Michael S. Brown is a Professor and Canada Research Chair at York University's Department of Electrical Engineering and Computer Science (EECS). He also serves as Senior Director at the Samsung AI Center in Toronto, Canada. His research focuses on computer vision, image processing, and computer graphics, with a deep specialization in camera imaging pipelines, color theory, and AI-driven ISP optimization. He has organized major conferences like ProCams, eHeritage, WACV, and ICCV, and holds editorial roles in journals like TPAMI and IJCV. He is renowned for his work on computational color constancy, ISP hardware algorithms, and AI-based image enhancement techniques. His recent ICCV 2023 tutorial detailed modern camera pipelines and AI applications in ISP components. His research is supported by grants from NSERC, Samsung, Adobe, Google, and Microsoft. Brown advises numerous graduate students and has mentored over 30 alumni now in academia and industry roles at Meta, Samsung, Microsoft, and startups. His lab focuses on camera systems, noise modeling, and cross-platform color management, with contributions to open-source datasets and software platforms for ISP experimentation.
Professor Andrew Chadwick is a leading scholar in political communication and digital media studies at Loughborough University, where he directs the Online Civic Culture Centre (O3C). He holds a BSocSc from Birmingham, MSc and PhD from the London School of Economics. His research focuses on hybrid media systems, disinformation dynamics, and digital politics. Key contributions include conceptualizing hybrid media events and analyzing misinformation in personal messaging platforms. His work has been recognized with prestigious awards, including the APSA Best Book Award. He supervises numerous PhD students exploring topics like social media influence, public opinion, and digital disinformation. Current projects include the Leverhulme-funded Everyday Misinformation Project examining numerical disinformation on messaging apps. His media engagements span BBC Radio 4 to The Washington Post. Research highlights include studies on misinformation warnings' ineffectiveness, vaccine hesitancy, and the role of celebrities in political representation. O3C serves as a hub for interdisciplinary research on digital civic culture. Chadwick’s academic leadership includes roles in editorial boards for top journals and international conference keynotes.
Dr. Muhammad Gulzari is an Assistant Professor at the School of Civil Engineering, University College Dublin (UCD). Previously, he held positions as Lecturer/Assistant Professor at the University of Galway (2023–2024), Adjunct Assistant Professor at Trinity College Dublin (2022–2023), and a Research Fellow at Trinity College Dublin (2021–2023). He earned his Ph.D. in Civil Structural Engineering from City University of Hong Kong (2021) and a B.Sc. in Civil Engineering from the University of Engineering and Technology Lahore (2017). Education: Bachelor of Engineering, Civil Engineering, University of Engineering and Technology Lahore Ph.D., Civil Structural Engineering, City University of Hong Kong Research Interests: His work focuses on structured materials and dynamics, including finite element modeling, phononic crystals, acoustic and mechanical metamaterials, vibration and noise control, and applications in structural health monitoring. He leads the Structured Materials and Dynamics Lab at UCD, exploring nonlinear and nonreciprocal metamaterials. Teaching and Awards: He has taught courses in Fluid Mechanics, Thermodynamics, and Combustion Engineering. Notable recognitions include the Seal of Excellence Award from the EU-Horizon MSCA Postdoctoral Fellowship (2021) and nominations for teaching excellence awards at Trinity College Dublin and University of Galway. Grants: TimberFlow: Enhancing Efficiency in Mass Timber Construction (Enterprise Ireland, 2025) Indoor Acoustic Quality via Acoustic Metamaterials (Enterprise Ireland, 2025) RUBBERPAVE: ELT Integration in Pavement Construction (Enterprise Ireland, 2024–2026) Deep Learning for Metamaterial Design (Irish Research Council, 2021–2023) Labs and Collaborations: His research group collaborates with industry partners like Amplitude Acoustics and G-frame Structures Ltd. Key projects include developing metamaterials for noise/vibration control and sustainable construction materials.
Demetris Christodoulou is an Associate Professor in Accounting, Governance and Regulation at the University of Sydney. He holds a BEcon from Piraeus University, an MSc(Fin) from the University of York (UK), and a PhD from Athens University of Economics and Business (AUEB). His research focuses on applying data analytics, econometrics, and visualization techniques to financial analysis, equity valuation, life insurance, and financial advice. He co-directs the PEMA research group, specializing in productivity and performance measurement analytics, and previously led the MEAFA research group (2007–2022). He has collaborated extensively with industry partners including Deloitte and Australian insurers, and developed training programs for over 1,000 executives. His work includes open-source contributions to Stata software and the Graph Workflow platform, alongside $662k in workshop-generated funds supporting academic programs. He has advised multiple PhD students and taught at leading universities globally. Education: BEcon in Economics (Econometrics), Piraeus University MSc in Finance, University of York (UK) PhD in Accounting and Financial Analysis, Athens University of Economics and Business His research interests span financial reporting models, life insurance underwriting strategies, and behavioral finance. Recent projects address dishonesty mitigation in insurance disclosures and the adviser effect on customer disclosures. He has published widely in top journals like the Review of Accounting Studies and Stata Journal , and his work was featured in The Australian for insights on insurance fraud reduction. He maintains international collaborations, including visiting roles at Columbia Business School and the University of Cyprus, and serves on advisory boards for organizations like Behaviour.ai. Publications highlight methodological innovations in econometrics and visualization, with 2025's upcoming Stata Journal paper advancing time-series analysis techniques. His grants include partnerships with industry on longitudinal studies of insured lives, aiming to improve risk modeling and public policy insights.
Kevin M. Lynch is a Professor of Mechanical Engineering at Northwestern University's McCormick School of Engineering, where he also serves as Director of the Center for Robotics and Biosystems. His research spans multiple domains of robotics, with particular expertise in dynamics, motion planning, and feedback control of mechanical systems. Dr. Lynch received his Ph.D. in Robotics from Carnegie Mellon University in 1996, with a thesis on "Nonprehensile Robotic Manipulation: Controllability and Planning" under advisor Prof. Matthew T. Mason. He earned his B.S.E. with honors in Electrical Engineering from Princeton University in 1989. His research interests focus on robotics, particularly dynamics, motion planning, and feedback control of mechanical systems. He investigates mechanics, planning, and control of robotic manipulation (juggling, throwing, pushing, rolling, vibration, etc.) and locomotion. His work also explores self-organizing systems, particularly decentralized control of mobile sensor networks and swarm robotics, underactuated dynamic systems, and physical human-robot interaction with industrial applications. Recent research has expanded into bio-inspired active electrosense, underwater robotics, control and optimization for robot swarms, swarm shape control, and functional electric stimulation. Dr. Lynch's recent publications demonstrate a strong trend toward rehabilitation robotics and human-robot interaction, particularly in lower-limb exoskeletons for gait training and rehabilitation. His work bridges fundamental robotics research with practical applications in healthcare, showing increasing integration of swarm robotics principles with human-centered design. The publications also reveal continued strong contributions to fundamental robotics theory, particularly in swarm formation control and manipulation dynamics. George Saridis Leadership Award in Robotics and Automation (2022) Harashima Award for Innovative Technologies (2017) IEEE Fellow (2010) Charles Deering McCormick Professor of Teaching Excellence (2007-10) Society of Automotive Engineers Ralph R. Teetor Educational Award (2007) Early Career Award in Robotics and Automation (2001) McCormick School of Engineering and Applied Science Teacher Of The Year Award (1998-1999) NSF Career Award (1998) As Director of the Center for Robotics and Biosystems, Dr. Lynch oversees significant research grants and initiatives in robotics. He has made substantial contributions to robotics education through his "Modern Robotics" book and associated Coursera specialization, which has reached thousands of students worldwide. His professional service includes serving as Editor-in-Chief of IEEE Transactions on Robotics, where he oversaw a 90% increase in submissions during his tenure. Dr. Lynch leads research groups focusing on swarm robotics and rehabilitation robotics, with particular emphasis on the Center for Robotics and Biosystems at Northwestern University. His teams integrate expertise from mechanical engineering, electrical engineering, computer science, and rehabilitation medicine to develop innovative robotic systems for both industrial applications and healthcare solutions.
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.
Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
André DeHon is the Oliver C. Boileau Jr. and Nan Eleze Boileau Professor of Electrical Engineering at the University of Pennsylvania, with affiliations in the Electrical and Systems Engineering (ESE) and Computer and Information Science (CIS) departments. He founded the Penn Computer Engineering (CMPE) program and directs the DARPA-sponsored CyberSavvy Security Center. His research spans reconfigurable computing, FPGA architecture, interconnect design, hardware security, and energy efficiency, with a focus on adapting to fabrication imperfections and runtime variability. His work explores programmable substrates (e.g., CMOS VLSI, molecular electronics) and algorithmic mappings to create resilient, efficient systems. Current projects include runtime feedback mechanisms (REFINE), partial reconfiguration (ExHiPR), and security frameworks like SCALPEL and μSCOPE. Recent publications highlight advancements in FPGA compilation (PLD, HiPR), security (SCALPEL, μSCOPE), and interconnect optimization (Asymmetry in NoC). These studies intersect computer architecture, security, and embedded systems. Scientific awards and recognitions: 2023 IEEE Fellow 2020 IEEE Mary Kenneth Keller Teaching Award 2018 ACM Fellow TCFPGA Hall of Fame (2019) Best Paper at ICFPT 2015 Academic leadership: He chairs the ACM/SIGDA Technical Committee on FPGAs and Reconfigurable Computing, previously held faculty roles at Caltech (1999–2006), and was a postdoc at UC Berkeley (1996–1999). Education includes MIT SB (1990), SM (1993), and PhD (1996) in Electrical Engineering and Computer Science.
John Davis is a Professor in the Department of Physics at the University of Alberta, Faculty of Science. He holds a PhD and MSc from Northwestern University and a Bachelor’s from Washington University. His research focuses on nanomechanics, superfluidity, and superconductivity, particularly in confined geometries and quantum properties of nanomechanical systems. His lab develops superfluid-based technologies for dark matter detection and precision measurement. He has held academic positions since 2010, including roles at the Canadian Institute for Advanced Research and postdoctoral training at the University of Alberta with Prof. Mark R. Freeman. Education: PhD in Physics (2008), Northwestern University MSc in Physics (2003), Northwestern University Bachelor’s in Physics with Honors (2001), Washington University Research Interests: Superfluid nanomechanical resonators Ultralow-temperature superfluid 3He Nanofluidic cavity quantum electrodynamics Quantum-limited torque magnetometry Applications in dark matter detection and gravitational wave sensing His recent work emphasizes magnomechanics and optomechanical transduction , integrating superfluid systems with quantum sensors. Articles highlight advancements in cryogenic devices, nonlinear dynamics, and hybrid quantum systems. Ongoing projects include the HElium-based Light Operated Superfluid (HELIOS) dark matter detector. Grants & Labs: His lab operates a cryogen-efficient low-temperature facility, focusing on microfluidic quantum fluid experiments. Collaborations involve advanced photonic crystal cavities and diamond-based optomechanical platforms.
Kaiyu Hang is an Assistant Professor in the Department of Computer Science at Rice University, where he directs the Robotics and Physical Interactions Lab (RobotΠ Lab). His research spans multiple domains of robotics with a focus on physical interaction systems. Before joining Rice, he completed his postdoc at Yale University, earned his Ph.D./M.Sc. at KTH Royal Institute of Technology, and received his B.Eng. from Xi'an Jiaotong University. His research interests include robotic manipulation, grasping, in-hand manipulation, optimization, planning, learning, estimation, and control systems. He develops algorithms that enable robots to physically interact with other robots, people, and the world across scales from small grasping tasks to large-scale dual-arm and multi-robot manipulation systems. His work has practical applications in factories, kitchens, hospitals, warehouses, and construction sites. His recent publications demonstrate strong trends in in-hand manipulation techniques, energy-efficient drone operations, and benchmarking frameworks for robotic grasping. The 2025 IROS papers accepted highlight his leadership in developing standardized competition frameworks for evaluating robotic manipulation capabilities across diverse hardware platforms. ASME Rising Star of Mechanical Engineering (2024) NSF CAREER Award (2023) Multiple finalist awards at IEEE-RAS Humanoids and ICRA conferences Junior Fellowship Award from Institute for Advanced Study, HKUST (2017-2018) As an educator, he has taught multiple robotics courses including COMP 462/562: Introduction to Modern Robotics and COMP 461: Senior Design in A Robotized World. He serves as Faculty Advisor for the Rice Robotics Club and participates in graduate admissions. His lab actively recruits Ph.D. students and offers research opportunities for undergraduate and master's students who have completed core robotics courses.
**Dr. JIN Chen** is an **Associate Professor** in the **Department of Information Systems and Analytics** at the **National University of Singapore (NUS) School of Computing**. He holds a PhD in Industrial Engineering and Management Science from Northwestern University (U.S.) and joined NUS in 2018 after postdoctoral work at the Wharton School, University of Pennsylvania. His research focuses on **online platforms**, **information systems operations**, and **digital transformation**, with a particular interest in addressing challenges like fake information dissemination and platform competition. His work bridges **operations management** and **information systems**, emphasizing practical applications in e-commerce and fintech. **Education**: B.Sc. (Shanghai Jiao Tong University) M.Sc. & Ph.D. (Northwestern University, U.S.) **Research Interests**: Platform design and competition Information bundling and pricing strategies Consumer search behavior Right-to-repair policies and environmental impacts **Awards & Recognition** (selected): Finalist, MSOM Service Management SIG Best Paper Award (2024) Nomination for NUS Young Researcher Award (2022–2024) Winner, INFORMS ENRE Young Researcher Prize (2022) Teaching Excellence Awards (2021–2023) **Key Projects**: Optimal information structure design to combat fake orders/reviews Environmental and welfare implications of right-to-repair laws Strategic bundling and pricing in multi-service platforms
Dr. Fumiya Iida is a researcher affiliated with the University of Cambridge , contributing to interdisciplinary research through Cambridge Reproduction and the Department of Engineering . His work spans bio-inspired robotics , soft robotics , and embodied intelligence , with a focus on biomechanics and human-robot interaction. His research integrates evolutionary robotics , reservoir computing , and tactile sensing , aiming to bridge engineering, physiology, and synthetic biology. Recent publications highlight innovations in Soft robotic actuation Robust control systems Multimodal sensor integration Human-robot collaborative tasks Dr. Iida's 15 most recent 2025 articles emphasize reservoir computing , soft sensor design , and adaptive motor coordination , reflecting his commitment to advancing embodied intelligence in robotics. No formal awards or student advisement details were found in the provided texts.
Yonatan Bisk is an Assistant Professor at Carnegie Mellon University (CMU) in the School of Computer Science , with dual appointments in the Language Technologies Institute and Robotics Institute . His research bridges Natural Language Processing (NLP) with robotics, focusing on grounded and embodied language understanding. Assistant Professor, Language Technologies Institute, CMU (2021–Present) Courtesy Appointment, Robotics Institute, CMU Research Themes : Language as a social codification of embodied experience Interpretable multimodal model training Human-robot collaboration frameworks Embodied question-answering systems Selected Trends : His recent publications show increasing focus on cross-modal attention mechanisms (Vid2Robot), error detection in toolchains (Tools Fail), and theory-of-mind reasoning in language agents (SOTOPIA). Multimodal integration spans vision, audio, and robotic control contexts (ANAVI). Labs & Collaborations : Founder of CLAW Lab (Connecting Language to Action and the World) Collaborations with Microsoft Research, Meta Inc, and CMU's REAL (Robotics, Embodied AI, Learning) community
Shai Ben-David is a Professor and University Research Chair at the Department of Computer Science, University of Waterloo. He is affiliated with the Cheriton School of Computer Science and can be reached at shai@uwaterloo.ca . His office is located in DC 2643. Education: Ph.D., Hebrew University, Jerusalem, Israel (1987) M.Sc., Hebrew University Jerusalem, Israel (1979) B.Sc., Hebrew University Jerusalem, Israel (1978) Research interests focus on foundational aspects of machine learning theory, including unsupervised learning (clustering), domain adaptation, fairness, interpretability, and alternative approaches to worst-case computational complexity. He also explores logic applications in computer science theory. His research trends emphasize theoretical challenges in machine learning, particularly clustering, fairness in representations, and the interplay between computational feasibility and learnability. He investigates how unlabeled data and sample compression techniques impact learning robustness and efficiency. No scientific awards are listed. His advising record shows no formal advisees listed here. Grants and funding details are not provided in the text. He has contributed to organizing events like the Dagstuhl Seminar on Foundations of Unsupervised Learning (2017) and co-edited MFCS 2016 proceedings. His work addresses both theoretical questions and practical gaps in ML implementation.
Kostas Bekris is a Professor in the Department of Computer Science at Rutgers University, specializing in Robotics and Artificial Intelligence. His research focuses on motion planning, autonomous manipulation, and robot control, with notable contributions to tensegrity robotics, perception-driven systems, and large-scale package handling. He leads a team conducting groundbreaking work in robotics, supported by grants from NSF, NASA, and industry collaborators like ExxonMobil. His group emphasizes interdisciplinary approaches, combining machine learning, topological methods, and differentiable physics modeling to advance robot capabilities in complex environments. Education details are not explicitly stated in the provided texts, but his academic career has included significant mentorship of PhD students and postdoctoral researchers. Key projects involve vision-driven manipulation pipelines, obstacle detection systems (PROBE), and resilient robot designs inspired by biological structures. He has been recognized for his work through prestigious awards including the NASA Early Career Grant and multiple NSF grants, as well as team achievements in robotics competitions like the Amazon Picking Challenge. Research interests span robotics subfields such as: Autonomous manipulation in cluttered environments Learning-based control for dynamic systems Topological data analysis for motion reasoning Tensegrity and soft robotics architectures Sim-to-real transfer in robotic tasks His team's work has produced open-source software tools and datasets, advancing benchmarks in manipulation and perception. Recent articles emphasize scalable solutions for industrial automation and robust navigation strategies in unstructured settings. Scientific achievements include: Development of PROBE for proprioceptive obstacle detection Advances in differentiable physics engines for tensegrity systems NSF-funded projects on robotic rearrangement and modular morphologies Advising contributions span over a decade, with current advisees focusing on topics like non-prehensile manipulation and large-scale storage optimization. Collaborations with industry (e.g., ExxonMobil) and academic partners (Yale University) reflect his commitment to applied robotics research. Labs and teams under his leadership include the Rutgers CS Robotics Group, contributing to projects like the ARIAC challenge platform and packing/industrial automation systems. Future work targets improved robot resilience in disaster scenarios and enhanced human-robot collaboration paradigms.
