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Krishnaiyan ThulasiramanView profile

Professor

• Graph Theory
• Combinatorial Optimization
• Algorithms
• +7 more

Krishnaiyan Thulasiraman is a Professor and Hitachi Chair Emeritus at the School of Computer Science, University of Oklahoma. He holds adjunct and emeritus positions at the University of Waterloo (Electrical and Computer Engineering) and Concordia University (Electrical and Computer Engineering). His career spans over five decades, including roles as Chair of Electrical and Computer Engineering at Concordia (1981–1994) and faculty appointments at IIT Madras (1965–1981). Education: PhD (1968), M.S. (1965), and B.E. (1963) in Electrical Engineering from institutions in India, including the Indian Institute of Technology Madras (IITM). His research focuses on graph theory, combinatorial optimization, algorithms, and their applications to fault diagnosis, network science, and VLSI systems. He pioneered work in network science and engineering, with contributions to logical topology design, survivable networks, and graph-based circuit analysis. Research interests span graph theory applications in circuits, systems, and computing; fault-tolerant network design; and network science fundamentals. His work bridges theoretical foundations with practical applications in WDM optical networks, power systems, and distributed computing systems. Key awards include the IEEE CAS Society Technical Achievement Award (2006), AAAS Fellowship (2007), and recognition as a 2017 Pioneer of Circuits and Systems. He has authored/co-authored over 100 papers, three books, and edited a major handbook. Professional roles include Deputy Editor-in-Chief of IEEE Transactions on Circuits and Systems, and leadership in IEEE technical committees. Collaborations span academia and industry, with partnerships at institutions like Arizona State University, Tsinghua University, and the Tokyo Institute of Technology. His research teams have addressed challenges in network reliability, algorithmic graph theory, and distributed systems.

Ankur MoitraView profile

Professor

• Theoretical Computer Science
• Machine Learning
• Algorithms
• +3 more

Ankur Moitra is the Norbert Wiener Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Electrical Engineering and Computer Science and the Theory of Computation research group. His work focuses on foundational aspects of algorithms, machine learning, and theoretical computer science, with applications to cryptography, quantum computing, and statistical learning. He leads research initiatives in algorithmic robustness, tensor decomposition methods, and reinforcement learning. Moitra’s research spans algorithm design, computational complexity, and probabilistic models. Notable contributions include robust learning algorithms for mixtures of distributions, tensor decomposition techniques, and theoretical analyses of reinforcement learning and adversarial machine learning. His work bridges mathematical theory with practical computational challenges, often addressing algorithmic guarantees under noise or adversarial conditions. His recent publications highlight advancements in areas such as cryptographic watermarking, quantum Hamiltonian learning, and efficient sampling from complex distributions. Despite no explicitly listed awards, his contributions have influenced major areas of theoretical computer science. His research group collaborates across MIT’s School of Science and the Computer Science and Artificial Intelligence Laboratory (CSAIL).

• Computer Vision
• Deep Learning
• Object Detection
• +3 more

Chunluan Zhou is a researcher at Nanyang Technological University (NTU), Singapore, with a PhD in Computer Vision and Deep Learning. His research focuses on object detection, pedestrian detection, transformer-based tracking, and occlusion reasoning. He has published extensively in top-tier conferences and journals such as ICCV, CVPR, ECCV, TIP, and TCSVT. Education: PhD from NTU under Prof. Junsong Yuan and Prof. Kai-Kuang Ma, M.Eng from Zhejiang University, and B.Eng from Harbin Institute of Technology. Research Interests: Computer Vision and Deep Learning, with emphasis on object detection, occlusion reasoning, transformer networks, and video analysis. His work addresses challenges like heavily occluded pedestrian detection, weakly supervised learning, and scene-debiasing action recognition. Professional Contributions: Active reviewer for top conferences (CVPR, ICCV, ECCV, NeurIPS) and journals (TIP, TCSVT, PR). His recent research includes scene-debiasing open-set action recognition (SOAR), cyclic self-training for object detection, and transformer-based visual tracking methods like AiATrack. Labs/Teams: Collaborates with Prof. Junsong Yuan's research group, focusing on computer vision and deep learning applications.

Zvi GalilView profile

Professor

• Algorithms
• Complexity Theory
• Cryptography
• +3 more

Zvi Galil is a distinguished academic and former Dean of Computing at Georgia Institute of Technology (2010-2019). He holds the title of Storey Chair and serves as Executive Advisor for Online Programs. His academic journey includes leadership roles at Columbia University (Fu Foundation School of Engineering Dean, 1995-2007) and Tel Aviv University (President, 2007-2009). He earned degrees in Applied Mathematics from Tel Aviv University and a PhD in Computer Science from Cornell University. Galil’s research focuses on algorithms, complexity theory, cryptography, and stringology. He has authored over 200 papers and edited 5 books, with contributions to graph algorithms, parallel computing, and data structures. He is a Fellow of the ACM and American Academy of Arts and Sciences, and a member of the National Academy of Engineering. His work has influenced fields like online education through initiatives like OMSCS (Georgia Tech’s Online Master of Science in Computer Science). Educations: BS and MS (summa cum laude), Applied Mathematics, Tel Aviv University PhD, Computer Science, Cornell University His research trends span dynamic graph algorithms, real-time string processing, and scalable graph isomorphism techniques. He has also contributed to foundational areas like suffix trees and text indexing. His awards include the Columbia Great Teacher Award (2009) for pedagogical excellence. Awards: ACM Fellow Member, American Academy of Arts and Sciences Member, National Academy of Engineering Columbia Society of Graduates Great Teacher Award Galil advises on online education programs and collaborates with the Algorithms and Randomness Center (ARC) at Georgia Tech. He has held editorial roles at major journals and advised Oxford University Press on computer science publications.

Magdalena Regina Tyzlik-CarverView profile

Associate Professor

• Critical Data Studies
• Digital Communication
• Curating Data
• +7 more

Magdalena Regina Tyzlik-Carver is an Associate Professor of Digital Communication and Culture at Aarhus University’s Department of Digital Design and Information Studies. Her research focuses on curating data, critical data practices, posthuman theory, and new materialism, with an emphasis on digital transformation driven by AI, semantic web, and data infrastructures. She co-directs the Centre for Critical Data Practices and is affiliated with the Digital Aesthetics Research Centre and Environmental Media and Aesthetics programs. Her work includes participatory methods for data practices beyond BigTech extractivism, as well as exhibitions like Fermenting Data and Ghost Factory . She has co-authored Boundary Images (2023) and edited Executing Practices (2018). Her research explores speculative methods, open data principles, and knowledge graphs to reimagine data futures. She has curated exhibitions addressing networked images, automated systems, and digital art history. Key projects include Fermenting Data (2021), ScreenShots (2019), and Corrupting Data (2017). She holds a PhD and is an associate researcher at London South Bank University’s Centre for the Study of the Network Image. Recent publications include speculative curatorial frameworks in Companion to Curatorial Futures (2025) and explorations of distance in digital spaces ( Everything Is a Matter of Distance , 2025). Her work bridges theory and practice, advocating for inclusive, ethical data practices through exhibitions, academic writing, and collaborative research initiatives.

Qin ZhangView profile

Professor

• Algorithms for Big Data
• Theoretical Foundations of Machine Learning
• Quantum Information and Computing
• +4 more

Roles and Affiliations: Professor of Computer Science at Indiana University Bloomington, Adjunct Professor in Mathematics. Previously at IBM Almaden Research Center and Aarhus University's Center for Massive Data Algorithmics. PhD from HKUST in Computer Science and Engineering. Education: PhD in Computer Science, HKUST, advised by Mordecai Golin and Ke Yi Research Interests: Focuses on algorithms for big data, including communication-efficient distributed computation, streaming/sketching algorithms, and quantum data management. Explores theoretical foundations of machine learning, particularly distributed learning. Leads projects funded by NSF grants on parallel reinforcement learning, distributed graph algorithms, and noisy data processing. Publications and Awards: Over 100 papers in top venues like FOCS, SIGMOD, and NeurIPS. Won the Best Paper Award at SPAA 2017 for distributed clustering work. Invited to special journal issues for top conferences. Teaching and Grants: Teaches advanced courses on sublinear algorithms and algorithm design. PI on NSF grants totaling over $2M, including collaborative projects on distributed computing and epidemiological modeling. Advises PhD students in areas like quantum algorithms and reinforcement learning. Service: Served on program committees for SIGMOD, NeurIPS, ICML, and PODS. Organizes theory seminars and Midwest Theory Day workshops.

Michele CosciaView profile

Associate Professor

• Network Science
• Complex Networks
• Digital Humanities
• +10 more

Michele Coscia is an Associate Professor in the Department of Computer Science at the IT University of Copenhagen (ITU), where he conducts research at the intersection of network science, digital humanities, and data analytics. He leads the NERDS research group and supervises PhD students and postdoctoral researchers working on financial crime detection, archaeological networks, and work environment modeling. PhD in Computer Science, University of Pisa (2012) Former researcher at the Center for International Development (CID), Harvard University (6 years) Visiting researcher at Barabási Lab, Northeastern University His research focuses on developing and applying network science methodologies such as noise-corrected backboning , node attribute analysis , and network variance to study complex systems. His work spans diverse domains including: Archaeology : Inferring social and biological relationships from material culture at Neolithic sites like Çatalhöyük. Cultural Analytics : Mapping Italian music networks, analyzing Wikipedia’s gender bias, and studying ideological polarization on social media. Social Media Dynamics : Investigating meritocracy vs. topocracy, intolerance feedback loops, and information virality on platforms like Reddit and Twitter. Sports Analytics : Analyzing predictability trends in team sports and the impact of economic systems on league competitiveness. His publications appear in high-impact journals such as Science Advances , EPJ Data Science , and Applied Network Science . He is the author of The Atlas for the Aspiring Network Scientist , a comprehensive open-access textbook now in its second edition, which covers graph theory, machine learning on graphs, and statistical foundations of network analysis. Recent trends in his work show a growing emphasis on interdisciplinary applications of network science, particularly in archaeology and cultural studies, often in collaboration with institutions such as Aarhus University and the National Research Center for Work Environment. His research consistently promotes open science, with datasets and code publicly shared. Co-PI on a Villum Synergy project applying network analysis to Roman Empire archaeological data Active contributor to the CUDAN (Cultural Data Analytics) community Developing methods for uncertain and incomplete network data Michele Coscia’s work demonstrates a strong commitment to methodological innovation and real-world impact across the humanities, social sciences, and computational domains.

• Computational Biology
• Bioinformatics
• Privacy-Preserving Data Mining
• +5 more

Kana Shimizu is a Professor at Waseda University’s Faculty of Science and Engineering, School of Fundamental Science and Engineering, and a leading authority on privacy-preserving algorithms for genome and biomedical data. Since obtaining her Dr.Eng. from Waseda, she has held positions at AIST and a visiting investigator role at Memorial Sloan-Kettering Cancer Center, advancing to full Professor at Waseda in 2018. Education: Dr.Eng., Waseda University M.Eng., Waseda University (Dept. of Information Science) B.Eng., Waseda University (School of Science and Engineering) Research Interests: Prof. Shimizu integrates cryptography with life-science data analytics, developing homomorphic encryption and secret-sharing protocols that enable secure genome search, private chemical database queries, and privacy-preserving machine learning. She also contributes to computational biology through algorithms for next-generation sequencing, structural bioinformatics, and prediction of intrinsically disordered proteins. Publication Trends: Her recent works (2022-2024) emphasize practical privacy technologies—secure range queries, function secret sharing, and gradient-clipping for synthetic genomes—while earlier high-impact papers established fast similarity search (SlideSort), binding-site databases (PoSSuM), and encrypted genomic search using the Burrows-Wheeler transform, collectively bridging algorithmic innovation and real-world biomedical privacy needs. Scientific Awards: KDDI Foundation Achievement Award 2022 MEXT Commendation for Science and Technology (Research) 2018 Multiple best-paper/demo awards at CSS, IIBMP, and domestic bioinformatics meetings Grants & Advising: She currently leads JSPS KAKENHI (S) on “Compressed Secure Computation for Large-Scale Data” and an AMED project on precise cancer-genome graphs. She serves as advisor/evaluator for JST PRESTO, AMED, NBDC, and the Tokyo High Court, and has supervised numerous bachelor theses at Waseda. Labs & Teams: Prof. Shimizu heads a laboratory within the School of Fundamental Science and Engineering at Waseda, collaborating with national centers (AIST, RIKEN) and international partners (MSKCC, Finland Tekes/AF), focusing on encrypted bioinformatics platforms and high-performance genomic algorithms.