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Fitsum Meshesha KifetewView profile

Researcher

• Software Testing
• Search-based Software Engineering
• Requirements Engineering
• +4 more

Fitsum Meshesha Kifetew is a tenured researcher at Fondazione Bruno Kessler (FBK) in Trento, Italy, where he works in the Software Engineering unit within the Digital Industry center. With over a decade of experience in software engineering research, he has established himself as a leading expert in automated software testing and requirements engineering. His work bridges theoretical foundations with practical applications, particularly through search-based software engineering techniques that have significantly advanced the field of test automation. PhD in 2015 from University of Trento/Fondazione Bruno Kessler Thesis: 'Evolutionary Test Case Generation via Many Objective Optimization and Stochastic Grammars' Supervised by Paolo Tonella and Roberto Tiella Kifetew's research primarily focuses on automated software testing and requirements engineering, with particular emphasis on search-based software engineering (SBSE) techniques. His work spans multiple domains including test generation, software adaptation and evolution, and requirements analysis. He has pioneered approaches that combine evolutionary algorithms with model-based testing, significantly advancing the state of the art in automated testing. His recent work explores the intersection of AI and software testing, particularly through large language models for test oracle generation and reinforcement learning for game testing. An analysis of Kifetew's recent publications reveals a strong trajectory in evolving testing methodologies from traditional code-based approaches toward more sophisticated model-based and AI-enhanced techniques. His work shows increasing focus on energy efficiency in testing processes, adaptation to complex domains like game testing and safety-critical systems, and integration of machine learning to address longstanding challenges like the test oracle problem. The research demonstrates consistent innovation in applying multi-objective optimization to software testing challenges. His notable recognitions include: IEEE Computer Society TCSE 10 Year Most Influential Paper Award for 'Reformulating branch coverage as a many-objective optimization problem' Best paper award at ICST'2025 Tool Competition UAV Testing Track Best paper award at SSBSE'18 for 'Incremental Control Dependency Frontier Exploration for Many-Criteria Test Case Generation' Kifetew has played significant leadership roles in major software engineering conferences, serving as Program Co-Chair for ICST 2022 and participating in numerous program committees. He has secured substantial research funding through EU projects including iv4XR (H2020 grant 856716), GAUSS (PRIN project funded by MIUR), and SUPERSEDE (H2020 grant 644018). His work has resulted in multiple research prototypes including DynaMOSA, Stochastic Grammar Based Testing, and EvoSuite-SVD, which have been widely adopted in both academic and industrial settings. He leads research on intelligent verification and validation for extended reality systems through the iv4XR project and has contributed to tools for model-based player experience testing. His work bridges theoretical computer science with practical software engineering challenges, particularly in the domains of game testing, safety-critical systems, and requirements engineering.

Simone OpelView profile

Researcher

• Computer Science Education
• Data Science Education
• Artificial Intelligence Education
• +4 more

Dr. Simone Opel serves as a Specialist Media Didactician at the Faculty of Media and Information Technology at FernUniversität in Hagen, Germany. Her academic role focuses on computer science education with particular emphasis on vocational-academic pathways, data science pedagogy, and ethical considerations in technology education. She teaches Master's level courses including 'Introduction to scientific work' and 'Introduction to Data Science Research,' while contributing to curriculum development in computer science didactics and data ethics. Her educational background features a PhD in Natural Sciences (2020) with research on competency modeling for IT specialist training, complemented by a First State Examination for Vocational School Teaching from Friedrich-Alexander-Universität Erlangen-Nürnberg. Earlier studies include a Diplom-Ingenieur in Telecommunications from Georg-Simon-Ohm-Hochschule in Nuremberg. Dr. Opel's research centers on bridging vocational training and higher education through competency-based approaches. Her work addresses critical challenges in AI literacy development, ethical frameworks for data science education, and recognition of prior professional experience. She investigates how workplace skills from vocational training can inform academic curriculum design, with particular focus on the evolving IT sector. Her publications reveal increasing attention to generative AI in educational contexts and ethical dimensions of technology education. Analysis of her publication trends shows consistent focus on practical implementation strategies across educational levels. Recent work demonstrates growing emphasis on ethical considerations in AI education, reflecting societal concerns about responsible technology development. Her research combines theoretical frameworks like the Four-Component Instructional Design model with practical classroom applications, creating valuable resources for educators navigating the digital transformation of curricula. Co-author of influential 'Ethical Use of Artificial Intelligence (AI) in Higher Education – A Handbook' Contributor to German Computer Science Society (GI) position papers on AI in education Developer of AsTRA assessment tool for recognizing prior professional experience Key participant in national initiatives restructuring IT vocational training As an educator, Dr. Opel supervises Master's students and develops innovative teaching materials that connect theoretical knowledge with practical application. Her work on permeability between vocational and academic pathways has led to concrete tools for recognizing workplace competencies within higher education frameworks. She collaborates with interdisciplinary research teams across German universities to develop simulation games and virtual tutors that enhance data science learning experiences. Dr. Opel actively participates in research networks focusing on educational technology and vocational-academic transition. Current projects include developing educational games for the data science life cycle and prototyping adaptive virtual tutors. Her work informs national policy discussions on restructuring IT vocational training and strengthening connections between different educational sectors in Germany's dual education system.

Mandy GoramView profile

Researcher

• Adaptive Learning Systems
• Learning Analytics
• Context-Based Adaptive Systems
• +5 more

Mandy Goram is a researcher at FernUniversität in Hagen affiliated with the Faculty of Mathematics and Computer Science and Cooperative Systems research group. She holds an M.Sc. in Practical Computer Science and B.Sc. in Business Information Systems from the same institution and has lectured at Informatica Feminale summer school since 2015. Her educational background includes: M.Sc. in Practical Computer Science specializing in Humans and Computers, Recommender Systems, and Intelligent Assistants B.Sc. in Business Information Systems specializing in Human-Computer Interaction and Database Systems Germany Scholarship recipient Goram's research centers on adaptive personalized learning environments (APLE project) supporting self-regulation in distance education. She develops context-based adaptive assistance systems using machine learning, NLP, and information extraction while prioritizing GDPR compliance. Her work bridges AI, multi-agent systems, and human-computer interaction with applications in conversational interfaces and learning analytics. Analysis of her 11 publications (2013-2020) reveals consistent focus on context-aware adaptive systems integrating legal requirements, particularly GDPR. Key trends include explainable AI for educational settings, privacy-preserving collaboration environments, and practical applications in business intelligence and customer experience management across insurance and banking sectors. No scientific awards were documented in the source material. She has supervised three theses: Stefan Walter's 2021 Master's on GDPR-compliant conversation partner identification, Yannik Worthmann's 2020 Bachelor's on context-based learning materials under GDPR, and Arda Treijner's 2019 Bachelor's on user data tracking for meinDorf55+. Goram leads the ongoing APLE project funded by FernUniversität Hagen's Research Focus DDLL with a 36-month duration. As part of the Cooperative Systems research group, she collaborates with Dirk Veiel on context-based adaptive systems emphasizing 'Compliance by Design' principles. Her work integrates technical innovation with regulatory frameworks through multi-agent architectures and deep learning applications.

Andreas KleineView profile

Professor

• Operations Research
• Data Envelopment Analysis
• Multi-Criteria Decision Making
• +4 more

Univ.-Prof. Dr. Andreas Kleine is a distinguished Professor at Distance University Hagen, holding the Chair of Business Administration, specializing in Quantitative Methods and Business Mathematics within the Faculty of Economics and Business Administration. Since 2011, he has led this department while also serving in significant administrative roles including as Vice Rector for Research and the Promotion of Young Academics (2016-2022) and currently as Ombudsperson for ensuring good scientific practice since March 2022. He is an active member of the research focus Energy, Environment & Sustainability and the research group Management of Energy-Flexible Factories (MaxFab). Saarland University - Diplom-Kaufmann in Business Administration (1989) Habilitation in Business Administration (2001) with thesis "Efficient Alternatives, Productions, and Organizations" University of Hohenheim - Chair of Operations Research (2002) and Department of Quantitative Methods Aston Business School - Visiting Scholar (2005) Württemberg Academy of Administrative and Economic Sciences - Teaching Experience Professor Kleine's research centers on applying quantitative methods to business administration problems, particularly where information is incomplete or multiple objectives must be balanced simultaneously. His work spans Data Envelopment Analysis, Multi-Criteria Decision Making, Optimization of renewable energies, and Risk Measurement. He has extensive experience in energy economics, focusing on how energy consumption and emissions in economic units can be recorded and operational processes optimized through energy flexibility potential. His research also explores optimal control of PV systems, forecasting electricity generation from renewables, and sustainability in production planning. Analyzing his recent publications reveals a strong trend toward sustainable operations research with emphasis on energy-flexible production systems, eco-efficient manufacturing, and multi-criteria optimization for sustainability. His work bridges theoretical operations research with practical applications in energy markets, production planning, and environmental management, often employing advanced methods like Data Envelopment Analysis and Pareto optimization to address complex sustainability challenges. Editor in Chief: Lecture Notes in Economics and Mathematical Systems Editorial Board: Journal of Business Economics Scientific Committee: International Conference on Data Envelopment Analysis (2023, 2017, 2016, 2015) Chair of Program Committee: VHB Annual Conference 2017 (St. Gallen) Chair of Scientific Commission "Operations Research" of VHB (2011-2015) Professor Kleine has served as reviewer for numerous prestigious journals including Annals of Operations Research, European Journal of Operational Research, and Journal of Business Economics. His research has been funded by both the German Research Foundation (DFG) and industry partners like TransnetBW for projects related to renewable energy market integration. He has played a key role in establishing the Research and Graduate Services at Distance University and served on the CRIS.NRW steering committee as representative of Vice Rectors for Research in North Rhine-Westphalian universities. As a member of the interdisciplinary research project MaXFab (Management Energieflexibler Fabriken) and the research focus on Energy, Environment & Sustainability, Professor Kleine collaborates with engineers, economists, and environmental scientists to develop practical solutions for energy-flexible production systems. His work with the Ombuds office for scientific integrity demonstrates his commitment to maintaining high research standards across the university.

Qinghua LuView profile

Professor

• Responsible AI
• Software Engineering for AI
• AI Safety
• +5 more

Dr Qinghua Lu serves as Group Leader of the Software Systems Research Group at CSIRO's Data61 in Australia, where she leads pioneering research at the intersection of artificial intelligence and software engineering. With over 200 publications in premier international venues, she has established herself as a global authority in Responsible AI and Software Engineering for AI. Her research program focuses on two critical areas: Agent Engineering and Evaluation, and AI Safety/Responsible AI. She has developed architectural patterns for foundation model-based agents, reference architectures for trustworthy AI systems, and multi-layered safety frameworks including the influential Swiss Cheese Model for AI Safety. Her work bridges theoretical research with practical applications through tools like Prompt Sapper for AI chain development and SoapOperaTG for knowledge graph-based testing. Dr Lu actively shapes national and international AI policy as a contributor to Australia's AI Safety Standard, Frontier Model Forum, OECD.AI's trustworthy AI metrics, and the EU General-Purpose AI Code of Practice. She has received numerous accolades including the 2023 Asia-Pacific Women in AI Trailblazer Award and an ACM Distinguished Paper Award for her seminal work 'Towards a Roadmap on Software Engineering for Responsible AI'. As a prolific author, her book 'Responsible AI: Best Practices for Creating Trustworthy AI Systems' (2023) became Amazon's No. 3 AI book bestseller, recognized as the world's first responsible AI book for practitioners. Her follow-up work 'Engineering AI Systems: Architecture and DevOps Essentials' (2025) further establishes her leadership in AI system engineering. Dr Lu maintains active leadership in the academic community as Area Chair of Software Engineering for AI at ICSE'26, Program Chair for AIware'25 and CAIN'25, and associate editor for IEEE Transactions on AI. Her editorial service extends to multiple prestigious journals including Engineering Applications of Artificial Intelligence and IEEE Communications Surveys and Tutorials.

Annette G. Beck-SickingerView profile

Professor

• Protein Engineering
• Biomaterials
• Chemokine Biology
• +4 more

Annette G. Beck-Sickinger is Professor of Bioorganic Chemistry and Biochemistry at the Institute of Biochemistry within Leipzig University's Faculty of Life Sciences. Her research focuses on chemical protein modification to characterize matrix-protein interactions, specifically developing strategies to improve implant biocompatibility through peptide-mediated immobilization of protein mediators and investigation of immune-modulatory chemokine interactions with extracellular matrix components. Her primary research areas include protein engineering, biomaterials science, and chemokine biology, with emphasis on glycosaminoglycan interactions (particularly for IL-8 and CXCL12/SDF-1), peptide-based surface coatings for controlled cytokine release, and light-activated chemokine systems. Current projects target wound healing enhancement via ECM-inspired biomaterials and reduction of implant-related inflammation through modified chemokine analogues. Analysis of her 2013-2021 publications reveals consistent innovation in biomaterial design for medical applications, characterized by interdisciplinary approaches combining synthetic chemistry, structural biology, and translational medicine. Key trends include enzyme-responsive release systems (MMP-9 mediated), photocontrol of protein activity, and mechanistic studies of chemokine-glycosaminoglycan binding to modulate immune responses. No scientific awards are mentioned in the available documentation. While specific advisees are not listed, her extensive publication record with multiple student co-authors (e.g., Bellmann-Sickert, Baumann, Panitz) indicates active supervision of graduate researchers. Her described projects on implant biocompatibility and wound healing suggest ongoing grant support, though specific funding sources are not detailed in the provided text. Her laboratory at Leipzig University's Institute of Biochemistry operates at the chemistry-biology interface, utilizing techniques including expressed protein ligation, NMR spectroscopy, and surface characterization. Collaborations with polymer scientists (Schnabelrauch, Hacker), biophysicists (Abel), and immunologists (Pisabarro) demonstrate a multidisciplinary research ecosystem focused on translating protein engineering into clinical solutions for regenerative medicine.

Manfred EppeView profile

Researcher

• Reinforcement Learning
• Cognitive Robotics
• Machine Learning
• +5 more

Manfred Eppe serves as Chief Engineer at the Institute for Data Science Foundations, Hamburg University of Technology, where he leads the cognitive robotics laboratory and contributes to teaching the machine learning lecture. His research centers on computational cognitive models of representation learning and reinforcement learning for simulated and physical robotic agents, with applications spanning neuro-semantic systems and cognitive robotics. His educational background includes a Ph.D. in Computer Science from the University of Bremen (2014), followed by postdoctoral research at the University of Hamburg (2016-2021), UC Berkeley (2015), and IIIA-CSIC in Barcelona (2015) within the COINVENT project. These positions focused on data-driven neuro-semantic systems, human-robot interaction using neurocognitive methods, and AI-based concept representation. Eppe's research integrates reinforcement learning with cognitive modeling to develop agents capable of representation learning, concept blending, and commonsense reasoning. His work bridges symbolic AI and neural approaches, emphasizing neurocognitively inspired methods for natural language understanding and robotic applications in physical environments. He has secured over €1.5 million in research funding as principal investigator for DFG projects MoReSpace and LeCAREbot, alongside prior grants from Volkswagen Stiftung and DAAD. His teaching portfolio includes Data Science at West Coast University of Applied Sciences and the "From Data to Knowledge" course at Universität Hamburg, with thesis supervision limited to reinforcement learning topics using the Scilab-RL virtual robotics platform. Eppe directs the cognitive robotics laboratory, which develops the Scilab-RL framework for simulating robotic agents and testing learning algorithms. The lab emphasizes open collaboration and practical applications of computational cognitive models in real-world robotic systems.

Kexin PeiView profile

Assistant Professor

• Security
• Software Engineering
• Machine Learning

Kexin Pei is a Neubauer Family Assistant Professor at the Department of Computer Science, University of Chicago. She received her PhD from the Department of Computer Science at Columbia University. Her academic career spans research and teaching in the fields of Security, Software Engineering, and Machine Learning. Her educational background includes: PhD in Computer Science from Columbia University Dr. Pei's research interests focus on developing data-driven program analysis to improve the security and reliability of both traditional and AI-based software systems. She is particularly interested in creating machine learning models that can reason about program structure and behavior to precisely and efficiently analyze, detect, and fix software bugs and vulnerabilities. Her work bridges the gap between theoretical computer science and practical security applications, with significant contributions to binary analysis, program understanding, and AI security. Her research output shows a strong trend toward integrating machine learning with traditional program analysis techniques. Many of her recent publications explore how neural networks and language models can enhance software security and reliability, particularly in binary analysis, vulnerability detection, and code understanding. Her work often involves developing novel frameworks that combine execution traces, code structure, and semantic understanding to create more robust analysis tools. Dr. Pei has received several prestigious awards for her research: Best Paper Award at MASEC@NeurIPS 2023 Best Paper Award Runner-Up in CSAW 2018 Top-10 Finalist of Applied Research Competition ACM SigMobile Research Highlight MLSec @NIPS'17 CACM research highlight Distinguished Artifact Award at FSE 2016 Dr. Pei actively mentors students and has advised several PhD and Master's students. Her group includes PhD students Jie Zhu, Weichen Li, and Jun Yang (with Shan Lu), as well as MS students Sam Huang and Yiming Cheng (with Junchen Jiang). She has also worked with numerous student collaborators and visiting students from institutions including MIT, Georgia Tech, and UChicago. Her research has been supported by grants from various sources, including collaborations with Google DeepMind and Microsoft Research where she completed internships. Dr. Pei is involved in several research labs and teams, including collaborations with the CUMLSec group (as seen in repositories like trex and XDA on GitHub). Her work often involves interdisciplinary teams combining expertise in security, machine learning, and software engineering to tackle complex problems in program analysis.

Ashutosh TrivediView profile

Associate Professor

• Formal Methods
• Reinforcement Learning
• Software Engineering
• +4 more

Ashutosh Trivedi is an Associate Professor of Computer Science at the University of Colorado Boulder, specializing in formal methods for safety-critical learning-enabled systems. His research bridges computer science, control theory, and machine learning with a focus on trustworthy AI systems that operate safely, fairly, and responsibly. His research interests encompass Formal Methods , Reinforcement Learning , Software Fairness , and Software Accountability . Trivedi develops mathematical approaches to bring precision to AI system design, using formal languages, automata, and logic to transform vague natural-language instructions into clear specifications. His work emphasizes principled interaction with AI, creating explainable and reliable systems through techniques like reinforcement learning algorithms for cardiac pacemaker design, SAT solvers for grounding LLM outputs, and formal logic for capturing legal obligations. His recent publications reveal a strong trend toward integrating formal verification with machine learning systems, particularly focusing on fairness testing, safety constraints in reinforcement learning, and neurosymbolic approaches. The research spans applications from legal-critical software to medical devices, with consistent emphasis on accountability and ethical considerations in AI systems. 2022 NSF CAREER award Liverpool Fellowship Distinguished Paper Award at CAV for Regular Reinforcement Learning Senior Member of the ACM Royal Society Wolfson Visiting Fellowship NeuS 2025 Disruptive Idea Award Trivedi actively mentors PhD students, having supervised multiple successful thesis defenses including Shadi Tasdighi Kalat on multi-agent games, Mateo Perez on formal languages for reinforcement learning, John Komp on pacemaker therapy, Vishnu Murali on functional proofs, and Taylor Dohmen on sequential optimization. His group, the Programming Languages and Verification (CUPLV) at CU Boulder, focuses on making AI systems more trustworthy through mathematical precision and formal verification techniques. Currently on sabbatical at the University of Liverpool, Trivedi continues to bridge the gap between machine learning and responsible system design, helping AI systems earn the trust we increasingly place in them for critical applications.

Lingming ZhangView profile

Associate Professor

• Software Engineering
• Programming Languages
• Machine Learning
• +5 more

Lingming Zhang is an Associate Professor in the Department of Computer Science at the Grainger College of Engineering, University of Illinois Urbana-Champaign. He maintains an active research program focused on the intersection of Software Engineering, Programming Languages, and Machine Learning, with particular emphasis on LLM-based software testing, repair, and synthesis. His research has resulted in over 100 publications with an h-index exceeding 50. His work has practical impact, having helped detect over 1,000 bugs and vulnerabilities in open-source projects from Apache and GitHub, as well as software systems from major technology companies including eBay, Google, Meta/Facebook, Microsoft, NVIDIA, OctoML, Oracle, and Yahoo!. Zhang's research interests span several key areas in software engineering and programming languages: LLM-based software testing, debugging, and repair Code generation and understanding with large language models Automated program repair Software testing and fuzzing techniques Deep learning for software engineering His recent publications demonstrate a strong focus on leveraging large language models for software engineering tasks, with notable contributions including Agentless, TitanFuzz, AlphaRepair, and ChatRepair. His group has also released influential open-source code language models including StarCoder2 and Magicoder, which have garnered over 1 million downloads worldwide. Zhang has received numerous prestigious awards and recognitions for his work: ACM Distinguished Member ACM SIGSOFT Early Career Researcher Award NSF CAREER Award UIUC Dean's Award for Excellence in Research Multiple ACM SIGSOFT Distinguished Paper Awards He has successfully mentored numerous PhD and MS students, many of whom have gone on to positions at top technology companies and academic institutions. His research is supported by grants from major technology companies including Alibaba, Amazon, Google, Kwai Inc, Meta/Facebook, NVIDIA, and Samsung. Currently, he serves as program co-chair for ASE 2025 and LLM4Code 2025, demonstrating his leadership in the software engineering research community.

Peng DiView profile

Adjunct Associate Professor

• Program Analysis
• Compiler
• Parallel Computing
• +5 more

Peng Di serves as an Adjunct Associate Professor at the University of New South Wales, Sydney while working as a Senior Staff Engineer and leading the Intelligent Platform Engineering team at Ant Group in Hangzhou, China. They also serve as an Industrial PhD Mentor at Zhejiang University. PhD from UNSW under supervision of Scientia Professor Jingling Xue Former Research Associate at Compiler Research Group Former Technical Expert of AI compiler at Huawei Peng Di's research spans the intersection of programming languages, artificial intelligence, and software engineering. Their work focuses on leveraging large language models for software development, program analysis techniques, compiler technologies, and security applications. They have pioneered approaches to integrate code large language models with traditional program analysis, creating hybrid systems that enhance software development efficiency and security. Their research in parallel computing and optimizations for heterogeneous systems has resulted in practical industrial applications, particularly in the domain of AI compilers and microservices architecture. Recent publications demonstrate a clear trend toward integrating graph-based representations with large language models for code understanding, advancing static analysis techniques using datalog and neural networks, and applying these innovations to practical industrial settings. Their work bridges theoretical program analysis with real-world software engineering challenges, particularly in microservices architectures and security-critical applications. T-Star Award (Ant Group's highest technological award, 2024) ACM SIGSOFT Distinguished Paper Award (2025) Distinguished Dissertation Award by the CCF Systems Software (2024) distinguished paper award of ASE'24 (2024) Peng Di leads the Intelligent Platform Engineering team at Ant Group, which has developed several significant open-source projects including CodeFuse (a pretrained code large language model), Ling-Coder-Lite (a MoE code LLM), and OpenDeRisk (an AI-native risk intelligence system). Their team collaborates with academic institutions including UNSW, East China Normal University, ETH Zurich, Chinese Academy of Sciences, and Nanjing University on cutting-edge research. Peng Di actively mentors PhD students as an Industrial PhD Mentor at Zhejiang University and recruits technical experts and interns for research in machine learning, program analysis, and AI software engineering. The Ant Intelligent Platform Engineering team, led by Peng Di, is committed to building a safe and trusted framework for intelligent, automated software engineering. They manage the CodeFuse AI Coding and DeRisk AIOps communities, which encompass pre-trained Code LLMs, agents, frameworks, toolchains, and databases. Their team has also contributed to agent development platforms Tbox and WeaveFox, with the latter being an AI frontend development platform launched by Ant Group.

Amin Milani FardView profile

Associate Professor

• Software Engineering
• Cybersecurity
• Software Testing
• +5 more

Amin Milani Fard is an Associate Professor of Computer Science at New York Institute of Technology - Vancouver Campus, and a visiting faculty member in Management Information Systems at Simon Fraser University's Beedie School of Business in Vancouver, Canada. Previously, he served as an Assistant Professor at NYIT Vancouver from 2018 to 2023. Dr. Milani Fard received his Ph.D. in Computer Software Engineering from the University of British Columbia (2012-2017), his M.Sc. in Computer Science from Simon Fraser University (2009-2010), and his B.Sc. in Computer Software Engineering from Ferdowsi University of Mashhad. His academic journey began with notable achievements including the 1st Rank Khwarizmi Award (awarded by Iran's President Mohammad Khatami) and an Exceptional Talents Admission Award. His research spans multiple disciplines, primarily focusing on Security, Privacy, and Assurance of Software and Information , with significant contributions to software testing, blockchain security, financial market prediction, and machine learning applications. His work on JavaScript security code smells, Ethereum smart contract vulnerabilities, and financial market prediction using multimodal data has been widely recognized. His publications demonstrate a consistent pattern of high-quality research with numerous papers in top conferences including ASE, ICST, and IEEE journals. Dr. Milani Fard's research portfolio shows a clear evolution from foundational work in software testing and JavaScript analysis toward more complex applications in financial technology and AI-driven security solutions. His most recent work focuses on integrating LLM technologies with security applications and advancing financial prediction models using sophisticated time series analysis. Scientific Awards and Recognition: Most Influential Paper Award at IEEE SCAM 2023 Presidential Excellence Award Finalist for Teaching at NYIT (2023) Best Paper Award at ECIR 2019 Multiple research grants including NSERC Alexander Graham Bell Canada Graduate Scholarship IEEE Best Paper Award Nominee at ICST 2017 As an active researcher, Dr. Milani Fard serves on program committees for major conferences including ASE and has contributed significantly to the software engineering community through his publications and research leadership. His work bridges theoretical computer science with practical applications in cybersecurity and financial technology, demonstrating both academic rigor and real-world impact.

Maliheh IzadiView profile

Assistant Professor

• Software Engineering
• Machine Learning
• Large Language Models for Code
• +7 more

Maliheh Izadi is a tenure-track assistant professor in the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), Netherlands. She leads the AISE (AI-enabled Software Engineering) research lab and serves as the scientific manager for the TU Delft/JetBrains Collaboration (AI4SE). She is also a member of the Software Engineering Research Group (SERG) at TU Delft and actively supervises PhD, MSc, and BSc students. Dr. Izadi's research focuses on enhancing software development tools through building smarter software and tailoring machine learning and NLP techniques to source code. Her primary interests include building and tailoring large language models (LLMs) and autonomous agents to source code, with specific focus areas including evaluation, benchmarking, model memorization, IDE integration, in-IDE Human-AI interaction, and extending models' capabilities to low-resource programming languages. Her work bridges the gap between deep learning and source code analysis, with applications in code understanding, generation, documentation, and developer productivity enhancement. Her recent publications reveal a strong trend toward evaluating and improving LLMs for code, with emphasis on safety, usability, and efficiency. She has made significant contributions to benchmark development, harmfulness assessment of LLMs in programming contexts, and improving the integration of AI tools within development environments. Her research consistently addresses real-world industrial challenges while advancing theoretical understanding of model behavior. Google Research Scholar Award (2025) for proposal on Tackling LLM Hallucinations Amazon Research Award (2024) for proposal on Addressing Memorization in Code LLMs ACM SIGSOFT Distinguished Paper Award (2025) for How Much Do Code Language Models Remember? ACM SIGSOFT Distinguished Paper Award (2024) for A Transformer-Based Approach for Smart Invocation of Automatic Code Completion Best Tool Award at SaTML'22 competition for STACC Best Tool Award at NLBSE'22 competition for Catiss Dr. Izadi actively collaborates with industry partners, particularly JetBrains Research, where she leads the AI4SE ICAI lab. She has supervised multiple PhD and master's students and has served on program committees for major software engineering conferences including ASE, ICSE, FSE, and MSR. Her research has been published in top venues such as IEEE/ACM ICSE, FSE, ASE, TOSEM, EMSE, MSR, ICSME, SANER, and JSS. She is also organizing the First International workshop on Autonomous Agents in Software Engineering (AgenticSE) co-located with ASE'25.

Tianyi ZhangView profile

Assistant Professor

• Software Engineering
• Human-Computer Interaction
• Artificial Intelligence
• +5 more

Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.

Fatemeh Hendijani FardView profile

Assistant Professor

• Code Intelligence
• Transfer Learning
• AI4SE
• +5 more

Fatemeh Hendijani Fard is an Assistant Professor in the Department of Computer Science at the University of British Columbia's Okanagan campus. She serves as a graduate student supervisor and teaches courses in Computer Science and Data Science. Dr. Fard is a member of the CITECH program and MMRI, part of the Killam family of scholars, and an active member of both IEEE and ACM. Her research focuses on the intersection of Natural Language Processing and Software Engineering, with particular emphasis on developing code intelligence models for low-resource programming languages like R. She conducts empirical studies and develops techniques to improve the computational efficiency of code-language models while making them accessible to communities with restricted GPU access. Her work strongly advocates for Diversity and Inclusion in STEM, particularly for underrepresented females. Analysis of Dr. Fard's recent publications reveals a strong research trajectory in adapting Large Language Models for code intelligence with a focus on efficiency and accessibility. Her work spans multiple dimensions including code summarization, method name prediction, code search, code clone detection, and program repair, with special attention to low-resource programming languages. A notable trend is her exploration of adapter-based approaches for knowledge transfer that reduce computational requirements while maintaining performance. Izaak Walton Killam Memorial Scholarship Alberta Innovates Technology Futures (AITF) NSERC Discovery NSERC CREATE Mitacs Accelerate UBC Start-up Fund Dr. Fard has secured significant research funding including NSERC Discovery, NSERC CREATE, Mitacs Accelerate, and UBC Start-up funds to support her work on code intelligence for low-resource programming languages. She actively serves as a graduate student supervisor, guiding research in areas related to code representation learning and mining software repositories. Her service to the academic community is extensive, having served on program committees for major conferences including FSE, MSR, ASE, SANER, and ICSME across multiple years. Dr. Fard leads research initiatives focused on making code intelligence accessible to communities working with understudied programming languages. Her team conducts empirical studies and develops new techniques specifically designed for low-resource languages, with particular attention to the R programming language. This work addresses diversity and inclusion in AI tools by ensuring developers with limited computational resources can benefit from advances in neural networks and automated tools.