Rachee Singh is an Assistant Professor of Computer Science at Cornell University, leading the sysphotonics research group. She concurrently serves as an Amazon Scholar within the SageMaker Hyperpod teams, specializing in large-scale machine learning infrastructure development for cloud environments. Her research focuses on photonic interconnect systems for server-scale, rack-scale, and long-haul communication networks, targeting performance optimization for distributed machine learning and planet-scale cloud workloads. Key specialties include optical network design, fault-tolerant WAN architectures, and energy-efficient datacenter interconnects, with strong emphasis on practical deployment in real-world systems. Her group bridges theoretical networking principles with applied AI infrastructure challenges. Recent publications demonstrate concentrated innovation in photonic network optimization for ML workloads, particularly in wavelength management, collective communication algorithms, and chip-to-chip photonic fabrics. This work spans optical physics, distributed systems, and machine learning, revealing a trajectory toward sustainable, high-performance AI infrastructure. Scientific recognition includes: Amazon Research Award (2023) Cisco Research Award Dr. Singh actively mentors graduate researchers including Jonathan Aimuyo, Byungsoo Oh, and Arjun Devraj, whose co-authored publications form the core of her group's output. Research funding is secured through competitive grants from the NSF (including a $1M award for chip-to-chip photonic fabrics), SRC/DARPA JUMP 2.0 program, Cisco, and Cornell's Atkinson Center for Sustainability. The sysphotonics group operates as Cornell's hub for photonic network systems research, developing programmable integrated photonics solutions and collaborating with Amazon on SageMaker Hyperpod for next-generation ML infrastructure.
Professor Tony Shardlow is affiliated with the Department of Mathematical Sciences at the University of Bath , UK. His research spans Stochastic Differential Equations , Bayesian Inverse Problems , Statistical Shape Modelling , and Numerical Analysis , with applications in data science, medical imaging, and computational physics. Labs/Teams : IMI (Institute for Mathematical Innovation), Prob-L@b (Probability Laboratory at Bath), SAMBa (EPSRC Centre for Doctoral Training in Statistical Applied Mathematics). Recent Research Trends : Focus on geometric shape analysis using flow fields, stochastic PDEs for particle dynamics, and Bayesian inference techniques in industrial and medical contexts. Collaborative work bridges computational mathematics with applications in hip dysplasia assessment and pesticide delivery systems. Advising : Supervised Fengpei Wang's PhD thesis on dimension reduction and Sinkhorn algorithms. Collaborates with researchers like N. D. F. Campbell and C. Poon. Teaching : Offers MA30170 - Numerical Solution of Elliptic PDEs.
Sebastiano Battiato is a Full Professor of Computer Science at the University of Catania's Department of Mathematics and Computer Science. He serves as Scientific Coordinator of the PhD Program in Computer Science and Deputy Rector for Strategic Planning and Information Systems at the University of Catania. As Director and Co-Founder of the International Computer Vision Summer School (ICVSS), he has significantly contributed to computer vision education globally. Education: Bachelor's degree in Computer Science (summa cum laude), University of Catania, 1995 Ph.D. in Computer Science and Applied Mathematics, University of Naples, 1999 Professor Battiato's research primarily focuses on Computer Vision, Imaging Technology, and Multimedia Forensics . His work spans from developing ISP algorithms for embedded devices to creating advanced techniques for image enhancement, coding, and forensic analysis. He has pioneered research in social media forensics, developing methods to determine if images have been processed through specific social platforms. His research has practical applications in assistive technologies, retail, digital marketing, and medical fields. His scholarly output shows a consistent focus on digital forensics and image processing, with an increasing emphasis on social media forensics in recent years. The research trajectory demonstrates progression from foundational image processing techniques to sophisticated forensic applications capable of addressing modern challenges like deepfakes and social media manipulation. Scientific Awards: 2017 PAMI Mark Everingham Prize for the series of annual ICVSS schools 2011 Best Associate Editor Award of IEEE Transactions on Circuits and Systems for Video Technology Professor Battiato has coordinated IPLab's participation in numerous large-scale research projects funded by national and international bodies as well as private companies. He has served as principal investigator on many international and national research projects, demonstrating strong leadership in securing research funding. His editorial work includes serving as associate editor for the SPIE Journal of Electronic Imaging and IET Image Processing Journal, and membership on several other editorial boards. As Director of IPLab research lab (http://iplab.dmi.unict.it), Professor Battiato leads a team focused on computer vision and digital forensics. The lab collaborates extensively with law enforcement agencies through iCTLAB, a university spinoff he founded that provides digital forensic services. IPLab is recognized for its contributions to image/video forensics, with techniques implemented in commercial forensic software like AMPED Authenticate.
Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.
Matthew Stephenson is a Lecturer at Flinders University's College of Science and Engineering, specializing in Artificial Intelligence applications for games. He leads the Data for Decisions initiative within the Factory of the Future Transdisciplinary Hub, focusing on AI-powered scenario generation for smart digital twins. Additionally, he is a member of IRL CROSSING, an international lab studying human-autonomous agent teaming dynamics. PhD in Computer Science (Australian National University, 2019) B.Sc.(Hons) in Computer Science (University of Canterbury, 2015) His research applies AI, Machine Learning, and Data Science to game domains, including intelligent agent development for physics-based environments, procedural content generation, and game analytics. He also investigates deceptive behaviors in multi-agent systems and leverages games as testbeds for real-world AI solutions. Recent publications focus on large language models for game benchmarking, physical reasoning challenges, and evolutionary game generation. Scientific awards include an honourable mention at Foundations of Digital Games (FDG'18). He supervises students in procedural generation, game AI, and physics-based task creation, with teaching roles in computational intelligence and neural networks courses.
Simon Crouch is a Senior Research Fellow in Biostatistics at the University of York's Health Sciences department. With a strong mathematical background from Cambridge and Warwick, he leads the analytics team within the Epidemiology and Cancer Statistics Group and works closely with the Haematological Malignancy Research Network (HMRN) and Cardiovascular Health team. His work focuses on statistical modeling of complex epidemiological data related to hematological malignancies. University of Cambridge: MA, MMath in Mathematics University of Warwick: PhD in Mathematics University of Lancaster: MSc in Medical Statistics Dr. Crouch specializes in the statistical modeling of complex epidemiological data, with particular focus on hematological malignancies. His research encompasses predictive modeling, event history analysis, and machine learning applications in cancer epidemiology. He has made significant contributions to understanding myelodysplastic syndromes, lymphoma classification, and survival analysis in blood cancers through population-based studies. His work often involves collaboration with international registries including the European Myelodysplastic Syndromes Registry (EUMDS) and the Haematological Malignancy Research Network. Analysis of his recent publications reveals a strong focus on myelodysplastic syndromes (MDS), with particular attention to risk stratification, survival analysis, and treatment outcomes. His work increasingly incorporates genomic and molecular data to refine disease classification and prediction models. The trend shows progression from purely statistical methodology development toward integrated translational research that combines clinical, genomic, and epidemiological data to improve patient outcomes. Extensive publication record with 113 research outputs including 66 articles, 21 patents, and numerous meeting abstracts Active participation in major international research consortia including MDS-RIGHT and ImmunAID Significant contributions to the development of statistical methodologies for cancer epidemiology Dr. Crouch actively supervises PhD students in mathematical and statistical modeling applied to cancer epidemiology, with particular interest in time-to-event models, complex longitudinal models, and simulation techniques. His research has been supported through multiple projects, including the European Myelodysplastic Syndromes Registry and the MDS-RIGHT project focused on facilitating informed decision-making in hemato-oncology. He contributes to the Advanced Health and Social Statistics module for postgraduate students at the University of York.
Benjamin Eysenbach is an Assistant Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science since 2023. His research focuses on developing principled reinforcement learning (RL) algorithms that improve simplicity, scalability, and robustness in state-of-the-art systems, particularly through probabilistic inference techniques. Ph.D., Machine Learning, Carnegie Mellon University (2023) B.S., Mathematics, Massachusetts Institute of Technology Research interests center on reinforcement learning with emphasis on long-horizon reasoning, exploration strategies, and robustness. He explores intersections with probabilistic inference and self-supervised learning to enhance RL capabilities. Recent publications highlight trends in contrastive learning for goal-conditioned RL, temporal distance modeling , and hierarchical control . Key themes include reward-free learning, scalable architectures, and uncertainty quantification in decision-making systems. 2025: Junior Faculty Award for Excellence in Research and Teaching, Princeton School of Engineering and Applied Science Eysenbach's work bridges theoretical foundations with practical implementations in AI training frameworks, emphasizing performance optimization and safety mechanisms.
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Leland Bybee is an Assistant Professor of Finance at the University of Chicago Booth School of Business . He leverages machine learning and natural language processing to address economic and financial questions, particularly focusing on belief measurement with applications to asset pricing and behavioral economics. Ph.D. in Financial Economics, Yale School of Management (2024) M.S. in Statistics, University of Michigan (2017) B.A. in Economics, University of Chicago (2013) His research integrates computational methods with economic theory to analyze: Textual analysis of business news for macroeconomic tracking Narrative-driven asset pricing models Memory-based belief formation using kernel methods Macroeconomic determinants of currency returns He has received multiple awards including: Dimension Fund Advisors Distinguished Paper Award BlackRock Applied Research Award HEC Top Finance Graduate Award The Brattle Group PhD Candidates Award EFA Engelbert Dockner Memorial Prize Bybee teaches Machine Learning in Finance and participates in finance seminars, contributing computational tools like regIPCA (Python) and changepointsHD (R) to the research community.
Adrian Weller is a Director of Research in Machine Learning at the University of Cambridge and Head of Safe and Ethical AI at The Alan Turing Institute, where he also serves as a Turing Fellow. He additionally directs the Trust and Society programme at the Leverhulme Centre for the Future of Intelligence. His career includes senior roles in finance and advisory positions for governmental AI ethics bodies. His research integrates technical and societal dimensions of artificial intelligence, with major foci including: Foundational ML : Statistical methods, high-dimensional inference, causality, and Monte Carlo techniques Trustworthy AI : Fairness, privacy, bias mitigation, and algorithmic transparency Applied Domains : Computer vision, reinforcement learning, and bio-applications of ML Socio-technical Systems : Policy frameworks, human perceptions of algorithms, and ethical deployment Notable recognition includes an MBE (2022) for pioneering contributions to digital innovation. His work actively informs UK and EU AI policy discussions.
Mariarosaria Taddeo is Professor of Digital Ethics and Defence Technologies at the Oxford Internet Institute (University of Oxford) , where she also serves as DPhil Programme Director for Information, Communication and the Social Sciences. She is a Senior Research Fellow at the Alan Turing Institute and holds advisory roles at institutions including the Ministry of Defence (UK) Ethics Advisory Panel , the BRAID Programme , and the Leonardo Foundation . Education PhD (Doctor Europeus) in Philosophy from University of Padua Mariarosaria Taddeo’s research spans Digital Ethics, Philosophy of Technology, Cybersecurity Ethics, and AI Governance , with a focus on national defence applications. Her work addresses trust in AI , cyber conflict regulation , and ethical frameworks for autonomous weapons . She has published extensively in journals like Nature , Science , and Minds and Machines , including studies on data philanthropy , digital well-being , and quantum technology ethics . Scientific Awards 2010 Simon Award for Outstanding Research in Computing and Philosophy 2016 World Technology Award for Ethics 2018 InspiringFifty: Top 50 Italian women in technology ORBIT listings (2018, 2020) of top 100 women in AI Ethics 2020 Women’s Forum for Economy and Society: Outstanding Rising Talents ComputerWeekly Top 100 Influential Women in UK Technology (2020, 2023) Her projects include the UK MOD-funded AI Ethics Principles initiative, the NATO Cooperative Cyber Defence Centre of Excellence ethical guidance project, and contributions to the PETRAS IoT Research Hub . She advocates for ethical AI implementation in defence and digital governance, influencing EU policy through the CEPS Task Force on AI and Cybersecurity .
Professor Marios C. Angelides is a full-time faculty member at Brunel University London , serving as Professor of Computing and Divisional Lead within the College of Engineering, Design and Physical Sciences . He leads the Creative Computing Research Group under the Institute of Digital Futures and contributes to the Digital Media department at Brunel Design School. BSc (First Class Honours) and PhD in Computing from the London School of Economics (LSE) Chartered Engineer (CEng) and Chartered Fellow of the British Computer Society (FBCS CITP) His research focuses on Creative Computing , specifically applying Machine Learning , Serious Gaming , and Cognitive Modeling to develop Smart IoT Applications . His work spans autonomous drone fleets for environmental monitoring, cybersecurity middleware for Android systems, wearable technology for lifestyle recommendations, and historical analysis of Alan Turing’s legacy in modern AI. Recent publications highlight trends in deploying Machine Learning for: IoT systems optimization Autonomous aerial/underwater vehicle coordination Deepfake detection using Turing’s Imitation Game Energy allocation in CubeSats via gaming mechanics Scientific recognition includes being Deputy Editor of The Computer Journal and runner-up for the 2016 Oxford University Press Wilkes Award . He has supervised PhD students in topics like Smart Android Middleware for Cybersecurity and Wearable Recommendation Systems , with active involvement in editorial boards and international conferences.
Miklos Z. Racz is an Assistant Professor at Northwestern University with a joint appointment in the Department of Computer Science and the Department of Statistics and Data Science. He is affiliated with the IDEAL Institute. Previously, he was an Assistant Professor at Princeton University (ORFE Department) and a postdoc at Microsoft Research. His research focuses on probability, statistics, computer science, and information theory, with emphasis on combinatorial statistics, discrete probability, and applied probability. Key interests include statistical inference on random discrete structures like random graphs, community detection, latent geometry inference, and DNA data storage. He has advised numerous PhD and undergraduate students. Education: PhD in Statistics (UC Berkeley, 2015), MS in Computer Science (UC Berkeley), MS in Mathematics (Budapest University of Technology and Economics). Research interests span random graph theory, network analysis, information cascades, and computational biology. He teaches courses like Mathematical Foundations of Computer Science and Probability for Statistical Inference. His work has been published in top venues like Annals of Applied Probability, NeurIPS, and IEEE journals. Notable contributions include breakthroughs in graph matching algorithms for stochastic block models, community recovery, and DNA synthesis optimization. His research has practical applications in data storage and network science.
Jose Israel Rodriguez is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison, with additional affiliations in the Department of Electrical & Computer Engineering and the Institute for Foundations of Data Science. He joined UW Madison in Fall 2020 after completing postdoctoral positions at the University of Chicago (with Lek-Heng Lim) and Notre Dame (with Jonathan Hauenstein). Rodriguez earned his PhD in 2014 from UC Berkeley under the supervision of Bernd Sturmfels. His research focuses on applied algebraic geometry and algebraic methods for statistics, with particular interests in nonlinear algebra and nonlinear eigenvalue problems, algebraic statistics and nearest point problems, and applications of monodromy and Galois groups. Rodriguez has made significant contributions to numerical algebraic geometry, particularly in solving polynomial systems, maximum likelihood estimation, and Euclidean distance degree calculations. His work bridges theoretical mathematics with practical computational methods. Rodriguez's recent publications demonstrate a strong trend toward developing numerical methods for solving complex algebraic problems with applications in statistics, optimization, and engineering. His research shows increasing sophistication in handling decomposable systems, multiparameter eigenvalue problems, and braid group computations, often implementing these methods in software tools like Macaulay2. His work connects abstract algebraic geometry with concrete computational approaches. NSF Postdoctoral Fellow Provost's Postdoctoral Scholar Rodriguez currently advises PhD students Julia Lindberg (expected graduation May 2022, joint with B. Lesieutre) and Zinan Wang. He has organized numerous seminars and conferences including SIAM_SAGA, Algebra in Statistics and Computation Seminar, and Applied Algebra Seminar. His research has been supported by various grants that enable his work in numerical algebraic geometry and its applications. Rodriguez is actively involved in the algebraic geometry and statistics communities, organizing several seminars and minisymposia at major conferences. He has developed several software tools including implementations for decomposable sparse polynomial systems, multiregeneration, algebraic optimization, Galois groups, and maximum likelihood obstruction functions. His work connects theoretical mathematics with practical computational applications across various domains.
Yingli Qin is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, part of the Faculty of Mathematics. His research focuses on high-dimensional statistics and random matrix theory, with applications to covariance matrix analysis and hypothesis testing. He holds a PhD in Statistics from Iowa State University, alongside MA and BSc degrees in Mathematics and Statistics from Iowa State University and Northeast Normal University, China. Education : PhD in Statistics, Iowa State University, USA MA in Statistics, Iowa State University, USA BSc in Applied Mathematics, Northeast Normal University, China Research Interests : Qin’s work emphasizes high-dimensional statistical methodologies, including covariance matrix estimation, spectral distribution analysis, and the application of random matrix theory to address challenges in large-scale data. His contributions include developing bias-reduced estimators and testing frameworks for high-dimensional datasets. Publications : Qin has published extensively in top-tier journals such as the Annals of Statistics , Journal of Multivariate Analysis , and Biometrika , with a focus on advancing statistical theory for high-dimensional settings. Teaching : He teaches advanced courses including Multivariate Analysis (Stat 923), Estimation and Hypothesis Testing (Stat 850/450), and Mathematical Statistics (Stat 330).
