The study aims to explore the transitions experienced by international Women of Color (IWoC) engineers in the US as they navigate their academic and professional lives. Motivated by the lack of research on IWoC’s experiences, specifically around transition points of their lives, four international Women of Color participated in this qualitative auto-ethnographic deep-dive. All four researchers have attended college in the United States for their high educational degrees focused on education/engineering education and are currently involved in engineering education scholarship work.
Category: Sreyoshi Bhaduri, Ph.D.
Sreyoshi Bhaduri, Ph.D.
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Outsiders: Pathways and Perspectives from Engineering Education PhDs Outside Academia
This article presents a critical exploration and recommendation based on the lived experiences of PhD graduates in Engineering Education who have ventured into non-academic career paths. The work is rooted in an auto-ethnographic research approach, and the report aims to mimic a live virtual panel. It seeks to elucidate the experiences and challenges faced by PhD graduates who diverged from traditional academic roles to pursue careers in industry, entrepreneurship, consulting, and pre-college leadership. These narratives reveal a complex landscape of motivations, perceived hierarchical barriers, and under-recognition within academic and non-academic sectors, highlighting a divide between industry and academia. The paper delves into the unique challenges faced by non-academic engineering educators, such as confronting a culture that often questions their value outside traditional faculty roles and the overarching perception that non-research roles are less significant. Despite these challenges, the authors argue for the vital role these professionals play in bridging the gap between research, instruction, and practical application in engineering education. They emphasize the importance of ASEE or similar professional societies in recognizing and leveraging the diverse contributions of non-academic engineering educators to foster a more inclusive and supportive community. Key takeaways and recommendations include the necessity for ASEE and similar bodies to shift normative expectations, create inclusive and equitable environments, and actively value diverse career trajectories. The paper calls for actionable strategies to build more inclusive professional communities, create safe spaces for discussing career diversity, and establish stronger connections between current students and diverse alums. The overarching goal is to cultivate an environment where all forms of contribution to engineering education are valued, encouraging a broader spectrum of career considerations among graduates and professionals. The authors seek not only to share insights but also to galvanize a nascent community of like-minded engineering educators aspiring or working outside the traditional academic sphere.
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(Multi-disciplinary) Teamwork makes the (real) dream work: Pragmatic recommendations from industry for engineering classrooms
Many students choose to major in engineering to join the community of professional engineers and gain exposure to the field through their college experience. However, research suggests that engineering graduates may not be adequately prepared for the workplace due to the complexities of engineering work. Engineering work involves complexity, ambiguity, and contradictions, and developing innovation skills requires analyzing real-world problems that are often ill-defined and multifaceted. Therefore, it is essential for engineering students to have opportunities to work in multi-disciplinary teams to develop their skills in problem-solving and innovation. This emphasis on the need for exposure to multi-disciplinary problem solving holds true not only for undergraduate engineers in training, but also for graduate students focused on engineering education.
This paper draws from experiences of a multi-disciplinary team (including engineers, scientists, UX researchers, Industrial-Organization (I-O) psychologists, economists, and program and product managers) studying talent management in the tech industry, to present lessons learned from leading with science to understand, inform, and improve employee experiences at a large private technology company. Our paper exemplifies how projects in industry leverage multi-disciplinary expertise and presents recommendations for new graduates and engineering professionals. Ultimately, this paper affords an opportunity for educators to expand on examples of how multiple disciplines come together to study engineers in the workforce.
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Beyond the algorithm: Empowering ai practitioners through liberal education
As AI technology continues to transform society, there is a growing need for engineers and technologists to develop interdisciplinary skills to address complex, society-wide problems. However, there is a gap in understanding how to effectively design and deliver inter-disciplinary education programs for AI-related training. This paper addresses this gap by reporting on a successful summer school program that brought together specialists from around the world to engage in deliberations on responsible AI, as part of a Summer School in Responsible AI led by Mila – Quebec Artificial Intelligence Institute. Through deep dive auto-ethnographic reflections from five individuals, who were either organizers or participants, augmented with end-of-program feedback, we provide a rich description of the program’s planning, activities, and impact. Specifically, our study draws from engineering education research, bridging the gap between research and practice to answer three research questions related to the program: (1) How did the program design enable a more effective understanding of interdisciplinary problem-sets? (2) How did participants experience the interdisciplinary work of the program? (3) Did the program affect participants’ impact on interdisciplinary problem-sets after the program? Our findings highlight the benefits of interdisciplinary, holistic, and hands-on approaches to AI education and provide insights for fellow engineering education researchers on how to design effective programs in this field.
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Reimagining AI Conference Mission Statements to Promote Inclusion in the Emerging Institutional Field of AI
AI conferences play a crucial role in education by providing a platform for knowledge sharing, networking, and collaboration, shaping the future of AI research and applications, and informing curricula and teaching practices. This work-in-progress, innovative practice paper presents preliminary findings from textual analysis of mission statements from select artificial intelligence (AI) conferences to uncover information gaps and opportunities that hinder inclusivity and accessibility in the emerging institutional field of AI. By examining language and focus, we identify potential barriers to entry for individuals interested in the AI domain, including educators, researchers, practitioners, and students from underrepresented groups. Our paper employs the use of the Language as Symbolic Action (LSA) framework [1] to reveal information gaps in areas such as no explicit emphasis on DEI, undefined promises of business and personal empowerment and power, and occasional elitism. These preliminary findings uncover opportunities for improvement, including the need for more inclusive language, an explicit commitment to diversity, equity, and inclusion (DEI) initiatives, clearer communications about conference goals and expectations, and emphasis on strategies to address power imbalances and promote equal opportunities for participation. The impact of our work is bi-fold: 1) we demonstrate preliminary results from using the Language as Symbolic Action framework to text-analysis of mission statements, and 2) our preliminary findings will be valuable to the education community in understanding gaps in current AI conferences and consequently, outreach. Our work is thus of practical use for conference organizers, engineering and CS educators and other AI-related domains, researchers, and the broader AI community. Our paper highlights the need for more intentional and inclusive conference design to foster a diverse and vibrant community and community of AI professionals.
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Bridging the Gap: Exploring Semiconductors Exposure and Motivation among Multidisciplinary Engineering Students
Several educational initiatives are currently underway to address workforce challenges in the semiconductors industry. Assessing students’ exposure to and motivation for semiconductors-related topics is an essential initial step toward recognizing areas where primary efforts should be concentrated. The primary objective of this study is to assess students’ awareness and motivation concerning semiconductors in the context of a multidisciplinary introduction to electrical engineering course. Through quantitative analysis and the administration of an existing validated survey instrument, we aim to explore students’ exposure to semiconductors-related topics and potential correlations between awareness, motivation, and demographic variables, including gender and class standing. The instrument was administered to a cohort of 255 students enrolled in a multidisciplinary course covering the fundamentals of electrical engineering. Preliminary data indicates that only 9% of the students in this cohort haven taken a class about semiconductors and only 3% have some interest in pursuing a career in the semiconductors field. The results of this analysis hold several significant implications for engineering education and the semiconductor industry. Firstly, the limited exposure to and interest in semiconductors among engineering students suggest the need for curriculum alignment with the demands of the semiconductor industry and interdisciplinary education. By doing so, we empower students from diverse disciplines to contribute to technological advancements, innovation, and problem-solving fostering a more inclusive, diverse, and well-rounded workforce within the semiconductor sector.
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Decoding the Diversity: A Review of the Indic AI Research Landscape
This review paper provides a comprehensive overview of large language model (LLM) research directions within Indic languages. Indic languages are those spoken in the Indian subcontinent, including India, Pakistan, Bangladesh, Sri Lanka, Nepal, and Bhutan, among others. These languages have a rich cultural and linguistic heritage and are spoken by over 1.5 billion people worldwide. With the tremendous market potential and growing demand for natural language processing (NLP) based applications in diverse languages, generative applications for Indic languages pose unique challenges and opportunities for research. Our paper deep dives into the recent advancements in Indic generative modeling, contributing with a taxonomy of research directions, tabulating 84 recent publications. Research directions surveyed in this paper include LLM development, fine-tuning existing LLMs, development of corpora, benchmarking and evaluation, as well as publications around specific techniques, tools, and applications. We found that researchers across the publications emphasize the challenges associated with limited data availability, lack of standardization, and the peculiar linguistic complexities of Indic languages. This work aims to serve as a valuable resource for researchers and practitioners working in the field of NLP, particularly those focused on Indic languages, and contributes to the development of more accurate and efficient LLM applications for these languages.
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Leveraging AI in education
To stay ahead, it is essential to adapt to the rise of AI by intelligently incorporating it into all levels of the education process
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Parameter efficient fine tuning: A comprehensive analysis across applications
The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging, primarily through the adaptation of pre-trained models for specific tasks. Traditional fine-tuning methods, involving adjustments to all parameters, face challenges due to high computational and memory demands. This has led to the development of Parameter Efficient Fine-Tuning (PEFT) techniques, which selectively update parameters to balance computational efficiency with performance. This review examines PEFT approaches, offering a detailed comparison of various strategies highlighting applications across different domains, including text generation, medical imaging, protein modeling, and speech synthesis. By assessing the effectiveness of PEFT methods in reducing computational load, speeding up training, and lowering memory usage, this paper contributes to making deep learning more accessible and adaptable, facilitating its wider application and encouraging innovation in model optimization. Ultimately, the paper aims to contribute towards insights into PEFT’s evolving landscape, guiding researchers and practitioners in overcoming the limitations of conventional fine-tuning approaches.
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Several presentations, including “Taking the Lead in SWE: An Asian Perspective.”
Dr. Bhaduri will be presenting at the Society of Women Engineers, with whom she is a Senator. Several events are online for interested persons. Dr. Bhaduri is an accomplished technologist and educator, as well as being excellent speaker and is appreciated for her wit, her precision, and her kindness.
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Excellence, Belonging, and the American Dream: An Auto-ethnography on Being International in Engineering
This research paper uses an auto-ethnographic approach to highlight experiences of women of color who are international in U.S. engineering workforce and classrooms. Three preliminary themes are highlighted in this paper. The first is the theme related to the notion of Excellence, and how the definitions of success for immigrating or international engineers may be tied to maintaining visa status. The second theme centers around the lack of Sense of Belonging expressed by participant-authors. The final theme was around Chasing the American Dream and the resultant sacrifices. We hope this paper not only provides an overview of some of the unique challenges faced by international women of color, but also drives more interest in better understanding this oft-overlooked group in engineering.
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Work in progress: Coloring Outside the Lines-Exploring the Potential for Integrating Creative Evaluation in Engineering Education
Extant cultures within academic institutions that educate and train the next generations of STEM professionals tend to privilege long-held majority perspectives of knowing, thinking, and doing in science and engineering. To more intentionally recruit and include diverse groups of students into our educational programs, it is imperative that we develop and adopt unique pedagogical and assessment approaches that move beyond didactics, leverage experiential learning, and embrace a variety of student backgrounds and identities. In this paper, we demonstrate how visual methods-based assessments can serve as an impactful alternative to more traditional forms. We start by introducing three examples currently used in STEM curricula, and then by describing how these assessments promote autonomy and creativity as students make meaning of STEM and of themselves as STEM professionals. We conclude the description of each assessment example by identifying key considerations for STEM instructors when attempting to implement such assessments in their own contexts.