Jan Dreier is a Research Fellow at the Institute of Logic and Computation at Vienna University of Technology. His research centers on structural graph theory and algorithmic meta-theorems, particularly exploring the boundaries of tractability for model checking problems. Dreier's work bridges theoretical computer science and discrete mathematics, focusing on graph decompositions, parameterized complexity, and logical expressiveness. Key research themes include monadic stability in graph classes, applications of model theory to computer science, and efficient algorithms for logical queries on structured graphs. His publication record shows consistent focus on graph sparsity concepts and algorithmic applications of logic, with recent work expanding into approximation methods for counting queries. Research demonstrates sophisticated applications of combinatorial methods to fundamental problems in computational complexity.
Eric V. Mazumdar is an Assistant Professor at the California Institute of Technology (Caltech), jointly appointed in the departments of Computing and Mathematical Sciences and Economics. He holds a B.S. from MIT (2015) and a Ph.D. from UC Berkeley (2021), co-advised by Michael Jordan and Shankar Sastry. His research bridges machine learning and economics, focusing on deploying algorithms into societal systems through theoretical and practical lenses. Key areas include strategic classification, multi-agent reinforcement learning, and distributionally robust optimization, with applications in healthcare, online markets, and intelligent infrastructure. Education: B.S., Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2015 Ph.D., Electrical Engineering and Computer Science, University of California, Berkeley, 2021 Research Interests: Mazumdar’s work emphasizes understanding learning algorithms in strategic environments, including min-max optimization, game theory, and multi-agent systems. He explores how algorithms interact with human and algorithmic agents in dynamic systems, with practical applications in healthcare delivery, e-commerce, and autonomous systems testing. Awards: NSF CAREER Award (2023) Simons Institute Research Fellowship in Learning in Games Grants & Funding: Supported by NSF, DARPA, Amazon, and other organizations. His NSF CAREER grant focuses on strategic interactions in societal-scale systems. Teaching: Courses include Networks: Structure & Economics (CMS/CS/EE/IDS 144) and Topics in Learning and Games (CMS/Ec 248). At UC Berkeley, he contributed to courses like Data, Inference, and Decisions (DS 102). Students & Postdocs: Current students: Lauren Conger, Tinashe Handina, Yizhou Zhang Postdocs: Zaiwei Chen, Laixi Shi, Kishan Panaganti (co-advised with Adam Wierman)
Prof. Eleni Chatzi is a Full Professor and Chair of Structural Mechanics at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. She holds a PhD from Columbia University (2010) and has held roles from Assistant to Full Professor at ETH since 2010. Her research focuses on intelligent structural monitoring and data-driven asset management, emphasizing nonlinear dynamics and sensor integration. Affiliations : Institute of Structural Engineering, European Academy of Wind Energy (EAWE President), Swiss Community for Computational Methods (SWICCOMAS Chair) Research interests include Structural Health Monitoring (SHM), system identification, and advanced simulation tools. She pioneered work on data-driven diagnostics and self-aware infrastructure, supported by grants like the ERC Starting Grant (2015). Awards include the 2020 Walter L. Huber Prize and 2024 SHM Person of the Year Award. Her work spans wind energy infrastructure, metamaterials for vibration control, and AI-driven structural analytics. Over 600 publications and 200k+ citations highlight her impact. She teaches computational science and structural dynamics in ETH's programs and collaborates globally on sustainable infrastructure projects.
David Bindel is an Associate Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences, College of Engineering, and Cornell Ann S. Bowers College of Computing and Information Science. He earned his Ph.D. in Mathematics from the University of California, Berkeley in 2006. His research focuses on applied numerical linear algebra, eigenvalue problems, and their applications in plasma physics, network analysis, and nonlinear systems. He develops methods for analyzing complex systems, including magnetic confinement in stellarators, stability of MHD systems, and community detection in networks. His work bridges theoretical foundations with practical computational tools, such as formal verification of linear algebra algorithms and scalable Gaussian process models. Bindel’s research explores the interplay between structure and computation, leveraging eigenvalue analysis to address challenges in computer vision, opinion dynamics, and engineering design. He has contributed to advancements in numerical methods for large-scale systems, including iterative solvers, spectral approximation techniques, and stochastic optimization. His interdisciplinary approach spans applied mathematics, computer science, and physics, with applications in fusion energy, machine learning, and network science. Recent work highlights include high-order expansions for magnetic confinement, adaptive filtering for dynamical systems, and Bayesian optimization strategies. His publications emphasize rigorous analysis alongside computational scalability, addressing both theoretical and practical aspects of modern scientific computing. Despite no explicitly listed awards, his contributions reflect significant impact in his fields.
Illya V. Hicks is a Professor in the Computational and Applied Mathematics Department at Rice University. He holds a PhD from Rice University (2000) and a BS from Texas State University (1995). His research focuses on combinatorial optimization, integer programming, graph theory, and matroid theory, with applications in social networks, cancer treatment, and network design. He has advised numerous doctoral, post-doctoral, and masters students. Education: PhD and MA in Computational and Applied Mathematics, Rice University, 2000 BS in Mathematics, Texas State University, 1995 Research Interests: Utilizing graph decomposition techniques to solve NP-complete problems, including branch decompositions and matroid circuit problems. Applications include sensor network design, healthcare logistics, and algorithmic graph theory. Awards: Recognized with the 2015 Presidential Mentoring Award (Rice University), 2010 Forum Moving Spirit Award (INFORMS), and the 2005 Optimization Prize for Young Researchers. Grants and Projects: Includes NSF-funded research on branch decomposition techniques, submodular optimization, and healthcare service distribution. Active in promoting minority participation in operations research through travel grants and mentoring initiatives. Labs/Teams: Engaged in collaborative research on graph algorithms, combinatorial optimization, and interdisciplinary applications in healthcare and engineering.
John Evans is an Associate Professor and Jack Rominger Faculty Fellow in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the Applied Mathematics program. He serves as Associate Chair for Undergraduate Curriculum and is part of the Aerospace Mechanics Research Center (AMREC). His research focuses on computational mechanics, particularly fluid dynamics, fluid-structure interaction, and turbulence modeling using high-order and structure-preserving methods. Evans holds a PhD (2011) and MS (2008) in Computational and Applied Mathematics from the University of Texas at Austin, and dual BS/MS degrees in Mathematics and Applied Mathematics from Rensselaer Polytechnic Institute (2006). Before joining CU Boulder, he was a postdoctoral fellow at the Institute for Computational Engineering and Sciences (ICES). His research interests include isogeometric analysis, immersed methods, and data-driven turbulence modeling. Notable contributions include development of divergence-conforming discretizations for incompressible flows, stabilized collocation methods, and invariant subgrid stress models. He leads the AMREC lab and collaborates on plasma-fueled propulsion systems and geometrically sensitive simulations. Key Awards: 2021: Rocky Mountain AIAA Educator of the Year 2021: Gallagher Young Investigator Medal 2019-2021: Clarivate Highly Cited Researcher Professional Activities: Editor of Engineering Computations, Senior AIAA Member, Simons Visiting Professor (2019) Evans' work bridges advanced numerical methods with real-world engineering challenges. His lab develops open-source tools like XIGA for multi-material problems and focuses on immersive simulation environments. Current projects explore turbulence closure models, plasma propulsion, and topology optimization with B-spline-based approaches.
Dmitri Strukov is a Professor at the University of California, Santa Barbara in the Department of Electrical and Computer Engineering. His work spans material science, electrical engineering, and computer science, focusing on novel computing paradigms using emerging memory devices. Education: PhD in Electrical and Computer Engineering from SUNY Stony Brook, MS in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests include neuromorphic computing , non-volatile memory applications , and mixed-signal circuits for machine learning and hardware security. His group develops memristive crossbar arrays and 3D NAND flash for energy-efficient systems. Scientific Leadership features Fellow of IEEE and Distinguished Lecturer roles. His work has been recognized with best paper awards at ASPLOS’19 and Computing Frontiers’13. Students: Mentored PhD graduates in neurocomputing, security, and memristor design including Z. Fahimi, S. Larimian, M.R. Mahmoodi, and X. Guo. Grants: Funded by AFOSR, ARO, DARPA, NSF, and industry leaders like Google and Samsung. Labs: Utilizes UCSB’s nanofabrication center and advanced tools for memristor characterization.
Antony Franklin is an Associate Professor and Head of the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IITH), India. He leads the Networked Wireless Systems (NeWS) Lab and is actively involved in 5G research, wireless networks, and cybersecurity. He earned his Ph.D. from IIT Madras in 2010. Education: Ph.D. in Computer Science and Engineering, IIT Madras, 2010 Research Interests: His research spans wireless networks , 5G systems , mobile edge computing , low-latency transport protocols , and cybersecurity . He focuses on next-generation mobile systems, especially 5G, aiming to meet the demands of ultra-low latency, high data rates, and quality of experience for services like IoT, autonomous driving, and VR. He has been involved in developing testbeds and prototypes to validate real-world performance of 5G technologies, including network slicing, LTE-WiFi integration, and edge computing. Scientific Awards: Best Academic Demo Award, IEEE COMSNETS 2018 Second Best Paper Award, IEEE ANTS 2017 Grants & Projects: Network Slice Life-cycle Management for 5G Mobile Networks – SPARC, MHRD (2019–2021) DNS/IPv6 for IoT Security – NASSCOM (2018–2019) CCRAN: Energy Efficiency in Cloud RAN – Intel India (2018–2021) End-to-End 5G Test-Bed – DoT, GoI (2018–2021) M2SMART Smart Cities – SATREPS (2018–2023) Ultra-Reliable Low Latency Protocols – STINT, Sweden (2017–2018) Low Latency 5G Protocols – SERB ECR (2016–2019) Lab & Team: He leads the NeWS Lab (Networked Wireless Systems) at IITH, which focuses on 5G, wireless networks, and IoT systems. The lab is actively involved in developing real-world testbeds and publishing in top-tier conferences and journals.
Wim Gevers is a faculty member at the Université libre de Bruxelles (ULB) and leads the CS4S – Cognitive Control & Sleep laboratory within the CRCN research centre. His work bridges cognitive psychology, neuroscience and sleep research to understand how the brain exerts control over thoughts and actions and how sleep contributes to these processes. Research Interests Cognitive Control & Metacognition: Investigating how subjective experiences such as confidence and the "urge-to-err" guide strategic adjustments in behaviour. Working Memory & Ordinal Cognition: Examining how order information is maintained and manipulated, and how these processes relate to mathematical competence. Sleep, Memory & Decision Making: Exploring how sleep-dependent consolidation influences motor learning and decision strategies. Across his 2022–2025 publications a clear trend emerges: a focus on metacognitive monitoring —how humans evaluate their own cognitive states—and the role of emotional and temporal context in shaping those evaluations. Studies range from reaction-time introspection and confidence judgements in perceptual tasks to the impact of aging and depression on metacognitive accuracy. Doctoral Supervision & Mentoring Whitney Stee (PhD 2024) – Sleep-dependent structural brain reorganization & motor learning Gaia Corlazzoli (PhD 2024) – Subjective experience in decision-making Myrtille Dewulf (PhD 2023) – Ordinal coding mechanisms in working memory Rebeca Sifuentes-Ortega (PhD 2023) – REM sleep and memory reactivation All dissertations were defended at ULB, Faculté des Sciences psychologiques et de l’éducation, with Wim Gevers formally listed as Promotor . Laboratory & Collaborative Networks As head of CS4S, Gevers coordinates a multidisciplinary team that combines behavioural experimentation, EEG/MEG, computational modelling and sleep polysomnography. The lab is embedded in the larger CRCN ecosystem, fostering collaborations with groups such as CO3 (consciousness), LCLD (language & deafness), and UR2NF (neurofunctional imaging).
Joydeep Biswas is an Associate Professor in the Computer Science Department at the University of Texas at Austin, where he serves as the Director of the Autonomous Mobile Robotics Laboratory (AMRL). He is also affiliated with Texas Robotics, the UT Machine Learning Laboratory, and UT Good Systems. Previously, he was an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. Dr. Biswas earned his PhD in Robotics from Carnegie Mellon University in 2014 and his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008. His educational background has provided him with a strong foundation in both theoretical and applied aspects of robotics and artificial intelligence. Dr. Biswas's research focuses on enabling long-term autonomy for mobile robots operating in human environments. His work spans robot perception, motion planning, control systems, and AI, with the ultimate goal of creating self-sufficient autonomous mobile robots that can perform tasks accurately and robustly in real-world settings. He is particularly interested in perception, planning, and failure recovery for autonomous mobile robots, which supports his vision of having autonomous service mobile robots deployed at campus-to-city scale, both indoors and outdoors, performing assistive tasks over deployments spanning years. His IJCAI 2019 Early Career Spotlight talk summarizes much of his research to date and ongoing interests. His recent research has shown a strong trend toward social navigation, human-robot interaction, and the application of machine learning techniques to robotics problems. There's a clear progression from fundamental robotics research toward more complex, real-world applications that require robots to understand and navigate human social spaces effectively. His work increasingly integrates large language models and other advanced AI techniques with traditional robotics approaches, as evidenced by his recent publications on topics like preference-conditioned navigation, social navigation benchmarks, and instruction-following navigation systems. Dr. Biswas has received numerous prestigious awards including the NSF CAREER Award (2021), J.P. Morgan Faculty Research Award (2019), Amazon Research Award (2019), and a grant from Northrop Grumman Mission Systems (2018). These awards recognize his innovative contributions to the field of robotics and autonomous systems. As a dedicated educator and mentor, Dr. Biswas actively supervises PhD and master's students, with his PhD student Sadegh Rabiee winning the student poster award at the Northrop Grumman University Symposium 2019. He has secured significant grant funding from the National Science Foundation for projects including 'Introspective Perception and Planning for Long-Term Autonomy' and 'Interactive Synthesis and Repair For Robot Programs,' demonstrating his ability to secure competitive research funding and his commitment to advancing the field. Dr. Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL), which serves as a hub for interdisciplinary research in mobile robotics. The lab has developed notable resources such as the UT Campus Object Dataset (CODA) for 3D perception research and SOCIALGYM, a framework for benchmarking social robot navigation. His team regularly deploys robots on the UT Austin campus and in urban environments to test and refine their approaches in realistic settings, bridging the gap between simulation and real-world application.
Seth Lewis Gilbert is a Professor and Head of the Department of Computer Science at the National University of Singapore (NUS), within the School of Computing . He holds the Dean's Chair Associate Professor title and focuses on algorithms for large-scale distributed systems , emphasizing scalability and fault-tolerance . His work spans wireless networks , contention resolution , dynamic networks , and blockchain protocols . Ph.D. in Computer Science, MIT (2007) M.S. in Computer Science, MIT (2003) B.S. in Electrical Engineering & Mathematics, Yale University (1999) His research explores trusted coordination in systems with untrusted and unreliable participants, addressing challenges like Byzantine agreement , load balancing , and contention resolution . He has pioneered formal frameworks for accountability in distributed protocols and developed novel algorithms for asynchronous task allocation . Recent publications include work on consensus protocols , leader election , and contention resolution , reflecting his focus on dynamic network environments . His research has earned recognition at venues like DISC, ICDCS, and CCS. Scientific Awards & Honors : Young Researcher Award (NUS, 2014) Faculty Teaching Excellence Award (2013/14, 2014/15, 2015/16) School of Computing Teaching Excellence Honour Roll (2016-2021) Best Paper Awards at DISC (2023, 2022), ICDCS (2022), IPDPS (2022) Best Student Paper Award at DISC (2022) He serves on the Steering Committee for DISC as Treasurer and has chaired program committees for DISC, OPODIS, and SPAA. His work bridges foundational algorithmic theory with practical applications in blockchains and dynamic networked systems .
Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.
Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.
Ming Yin is an Associate Professor in the Department of Computer Science at Purdue University. Her research bridges human-computer interaction, applied artificial intelligence, computational social science, and behavioral sciences. She focuses on leveraging human behavior data to design intelligent systems that balance machine efficiency with human understanding, trust, and engagement. Education : PhD in Computer Science (Harvard University, 2017), B.E. in Computer Software (Tsinghua University, 2011) Previous Roles : Postdoctoral researcher at Microsoft Research New York City (2017–2018) Teaching : Courses on AI, Human-AI Interaction, Data Mining, and Human-Centered Computing Research Interests center on social computing, crowdsourcing, human-AI interaction, and ethical AI. She employs experimental and computational methods to study how human behavior can improve AI systems' design, fairness, and user trust. Her work has significant implications for gig economy platforms, decision support systems, and algorithmic accountability. Scientific Contributions include over 15 recent articles in top venues like CHI, IJCAI, and ACL. These works explore topics such as LLM-driven trust calibration, adversarial social influence, and ethical AI design. Her research has been recognized with the NSF CAREER Award Siebel Scholar (Class of 2017) Multiple Best Paper and Honorable Mention Awards at CHI, CSCW, and HCOMP Teaching Expertise spans courses like Introduction to Artificial Intelligence (CS 471), Human-AI Interaction (CS 592-HAI), and Data Mining (CS 573). She emphasizes project-based learning and designing systems for real-world problems, such as "learning in a new era" in her 2025 HCI course.
Brendan O'Connor is an Associate Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His research focuses on computational social science and natural language processing (NLP), particularly exploring how social factors influence language technologies and using text analysis to understand societal trends. His work includes studies on racial bias in NLP, political event analysis, and social media linguistics. He holds a PhD in Machine Learning from Carnegie Mellon University (2014) and dual MS/BS in Symbolic Systems from Stanford University (2006). Education: PhD in Machine Learning, Carnegie Mellon University (2014) MS in Symbolic Systems, Stanford University (2006) BS in Symbolic Systems, Stanford University (2006) Research interests span AI ethics, social media analysis, and computational methods for studying language and society. Notably, he investigates racial disparities in NLP systems, linguistic variation in African American English, and event detection in news and social media. His work has been recognized with NSF CAREER and Google Faculty awards, and his research has been cited thousands of times. His lab, the Statistical Social Language Analysis Lab, develops tools for analyzing large-scale text data. He is affiliated with the Center for Data Science, Center for Intelligent Information Retrieval, and Computational Social Science Institute. Recent projects include analyzing global news coverage of critical events and developing frameworks for zero-shot argument explication. Awards and Honors: NSF CAREER Award Google Faculty Research Award Best Paper Award Advising and Grants: O'Connor has advised projects on social media polling representativeness and demographic analysis. His grants include collaborative research on sociopolitical event extraction and bias mitigation in AI systems. He has also contributed to platforms like Rookie for news archive exploration and ezCoref for coreference resolution. Labs/Teams: Leads the Statistical Social Language Analysis Lab and collaborates with the UMass NLP Group and Harvard Institute for Quantitative Social Science. His work bridges NLP with social science methodologies, emphasizing transparency in algorithms and causal inference using text data.
