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.
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.
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.
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.
Christian Igel is a Professor at the Department of Computer Science, University of Copenhagen, and serves as director of the SCIENCE AI Centre . He is also a co-lead of the Pioneer Centre for Artificial Intelligence in Denmark. His academic journey includes a Doctoral degree from Bielefeld University (2002) and a Habilitation degree from Ruhr-University Bochum (2010). Igel is a Juniorprofessor (2002–2010) and has held editorial roles at journals like KI - Künstliche Intelligenz and Artificial Intelligence Journal . Doctoral degree: Faculty of Technology, Bielefeld University, Germany (2002) Habilitation degree: Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany (2010) His research spans Machine Learning , focusing on Support Vector Machines , Evolution Strategies , Reinforcement Learning , Deep Neural Networks , and PAC-Bayesian Analysis . He applies these methods to Environmental Monitoring , Medical Diagnostics , and Climate Research . Recent publications highlight work on adversarial machine learning , environmentally sustainable AI , and tree resource mapping using deep learning. His scientific awards include being a ELLIS Fellow . Igel’s software tools like Shark , woody , and Multi-Planar UNet are widely used in research and industry. Notable grants and collaborations involve projects with European Lab for Learning and Intelligent Systems (ELLIS) , SCIENCE AI Centre , and international teams in Denmark , Germany , and France . His lab leadership emphasizes open-source frameworks and reproducible research. Editorial Roles: German Journal on Artificial Intelligence , Evolutionary Computation Journal , Artificial Intelligence Journal Software Projects: Shark , woody , Multi-Planar UNet , U-Time Collaborations: SCIENCE AI Centre , Pioneer Centre for Artificial Intelligence , European Lab for Learning and Intelligent Systems
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.
Qixuan Chen, PhD, is an Associate Professor of Biostatistics at Columbia University Mailman School of Public Health. She obtained her PhD from the University of Michigan in 2009, with dual expertise in biostatistics and survey sampling. Education: BA in Economics (Nankai University), MS in Applied Statistics (Bowling Green State University), PhD in Biostatistics (University of Michigan) Her research focuses on advanced statistical methods for complex surveys, causal inference, and handling missing data. Key contributions include developing Bayesian predictive inference frameworks using machine learning and regularized regression for integrating administrative records with survey samples. Recent publications emphasize environmental health applications, including measurement error correction for immunoassays and variable selection in multiply imputed data. Her work bridges biostatistics with computational methods for data integration. Scientific Awards: NIEHS Career Development Award, Teaching Award, Calderone Research Prize, Bryant Scholarship, Hutzinger Award She actively contributes to public health through dashboards like the New York City Neighborhoods COVID-19 tracker and PRIME radiology diagnostics platform. Grants such as R01ES035784 support her ongoing work in exposure-response analysis.
Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis, with affiliate status at Indiana University's Ostrom Workshop and as Research Director at Metagov. His research focuses on computational social science approaches to understanding self-governance in complex social systems, particularly through the lens of online communities as model institutions. Education: Ph.D. in Cognitive Science and Informatics (complex systems), Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey specializes in computational approaches to institutional analysis and the cognitive science of strategic behavior . His work examines how communities design governance systems to overcome collective action problems, with emphasis on: Emergent institutional structures in digital commons Policy-as-data through NLP and institutional grammar frameworks Cognitive mechanisms underlying cooperative behavior Design principles for participatory change in online platforms His methodology integrates large-scale data analysis, web-based experiments, and computational modeling across diverse contexts including Minecraft, Reddit, and professional sports ecosystems. Publication Trends: Recent publications (2023-2025) demonstrate a cohesive trajectory toward computational institutional analysis, with increasing focus on NLP-driven policy analysis (e.g., NLP4Gov), decentralized governance architectures (DAOs, multi-level platform governance), and the cognitive foundations of collective action. His work consistently bridges theoretical institutional analysis with practical applications in digital community design, showing particular growth in translating Ostrom's design principles into computational frameworks. Awards: Honorable Mention Award for Best Paper at ACM CSCW 2019 Advising and Grants: Frey mentors students interested in data science applications at the intersection of communication, cognition, and complex systems, emphasizing resourcefulness and intellectual curiosity. His research has secured substantial funding from: National Science Foundation (NSF) NASA Ford Foundation Google Open Source Foundation He actively encourages aspiring graduate students with strong self-directed research skills to explore computational approaches to social phenomena. Labs and Teams: He leads the Computational Communication Lab at UC Davis and co-directs the Institutional Grammar Research Initiative. Through Metagov, he develops the 'Governance API' framework for modular community governance. His past affiliations include Disney Research (Walt Disney Imagineering) where he applied complexity science to theme park systems, and the New England Complex Systems Institute (NECSI). Current collaborations span Ethereum governance, Minecraft server ecosystems, and Colorado's cannabis monitoring infrastructure.
Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he directs the UW Robot Learning Laboratory. He also serves as a Principal Research Scientist in the Seattle Robotics Lab at NVIDIA Research and co-chairs the IEEE Robotics and Automation Society Technical Committee on Robot Learning. Dr. Boots received his Ph.D. from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, where he was a member of the Sense, Learn, Act (SELECT) Lab co-directed by Carlos Guestrin and his advisor Geoff Gordon. Prior to joining the University of Washington faculty, he was an Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech, and before that, he completed a post-doc in the Robotics and State Estimation Lab directed by Dieter Fox at the University of Washington. Professor Boots' research focuses on the intersection of machine learning, artificial intelligence, and robotics, with particular emphasis on developing theory and systems that tightly integrate perception, learning, and control. His work spans computer vision, state estimation, localization and mapping, high-speed navigation, motion planning, and robotic manipulation. His group develops algorithms drawing from deep learning and neural networks, nonparametric statistics, graphical models, nonconvex optimization, quantum physics, online learning, reinforcement learning, and optimal control. The research demonstrates a strong theoretical foundation while maintaining practical relevance to real-world robotic systems. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with robotics, particularly in model predictive control, motion planning, and learning-based approaches to robot control. His work shows increasing focus on developing theoretically grounded methods that can handle the complex, nonlinear dynamics of real-world robotic systems while maintaining computational efficiency. The publications span top venues including ICRA, CoRL, IROS, and NeurIPS, demonstrating broad impact across multiple subfields of robotics and AI. Finalist for Best Systems Paper at Conference on Robot Learning (CoRL-2021) Multiple papers selected for oral presentations at top robotics conferences Work recognized for theoretical contributions and practical applications in robot learning As director of the UW Robot Learning Laboratory, Boots leads a vibrant research group focused on fundamental and applied research in robot learning. The lab maintains strong collaborations with NVIDIA Research and has produced numerous high-impact publications that bridge theory and practice. Professor Boots teaches courses in autonomous robotics, machine learning, and reinforcement learning, contributing to both undergraduate and graduate education at the University of Washington.
Murat Kantarcioglu is a Professor of Computer Science at Virginia Tech, affiliated with the College of Engineering. He is also a Faculty Fellow at the Commonwealth Cyber Initiative (CCI) and directs the Data Security and Privacy Lab. Previously, he held the Ashbel Smith Professorship at the University of Texas at Dallas. His research focuses on data and AI security, privacy, blockchain, and cybersecurity. He has received notable awards, including the NSF CAREER Award and IEEE Technical Achievement Award, and is a Fellow of AAAS and IEEE. Education: Ph.D. in Computer Science (Purdue University), B.S. in Computer Engineering (Middle East Technical University). Research Interests: Privacy-preserving machine learning and data analytics Adversarial machine learning and cybersecurity Blockchain technology and applications Healthcare data security and genomics privacy Risk and incentive models for assured data sharing Awards and Recognition: NSF CAREER Award AMIA Homer R. Warner Award IEEE ISI Technical Achievement Award Fellow of AAAS and IEEE Distinguished Member of ACM Advising and Labs: Directed over 20 PhD/Master’s students, many in cybersecurity and privacy domains. Founder and director of Virginia Tech’s Data Security and Privacy Lab. Associate at Harvard’s University Data Privacy Lab. Service and Leadership: Extensive program committee roles in top conferences (KDD, AAAI, IEEE ICDE). Former CCI co-chair for IEEE TrustCom. Co-authored influential textbooks on adversarial machine learning.
Travis Desell is a Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT), part of the B. Thomas Golisano College of Computing and Information Sciences. His research focuses on data science and machine learning applied to large-scale datasets using high-performance and distributed computing. He specializes in neuro-evolution, combining evolutionary algorithms with neural networks, particularly through his EXACT and EXAMM algorithms. He leads the D2S2 Lab and has developed the SALSA programming language based on the actor model. Currently funded projects include the National General Aviation Flight Information Database (NGAFID) and an NSF award exploring contextual bandits for decision-making in cyber-physical systems. His work emphasizes practical scientific applications, including stock forecasting, power plant data prediction, and explainable time series models. Education details are not explicitly provided, but his roles and publications indicate advanced academic credentials. Research interests span neuro-evolutionary techniques, recurrent neural networks, and distributed computing frameworks. Key projects include EXAMM for time series forecasting and NGAFID for flight safety analysis. Collaborations involve students and teams at RIT and beyond, with a focus on advancing AI-driven solutions in dynamic environments. Lab affiliations include the D2S2 Lab, where he mentors students and conducts cutting-edge research. Current opportunities exist for PhD students with backgrounds in software engineering and expertise in areas like NLP, web development, and distributed systems.
Sri AravindaKrishnan Thyagarajan is a Lecturer at The University of Sydney's School of Computer Science, specializing in cryptography. He focuses on enhancing security and fairness in distributed systems, blockchains, and multi-party computations with applications in post-quantum cryptography. His work spans top-tier conferences like ACM CCS and IEEE SP. Education: B.Tech, Computer Science & Engineering, National Institute of Technology Trichy, India (2011–2015) M.Sc., Computer Science, Saarland University, Germany (2015–2016) Ph.D., Applied Cryptography, Friedrich Alexander Universität Erlangen-Nürnberg, Germany (2016–2021) Research Interests: Cryptography (classical and post-quantum), blockchain security, cryptocurrencies, fairness in multi-party computations, privacy-preserving protocols, and decentralized systems. Recent Articles: Focus on cryptographic protocols for blockchains, threshold cryptography, verifiable randomness, and secure atomic swaps. Recent work includes advancements in post-quantum signatures, distributed randomness generation, and privacy-preserving payment systems. Awards: Nominated for GI Dissertationspreis 2021 Advising & Grants: Advising Ankit Kapoor. Active in program committees for CCS, IEEE SP, and FC. Currently Program Chair for Crypto Valley Conference 2025. Labs/Teams: Part of the Sydney Blockchain Centre and cybersecurity cluster at the University of Sydney. Collaborates globally with institutions like NTT Research, Carnegie Mellon University, and Bocconi University.
Professor Yizhou Sun is affiliated with the University of California Los Angeles (UCLA) and the Henry Samueli School of Engineering and Applied Science . Her academic work focuses on Machine Learning , Artificial Intelligence , and Graph Neural Networks within the Computer Science department. Her research spans High-Level Synthesis , Causal Inference , and Computational Biology , with recent publications addressing neural network compression, language model safety, and dynamical system modeling. The trends in her recent 2025 and 2024 publications emphasize Deep Learning , Graph Theory , and Language Model Optimization , reflecting interdisciplinary applications in Biomedical Data , Hardware Design , and Physical Simulation .
Dr. Ava Hedayatipour is an Assistant Professor of Electrical Engineering at California State University at Long Beach (CSULB), where she joined in Fall 2020. She holds a Ph.D. from the University of Tennessee, Knoxville (2020), and degrees from Iran University of Science and Technology (B.S., 2012) and Shahid Rajaee Teacher Training University (M.S., 2015). Her research focuses on analog/mixed-signal circuit design, bio-implantable devices, low-power systems, and hardware security. Notable contributions include a first-of-its-kind integrated secure multimodal sensor and a flexible paper electrode for remote electrochemical experiments. Education: Ph.D., Electrical Engineering, University of Tennessee, Knoxville, 2020 M.S., Electrical Engineering, Shahid Rajaee Teacher Training University, Iran, 2015 B.S., Electrical Engineering, Iran University of Science and Technology, 2012 Research Interests: Analog and mixed-signal circuit design Biomedical devices and lab-on-chip applications Low-power, low-noise microelectronics Hardware security for IoT and biomedical sensors Flexible electrodes for wearable systems Awards: University of Tennessee Fellowship Award (2019) Outstanding Teaching Assistant Award (2018) BEST PAPER AWARD at IEEE DCAS 2025 2nd Place Winner at IEEE BIOCAS 2023 Innovation Challenge Advising & Grants: Lead CSULB LEAP program project on medical imaging braces Funded NSF project on chaotic analog security (2018–present) Collaborated with industry partners like Applied Medical and Synaptics Labs & Teams: Next Generation Wearable Lab at CSULB Focus on sensor design, hardware security, and biomedical applications
Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.
