg) Postdoctoral scholar at MIT (2011–2012, advisor: Greg Wornell) Internships at IBM Almaden (2010) and HP Labs (2008) Research Interests : Focus on algorithmic and statistical aspects of machine learning, error-correcting codes, optimization, signal processing, and distributed systems. Key areas include clustering algorithms, compressed sensing, federated learning, and information-theoretic foundations of data science. He has contributed to theoretical guarantees for learning mixtures, distributed optimization, and robust coding schemes. Recent Articles Trends : Recent work emphasizes distributed optimization (e.g., vqSGD, Byzantine-resilient algorithms), sparse recovery in high-dimensional models, and theoretical foundations of learning (e.g., support recovery, parameter estimation). His research bridges information theory and machine learning, with applications in storage systems and large-scale data processing. Awards & Roles : 2020 EURASIP JASP Best Paper Award Co-PI and co-leader of the NSF AI Institute for Learning-Enabled Optimization at Scale Editorships: IEEE Transactions on Information Theory and Foundations and Trends in Communications Grants & Labs : Leads the EnCORE Institute as UCSD Site Lead, focusing on theoretical perspectives of large language models and computational-statistical gaps. Active in organizing workshops on topics like LLMs, clustering, and distributed optimization. His research is funded by NSF and industry collaborations. Teaching : Courses include Algorithms for Data Science , Probability and Statistics for Data Science , and Coding Theory , emphasizing foundational theory and scalable methods.
Adam Doupé is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) and Director of the Center for Cybersecurity and Trusted Foundations (CTF). He holds a Ph.D. and M.S. in Computer Science from the University of California, Santa Barbara. His research focuses on cybersecurity, vulnerability analysis, web security, and hacking competitions. Notable awards include the NSF CAREER Award (2017), Best Teacher Award, and Outstanding Assistant Professor Award from ASU's Fulton Schools of Engineering. Education: Ph.D. and M.S. in Computer Science, UC Santa Barbara (2014, 2009). Research emphasizes automated vulnerability analysis, binary analysis, and cybersecurity education. Key contributions include frameworks like SCAMNet and SENSAI for fraud detection, and tools like Fuzz to the Future for uncovering future vulnerabilities. Recent articles highlight advancements in phishing ecosystem analysis, browser fingerprinting mitigation, and compiler-aware decompilation. Awards reflect his impact in both teaching and research. Advising and grants support his work in secure systems and ethical hacking. He co-leads the SEFCOM lab with Drs. Ahn, Shoshitaishvili, Wang, and Bao, and hosts CTF Radiooo for cybersecurity discussions.
Joel Blanchard, PhD, is an Associate Professor in the Departments of Neuroscience and Cell, Developmental & Regenerative Biology at the Icahn School of Medicine at Mount Sinai. He is also an Investigator at the Black Family Stem Cell Institute and the Ronald Loeb Center for Alzheimer’s Disease. His research focuses on developing human brain models using induced pluripotent stem cells to study and treat neurodegenerative diseases. Education: BS, St. Lawrence University MLA, Harvard University PhD, The Scripps Research Institute & MIT Research Interests: Dr. Blanchard’s lab is dedicated to understanding the genetic and environmental vulnerabilities that lead to neurodegeneration. His team develops cutting-edge 3D brain organoids and blood-brain barrier models to investigate diseases like Alzheimer’s, Parkinson’s, and ALS. A major focus is on the role of APOE4 in myelination, cholesterol metabolism, and vascular dysfunction. Scientific Awards: Friedman Brain Institute Scholar Award (2021) ISSCR Merit Award (2019) Glenn Foundation Postdoctoral Fellowship (2018) California Institute for Regenerative Medicine Pre-doctoral Fellowship (2011) Funding & Grants: Dr. Blanchard’s lab is currently funded by NASA, NIH, and the Cure Alzheimer’s Fund. Active projects include the development of miBrain (multicellular integrated brain tissue), blood-brain barrier modeling, and APOE4-mediated mechanisms of neurodegeneration. Lab & Team: The Blanchard Lab is a multidisciplinary team of postdocs, graduate students, and research associates. The lab is actively recruiting postdoctoral fellows and is known for its collaborative, innovative environment focused on translational neuroscience.
Bernhard von Stengel is a Professor of Mathematics at the Department of Mathematics, London School of Economics and Political Science . His work bridges game theory, computational complexity , and mathematical economics , with a focus on equilibrium computation and algorithmic aspects. Developed Game Theory Explorer , open-source software for analyzing strategic and extensive-form games. Organized major workshops like What is Strategic Information? (2024) and Game Theory and Machine Learning (2023). Authored the textbook Game Theory Basics (Cambridge University Press, 2021). His research spans zero-sum games , correlated equilibrium , inspection games , and communication over noisy channels . Recent work includes characterizing the Condorcet dimension of metric spaces (2024) and stable-set bounds for Nash equilibria in bimatrix games. He has collaborated with institutions like the Game Theory Society and contributed to public discourse via talks on algorithms' societal impact (2021) and game theory in politics (2020).
Ivana Nikoloska is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She is affiliated with the Center for Quantum Materials and Technology Eindhoven and BIASlab (Bayesian Intelligence and Stochastic Agents Lab). Her academic career includes prior roles as a Research Associate at King’s College London and a Visiting Researcher at Aalborg University. PhD: Monash University, Australia (2023) MSc & Dipl.-Ing.: University of Ss. Cyril and Methodius, North Macedonia Research Interests span foundational and applied machine learning, quantum computing, and information/communication engineering. Her work focuses on integrating Bayesian inference, variational methods, and quantum technologies for tasks like signal processing, channel estimation, and power control optimization. Quantum Machine Learning Bayesian Simulation-Based Inference Meta-learning for Wireless Systems Hybrid Quantum-Classical Architectures Stochastic Signal Processing Quantum Sensing & Metrology Notable Trends in Publications include quantum recurrent neural networks with adaptive gating, Bayesian frameworks for quantum sensing, and meta-learning applications in communication systems. She explores variational inference for planning and robust algorithms for channel estimation under non-ideal conditions.
Aravind Machiry is an Assistant Professor at Purdue University's Electrical and Computer Engineering Department and a founding member of the Purdue Systems and Software Security (PurS3) Lab . His research focuses on system security, particularly vulnerability detection, prevention, and secure system development using static/dynamic program analysis, fuzzing, type systems, and machine learning. Designing practical solutions for software and embedded system security Recipient of NSF CAREER and Amazon Research awards Active participant in SPLASH 2025 as OOPSLA Review Committee member His recent work includes automated vulnerability detection in embedded software, spatial memory safety enhancements, and security analysis of GitHub workflows. He has received recognition for his research through multiple distinguished paper awards and industry funding. Selected scientific awards include NSF CAREER Award (2024) Amazon Research Award (2022) Test of Time Award at FSE 2023 for DynoDroid Distinguished Paper Award at OOPSLA 2022 for 3c Qualcomm Innovation Fellowship (2025) His research team has developed frameworks like ARGUS for taint analysis of CI/CD workflows and FuzzUEr for UEFI interface fuzzing, discovering hundreds of critical vulnerabilities in open-source projects and thousands of command injection flaws in GitHub repositories.
Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.
Priya Narasimhan is a Professor of Electrical & Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. Her research focuses on dependable distributed systems, fault-tolerance, embedded systems, mobile systems, and sports technology. She leads the Intel Science and Technology Center in Embedded Computing (ISTC-EC) and founded YinzCam, a CMU spin-off providing mobile live streaming to sports venues. She holds multiple awards, including the Sloan Fellowship and NSF CAREER Award. Education: Ph.D. and M.S. in Electrical & Computer Engineering from UC Santa Barbara. Notable roles include former CTO of Eternal Systems, Director of Intel Labs Pittsburgh, and Director of CMU's CyLab Mobility Research Center. Research spans failure diagnosis in distributed systems, live upgrades, mobile cloud computing, football technology, assistive tech for the blind (Trinetra), and civic tech (iBurgh). Over 30+ students advised across Ph.D., M.S., and undergraduate programs. Active in entrepreneurship, teaching (courses like 18-349 Embedded Systems), and industry collaborations.
Lars Davidson is a Professor in the Department of Fluid Dynamics at Chalmers University of Technology. His research focuses on numerical simulations of fluid flow and heat transfer, with an emphasis on turbulence modeling for Large Eddy Simulation (LES) and hybrid LES/RANS methods. He has developed computational codes CALC-BFC and CALC-LES based on finite-volume techniques, and recently integrated machine learning to enhance wall functions and turbulence models. Key projects include Hybrid LES/RANS for wall-bounded flows Machine learning applications in fluid dynamics Aeroacoustic noise reduction in automotive and aerospace systems Wind turbine load analysis in forested regions . His publications span 302 articles in journals and conferences, with recent work on Neural networks for turbulence closure Plasma actuators for drag reduction Lattice Boltzmann wall-modeled LES . Collaborations include teams at Volvo, Siemens, and international research groups.
Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.
Pearl Sandick is a Professor in the Department of Physics and Astronomy and Interim Dean in the College of Science at the University of Utah. She has previously served as Associate Chair of the Department of Physics and Astronomy and Associate Dean for Faculty and Research in the College of Science. Her academic journey at the University of Utah began in 2011 as an Assistant Professor, progressing to Associate Professor in 2017, and achieving the rank of Professor in 2022. Her educational background includes: BA in Mathematics from New York University (2003) PhD in Physics from the University of Minnesota (2008) Sandick is a theoretical particle physicist whose research focuses on physics beyond the Standard Model, with particular emphasis on dark matter. Her work spans theoretical modeling, connections to astrophysical observations, and implications for experimental detection. She investigates various dark matter candidates and their potential signatures in current and future experiments, including collider searches, direct detection experiments, and indirect detection through astrophysical observations. Her research also extends to connections between particle physics and cosmology, including early universe phenomena and implications for cosmic structure formation. She has developed computational tools like MADHAT for dark matter analysis and has made significant contributions to understanding how stellar evolution can constrain axion physics. Her scholarly contributions have been recognized with several prestigious awards: University of Utah Early Career Teaching Award (2016) University of Utah Distinguished Mentor Award Linda K. Amos Award for Distinguished Service to Women University of Utah Presidential Scholar Sandick has been actively involved in mentoring graduate students, as evidenced by her teaching of PhD thesis research and Master's research courses. She has secured significant research funding from the National Science Foundation and other agencies to support her work on dark matter, dark energy, and new physics. Her grant portfolio includes projects on theoretical particle physics, connections to astrophysical observations, and studies on graduate education reform following a departmental tragedy. She is an active member of the American Physical Society, having served as Chair of the regional Four Corners Section in 2021-2022, demonstrating her commitment to the broader physics community and leadership in her field.
Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications
Ken Duffy is a Professor and Chair of the Department of Mathematics at Northeastern University, with a joint appointment in the Department of Electrical and Computer Engineering. He joined Northeastern in 2023 and previously served as Interim Chair of the latter department. Previously, he was a professor at the National University of Ireland, Maynooth, where he directed the Hamilton Institute (2016–2022) and co-directed the Science Foundation Ireland Centre for Research Training in Foundations of Data Science. He earned a PhD in Mathematics from Trinity College Dublin. His research focuses on collaborative, multi-disciplinary algorithm design using probability and statistics, with applications in digital circuits, DNA, and network coding. Notable contributions include the Royal Statistical Society’s Applied Probability Section (co-founded in 2011) and numerous award-winning papers in IEEE conferences and journals. Recent work emphasizes decoding algorithms like GRAND (Guessing Random Additive Noise Decoding), applied to error correction, wireless systems, and biomedical imaging. His articles address topics like soft-output decoding, interference mitigation, and cellular lineage tracing. Awards: Best Paper Awards (IEEE ICC 2015, IEEE TNSE 2019), COMSNETS Best Demo (2022–2023), and the IEEE Ellersick Award (2024). Advising: The SFI Centre he co-directed funded over 120 PhD students. Labs/Teams: Hamilton Institute, Royal Statistical Society’s Applied Probability Section, and collaborative projects in cellular dynamics and secure communication.
Edward Kim is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics. His research spans computer vision, sparse coding, neuromorphic computing, and AI, with a focus on neuro-inspired machine learning and robust, interpretable models. Research Interests: Computer Vision Sparse Coding and Dictionary Learning Neuromorphic and Spiking Neural Networks Explainable and Adversarially Robust AI Multimodal Learning Medical Image Processing His recent publications highlight a strong trend in developing biologically inspired, robust, and interpretable machine learning models, particularly using sparse coding and spiking neural networks. Themes include adversarial robustness, model confidence calibration, and cross-modal integration. His work often bridges neuroscience and AI, aiming to create more human-like and trustworthy systems. Scientific Awards: NSF CAREER Award (2019) Longsview Fellow (collaborative project, 2021) Dr. Kim advises several graduate students in the SPARSE Lab and has secured significant research funding from the NSF, DARPA, and the Bill & Melinda Gates Foundation. His grants focus on ethical AI, racial bias in ML, and digital health platforms. He also contributes to academic leadership as a Provost Fellow at the Drexel Solutions Institute and co-chair of computer vision tracks at major conferences. Labs and Teams: He leads the SPARSE (SPiking And Recurrent SOFTwarE) Coding Lab, which investigates biologically inspired learning models beyond traditional deep learning. The lab integrates neuroscience principles to improve stability, interpretability, and robustness in AI systems.
